stevenbucaille/image-classification-models-dataset

model_id stringlengths 7 105	model_card stringlengths 1 130k	model_labels listlengths 2 80k
qubvel-hf/vit-base-beans	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans This model is a fine-tuned version of [timm/resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.4913 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.07 \| 1.0 \| 130 \| 1.0683 \| 0.4135 \| \| 1.0523 \| 2.0 \| 260 \| 1.0356 \| 0.6241 \| \| 1.0439 \| 3.0 \| 390 \| 1.0045 \| 0.6617 \| \| 1.0056 \| 4.0 \| 520 \| 0.9671 \| 0.7293 \| \| 0.9853 \| 5.0 \| 650 \| 0.9245 \| 0.7895 \| \| 0.9581 \| 6.0 \| 780 \| 0.8744 \| 0.7820 \| \| 0.9044 \| 7.0 \| 910 \| 0.8172 \| 0.7820 \| \| 0.869 \| 8.0 \| 1040 \| 0.7737 \| 0.8271 \| \| 0.8804 \| 9.0 \| 1170 \| 0.7098 \| 0.8271 \| \| 0.7757 \| 10.0 \| 1300 \| 0.6705 \| 0.8120 \| \| 0.7694 \| 11.0 \| 1430 \| 0.6382 \| 0.8571 \| \| 0.7966 \| 12.0 \| 1560 \| 0.6088 \| 0.7895 \| \| 0.7425 \| 13.0 \| 1690 \| 0.5724 \| 0.8496 \| \| 0.7698 \| 14.0 \| 1820 \| 0.5665 \| 0.8195 \| \| 0.6632 \| 15.0 \| 1950 \| 0.5308 \| 0.8571 \| \| 0.6162 \| 16.0 \| 2080 \| 0.5262 \| 0.8346 \| \| 0.6128 \| 17.0 \| 2210 \| 0.5081 \| 0.8421 \| \| 0.685 \| 18.0 \| 2340 \| 0.4913 \| 0.8571 \| \| 0.6614 \| 19.0 \| 2470 \| 0.4937 \| 0.8496 \| \| 0.6934 \| 20.0 \| 2600 \| 0.5027 \| 0.8571 \| ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.5.0+cu118 - Datasets 2.21.0 - Tokenizers 0.20.0	[ "angular_leaf_spot", "bean_rust", "healthy" ]
alyzbane/convnextv2-tiny-1k-224-finetuned-barkley	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnextv2-tiny-1k-224-finetuned-barkley This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0083 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 - Top1 Accuracy: 1.0 - Error Rate: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| Top1 Accuracy \| Error Rate \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|:-------------:\|:----------:\| \| 1.4696 \| 1.0 \| 38 \| 1.1807 \| 0.7273 \| 0.6513 \| 0.6180 \| 0.6768 \| 0.6513 \| 0.3232 \| \| 0.7197 \| 2.0 \| 76 \| 0.3719 \| 0.9439 \| 0.9408 \| 0.9404 \| 0.9434 \| 0.9474 \| 0.0566 \| \| 0.2388 \| 3.0 \| 114 \| 0.1489 \| 0.9688 \| 0.9671 \| 0.9671 \| 0.9716 \| 0.9671 \| 0.0284 \| \| 0.1048 \| 4.0 \| 152 \| 0.0730 \| 0.9868 \| 0.9868 \| 0.9868 \| 0.9878 \| 0.9868 \| 0.0122 \| \| 0.1103 \| 5.0 \| 190 \| 0.0288 \| 0.9868 \| 0.9868 \| 0.9868 \| 0.9878 \| 0.9868 \| 0.0122 \| \| 0.072 \| 6.0 \| 228 \| 0.0537 \| 0.9877 \| 0.9868 \| 0.9869 \| 0.9868 \| 0.9868 \| 0.0132 \| \| 0.0248 \| 7.0 \| 266 \| 0.0083 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 0.0 \| \| 0.0371 \| 8.0 \| 304 \| 0.0653 \| 0.9819 \| 0.9803 \| 0.9802 \| 0.9800 \| 0.9803 \| 0.0200 \| \| 0.0626 \| 9.0 \| 342 \| 0.2271 \| 0.9545 \| 0.9408 \| 0.9404 \| 0.95 \| 0.9408 \| 0.0500 \| \| 0.07 \| 10.0 \| 380 \| 0.0304 \| 0.9936 \| 0.9934 \| 0.9934 \| 0.9933 \| 0.9934 \| 0.0067 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.19.1	[ "iinstia bijuga", "mangifera indica", "pterocarpus indicus", "roystonea regia", "tabebuia" ]
bombshelll/swin-brain-abnormalities-classification	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-brain-abnormalities-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2060 - Accuracy: 0.9531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.8774 \| 0.9892 \| 23 \| 0.5766 \| 0.7688 \| \| 0.4681 \| 1.9785 \| 46 \| 0.2226 \| 0.9363 \| \| 0.2733 \| 2.9677 \| 69 \| 0.1654 \| 0.9296 \| \| 0.2105 \| 4.0 \| 93 \| 0.1851 \| 0.9414 \| \| 0.1705 \| 4.9892 \| 116 \| 0.2025 \| 0.9397 \| \| 0.1716 \| 5.9785 \| 139 \| 0.1505 \| 0.9497 \| \| 0.1528 \| 6.9677 \| 162 \| 0.2124 \| 0.9430 \| \| 0.1377 \| 8.0 \| 186 \| 0.1841 \| 0.9464 \| \| 0.1275 \| 8.9892 \| 209 \| 0.1919 \| 0.9481 \| \| 0.115 \| 9.9785 \| 232 \| 0.1570 \| 0.9548 \| \| 0.1076 \| 10.9677 \| 255 \| 0.2035 \| 0.9481 \| \| 0.1001 \| 12.0 \| 279 \| 0.1805 \| 0.9514 \| \| 0.0932 \| 12.9892 \| 302 \| 0.2067 \| 0.9497 \| \| 0.0814 \| 13.9785 \| 325 \| 0.2107 \| 0.9481 \| \| 0.0767 \| 14.8387 \| 345 \| 0.2060 \| 0.9531 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "lesion", "normal", "tumor" ]
bombshelll/swin-brain-plane-classification	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-brain-plane-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0556 - Accuracy: 0.9778 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0606 \| 1.0 \| 5 \| 0.7887 \| 0.8741 \| \| 0.7256 \| 2.0 \| 10 \| 0.3949 \| 0.9111 \| \| 0.4331 \| 3.0 \| 15 \| 0.1731 \| 0.9704 \| \| 0.3165 \| 4.0 \| 20 \| 0.1250 \| 0.9630 \| \| 0.1827 \| 5.0 \| 25 \| 0.0847 \| 0.9704 \| \| 0.1488 \| 6.0 \| 30 \| 0.0919 \| 0.9630 \| \| 0.1425 \| 7.0 \| 35 \| 0.0604 \| 0.9778 \| \| 0.1487 \| 8.0 \| 40 \| 0.0718 \| 0.9778 \| \| 0.118 \| 9.0 \| 45 \| 0.0578 \| 0.9778 \| \| 0.1278 \| 10.0 \| 50 \| 0.0556 \| 0.9778 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "axial", "coronal", "sagittal" ]
bombshelll/swin-brain-modality-classification	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-brain-modality-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0197 - Accuracy: 0.9956 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7116 \| 1.0 \| 16 \| 0.1466 \| 0.9333 \| \| 0.0955 \| 2.0 \| 32 \| 0.0323 \| 0.9911 \| \| 0.0464 \| 3.0 \| 48 \| 0.0197 \| 0.9956 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "ct", "mri t1", "mri t2", "mri t2 flair" ]
kiranshivaraju/convnext2-tiny-finetuned-aug-pcb_data	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "bad", "good" ]
neuralhaven/mobilevit-xx-small-FireRisk	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mobilevit-xx-small-FireRisk This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9814 - eval_accuracy: 0.6146 - eval_precision: 0.5070 - eval_recall: 0.4671 - eval_f1: 0.4675 - eval_runtime: 164.589 - eval_samples_per_second: 130.878 - eval_steps_per_second: 1.027 - epoch: 1.0 - step: 550 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "very low", "low", "moderate", "high", "very high", "non-burnable", "water" ]
AnthoneoJ/ms6-train	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "food", "guru", "inside", "menu", "outside", "people" ]
bombshelll/swin-brain-tumor-type-classification	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-brain-tumor-type-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2978 - Accuracy: 0.9081 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.4824 \| 1.0 \| 21 \| 2.2558 \| 0.2420 \| \| 2.1213 \| 2.0 \| 42 \| 1.8165 \| 0.4170 \| \| 1.6613 \| 3.0 \| 63 \| 1.3678 \| 0.5671 \| \| 1.3237 \| 4.0 \| 84 \| 1.1940 \| 0.6060 \| \| 1.1543 \| 5.0 \| 105 \| 0.9205 \| 0.7049 \| \| 0.9317 \| 6.0 \| 126 \| 0.8121 \| 0.7314 \| \| 0.7891 \| 7.0 \| 147 \| 0.6553 \| 0.7986 \| \| 0.6812 \| 8.0 \| 168 \| 0.5720 \| 0.8180 \| \| 0.6348 \| 9.0 \| 189 \| 0.5364 \| 0.8180 \| \| 0.5488 \| 10.0 \| 210 \| 0.4780 \| 0.8428 \| \| 0.505 \| 11.0 \| 231 \| 0.4540 \| 0.8569 \| \| 0.4758 \| 12.0 \| 252 \| 0.3992 \| 0.8852 \| \| 0.4306 \| 13.0 \| 273 \| 0.4280 \| 0.8675 \| \| 0.3952 \| 14.0 \| 294 \| 0.4019 \| 0.8781 \| \| 0.3726 \| 15.0 \| 315 \| 0.3794 \| 0.8763 \| \| 0.3191 \| 16.0 \| 336 \| 0.3482 \| 0.8958 \| \| 0.3014 \| 17.0 \| 357 \| 0.3372 \| 0.8940 \| \| 0.2785 \| 18.0 \| 378 \| 0.3472 \| 0.8993 \| \| 0.2948 \| 19.0 \| 399 \| 0.3246 \| 0.9064 \| \| 0.2618 \| 20.0 \| 420 \| 0.3060 \| 0.9081 \| \| 0.2705 \| 21.0 \| 441 \| 0.3122 \| 0.9046 \| \| 0.2479 \| 22.0 \| 462 \| 0.3061 \| 0.9028 \| \| 0.2411 \| 23.0 \| 483 \| 0.3040 \| 0.9099 \| \| 0.2556 \| 24.0 \| 504 \| 0.2990 \| 0.9099 \| \| 0.2413 \| 25.0 \| 525 \| 0.2978 \| 0.9081 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "astrocitoma", "carcinoma", "ependimoma", "ganglioglioma", "germinoma", "glioblastoma", "granuloma", "meduloblastoma", "meningioma", "neurocitoma", "oligodendroglioma", "papiloma", "schwannoma", "tuberculoma" ]
neuralhaven/deit-tiny-patch16-224-FireRisk	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deit-tiny-patch16-224-FireRisk This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9478 - Accuracy: 0.6253 - Precision: 0.5221 - Recall: 0.5179 - F1: 0.5190 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 512 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.9714 \| 1.0 \| 138 \| 0.9923 \| 0.6114 \| 0.5167 \| 0.5118 \| 0.5070 \| \| 0.9482 \| 2.0 \| 276 \| 0.9760 \| 0.6094 \| 0.4985 \| 0.5138 \| 0.5030 \| \| 0.9263 \| 3.0 \| 414 \| 0.9398 \| 0.6314 \| 0.5226 \| 0.5202 \| 0.5180 \| \| 0.903 \| 4.0 \| 552 \| 0.9683 \| 0.6183 \| 0.5141 \| 0.5021 \| 0.5039 \| \| 0.8536 \| 5.0 \| 690 \| 0.9478 \| 0.6253 \| 0.5221 \| 0.5179 \| 0.5190 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "very low", "low", "moderate", "high", "very high", "non-burnable", "water" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v9	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v9 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.3109 - Accuracy: 0.625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 22 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.014 \| 8.3333 \| 100 \| 1.5028 \| 0.5 \| \| 0.6464 \| 16.6667 \| 200 \| 1.3109 \| 0.625 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v10	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v10 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.9495 - Accuracy: 0.875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.3924 \| 8.3333 \| 100 \| 1.0648 \| 0.75 \| \| 0.2925 \| 16.6667 \| 200 \| 0.9745 \| 0.875 \| \| 0.2696 \| 25.0 \| 300 \| 0.9495 \| 0.875 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v11	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v11 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.7050 - Accuracy: 0.875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 27 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.1396 \| 8.3333 \| 100 \| 0.7253 \| 0.875 \| \| 0.1215 \| 16.6667 \| 200 \| 0.7134 \| 0.875 \| \| 0.1175 \| 25.0 \| 300 \| 0.7050 \| 0.875 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v12	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v12 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.4316 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 27 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.0811 \| 8.3333 \| 100 \| 1.6611 \| 0.4167 \| \| 0.6509 \| 16.6667 \| 200 \| 1.4987 \| 0.5 \| \| 0.5656 \| 25.0 \| 300 \| 1.4316 \| 0.6667 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
AbdoulayeDIOP/lettuce-npk-vit	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/abdoulaye-diop/lettuce-npk-deficiency-prediction/runs/zjp8gyck) # lettuce-npk-vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1492 - Accuracy: 0.9524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.1633 \| 0.992 \| 31 \| 1.1239 \| 0.8571 \| \| 0.4802 \| 1.984 \| 62 \| 0.4213 \| 0.9048 \| \| 0.1386 \| 2.976 \| 93 \| 0.2501 \| 0.9524 \| \| 0.1003 \| 4.0 \| 125 \| 0.1879 \| 0.9524 \| \| 0.0871 \| 4.992 \| 156 \| 0.3482 \| 0.8571 \| \| 0.0702 \| 5.984 \| 187 \| 0.2048 \| 0.9524 \| \| 0.0594 \| 6.976 \| 218 \| 0.2824 \| 0.9048 \| \| 0.0425 \| 8.0 \| 250 \| 0.2567 \| 0.9524 \| \| 0.0398 \| 8.992 \| 281 \| 0.3363 \| 0.8571 \| \| 0.0348 \| 9.984 \| 312 \| 0.2518 \| 0.9524 \| \| 0.0411 \| 10.9760 \| 343 \| 0.0369 \| 1.0 \| \| 0.0445 \| 12.0 \| 375 \| 0.2288 \| 0.9524 \| \| 0.0353 \| 12.992 \| 406 \| 0.2364 \| 0.8571 \| \| 0.0384 \| 13.984 \| 437 \| 0.2255 \| 0.9524 \| \| 0.0331 \| 14.9760 \| 468 \| 0.0572 \| 1.0 \| \| 0.0252 \| 16.0 \| 500 \| 0.2103 \| 0.9524 \| \| 0.0337 \| 16.992 \| 531 \| 0.0295 \| 1.0 \| \| 0.0302 \| 17.984 \| 562 \| 0.2805 \| 0.9048 \| \| 0.0328 \| 18.976 \| 593 \| 0.2127 \| 0.9524 \| \| 0.0315 \| 19.84 \| 620 \| 0.1492 \| 0.9524 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "-k", "-n", "-p", "fn" ]
Encore02/vit-weldclassifyv4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-weldclassifyv4 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3301 - Accuracy: 0.9209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.8207 \| 0.6410 \| 100 \| 1.0336 \| 0.5647 \| \| 0.6506 \| 1.2821 \| 200 \| 1.1982 \| 0.5791 \| \| 0.5324 \| 1.9231 \| 300 \| 0.6060 \| 0.7770 \| \| 0.2486 \| 2.5641 \| 400 \| 0.7294 \| 0.7518 \| \| 0.1366 \| 3.2051 \| 500 \| 0.4832 \| 0.8417 \| \| 0.3124 \| 3.8462 \| 600 \| 0.8676 \| 0.7626 \| \| 0.0296 \| 4.4872 \| 700 \| 0.4233 \| 0.8885 \| \| 0.0723 \| 5.1282 \| 800 \| 0.4470 \| 0.8849 \| \| 0.0342 \| 5.7692 \| 900 \| 0.3406 \| 0.9173 \| \| 0.0055 \| 6.4103 \| 1000 \| 0.3301 \| 0.9209 \| \| 0.0048 \| 7.0513 \| 1100 \| 0.3471 \| 0.9173 \| \| 0.0036 \| 7.6923 \| 1200 \| 0.3346 \| 0.9137 \| \| 0.003 \| 8.3333 \| 1300 \| 0.3498 \| 0.9137 \| \| 0.003 \| 8.9744 \| 1400 \| 0.3549 \| 0.9101 \| \| 0.0027 \| 9.6154 \| 1500 \| 0.3569 \| 0.9137 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "a", "b", "c", "d" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v14	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v14 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.2003 - Accuracy: 0.8125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 22 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.1165 \| 8.3333 \| 100 \| 1.4525 \| 0.5 \| \| 0.7716 \| 16.6667 \| 200 \| 1.2092 \| 0.8125 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v15	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v15 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.1946 - Accuracy: 0.7917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 22 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.1388 \| 8.3333 \| 100 \| 1.4610 \| 0.5 \| \| 0.8121 \| 16.6667 \| 200 \| 1.2161 \| 0.7917 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
DouglasBraga/swin-tiny-patch4-window7-224-finetuned-leukemia.v2.2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-leukemia.v2.2 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5507 - Accuracy: 0.7638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.2349 \| 0.9984 \| 312 \| 0.5575 \| 0.7698 \| \| 0.2191 \| 2.0 \| 625 \| 0.5572 \| 0.7618 \| \| 0.2124 \| 2.9984 \| 937 \| 0.5580 \| 0.769 \| \| 0.2207 \| 4.0 \| 1250 \| 0.5500 \| 0.763 \| \| 0.2143 \| 4.9984 \| 1562 \| 0.5575 \| 0.7652 \| \| 0.2191 \| 6.0 \| 1875 \| 0.5486 \| 0.7728 \| \| 0.2063 \| 6.9984 \| 2187 \| 0.5594 \| 0.7615 \| \| 0.207 \| 8.0 \| 2500 \| 0.5405 \| 0.7695 \| \| 0.2273 \| 8.9984 \| 2812 \| 0.5568 \| 0.7672 \| \| 0.2136 \| 10.0 \| 3125 \| 0.5483 \| 0.7728 \| \| 0.2184 \| 10.9984 \| 3437 \| 0.5606 \| 0.7665 \| \| 0.212 \| 12.0 \| 3750 \| 0.5578 \| 0.761 \| \| 0.1903 \| 12.9984 \| 4062 \| 0.5371 \| 0.769 \| \| 0.2487 \| 14.0 \| 4375 \| 0.5582 \| 0.7645 \| \| 0.2025 \| 14.9984 \| 4687 \| 0.5414 \| 0.7778 \| \| 0.2207 \| 16.0 \| 5000 \| 0.5376 \| 0.7685 \| \| 0.2012 \| 16.9984 \| 5312 \| 0.5489 \| 0.7702 \| \| 0.2198 \| 18.0 \| 5625 \| 0.5560 \| 0.7752 \| \| 0.2171 \| 18.9984 \| 5937 \| 0.5570 \| 0.7725 \| \| 0.2116 \| 20.0 \| 6250 \| 0.5622 \| 0.7625 \| \| 0.2162 \| 20.9984 \| 6562 \| 0.5587 \| 0.7668 \| \| 0.224 \| 22.0 \| 6875 \| 0.5456 \| 0.7712 \| \| 0.212 \| 22.9984 \| 7187 \| 0.5647 \| 0.7652 \| \| 0.2084 \| 24.0 \| 7500 \| 0.5533 \| 0.7672 \| \| 0.2226 \| 24.9984 \| 7812 \| 0.5434 \| 0.7705 \| \| 0.2173 \| 26.0 \| 8125 \| 0.5738 \| 0.7675 \| \| 0.2216 \| 26.9984 \| 8437 \| 0.5557 \| 0.7672 \| \| 0.1918 \| 28.0 \| 8750 \| 0.5502 \| 0.7705 \| \| 0.199 \| 28.9984 \| 9062 \| 0.5456 \| 0.7675 \| \| 0.21 \| 29.9520 \| 9360 \| 0.5483 \| 0.7715 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
DeepTrader/vit-finetuned-1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-finetuned-1 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2750 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.1575 \| 1.0 \| \| No log \| 2.0 \| 2 \| 0.1166 \| 1.0 \| \| No log \| 3.0 \| 3 \| 0.2496 \| 1.0 \| \| No log \| 4.0 \| 4 \| 0.4093 \| 0.5 \| \| No log \| 5.0 \| 5 \| 0.6732 \| 0.5 \| \| No log \| 6.0 \| 6 \| 0.9200 \| 0.5 \| \| No log \| 7.0 \| 7 \| 1.0925 \| 0.5 \| \| No log \| 8.0 \| 8 \| 1.1963 \| 0.5 \| \| No log \| 9.0 \| 9 \| 1.2521 \| 0.5 \| \| No log \| 10.0 \| 10 \| 1.2750 \| 0.5 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cpu - Datasets 3.1.0 - Tokenizers 0.20.1	[ "ironed_tshirts", "not_ironed_tshirts" ]
gabriletomasin/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0740 - Accuracy: 0.9736 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0835 \| 1.0 \| 152 \| 0.1241 \| 0.9616 \| \| 0.8216 \| 2.0 \| 304 \| 0.0918 \| 0.9685 \| \| 0.6378 \| 3.0 \| 456 \| 0.0740 \| 0.9736 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1	[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v16	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v16 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.4158 - Accuracy: 0.6429 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 22 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.2148 \| 8.3333 \| 100 \| 1.6360 \| 0.3214 \| \| 0.8693 \| 16.6667 \| 200 \| 1.4230 \| 0.6429 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v17	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v17 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.3167 - Accuracy: 0.7857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.2481 \| 8.3333 \| 100 \| 1.6308 \| 0.2857 \| \| 0.8449 \| 16.6667 \| 200 \| 1.4119 \| 0.5714 \| \| 0.7993 \| 25.0 \| 300 \| 1.3354 \| 0.7143 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
kiranshivaraju/convnext-tiny-finetuned-aug-pcb_data	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "bad", "good" ]
Razertory/font-identifier	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # font-identifier This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0010 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 3.2404 \| 0.9524 \| 15 \| 3.1135 \| 0.06 \| \| 2.7846 \| 1.9683 \| 31 \| 2.4576 \| 0.33 \| \| 2.3956 \| 2.9841 \| 47 \| 1.7152 \| 0.58 \| \| 1.6171 \| 4.0 \| 63 \| 1.0931 \| 0.775 \| \| 1.2882 \| 4.9524 \| 78 \| 0.6347 \| 0.85 \| \| 0.7191 \| 5.9683 \| 94 \| 0.3957 \| 0.94 \| \| 0.5196 \| 6.9841 \| 110 \| 0.2080 \| 0.965 \| \| 0.3999 \| 8.0 \| 126 \| 0.1480 \| 0.965 \| \| 0.2476 \| 8.9524 \| 141 \| 0.0934 \| 0.985 \| \| 0.2176 \| 9.9683 \| 157 \| 0.0768 \| 0.99 \| \| 0.194 \| 10.9841 \| 173 \| 0.0365 \| 0.995 \| \| 0.1572 \| 12.0 \| 189 \| 0.0616 \| 0.985 \| \| 0.1381 \| 12.9524 \| 204 \| 0.0640 \| 0.985 \| \| 0.1291 \| 13.9683 \| 220 \| 0.0522 \| 0.985 \| \| 0.094 \| 14.9841 \| 236 \| 0.0442 \| 0.99 \| \| 0.1037 \| 16.0 \| 252 \| 0.0492 \| 0.99 \| \| 0.1067 \| 16.9524 \| 267 \| 0.0629 \| 0.985 \| \| 0.0912 \| 17.9683 \| 283 \| 0.0486 \| 0.985 \| \| 0.0702 \| 18.9841 \| 299 \| 0.0344 \| 0.99 \| \| 0.0677 \| 20.0 \| 315 \| 0.0242 \| 0.995 \| \| 0.0566 \| 20.9524 \| 330 \| 0.0295 \| 0.99 \| \| 0.0742 \| 21.9683 \| 346 \| 0.0300 \| 0.99 \| \| 0.0675 \| 22.9841 \| 362 \| 0.0159 \| 1.0 \| \| 0.0501 \| 24.0 \| 378 \| 0.0105 \| 0.995 \| \| 0.0651 \| 24.9524 \| 393 \| 0.0362 \| 0.995 \| \| 0.0665 \| 25.9683 \| 409 \| 0.0335 \| 0.985 \| \| 0.0533 \| 26.9841 \| 425 \| 0.0369 \| 0.99 \| \| 0.0487 \| 28.0 \| 441 \| 0.0296 \| 0.99 \| \| 0.0384 \| 28.9524 \| 456 \| 0.0177 \| 0.995 \| \| 0.038 \| 29.9683 \| 472 \| 0.0176 \| 0.995 \| \| 0.0342 \| 30.9841 \| 488 \| 0.0165 \| 0.995 \| \| 0.055 \| 32.0 \| 504 \| 0.0199 \| 0.995 \| \| 0.0418 \| 32.9524 \| 519 \| 0.0022 \| 1.0 \| \| 0.0447 \| 33.9683 \| 535 \| 0.0071 \| 0.995 \| \| 0.0436 \| 34.9841 \| 551 \| 0.0587 \| 0.98 \| \| 0.0307 \| 36.0 \| 567 \| 0.0244 \| 0.995 \| \| 0.0413 \| 36.9524 \| 582 \| 0.0227 \| 0.99 \| \| 0.0351 \| 37.9683 \| 598 \| 0.0323 \| 0.99 \| \| 0.0267 \| 38.9841 \| 614 \| 0.0510 \| 0.985 \| \| 0.0259 \| 40.0 \| 630 \| 0.0009 \| 1.0 \| \| 0.0245 \| 40.9524 \| 645 \| 0.0017 \| 1.0 \| \| 0.0227 \| 41.9683 \| 661 \| 0.0208 \| 0.995 \| \| 0.0458 \| 42.9841 \| 677 \| 0.0445 \| 0.99 \| \| 0.0263 \| 44.0 \| 693 \| 0.0339 \| 0.99 \| \| 0.0458 \| 44.9524 \| 708 \| 0.0124 \| 0.995 \| \| 0.0374 \| 45.9683 \| 724 \| 0.0253 \| 0.995 \| \| 0.0413 \| 46.9841 \| 740 \| 0.0025 \| 1.0 \| \| 0.0413 \| 47.6190 \| 750 \| 0.0010 \| 1.0 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.0 - Datasets 3.1.0 - Tokenizers 0.20.1	[ "abeezee", "aclonica", "baloo2", "bangers", "barlowcondensed", "deftonestylus", "kanit", "onean", "worksans", "dintalksans", "kingsbridgeexlt", "steelfishoutline", "adamina", "adventpro", "akronim", "aladin", "alef", "aleo", "alexbrush", "amaranth" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v18	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v18 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.5308 - Accuracy: 0.3571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 6 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2464 \| 12.5 \| 100 \| 1.6494 \| 0.2857 \| \| 0.9502 \| 25.0 \| 200 \| 1.5193 \| 0.3929 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v19	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v19 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.2440 - Accuracy: 0.75 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v20	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v20 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.6906 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 18 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.3392 \| 8.3333 \| 100 \| 0.8138 \| 0.8571 \| \| 0.2148 \| 16.6667 \| 200 \| 0.7027 \| 0.8571 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v21	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v21 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.5714 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.1615 \| 8.3333 \| 100 \| 0.6069 \| 0.8571 \| \| 0.1215 \| 16.6667 \| 200 \| 0.5686 \| 0.8571 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v22	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v22 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.5265 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.1127 \| 4.3478 \| 100 \| 0.5795 \| 0.8571 \| \| 0.0985 \| 8.6957 \| 200 \| 0.5159 \| 0.8571 \| \| 0.0799 \| 13.0435 \| 300 \| 0.5685 \| 0.8571 \| \| 0.078 \| 17.3913 \| 400 \| 0.5435 \| 0.8571 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
tiendoan/finetune-clip-vit-large-patch14	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetune-clip-vit-large-patch14 This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6545 - F1: 0.6242 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 0.7084 \| 0.3690 \| 100 \| 0.7173 \| 0.5405 \| \| 0.6818 \| 0.7380 \| 200 \| 0.7269 \| 0.5683 \| \| 0.7169 \| 1.1070 \| 300 \| 0.6949 \| 0.5683 \| \| 0.6957 \| 1.4760 \| 400 \| 0.6799 \| 0.5650 \| \| 0.6218 \| 1.8450 \| 500 \| 0.7344 \| 0.5766 \| \| 0.6406 \| 2.2140 \| 600 \| 0.6600 \| 0.6118 \| \| 0.6645 \| 2.5830 \| 700 \| 0.6581 \| 0.6113 \| \| 0.6546 \| 2.9520 \| 800 \| 0.6549 \| 0.6192 \| \| 0.6068 \| 3.3210 \| 900 \| 0.6542 \| 0.6224 \| \| 0.6351 \| 3.6900 \| 1000 \| 0.6545 \| 0.6242 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "not-sarcasm", "sarcasm" ]
jix0727/vit-base-beans	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0670 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2797 \| 1.0 \| 130 \| 0.2151 \| 0.9624 \| \| 0.1301 \| 2.0 \| 260 \| 0.1259 \| 0.9774 \| \| 0.1385 \| 3.0 \| 390 \| 0.0962 \| 0.9774 \| \| 0.0764 \| 4.0 \| 520 \| 0.0670 \| 0.9925 \| \| 0.1154 \| 5.0 \| 650 \| 0.0809 \| 0.9774 \| ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
limsoft0401/restnet-18	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v001	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v001 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.1914 - Accuracy: 0.8214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 1.4924 \| 0.5 \| \| No log \| 2.0 \| 12 \| 1.4798 \| 0.5 \| \| No log \| 3.0 \| 18 \| 1.4518 \| 0.5357 \| \| No log \| 4.0 \| 24 \| 1.4402 \| 0.5357 \| \| No log \| 5.0 \| 30 \| 1.4052 \| 0.5357 \| \| No log \| 6.0 \| 36 \| 1.3828 \| 0.6786 \| \| No log \| 7.0 \| 42 \| 1.3588 \| 0.6786 \| \| No log \| 8.0 \| 48 \| 1.3295 \| 0.6786 \| \| No log \| 9.0 \| 54 \| 1.3263 \| 0.7143 \| \| No log \| 10.0 \| 60 \| 1.3072 \| 0.75 \| \| No log \| 11.0 \| 66 \| 1.2918 \| 0.7143 \| \| No log \| 12.0 \| 72 \| 1.2718 \| 0.8214 \| \| No log \| 13.0 \| 78 \| 1.2728 \| 0.7857 \| \| No log \| 14.0 \| 84 \| 1.2628 \| 0.75 \| \| No log \| 15.0 \| 90 \| 1.2333 \| 0.7857 \| \| No log \| 16.0 \| 96 \| 1.2253 \| 0.7857 \| \| No log \| 17.0 \| 102 \| 1.2240 \| 0.7857 \| \| No log \| 18.0 \| 108 \| 1.2249 \| 0.7857 \| \| No log \| 19.0 \| 114 \| 1.2177 \| 0.7857 \| \| No log \| 20.0 \| 120 \| 1.2098 \| 0.7857 \| \| No log \| 21.0 \| 126 \| 1.2029 \| 0.8214 \| \| No log \| 22.0 \| 132 \| 1.1875 \| 0.8571 \| \| No log \| 23.0 \| 138 \| 1.1873 \| 0.8571 \| \| No log \| 24.0 \| 144 \| 1.2051 \| 0.7857 \| \| No log \| 25.0 \| 150 \| 1.1914 \| 0.8214 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v002	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v002 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.0084 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 12 \| 1.1281 \| 0.8571 \| \| No log \| 2.0 \| 24 \| 1.1248 \| 0.8571 \| \| No log \| 3.0 \| 36 \| 1.0930 \| 0.8571 \| \| No log \| 4.0 \| 48 \| 1.1040 \| 0.8214 \| \| No log \| 5.0 \| 60 \| 1.0646 \| 0.8214 \| \| No log \| 6.0 \| 72 \| 1.0540 \| 0.8214 \| \| No log \| 7.0 \| 84 \| 1.0309 \| 0.8571 \| \| No log \| 8.0 \| 96 \| 1.0274 \| 0.8571 \| \| No log \| 9.0 \| 108 \| 1.0155 \| 0.8571 \| \| No log \| 10.0 \| 120 \| 1.0079 \| 0.8571 \| \| No log \| 11.0 \| 132 \| 1.0175 \| 0.8571 \| \| No log \| 12.0 \| 144 \| 1.0029 \| 0.8571 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-in21k-rotated-dungeons-v003	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rotated-dungeons-v003 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.5927 - F1: 0.8548 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 14 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 12 \| 1.6942 \| 0.0095 \| \| No log \| 2.0 \| 24 \| 1.4463 \| 0.3217 \| \| No log \| 3.0 \| 36 \| 1.2808 \| 0.3690 \| \| No log \| 4.0 \| 48 \| 0.9816 \| 0.5798 \| \| No log \| 5.0 \| 60 \| 0.6291 \| 0.8548 \| \| No log \| 6.0 \| 72 \| 0.9176 \| 0.7226 \| \| No log \| 7.0 \| 84 \| 0.7163 \| 0.8548 \| \| No log \| 8.0 \| 96 \| 0.6404 \| 0.8548 \| \| No log \| 9.0 \| 108 \| 0.6627 \| 0.8548 \| \| No log \| 10.0 \| 120 \| 0.7034 \| 0.8177 \| \| No log \| 11.0 \| 132 \| 0.5796 \| 0.8548 \| \| No log \| 12.0 \| 144 \| 0.5746 \| 0.8548 \| \| No log \| 13.0 \| 156 \| 0.5902 \| 0.8548 \| \| No log \| 14.0 \| 168 \| 0.6146 \| 0.8548 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
gustavomalkomes/vit-base-patch16-224-in21k	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the chainyo/rvl-cdip dataset. It achieves the following results on the evaluation set: - Loss: 0.4223 - Accuracy: 0.8788 - Memory Allocated (gb): 1.49 - Max Memory Allocated (gb): 2.1 - Total Memory Available (gb): 126.62 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.0a0+git12138a8 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "advertisement", "budget", "presentation", "questionnaire", "resume", "scientific publication", "scientific report", "specification", "email", "file folder", "form", "handwritten", "invoice", "letter", "memo", "news article" ]
hungryhunglee/my_awesome_food_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
osada-pku/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0688 - Accuracy: 0.9748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2593 \| 1.0 \| 190 \| 0.1323 \| 0.9541 \| \| 0.1607 \| 2.0 \| 380 \| 0.0793 \| 0.9730 \| \| 0.1287 \| 3.0 \| 570 \| 0.0688 \| 0.9748 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 2.11.0 - Tokenizers 0.19.1	[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]
Danx15/vit-msn-small-finetuned-custom-logo	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-msn-small-finetuned-custom-logo This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset. It achieves the following results on the evaluation set: - eval_loss: 6.3923 - eval_model_preparation_time: 0.0035 - eval_accuracy: 0.0031 - eval_runtime: 9.5833 - eval_samples_per_second: 170.087 - eval_steps_per_second: 2.713 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Framework versions - Transformers 4.46.2 - Pytorch 2.4.1+cu121 - Datasets 2.20.0 - Tokenizers 0.20.3	[ "8ballpool - origin", "abinbev - origin", "acluofflorida - origin", "afkarena - draconic resurgence", "afkarena - original", "activision - origin", "acura - origin", "adroll - origin", "age-of-methology - age-of-methology-titans", "age-of-methology - age-of-methology-retold", "age-of-methology - origin", "aion - origin", "akčnítarifygomobil - origin", "amgen - origin", "ancestry - origin", "applestore - origin", "arknights - origin", "assasin's-creed-shadows - origin", "audicenterguatemala - origin", "aura kingdom - link of hearts(japan)", "aura kingdom - r(hongkong)", "aura kingdom - r(taiwan)", "aura kingdom - s", "aura kingdom - anotherfate", "aura kingdom - english", "aura kingdom - taiwan", "axetrader - axetraderfunding", "axetrader - axetraderhub", "axetrader - original", "azurlane - chinese", "azurlane - cross wave (en)", "azurlane - cross wave (jp)", "azurlane - korean", "azurlane - original", "bmw - 1917", "bmw - 1933", "bmw - 1954", "bmw - 1974", "bmw - 1979", "bmw - 2007", "bmw - present", "bmwgroupinösterreich - origin", "barilla - 1924-1936", "barilla - 1936-1949", "barilla - 1949-1952", "barilla - 1952-1954", "barilla - 1954-1969", "barilla - 2003-2015", "barilla - 2015-2022", "barilla - 2022-present", "battlenight - origin", "bayerbrasil - origin", "bestbuy - origin", "bingoblitz - app logo", "bingoblitz - option1", "bingoblitz - option2", "bitlife - bitlifede-lebenssimulation", "bitlife - bitlifeespañol-simuladordevida", "bitlife - option1", "bitlife - option2", "bitlife - option3", "bitlife - option4", "bitlife - option5", "bitlife - option6", "blackclover - english", "blackclover - japan", "blackdesert - mobile", "blackdesert - online", "blackdesert - online resmastered", "blackdesert - remastered", "blackdesert - standard", "blacklivesmatter - origin", "bleach - original", "bleach - thousand-year blood war", "bmbvalve - origin", "byronhollisterfordoñaanacountysheriff2022 - origin", "cdc - cdc-health-care-public", "cdc - original", "cdc - round", "callofduty - black-ops-6", "callofduty - mobilevn", "callofduty - warzonemobile", "caltrate - origin", "centrummedycznebody&mind - origin", "cerave - origin", "chainsaw-man - origin", "coinbeach - origin", "colgate - colgate - chinese", "colgate - colgate 1897", "colgate - colgate 1946", "colgate - colgate 1963", "colgate - colgate 1980", "colgate - colgate 2001", "colgate - colgate 2004", "colgate - colgate 2009", "colgate - colgate 2016", "colgate - colgate 2018", "com2us - origin", "converse - all star", "converse - converse 1963", "converse - converse 1977", "converse - converse 2003", "converse - converse 2007", "converse - converse 2011", "converse - converse 2017", "cricketwireless - origin", "deadpool-&-wolverine - 1st logo", "deadpool-&-wolverine - 2nd logo", "deadpool-&-wolverine - 3nd logo", "death note - 2003-2006", "death note - 2006-present", "death note - into the dark", "dell technologies - emc", "dell technologies - horizontal", "dell technologies - hybrid", "dell technologies - vertical", "demon slayer - english", "demon slayer - japanese", "diablo-vessel-of-hatred - hybrid", "diablo-vessel-of-hatred - picture logo", "dinsko - origin", "doom-the-dark-ages - origin", "dove - 1955", "dove - 1969", "dove - 2004-present", "dove - picture logo", "dragon age - dragon age 2", "dragon age - origins", "dragon age - origins ultimate edition", "dragon nest - dragon nest 1", "dragon nest - dragon nest 2 evolution", "dragon nest - world of dragon nest", "dragon raja - english", "dragon raja - origins", "dragon raja - the blazing dawn", "dragonballz - budokai tenkaichi", "dragonballz - horizontal", "dragonballz - vertical", "dream destination canada - version1", "dream destination canada - version2", "dream destination canada - version3", "dreamwork animation - 1998-2004", "dreamwork animation - 2004-2006", "dreamwork animation - 2007-2018", "dreamwork animation - 2014-2017", "dreamwork animation - 2016-now", "dreamwork animation - 2016-now picture logo", "dreamworkstation - spirit", "dreamworksanimation - origin", "dresslily - version2(horizontal)", "dresslily - version1", "dresslily - version3(vertical)", "dresslily - version4", "dunkin - dunkin donuts 1950", "dunkin - dunkin donuts 1956", "dunkin - dunkin donuts 1960", "dunkin - dunkin donuts 1961", "dunkin - dunkin donuts 1970", "dunkin - dunkin donuts 1976", "dunkin - dunkin donuts 1980", "dunkin - dunkin donuts 2002", "dunkin - dunkin donuts 2007", "dunkin - dunkin donuts 2019-present", "dunkin - dunkin coffee", "dunkin - dunkin donuts", "ecco - version1", "ecco - version2", "ecco - version3", "ecco - version4", "ecco - version5", "ecco - version6", "edhanesfornc - origin", "elden ring - shadow of the erdtree", "elden ring - wordmark logo", "eldenring - icon", "eterniumrudeboy - origin", "fallout - fallout 1", "fallout - fallout 2", "fallout - fallout 3", "fallout - fallout tatics", "fireemblemheroes - origin", "flintlock - version1", "flintlock - version2", "fortnite - fortnite original", "fortnite - fortnite stw 2017", "fortnite - wordmark logo", "fragpunk - origin", "furgonazos-recargados - origin", "gameofsultans - origin", "gap - origin", "gardenscapeslandscapecentre - origin", "gear-of-war-e-day - origin", "genshinimpact - origin", "gintama - english", "gintama - japanese", "glad2glowindonesia - origin", "gmail - 2004 version1", "gmail - 2004 version2", "gmail - 2004-2010", "gmail - 2010-2013", "gmail - 2013-2020", "gmail - 2020-present", "googleplay - origin", "grammarly - origin", "groupon - 2008", "groupon - 2012", "hcahealthcare - hybrid logo", "hcahealthcare - picture logo", "harrypotter - hogwartsmystery", "harrypotter - puzzles&spells", "hellmann's - 1913", "hellmann's - 1929-1945", "hellmann's - 1945-1988", "hellmann's - 1988-2001", "hellmann's - 2001-2004", "hellmann's - 2004-2005", "hellmann's - 2015-2017", "hellmann's - 2017-present", "hertz - origin", "honda - honda car 1961-1969", "honda - honda car 1969-1981", "honda - honda car 1981-2000", "honda - honda car 2000-2024", "honda - honda car 2024-present", "honda - honda car picture logo", "honda - honda motor 1947-1948", "honda - honda motor 1948-1953", "honda - honda motor 1953-1968", "honda - honda motor 1968", "honda - honda motor 1968-1973", "honda - honda motor 1973-1985", "honda - honda motor 1985-1988", "honda - honda motor 1988-2000", "honda - honda motor 2000-present", "hoyoverse - origin", "huawei - app gallery", "huawei - original", "huntexhunter - 1998", "hunterxhunter - 2011", "huyndai - origin", "hyundai - origin", "ikea - origin", "idleheroes - origin", "indeedbrasil - origin", "indiana-johns - origin", "industrywest - origin", "intel - 1968", "intel - 2006", "intel - 2020", "intuitturbotax - origin", "inversionesgrupojif - origin", "johnson'sbaby - origin", "kfc - 1956", "kfc - 1959", "kfc - 1978", "kfc - 1991", "kfc - 1997", "kfc - 2006", "kfc - 2014-2018", "kfc - 2018-present", "kariteinonderbroek - origin", "kia - 1953", "kia - 1964", "kia - 1986", "kia - 1994", "kia - 2012", "kia - 2021", "kinto-one - origin", "kraft - 1926", "kraft - 2012-present", "kraft - 1960-2012", "la-colombe-coffee-roasters - origin", "lastshelter-survival - origin", "lay's - 1932", "lay's - 1965", "lay's - 1986", "lay's - 1997", "lay's - 2003", "lay's - 2007", "lay's - 2019", "lexus - origin", "lifeisstrange - origin", "lineage2 - origin", "lysolus - origin", "madly-madagascar - origin", "mafiacity - version1", "mafiacity - version2", "mark-miller-subaru - origin", "mars - origin", "marvel - loki picture logo", "marvel - loki wordmark", "marvel - marvel studio", "marvel - origin", "marvelcontestofchampions - origin", "marvelstrikeforce - origin", "matchingtonmansion - origin", "mcdonald's - 1940", "mcdonald's - 1948", "mcdonald's - 1955", "mcdonald's - 1961", "mcdonald's - 1968", "mcdonald's - 1975", "mcdonald's - 1992", "mcdonald's - 1999", "mcdonald's - 2000", "mcdonald's - 2006", "mechabreak - origin", "microsoft - origin", "microsoft-flight-simulator - origin", "mobilelegends adversionenture - version1", "mobilelegends adversionenture - version2", "mobilelegends-bangbang - origin", "motorcity - origin", "my hero academy - 2014", "my hero academy - chinese", "my hero academy - japanese", "mythicsummon-idlerpg - origin", "nashvillejusticeleague - origin", "national night out - 2019", "national night out - 2023", "national night out - 2024", "national night out - version1", "national night out - version5", "nike - origin", "nintendo - origin", "nintendoswitch - origin", "novartiscareers - origin", "nộithấtxbox - origin", "o'reillyautoparts - origin", "oasis - origin", "one-punch-man - origin", "onepiece - origin", "optivista - origin", "papajohns - origin", "papermario - origin", "paw-patrol-the-mighty-movie - origin", "pepsi - origin", "perfect dark - 2025", "perfect dark - initiative", "perfect dark - before 2010", "perfectworldm-godswarcis - origin", "perfectworldmobile - origin", "pfizer - new version", "pfizer - old version", "photoshop - origin", "pizza hut - 1999", "pizza hut - 2008", "pizza hut - 2010", "pizza hut - 2014", "pizza hut - 2019", "pokemon - origin", "pringles - origin", "projectmakeover - origin", "purple - origin", "quaker - 1877", "quaker - 1946", "quaker - 1957", "quaker - 1970", "quaker - 1972", "quaker - 2007", "quaker - 2010", "quaker - 2011", "quaker - 2012", "quaker - 2017", "ragnarokonlinerpg - origin", "ragnarokx-nextgeneration - origin", "raid-shadowlegends - origin", "retailmenot - picture logo", "retailmenot - wordmark", "riseofkingdoms - origin", "rosewe - origin", "rotita - origin", "samsung - origin", "sanofibelgium - origin", "sanofiexpertos - origin", "sea of thieves - alternate logo", "sea of thieves - deluxe edition", "sea of thieves - picture logo", "sea of thieves - standard logo", "searchlightpictures - origin", "seresto - origin", "seven knights ii - horizontal", "seven knights ii - vertical", "simcitybuildit - origin", "snickersworkweardeutschland - origin", "spinmaster - origin", "st.judechildren'sresearchhospital-scienceandmedicine - origin", "stalker2 - origin", "starfield - origin", "starbucks - origin", "stateofdecay-3 - hybrid", "stateofdecay-3 - picture", "stateofdecay-3 - workmark", "steam - origin", "stellaartois - origin", "summonerswar - origin", "tresemmépolska - origin", "talesofwind - origin", "teckdesk - origin", "theants-undergroundkingdom - origin", "thehomedepotfoundation - origin", "thesevendeadlysins-idleadventure - origin", "theu.s.concealedcarryassociationforsavinglives - origin", "theuniversityofnorthcarolinaatchapelhill - origin", "thextishell - origin", "tommyhanes - origin", "toyota - origin", "trolls - origin", "tynenol - origin", "ugc - 1982-1988", "ugc - 1988-2001", "ugc - 2001-2011", "ugc - 2011-2018", "ugc - 2018-present", "unicorn-academy - origin", "unilever-food-solutions - origin", "universityofvirginia - origin", "vnggames - origin", "vikingcareers - origin", "visa - origin", "volkswagengroupservices - origin", "wwesupercard - season1+2", "wwesupercard - season3", "wwesupercard - season4", "wwesupercard - season5", "wwesupercard - season6", "wwesupercard - season7", "walgreens - picture logo", "walgreens - wordmark", "walmartcostarica - origin", "war and margic - version1", "war and margic - version2", "war and margic - version3", "war and margic - version4", "wellsfargo - origin", "westgame - origin", "winter-burrow - origin", "women's-health-beauty-award - origin", "world of warcraft - battle for azeroth", "world of warcraft - cataclysm", "world of warcraft - dragonflight", "world of warcraft - legion", "world of warcraft - mists of pandaria", "world of warcraft - shadowlands", "world of warcraft - the burning crusade", "world of warcraft - the war within", "world of warcraft - warlords of draenor", "world of warcraft - wrath of the lich king", "xbox - origin", "xenoblade chronicles - xenoblade chronicles definitive edition", "xenoblade chronicles - xenoblade chronicles 2 torna – the golden country", "xenoblade chronicles - xenoblade chronicles 3 future redeemed", "xenoblade chronicles - xenoblade chronicles 3d", "xenoblade chronicles - xenoblade chronicles x", "zalando - picture logo", "zalando - wordmark", "acer - origin", "adidas - 1950-1971", "adidas - 1967-present", "adidas - 1971-present", "adidas - 1991-present", "adidas - 2002-present", "adidas - 2005-present", "adidas - 2022-present", "alibaba - alibaba cloud hybrid", "alibaba - alibaba cloud picture logo", "alibaba - alibaba group", "alibaba - alibaba picture logo", "alibaba - alibaba.com", "atomfall - origin", "audible - hybrid", "audible - icon", "avowed - origin", "black-rifle-coffee - origin", "cement-australia - origin", "esteelauder - origin", "green-mountain-coffee - origin", "killer-of-the-flower-moon - origin", "lazada - origin", "naruto - origin", "netflix - origin", "nvidia - origin", "punk-bunny-coffee - origin", "south-of-midnight - origin", "the-witcher-wild-hunt - origin" ]
kiranshivaraju/convnext-large-finetuned-aug-pcb_data	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "bad", "good" ]
kiranshivaraju/swin-tiny-patch4-window7-224-finetuned-aug-pcb	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-aug-pcb This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5192 - Accuracy: 0.7465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5945 \| 1.0 \| 15 \| 0.5192 \| 0.7465 \| \| 0.5435 \| 2.0 \| 30 \| 0.4960 \| 0.7230 \| \| 0.5187 \| 3.0 \| 45 \| 0.4820 \| 0.7418 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "bad", "good" ]
kiranshivaraju/resnet-50-pac_aug	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-pac_aug This model is a fine-tuned version of [kiranshivaraju/resnet-50-pac_aug](https://huggingface.co/kiranshivaraju/resnet-50-pac_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6055 - Recall: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.6335 \| 1.0 \| 15 \| 0.6352 \| 0.0 \| \| 0.643 \| 2.0 \| 30 \| 0.6276 \| 0.0 \| \| 0.6434 \| 3.0 \| 45 \| 0.6228 \| 0.0 \| \| 0.6305 \| 4.0 \| 60 \| 0.6156 \| 0.0 \| \| 0.6339 \| 5.0 \| 75 \| 0.6167 \| 0.0 \| \| 0.6346 \| 6.0 \| 90 \| 0.6112 \| 0.0 \| \| 0.6235 \| 7.0 \| 105 \| 0.6095 \| 0.0 \| \| 0.6279 \| 8.0 \| 120 \| 0.6082 \| 0.0 \| \| 0.6241 \| 9.0 \| 135 \| 0.6073 \| 0.0 \| \| 0.6233 \| 10.0 \| 150 \| 0.6055 \| 0.0 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "bad", "good" ]
ricardoSLabs/CIDAUT	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CIDAUT This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0808 - Accuracy: 0.9861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 0.5989 \| 0.6759 \| \| No log \| 2.0 \| 8 \| 0.3383 \| 0.9444 \| \| 0.5387 \| 3.0 \| 12 \| 0.1813 \| 0.9769 \| \| 0.5387 \| 4.0 \| 16 \| 0.1283 \| 0.9676 \| \| 0.1494 \| 5.0 \| 20 \| 0.0808 \| 0.9861 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "editada", "real" ]
ricardoSLabs/CIDAUTv2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CIDAUTv2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2719 - Accuracy: 0.9259 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 0.7322 \| 0.5880 \| \| No log \| 2.0 \| 8 \| 0.6585 \| 0.5972 \| \| 0.7438 \| 3.0 \| 12 \| 0.6115 \| 0.7222 \| \| 0.7438 \| 4.0 \| 16 \| 0.5726 \| 0.7546 \| \| 0.5781 \| 5.0 \| 20 \| 0.4803 \| 0.7824 \| \| 0.5781 \| 6.0 \| 24 \| 0.4627 \| 0.8333 \| \| 0.5781 \| 7.0 \| 28 \| 0.4060 \| 0.8056 \| \| 0.4511 \| 8.0 \| 32 \| 0.3512 \| 0.8796 \| \| 0.4511 \| 9.0 \| 36 \| 0.2725 \| 0.9028 \| \| 0.296 \| 10.0 \| 40 \| 0.2719 \| 0.9259 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "editada", "real" ]
kiranshivaraju/convnext-large-224-finetuned-dog-vs-cat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnext-large-224-finetuned-dog-vs-cat This model is a fine-tuned version of [facebook/convnext-large-224](https://huggingface.co/facebook/convnext-large-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3586 - Recall: 0.9783 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 0.496 \| 0.9954 \| 164 \| 0.4792 \| 0.9201 \| \| 0.3996 \| 1.9970 \| 329 \| 0.3836 \| 0.9765 \| \| 0.373 \| 2.9863 \| 492 \| 0.3586 \| 0.9783 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "cat", "dog" ]
tiendoan/finetune-vit-base-patch16-224	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetune-vit-base-patch16-224 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "image-sarcasm", "multi-sarcasm", "not-sarcasm", "text-sarcasm" ]
e1010101/vit-384-tongue-image-segmented-augmented	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segmented-augmented This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5815 - Precision: 0.8308 - Recall: 0.9136 - F1: 0.8703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.081 \| 1.0 \| 327 \| 0.3693 \| 0.8464 \| 0.8970 \| 0.8710 \| \| 0.0151 \| 2.0 \| 654 \| 0.4906 \| 0.8171 \| 0.8904 \| 0.8521 \| \| 0.0066 \| 3.0 \| 981 \| 0.5194 \| 0.8416 \| 0.9003 \| 0.8700 \| \| 0.0029 \| 4.0 \| 1308 \| 0.5671 \| 0.8308 \| 0.9136 \| 0.8703 \| \| 0.0026 \| 5.0 \| 1635 \| 0.5815 \| 0.8308 \| 0.9136 \| 0.8703 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1	[ "crack", "red-dots", "toothmark" ]
obivine/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0156 - Accuracy: 0.9972 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.5582 \| 0.9697 \| 16 \| 0.0498 \| 0.9903 \| \| 0.0905 \| 2.0 \| 33 \| 0.0156 \| 0.9972 \| \| 0.0762 \| 2.9091 \| 48 \| 0.0167 \| 0.9972 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1	[ "cat", "dog" ]
soplac/test2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8384 - Accuracy: 0.75 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 1.1029 \| 0.125 \| \| No log \| 2.0 \| 2 \| 1.0384 \| 0.625 \| \| No log \| 3.0 \| 3 \| 0.9989 \| 0.75 \| \| No log \| 4.0 \| 4 \| 0.9557 \| 0.75 \| \| No log \| 5.0 \| 5 \| 0.8941 \| 0.75 \| \| No log \| 6.0 \| 6 \| 0.8952 \| 0.75 \| \| No log \| 7.0 \| 7 \| 0.8669 \| 0.75 \| \| No log \| 8.0 \| 8 \| 0.8705 \| 0.75 \| \| No log \| 9.0 \| 9 \| 0.8357 \| 0.75 \| \| 0.4538 \| 10.0 \| 10 \| 0.8384 \| 0.75 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "pastel colors runway fashion", "runway fashion outfit", "stripes runway fashion" ]
ricardoSLabs/drive_scene_time_day	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # drive_scene_time_day This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0484 - Accuracy: 0.9891 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.0701 \| 0.9855 \| 34 \| 0.0746 \| 0.9818 \| \| 0.0666 \| 2.0 \| 69 \| 0.0632 \| 0.9836 \| \| 0.0565 \| 2.9855 \| 103 \| 0.0556 \| 0.9873 \| \| 0.0412 \| 4.0 \| 138 \| 0.0602 \| 0.9864 \| \| 0.0397 \| 4.9275 \| 170 \| 0.0484 \| 0.9891 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "daytime", "night" ]
tiendoan/finetune_vit_base_patch16_224_1epoch	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetune_vit_base_patch16_224_1epoch This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "image-sarcasm", "multi-sarcasm", "not-sarcasm", "text-sarcasm" ]
matinbaig43/swinv2-tiny-patch4-window16-256-finetuned-plantdisease	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swinv2-tiny-patch4-window16-256-finetuned-plantdisease This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "pepper__bell___bacterial_spot", "pepper__bell___healthy", "potato___early_blight", "potato___late_blight", "potato___healthy", "tomato_bacterial_spot", "tomato_early_blight", "tomato_late_blight", "tomato_leaf_mold", "tomato_septoria_leaf_spot", "tomato_spider_mites_two_spotted_spider_mite", "tomato__target_spot", "tomato__tomato_yellowleaf__curl_virus", "tomato__tomato_mosaic_virus", "tomato_healthy" ]
pku-sasahara/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Accuracy: 0.9781 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2614 \| 1.0 \| 190 \| 0.1177 \| 0.9644 \| \| 0.1862 \| 2.0 \| 380 \| 0.0753 \| 0.9748 \| \| 0.1673 \| 3.0 \| 570 \| 0.0623 \| 0.9781 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 2.11.0 - Tokenizers 0.19.1	[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]
ricardoSLabs/pre_CIDAUTv1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pre_CIDAUTv1 This model is a fine-tuned version of [ricardoSLabs/drive_scene_time_day](https://huggingface.co/ricardoSLabs/drive_scene_time_day) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6832 - Accuracy: 0.5741 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 1.0035 \| 0.5741 \| \| No log \| 2.0 \| 8 \| 0.7069 \| 0.4583 \| \| 1.3203 \| 3.0 \| 12 \| 0.6761 \| 0.5741 \| \| 1.3203 \| 4.0 \| 16 \| 0.6869 \| 0.5741 \| \| 0.6943 \| 5.0 \| 20 \| 0.6832 \| 0.5741 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "editada", "real" ]
srusti03/swinv2-tiny-patch4-window16-256-finetuned-plantdisease	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swinv2-tiny-patch4-window16-256-finetuned-plantdisease This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0636 - Accuracy: 0.9777 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.103 \| 1.0 \| 516 \| 0.0636 \| 0.9777 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "pepper__bell___bacterial_spot", "pepper__bell___healthy", "potato___early_blight", "potato___late_blight", "potato___healthy", "tomato_bacterial_spot", "tomato_early_blight", "tomato_late_blight", "tomato_leaf_mold", "tomato_septoria_leaf_spot", "tomato_spider_mites_two_spotted_spider_mite", "tomato__target_spot", "tomato__tomato_yellowleaf__curl_virus", "tomato__tomato_mosaic_virus", "tomato_healthy" ]
soplac/stripes	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # stripes This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2177 - Accuracy: 0.9194 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.9032 \| 7 \| 0.6456 \| 0.7742 \| \| 0.6684 \| 1.9355 \| 15 \| 0.4995 \| 0.8629 \| \| 0.4818 \| 2.9677 \| 23 \| 0.3788 \| 0.8629 \| \| 0.3026 \| 4.0 \| 31 \| 0.2881 \| 0.9113 \| \| 0.3026 \| 4.9032 \| 38 \| 0.2530 \| 0.9032 \| \| 0.1806 \| 5.9355 \| 46 \| 0.2359 \| 0.9194 \| \| 0.1161 \| 6.9677 \| 54 \| 0.2288 \| 0.9194 \| \| 0.0894 \| 8.0 \| 62 \| 0.2043 \| 0.9435 \| \| 0.0894 \| 8.9032 \| 69 \| 0.2042 \| 0.9355 \| \| 0.0723 \| 9.0323 \| 70 \| 0.2177 \| 0.9194 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "not_stripes", "stripes" ]
ricardoSLabs/pre_CIDAUTv2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pre_CIDAUTv2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0031 - Accuracy: 0.9991 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.3603 \| 0.9639 \| 20 \| 0.2950 \| 0.8633 \| \| 0.068 \| 1.9759 \| 41 \| 0.0205 \| 0.9921 \| \| 0.0484 \| 2.9880 \| 62 \| 0.0384 \| 0.9885 \| \| 0.0211 \| 4.0 \| 83 \| 0.0082 \| 0.9982 \| \| 0.0145 \| 4.8193 \| 100 \| 0.0031 \| 0.9991 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "editada", "real" ]
cvmil/swin-base-patch4-window7-224-augmented	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-base-patch4-window7-224_11092024 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5135 - Accuracy: 0.8337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0366 \| 1.0 \| 400 \| 0.9471 \| 0.72 \| \| 0.8257 \| 2.0 \| 800 \| 0.7889 \| 0.7538 \| \| 0.7119 \| 3.0 \| 1200 \| 0.7232 \| 0.7775 \| \| 0.6969 \| 4.0 \| 1600 \| 0.6739 \| 0.7837 \| \| 0.6487 \| 5.0 \| 2000 \| 0.6371 \| 0.7863 \| \| 0.5956 \| 6.0 \| 2400 \| 0.6198 \| 0.7887 \| \| 0.5604 \| 7.0 \| 2800 \| 0.5941 \| 0.8025 \| \| 0.5732 \| 8.0 \| 3200 \| 0.5867 \| 0.795 \| \| 0.5578 \| 9.0 \| 3600 \| 0.5705 \| 0.8025 \| \| 0.5449 \| 10.0 \| 4000 \| 0.5575 \| 0.8113 \| \| 0.5419 \| 11.0 \| 4400 \| 0.5505 \| 0.8213 \| \| 0.5086 \| 12.0 \| 4800 \| 0.5385 \| 0.8213 \| \| 0.4929 \| 13.0 \| 5200 \| 0.5340 \| 0.8213 \| \| 0.4701 \| 14.0 \| 5600 \| 0.5297 \| 0.8187 \| \| 0.4803 \| 15.0 \| 6000 \| 0.5240 \| 0.8225 \| \| 0.4988 \| 16.0 \| 6400 \| 0.5197 \| 0.83 \| \| 0.4842 \| 17.0 \| 6800 \| 0.5165 \| 0.8313 \| \| 0.4917 \| 18.0 \| 7200 \| 0.5148 \| 0.8313 \| \| 0.4734 \| 19.0 \| 7600 \| 0.5140 \| 0.8325 \| \| 0.4714 \| 20.0 \| 8000 \| 0.5135 \| 0.8337 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.1+cpu - Datasets 3.0.1 - Tokenizers 0.20.1	[ "bacterial leaf blight", "brown spot", "healthy rice leaf", "leaf blast", "leaf scald", "narrow brown leaf spot", "rice hispa", "sheath blight" ]
ozair23/mobilenet_v2_1.0_224-finetuned-plantdisease	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mobilenet_v2_1.0_224-finetuned-plantdisease This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0702 - Accuracy: 0.9777 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\| \| 0.3974 \| 0.9996 \| 1145 \| 0.3599 \| 0.8979 \| \| 0.2155 \| 2.0 \| 2291 \| 0.1525 \| 0.9603 \| \| 0.2058 \| 2.9996 \| 3436 \| 0.1492 \| 0.9559 \| \| 0.1524 \| 4.0 \| 4582 \| 0.1025 \| 0.9694 \| \| 0.1274 \| 4.9996 \| 5727 \| 0.0928 \| 0.9706 \| \| 0.1141 \| 6.0 \| 6873 \| 0.0874 \| 0.9723 \| \| 0.1275 \| 6.9996 \| 8018 \| 0.1226 \| 0.9620 \| \| 0.1323 \| 8.0 \| 9164 \| 0.0702 \| 0.9777 \| \| 0.1212 \| 8.9996 \| 10309 \| 0.1257 \| 0.9607 \| \| 0.0981 \| 9.9956 \| 11450 \| 0.0750 \| 0.9751 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "apple___apple_scab", "apple___black_rot", "apple___cedar_apple_rust", "apple___healthy", "blueberry___healthy", "cherry_(including_sour)___powdery_mildew", "cherry_(including_sour)___healthy", "corn_(maize)___cercospora_leaf_spot gray_leaf_spot", "corn_(maize)___common_rust_", "corn_(maize)___northern_leaf_blight", "corn_(maize)___healthy", "grape___black_rot", "grape___esca_(black_measles)", "grape___leaf_blight_(isariopsis_leaf_spot)", "grape___healthy", "orange___haunglongbing_(citrus_greening)", "peach___bacterial_spot", "peach___healthy", "pepper,_bell___bacterial_spot", "pepper,_bell___healthy", "potato___early_blight", "potato___late_blight", "potato___healthy", "raspberry___healthy", "soybean___healthy", "squash___powdery_mildew", "strawberry___leaf_scorch", "strawberry___healthy", "tomato___bacterial_spot", "tomato___early_blight", "tomato___late_blight", "tomato___leaf_mold", "tomato___septoria_leaf_spot", "tomato___spider_mites two-spotted_spider_mite", "tomato___target_spot", "tomato___tomato_yellow_leaf_curl_virus", "tomato___tomato_mosaic_virus", "tomato___healthy" ]
KiViDrag/ViT_bloodmnist_std_60	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_bloodmnist_std_60 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3387 - Accuracy: 0.8913 - F1: 0.8681 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.7924 \| 0.0595 \| 200 \| 1.7254 \| 0.4106 \| 0.3373 \| \| 0.4527 \| 0.1189 \| 400 \| 1.2129 \| 0.6641 \| 0.5830 \| \| 0.4004 \| 0.1784 \| 600 \| 0.9461 \| 0.7547 \| 0.6592 \| \| 0.3657 \| 0.2378 \| 800 \| 0.7647 \| 0.8084 \| 0.7425 \| \| 0.3506 \| 0.2973 \| 1000 \| 0.6377 \| 0.8043 \| 0.7888 \| \| 0.3081 \| 0.3567 \| 1200 \| 0.6453 \| 0.8055 \| 0.7728 \| \| 0.2848 \| 0.4162 \| 1400 \| 0.6006 \| 0.8195 \| 0.7385 \| \| 0.28 \| 0.4756 \| 1600 \| 0.8017 \| 0.7097 \| 0.6680 \| \| 0.3041 \| 0.5351 \| 1800 \| 0.4496 \| 0.8586 \| 0.8187 \| \| 0.272 \| 0.5945 \| 2000 \| 0.7200 \| 0.7541 \| 0.7126 \| \| 0.259 \| 0.6540 \| 2200 \| 0.5110 \| 0.8131 \| 0.7867 \| \| 0.2524 \| 0.7134 \| 2400 \| 0.4057 \| 0.8633 \| 0.8343 \| \| 0.2439 \| 0.7729 \| 2600 \| 0.4060 \| 0.8604 \| 0.8288 \| \| 0.2422 \| 0.8323 \| 2800 \| 0.4496 \| 0.8627 \| 0.8229 \| \| 0.2332 \| 0.8918 \| 3000 \| 0.4147 \| 0.8586 \| 0.8157 \| \| 0.2192 \| 0.9512 \| 3200 \| 0.3414 \| 0.8756 \| 0.8578 \| \| 0.212 \| 1.0107 \| 3400 \| 0.4139 \| 0.8464 \| 0.8048 \| \| 0.1738 \| 1.0702 \| 3600 \| 0.5111 \| 0.8213 \| 0.7703 \| \| 0.1718 \| 1.1296 \| 3800 \| 0.3725 \| 0.8674 \| 0.8398 \| \| 0.1679 \| 1.1891 \| 4000 \| 0.4632 \| 0.8400 \| 0.8283 \| \| 0.1706 \| 1.2485 \| 4200 \| 0.4331 \| 0.8511 \| 0.8216 \| \| 0.1602 \| 1.3080 \| 4400 \| 0.4359 \| 0.8382 \| 0.8094 \| \| 0.1502 \| 1.3674 \| 4600 \| 0.5608 \| 0.7903 \| 0.7278 \| \| 0.1713 \| 1.4269 \| 4800 \| 0.3495 \| 0.8762 \| 0.8555 \| \| 0.1544 \| 1.4863 \| 5000 \| 0.5389 \| 0.8072 \| 0.7830 \| \| 0.1477 \| 1.5458 \| 5200 \| 0.3790 \| 0.8645 \| 0.8318 \| \| 0.1515 \| 1.6052 \| 5400 \| 0.4332 \| 0.8300 \| 0.7977 \| \| 0.1465 \| 1.6647 \| 5600 \| 0.5368 \| 0.8230 \| 0.7546 \| \| 0.1409 \| 1.7241 \| 5800 \| 0.4630 \| 0.8493 \| 0.8004 \| \| 0.1294 \| 1.7836 \| 6000 \| 0.3530 \| 0.8803 \| 0.8396 \| \| 0.1252 \| 1.8430 \| 6200 \| 0.3822 \| 0.875 \| 0.8410 \| \| 0.1273 \| 1.9025 \| 6400 \| 0.2833 \| 0.9042 \| 0.8802 \| \| 0.1196 \| 1.9620 \| 6600 \| 0.3610 \| 0.8791 \| 0.8407 \| \| 0.1018 \| 2.0214 \| 6800 \| 0.3968 \| 0.8581 \| 0.8354 \| \| 0.0692 \| 2.0809 \| 7000 \| 0.4695 \| 0.8458 \| 0.8122 \| \| 0.0674 \| 2.1403 \| 7200 \| 0.4450 \| 0.8534 \| 0.8136 \| \| 0.0615 \| 2.1998 \| 7400 \| 0.3819 \| 0.8721 \| 0.8483 \| \| 0.0574 \| 2.2592 \| 7600 \| 0.3725 \| 0.875 \| 0.8468 \| \| 0.067 \| 2.3187 \| 7800 \| 0.4728 \| 0.8481 \| 0.8078 \| \| 0.0684 \| 2.3781 \| 8000 \| 0.3483 \| 0.8873 \| 0.8590 \| \| 0.066 \| 2.4376 \| 8200 \| 0.3763 \| 0.8797 \| 0.8514 \| \| 0.0521 \| 2.4970 \| 8400 \| 0.4029 \| 0.8657 \| 0.8377 \| \| 0.0553 \| 2.5565 \| 8600 \| 0.4100 \| 0.8697 \| 0.8382 \| \| 0.0534 \| 2.6159 \| 8800 \| 0.3810 \| 0.8762 \| 0.8469 \| \| 0.0475 \| 2.6754 \| 9000 \| 0.4043 \| 0.8703 \| 0.8416 \| \| 0.054 \| 2.7348 \| 9200 \| 0.4014 \| 0.8762 \| 0.8460 \| \| 0.0526 \| 2.7943 \| 9400 \| 0.4015 \| 0.875 \| 0.8439 \| \| 0.0481 \| 2.8537 \| 9600 \| 0.4047 \| 0.8779 \| 0.8455 \| \| 0.0442 \| 2.9132 \| 9800 \| 0.3997 \| 0.8773 \| 0.8449 \| \| 0.0372 \| 2.9727 \| 10000 \| 0.4131 \| 0.8762 \| 0.8433 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]
KiViDrag/ViT_bloodmnist_std_45	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_bloodmnist_std_45 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2659 - Accuracy: 0.9065 - F1: 0.8909 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.6113 \| 0.0595 \| 200 \| 0.8908 \| 0.6846 \| 0.5917 \| \| 0.3578 \| 0.1189 \| 400 \| 0.5958 \| 0.7956 \| 0.7548 \| \| 0.3118 \| 0.1784 \| 600 \| 0.5688 \| 0.7810 \| 0.7132 \| \| 0.2815 \| 0.2378 \| 800 \| 0.5227 \| 0.7961 \| 0.7645 \| \| 0.266 \| 0.2973 \| 1000 \| 0.6554 \| 0.7687 \| 0.7229 \| \| 0.2353 \| 0.3567 \| 1200 \| 0.3328 \| 0.8838 \| 0.8615 \| \| 0.2297 \| 0.4162 \| 1400 \| 0.4696 \| 0.8592 \| 0.7990 \| \| 0.2267 \| 0.4756 \| 1600 \| 0.4362 \| 0.8493 \| 0.8117 \| \| 0.2266 \| 0.5351 \| 1800 \| 0.3286 \| 0.8838 \| 0.8407 \| \| 0.2047 \| 0.5945 \| 2000 \| 0.3614 \| 0.8697 \| 0.8382 \| \| 0.1948 \| 0.6540 \| 2200 \| 0.3144 \| 0.8843 \| 0.8546 \| \| 0.1953 \| 0.7134 \| 2400 \| 0.3805 \| 0.8657 \| 0.8180 \| \| 0.1728 \| 0.7729 \| 2600 \| 0.3364 \| 0.8820 \| 0.8339 \| \| 0.1658 \| 0.8323 \| 2800 \| 0.2873 \| 0.8978 \| 0.8743 \| \| 0.1594 \| 0.8918 \| 3000 \| 0.3062 \| 0.8914 \| 0.8580 \| \| 0.1649 \| 0.9512 \| 3200 \| 0.3313 \| 0.8867 \| 0.8577 \| \| 0.1508 \| 1.0107 \| 3400 \| 0.2117 \| 0.9217 \| 0.9133 \| \| 0.1062 \| 1.0702 \| 3600 \| 0.2978 \| 0.8919 \| 0.8756 \| \| 0.1091 \| 1.1296 \| 3800 \| 0.2832 \| 0.9019 \| 0.8831 \| \| 0.0993 \| 1.1891 \| 4000 \| 0.3275 \| 0.8943 \| 0.8718 \| \| 0.1001 \| 1.2485 \| 4200 \| 0.3420 \| 0.8896 \| 0.8568 \| \| 0.1092 \| 1.3080 \| 4400 \| 0.2594 \| 0.9130 \| 0.8909 \| \| 0.092 \| 1.3674 \| 4600 \| 0.3181 \| 0.8966 \| 0.8753 \| \| 0.1036 \| 1.4269 \| 4800 \| 0.2721 \| 0.9048 \| 0.8852 \| \| 0.0896 \| 1.4863 \| 5000 \| 0.3795 \| 0.8820 \| 0.8617 \| \| 0.0904 \| 1.5458 \| 5200 \| 0.2382 \| 0.9171 \| 0.8980 \| \| 0.0864 \| 1.6052 \| 5400 \| 0.3845 \| 0.8814 \| 0.8499 \| \| 0.0809 \| 1.6647 \| 5600 \| 0.3189 \| 0.8984 \| 0.8758 \| \| 0.0764 \| 1.7241 \| 5800 \| 0.3952 \| 0.8843 \| 0.8522 \| \| 0.0796 \| 1.7836 \| 6000 \| 0.3656 \| 0.8867 \| 0.8460 \| \| 0.0695 \| 1.8430 \| 6200 \| 0.3266 \| 0.8925 \| 0.8597 \| \| 0.0682 \| 1.9025 \| 6400 \| 0.3247 \| 0.8960 \| 0.8647 \| \| 0.06 \| 1.9620 \| 6600 \| 0.2349 \| 0.9223 \| 0.9055 \| \| 0.0498 \| 2.0214 \| 6800 \| 0.2578 \| 0.9176 \| 0.8952 \| \| 0.0296 \| 2.0809 \| 7000 \| 0.2592 \| 0.9211 \| 0.9070 \| \| 0.0251 \| 2.1403 \| 7200 \| 0.3249 \| 0.9048 \| 0.8797 \| \| 0.02 \| 2.1998 \| 7400 \| 0.2977 \| 0.9165 \| 0.8973 \| \| 0.0274 \| 2.2592 \| 7600 \| 0.3411 \| 0.9013 \| 0.8730 \| \| 0.0241 \| 2.3187 \| 7800 \| 0.3916 \| 0.9013 \| 0.8752 \| \| 0.0253 \| 2.3781 \| 8000 \| 0.2919 \| 0.9136 \| 0.8939 \| \| 0.0197 \| 2.4376 \| 8200 \| 0.3294 \| 0.9077 \| 0.8835 \| \| 0.0209 \| 2.4970 \| 8400 \| 0.3709 \| 0.8966 \| 0.8652 \| \| 0.0175 \| 2.5565 \| 8600 \| 0.3639 \| 0.9001 \| 0.8733 \| \| 0.0191 \| 2.6159 \| 8800 \| 0.3706 \| 0.9048 \| 0.8790 \| \| 0.0167 \| 2.6754 \| 9000 \| 0.3120 \| 0.9171 \| 0.8993 \| \| 0.0224 \| 2.7348 \| 9200 \| 0.3493 \| 0.9048 \| 0.8799 \| \| 0.015 \| 2.7943 \| 9400 \| 0.3398 \| 0.9130 \| 0.8889 \| \| 0.0155 \| 2.8537 \| 9600 \| 0.3707 \| 0.9036 \| 0.8758 \| \| 0.0129 \| 2.9132 \| 9800 \| 0.3467 \| 0.9118 \| 0.8909 \| \| 0.0126 \| 2.9727 \| 10000 \| 0.3470 \| 0.9095 \| 0.8874 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]
KiViDrag/ViT_breastmnist_std_60	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_breastmnist_std_60 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5157 - Accuracy: 0.7756 - F1: 0.6137 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5179 \| 0.2597 \| 20 \| 0.5240 \| 0.7436 \| 0.5385 \| \| 0.4306 \| 0.5195 \| 40 \| 0.4807 \| 0.7949 \| 0.6855 \| \| 0.4258 \| 0.7792 \| 60 \| 0.4812 \| 0.7949 \| 0.6518 \| \| 0.4613 \| 1.0390 \| 80 \| 0.4491 \| 0.8333 \| 0.7247 \| \| 0.4194 \| 1.2987 \| 100 \| 0.4573 \| 0.8333 \| 0.7247 \| \| 0.3693 \| 1.5584 \| 120 \| 0.4665 \| 0.8205 \| 0.6953 \| \| 0.3825 \| 1.8182 \| 140 \| 0.4695 \| 0.7821 \| 0.6733 \| \| 0.387 \| 2.0779 \| 160 \| 0.4410 \| 0.8205 \| 0.7248 \| \| 0.3341 \| 2.3377 \| 180 \| 0.4422 \| 0.8205 \| 0.7367 \| \| 0.3192 \| 2.5974 \| 200 \| 0.4457 \| 0.8205 \| 0.7111 \| \| 0.3062 \| 2.8571 \| 220 \| 0.4575 \| 0.8205 \| 0.7111 \| \| 0.2485 \| 3.1169 \| 240 \| 0.4526 \| 0.8333 \| 0.7383 \| \| 0.2415 \| 3.3766 \| 260 \| 0.4430 \| 0.8462 \| 0.7641 \| \| 0.2377 \| 3.6364 \| 280 \| 0.4529 \| 0.8333 \| 0.7247 \| \| 0.2417 \| 3.8961 \| 300 \| 0.4386 \| 0.8205 \| 0.7111 \| \| 0.1783 \| 4.1558 \| 320 \| 0.4467 \| 0.8333 \| 0.7383 \| \| 0.193 \| 4.4156 \| 340 \| 0.4724 \| 0.8077 \| 0.6823 \| \| 0.1736 \| 4.6753 \| 360 \| 0.4757 \| 0.8333 \| 0.7383 \| \| 0.1656 \| 4.9351 \| 380 \| 0.4677 \| 0.8333 \| 0.7383 \| \| 0.1214 \| 5.1948 \| 400 \| 0.4747 \| 0.8077 \| 0.6981 \| \| 0.0851 \| 5.4545 \| 420 \| 0.4782 \| 0.7949 \| 0.6698 \| \| 0.0893 \| 5.7143 \| 440 \| 0.4842 \| 0.8077 \| 0.6823 \| \| 0.0978 \| 5.9740 \| 460 \| 0.4883 \| 0.8077 \| 0.6823 \| \| 0.0518 \| 6.2338 \| 480 \| 0.4861 \| 0.8077 \| 0.6981 \| \| 0.0662 \| 6.4935 \| 500 \| 0.5017 \| 0.8077 \| 0.6981 \| \| 0.058 \| 6.7532 \| 520 \| 0.5092 \| 0.7949 \| 0.6518 \| \| 0.0511 \| 7.0130 \| 540 \| 0.5003 \| 0.8205 \| 0.7111 \| \| 0.0235 \| 7.2727 \| 560 \| 0.5041 \| 0.8077 \| 0.6823 \| \| 0.0204 \| 7.5325 \| 580 \| 0.5140 \| 0.8205 \| 0.7111 \| \| 0.0196 \| 7.7922 \| 600 \| 0.5122 \| 0.8205 \| 0.7111 \| \| 0.0108 \| 8.0519 \| 620 \| 0.5186 \| 0.8205 \| 0.7111 \| \| 0.012 \| 8.3117 \| 640 \| 0.5315 \| 0.8333 \| 0.7247 \| \| 0.0077 \| 8.5714 \| 660 \| 0.5319 \| 0.8205 \| 0.7111 \| \| 0.0187 \| 8.8312 \| 680 \| 0.5279 \| 0.8205 \| 0.7111 \| \| 0.0063 \| 9.0909 \| 700 \| 0.5304 \| 0.8205 \| 0.7111 \| \| 0.004 \| 9.3506 \| 720 \| 0.5312 \| 0.8205 \| 0.7111 \| \| 0.0044 \| 9.6104 \| 740 \| 0.5310 \| 0.8205 \| 0.7111 \| \| 0.0076 \| 9.8701 \| 760 \| 0.5323 \| 0.8205 \| 0.7111 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "malignant", "normal, benign" ]
Tianmu28/vit_google_vehicle_classification_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vehicle_classification This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0269 - Accuracy: 0.9917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0356 \| 1.0 \| 245 \| 0.0432 \| 0.9869 \| \| 0.0036 \| 2.0 \| 490 \| 0.0403 \| 0.9869 \| \| 0.0004 \| 3.0 \| 735 \| 0.0275 \| 0.9905 \| \| 0.0002 \| 4.0 \| 980 \| 0.0260 \| 0.9917 \| \| 0.0002 \| 5.0 \| 1225 \| 0.0261 \| 0.9917 \| \| 0.0001 \| 6.0 \| 1470 \| 0.0264 \| 0.9917 \| \| 0.0001 \| 7.0 \| 1715 \| 0.0267 \| 0.9917 \| \| 0.0001 \| 8.0 \| 1960 \| 0.0269 \| 0.9917 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6" ]
KiViDrag/ViT_bloodmnist_std_15	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_bloodmnist_std_15 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1123 - Accuracy: 0.9699 - F1: 0.9662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.4602 \| 0.0595 \| 200 \| 0.2846 \| 0.9019 \| 0.8995 \| \| 0.19 \| 0.1189 \| 400 \| 0.2421 \| 0.9118 \| 0.9056 \| \| 0.1612 \| 0.1784 \| 600 \| 0.1811 \| 0.9299 \| 0.9222 \| \| 0.1443 \| 0.2378 \| 800 \| 0.1166 \| 0.9556 \| 0.9491 \| \| 0.1105 \| 0.2973 \| 1000 \| 0.1091 \| 0.9603 \| 0.9560 \| \| 0.0996 \| 0.3567 \| 1200 \| 0.1631 \| 0.9433 \| 0.9292 \| \| 0.0913 \| 0.4162 \| 1400 \| 0.1724 \| 0.9393 \| 0.9285 \| \| 0.0708 \| 0.4756 \| 1600 \| 0.1206 \| 0.9591 \| 0.9540 \| \| 0.0829 \| 0.5351 \| 1800 \| 0.0888 \| 0.9685 \| 0.9625 \| \| 0.0624 \| 0.5945 \| 2000 \| 0.1379 \| 0.9579 \| 0.9519 \| \| 0.0652 \| 0.6540 \| 2200 \| 0.1158 \| 0.9685 \| 0.9667 \| \| 0.0495 \| 0.7134 \| 2400 \| 0.1169 \| 0.9655 \| 0.9642 \| \| 0.0425 \| 0.7729 \| 2600 \| 0.0944 \| 0.9679 \| 0.9643 \| \| 0.0405 \| 0.8323 \| 2800 \| 0.1280 \| 0.9650 \| 0.9605 \| \| 0.0382 \| 0.8918 \| 3000 \| 0.0762 \| 0.9778 \| 0.9755 \| \| 0.0336 \| 0.9512 \| 3200 \| 0.1064 \| 0.9708 \| 0.9697 \| \| 0.0318 \| 1.0107 \| 3400 \| 0.1001 \| 0.9720 \| 0.9682 \| \| 0.0162 \| 1.0702 \| 3600 \| 0.1018 \| 0.9737 \| 0.9720 \| \| 0.0165 \| 1.1296 \| 3800 \| 0.1431 \| 0.9614 \| 0.9537 \| \| 0.0133 \| 1.1891 \| 4000 \| 0.0808 \| 0.9766 \| 0.9736 \| \| 0.0146 \| 1.2485 \| 4200 \| 0.0912 \| 0.9737 \| 0.9707 \| \| 0.0091 \| 1.3080 \| 4400 \| 0.1006 \| 0.9761 \| 0.9747 \| \| 0.0074 \| 1.3674 \| 4600 \| 0.1114 \| 0.9702 \| 0.9680 \| \| 0.0134 \| 1.4269 \| 4800 \| 0.1200 \| 0.9725 \| 0.9705 \| \| 0.012 \| 1.4863 \| 5000 \| 0.1063 \| 0.9720 \| 0.9694 \| \| 0.0099 \| 1.5458 \| 5200 \| 0.1239 \| 0.9690 \| 0.9667 \| \| 0.006 \| 1.6052 \| 5400 \| 0.1308 \| 0.9731 \| 0.9677 \| \| 0.0057 \| 1.6647 \| 5600 \| 0.1479 \| 0.9702 \| 0.9682 \| \| 0.0107 \| 1.7241 \| 5800 \| 0.1194 \| 0.9720 \| 0.9684 \| \| 0.0122 \| 1.7836 \| 6000 \| 0.1083 \| 0.9708 \| 0.9691 \| \| 0.0081 \| 1.8430 \| 6200 \| 0.1087 \| 0.9725 \| 0.9690 \| \| 0.0055 \| 1.9025 \| 6400 \| 0.1063 \| 0.9766 \| 0.9731 \| \| 0.0039 \| 1.9620 \| 6600 \| 0.1530 \| 0.9679 \| 0.9631 \| \| 0.0075 \| 2.0214 \| 6800 \| 0.1052 \| 0.9778 \| 0.9764 \| \| 0.0022 \| 2.0809 \| 7000 \| 0.1340 \| 0.9673 \| 0.9628 \| \| 0.0024 \| 2.1403 \| 7200 \| 0.1034 \| 0.9761 \| 0.9742 \| \| 0.0014 \| 2.1998 \| 7400 \| 0.1039 \| 0.9772 \| 0.9751 \| \| 0.0007 \| 2.2592 \| 7600 \| 0.1032 \| 0.9801 \| 0.9792 \| \| 0.0008 \| 2.3187 \| 7800 \| 0.0984 \| 0.9807 \| 0.9797 \| \| 0.0013 \| 2.3781 \| 8000 \| 0.1034 \| 0.9766 \| 0.9752 \| \| 0.0013 \| 2.4376 \| 8200 \| 0.1049 \| 0.9766 \| 0.9749 \| \| 0.0013 \| 2.4970 \| 8400 \| 0.1006 \| 0.9772 \| 0.9756 \| \| 0.0018 \| 2.5565 \| 8600 \| 0.1157 \| 0.9749 \| 0.9703 \| \| 0.0011 \| 2.6159 \| 8800 \| 0.1049 \| 0.9784 \| 0.9779 \| \| 0.0007 \| 2.6754 \| 9000 \| 0.1167 \| 0.9755 \| 0.9721 \| \| 0.0003 \| 2.7348 \| 9200 \| 0.1058 \| 0.9772 \| 0.9746 \| \| 0.0008 \| 2.7943 \| 9400 \| 0.1049 \| 0.9796 \| 0.9767 \| \| 0.0009 \| 2.8537 \| 9600 \| 0.1084 \| 0.9807 \| 0.9787 \| \| 0.0005 \| 2.9132 \| 9800 \| 0.0999 \| 0.9807 \| 0.9787 \| \| 0.0001 \| 2.9727 \| 10000 \| 0.1001 \| 0.9813 \| 0.9796 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]
KiViDrag/ViT_bloodmnist_std_30	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_bloodmnist_std_30 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1697 - Accuracy: 0.9430 - F1: 0.9339 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.5658 \| 0.0595 \| 200 \| 1.2306 \| 0.5076 \| 0.4526 \| \| 0.2887 \| 0.1189 \| 400 \| 0.6368 \| 0.7751 \| 0.7410 \| \| 0.2406 \| 0.1784 \| 600 \| 0.6641 \| 0.7827 \| 0.7050 \| \| 0.2229 \| 0.2378 \| 800 \| 0.4808 \| 0.8072 \| 0.7832 \| \| 0.1955 \| 0.2973 \| 1000 \| 0.4868 \| 0.8002 \| 0.7827 \| \| 0.1654 \| 0.3567 \| 1200 \| 0.3306 \| 0.8657 \| 0.8466 \| \| 0.1627 \| 0.4162 \| 1400 \| 0.3754 \| 0.8732 \| 0.8367 \| \| 0.1479 \| 0.4756 \| 1600 \| 0.2421 \| 0.9118 \| 0.8949 \| \| 0.1501 \| 0.5351 \| 1800 \| 0.2125 \| 0.9235 \| 0.9076 \| \| 0.1372 \| 0.5945 \| 2000 \| 0.3706 \| 0.8616 \| 0.8337 \| \| 0.1194 \| 0.6540 \| 2200 \| 0.1552 \| 0.9451 \| 0.9370 \| \| 0.1194 \| 0.7134 \| 2400 \| 0.2345 \| 0.9194 \| 0.8992 \| \| 0.1135 \| 0.7729 \| 2600 \| 0.2121 \| 0.9287 \| 0.9113 \| \| 0.1032 \| 0.8323 \| 2800 \| 0.2023 \| 0.9299 \| 0.9152 \| \| 0.1006 \| 0.8918 \| 3000 \| 0.1784 \| 0.9451 \| 0.9376 \| \| 0.0814 \| 0.9512 \| 3200 \| 0.1273 \| 0.9533 \| 0.9484 \| \| 0.0842 \| 1.0107 \| 3400 \| 0.2012 \| 0.9363 \| 0.9240 \| \| 0.0426 \| 1.0702 \| 3600 \| 0.2221 \| 0.9340 \| 0.9280 \| \| 0.06 \| 1.1296 \| 3800 \| 0.2641 \| 0.9100 \| 0.9037 \| \| 0.0632 \| 1.1891 \| 4000 \| 0.1796 \| 0.9433 \| 0.9339 \| \| 0.0506 \| 1.2485 \| 4200 \| 0.2771 \| 0.8989 \| 0.8838 \| \| 0.0467 \| 1.3080 \| 4400 \| 0.1939 \| 0.9393 \| 0.9265 \| \| 0.0469 \| 1.3674 \| 4600 \| 0.1896 \| 0.9410 \| 0.9322 \| \| 0.0457 \| 1.4269 \| 4800 \| 0.1477 \| 0.9509 \| 0.9479 \| \| 0.0416 \| 1.4863 \| 5000 \| 0.2789 \| 0.9206 \| 0.9086 \| \| 0.043 \| 1.5458 \| 5200 \| 0.1832 \| 0.9463 \| 0.9389 \| \| 0.0412 \| 1.6052 \| 5400 \| 0.2100 \| 0.9404 \| 0.9337 \| \| 0.0358 \| 1.6647 \| 5600 \| 0.2368 \| 0.9287 \| 0.9135 \| \| 0.0376 \| 1.7241 \| 5800 \| 0.2668 \| 0.9252 \| 0.9096 \| \| 0.0385 \| 1.7836 \| 6000 \| 0.2145 \| 0.9398 \| 0.9291 \| \| 0.0273 \| 1.8430 \| 6200 \| 0.1995 \| 0.9433 \| 0.9302 \| \| 0.0251 \| 1.9025 \| 6400 \| 0.1900 \| 0.9486 \| 0.9395 \| \| 0.0298 \| 1.9620 \| 6600 \| 0.1617 \| 0.9597 \| 0.9526 \| \| 0.02 \| 2.0214 \| 6800 \| 0.1984 \| 0.9463 \| 0.9343 \| \| 0.0083 \| 2.0809 \| 7000 \| 0.1899 \| 0.9498 \| 0.9377 \| \| 0.0068 \| 2.1403 \| 7200 \| 0.2592 \| 0.9340 \| 0.9199 \| \| 0.0059 \| 2.1998 \| 7400 \| 0.2101 \| 0.9428 \| 0.9335 \| \| 0.0066 \| 2.2592 \| 7600 \| 0.2247 \| 0.9422 \| 0.9259 \| \| 0.0062 \| 2.3187 \| 7800 \| 0.2370 \| 0.9439 \| 0.9348 \| \| 0.0084 \| 2.3781 \| 8000 \| 0.2266 \| 0.9474 \| 0.9390 \| \| 0.0049 \| 2.4376 \| 8200 \| 0.2343 \| 0.9480 \| 0.9354 \| \| 0.0075 \| 2.4970 \| 8400 \| 0.2032 \| 0.9486 \| 0.9378 \| \| 0.0025 \| 2.5565 \| 8600 \| 0.1916 \| 0.9515 \| 0.9436 \| \| 0.0064 \| 2.6159 \| 8800 \| 0.2066 \| 0.9533 \| 0.9436 \| \| 0.004 \| 2.6754 \| 9000 \| 0.2404 \| 0.9445 \| 0.9321 \| \| 0.0029 \| 2.7348 \| 9200 \| 0.2402 \| 0.9439 \| 0.9322 \| \| 0.0008 \| 2.7943 \| 9400 \| 0.2256 \| 0.9468 \| 0.9365 \| \| 0.003 \| 2.8537 \| 9600 \| 0.2265 \| 0.9492 \| 0.9408 \| \| 0.002 \| 2.9132 \| 9800 \| 0.2278 \| 0.9515 \| 0.9419 \| \| 0.0013 \| 2.9727 \| 10000 \| 0.2175 \| 0.9504 \| 0.9422 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]
KiViDrag/ViT_bloodmnist_std_0	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_bloodmnist_std_0 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1010 - Accuracy: 0.9690 - F1: 0.9644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.3571 \| 0.0595 \| 200 \| 0.1183 \| 0.9597 \| 0.9577 \| \| 0.1349 \| 0.1189 \| 400 \| 0.1324 \| 0.9568 \| 0.9521 \| \| 0.093 \| 0.1784 \| 600 \| 0.1167 \| 0.9609 \| 0.9587 \| \| 0.0777 \| 0.2378 \| 800 \| 0.0855 \| 0.9755 \| 0.9715 \| \| 0.0559 \| 0.2973 \| 1000 \| 0.1004 \| 0.9667 \| 0.9649 \| \| 0.0473 \| 0.3567 \| 1200 \| 0.1123 \| 0.9696 \| 0.9668 \| \| 0.0395 \| 0.4162 \| 1400 \| 0.1074 \| 0.9690 \| 0.9676 \| \| 0.0338 \| 0.4756 \| 1600 \| 0.1189 \| 0.9632 \| 0.9608 \| \| 0.027 \| 0.5351 \| 1800 \| 0.1097 \| 0.9772 \| 0.9755 \| \| 0.0176 \| 0.5945 \| 2000 \| 0.0958 \| 0.9784 \| 0.9766 \| \| 0.0105 \| 0.6540 \| 2200 \| 0.1423 \| 0.9720 \| 0.9692 \| \| 0.0161 \| 0.7134 \| 2400 \| 0.1725 \| 0.9650 \| 0.9625 \| \| 0.0113 \| 0.7729 \| 2600 \| 0.1278 \| 0.9708 \| 0.9675 \| \| 0.0077 \| 0.8323 \| 2800 \| 0.1132 \| 0.9766 \| 0.9743 \| \| 0.0078 \| 0.8918 \| 3000 \| 0.1646 \| 0.9690 \| 0.9679 \| \| 0.007 \| 0.9512 \| 3200 \| 0.1128 \| 0.9737 \| 0.9718 \| \| 0.0036 \| 1.0107 \| 3400 \| 0.1489 \| 0.9725 \| 0.9735 \| \| 0.0047 \| 1.0702 \| 3600 \| 0.1232 \| 0.9796 \| 0.9787 \| \| 0.0158 \| 1.1296 \| 3800 \| 0.1597 \| 0.9673 \| 0.9615 \| \| 0.0082 \| 1.1891 \| 4000 \| 0.1633 \| 0.9731 \| 0.9731 \| \| 0.0029 \| 1.2485 \| 4200 \| 0.1312 \| 0.9784 \| 0.9770 \| \| 0.0029 \| 1.3080 \| 4400 \| 0.1311 \| 0.9778 \| 0.9760 \| \| 0.0005 \| 1.3674 \| 4600 \| 0.1121 \| 0.9825 \| 0.9818 \| \| 0.0039 \| 1.4269 \| 4800 \| 0.2170 \| 0.9626 \| 0.9587 \| \| 0.0097 \| 1.4863 \| 5000 \| 0.1750 \| 0.9690 \| 0.9693 \| \| 0.0065 \| 1.5458 \| 5200 \| 0.1327 \| 0.9778 \| 0.9768 \| \| 0.0047 \| 1.6052 \| 5400 \| 0.1401 \| 0.9761 \| 0.9744 \| \| 0.0035 \| 1.6647 \| 5600 \| 0.1273 \| 0.9801 \| 0.9803 \| \| 0.0001 \| 1.7241 \| 5800 \| 0.1269 \| 0.9784 \| 0.9777 \| \| 0.0 \| 1.7836 \| 6000 \| 0.1601 \| 0.9737 \| 0.9723 \| \| 0.0 \| 1.8430 \| 6200 \| 0.1328 \| 0.9772 \| 0.9765 \| \| 0.0 \| 1.9025 \| 6400 \| 0.1326 \| 0.9772 \| 0.9765 \| \| 0.0 \| 1.9620 \| 6600 \| 0.1333 \| 0.9772 \| 0.9765 \| \| 0.0022 \| 2.0214 \| 6800 \| 0.1839 \| 0.9755 \| 0.9749 \| \| 0.0008 \| 2.0809 \| 7000 \| 0.1914 \| 0.9702 \| 0.9683 \| \| 0.0008 \| 2.1403 \| 7200 \| 0.1954 \| 0.9731 \| 0.9725 \| \| 0.0008 \| 2.1998 \| 7400 \| 0.1592 \| 0.9743 \| 0.9737 \| \| 0.0 \| 2.2592 \| 7600 \| 0.1653 \| 0.9755 \| 0.9750 \| \| 0.0 \| 2.3187 \| 7800 \| 0.1649 \| 0.9749 \| 0.9747 \| \| 0.0 \| 2.3781 \| 8000 \| 0.1654 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.4376 \| 8200 \| 0.1646 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.4970 \| 8400 \| 0.1643 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.5565 \| 8600 \| 0.1713 \| 0.9749 \| 0.9747 \| \| 0.0 \| 2.6159 \| 8800 \| 0.1698 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.6754 \| 9000 \| 0.1698 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.7348 \| 9200 \| 0.1696 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.7943 \| 9400 \| 0.1696 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.8537 \| 9600 \| 0.1696 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.9132 \| 9800 \| 0.1697 \| 0.9755 \| 0.9756 \| \| 0.0 \| 2.9727 \| 10000 \| 0.1698 \| 0.9755 \| 0.9756 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]
KiViDrag/ViT_breastmnist_std_30	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_breastmnist_std_30 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3936 - Accuracy: 0.8269 - F1: 0.7315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5034 \| 0.2597 \| 20 \| 0.4719 \| 0.7436 \| 0.4708 \| \| 0.4414 \| 0.5195 \| 40 \| 0.4457 \| 0.7821 \| 0.6400 \| \| 0.3762 \| 0.7792 \| 60 \| 0.4212 \| 0.8205 \| 0.7248 \| \| 0.4059 \| 1.0390 \| 80 \| 0.3988 \| 0.8462 \| 0.7641 \| \| 0.3249 \| 1.2987 \| 100 \| 0.3829 \| 0.8333 \| 0.7606 \| \| 0.2991 \| 1.5584 \| 120 \| 0.4080 \| 0.8462 \| 0.7743 \| \| 0.2948 \| 1.8182 \| 140 \| 0.3932 \| 0.8462 \| 0.7833 \| \| 0.2667 \| 2.0779 \| 160 \| 0.4388 \| 0.8333 \| 0.7502 \| \| 0.2049 \| 2.3377 \| 180 \| 0.4047 \| 0.8333 \| 0.7606 \| \| 0.1639 \| 2.5974 \| 200 \| 0.4301 \| 0.8333 \| 0.7502 \| \| 0.1732 \| 2.8571 \| 220 \| 0.4028 \| 0.8333 \| 0.7606 \| \| 0.1138 \| 3.1169 \| 240 \| 0.3755 \| 0.8718 \| 0.8194 \| \| 0.1099 \| 3.3766 \| 260 \| 0.4019 \| 0.8590 \| 0.7886 \| \| 0.1285 \| 3.6364 \| 280 \| 0.3739 \| 0.8590 \| 0.7974 \| \| 0.1265 \| 3.8961 \| 300 \| 0.3714 \| 0.8590 \| 0.8051 \| \| 0.0735 \| 4.1558 \| 320 \| 0.3820 \| 0.8718 \| 0.8194 \| \| 0.0515 \| 4.4156 \| 340 \| 0.3910 \| 0.8462 \| 0.7833 \| \| 0.0577 \| 4.6753 \| 360 \| 0.3984 \| 0.8462 \| 0.7833 \| \| 0.0584 \| 4.9351 \| 380 \| 0.4314 \| 0.8590 \| 0.7974 \| \| 0.0241 \| 5.1948 \| 400 \| 0.4040 \| 0.8718 \| 0.8194 \| \| 0.015 \| 5.4545 \| 420 \| 0.4201 \| 0.8718 \| 0.8194 \| \| 0.023 \| 5.7143 \| 440 \| 0.4276 \| 0.8718 \| 0.8194 \| \| 0.0254 \| 5.9740 \| 460 \| 0.4271 \| 0.8846 \| 0.8342 \| \| 0.0086 \| 6.2338 \| 480 \| 0.4149 \| 0.8718 \| 0.8194 \| \| 0.012 \| 6.4935 \| 500 \| 0.4738 \| 0.8718 \| 0.8120 \| \| 0.0052 \| 6.7532 \| 520 \| 0.4314 \| 0.8846 \| 0.8342 \| \| 0.0123 \| 7.0130 \| 540 \| 0.4363 \| 0.8718 \| 0.8194 \| \| 0.0026 \| 7.2727 \| 560 \| 0.4477 \| 0.8846 \| 0.8342 \| \| 0.0018 \| 7.5325 \| 580 \| 0.4447 \| 0.8718 \| 0.8194 \| \| 0.0024 \| 7.7922 \| 600 \| 0.4588 \| 0.8718 \| 0.8194 \| \| 0.0076 \| 8.0519 \| 620 \| 0.4517 \| 0.8718 \| 0.8194 \| \| 0.0013 \| 8.3117 \| 640 \| 0.4535 \| 0.8718 \| 0.8194 \| \| 0.0012 \| 8.5714 \| 660 \| 0.4479 \| 0.8846 \| 0.8342 \| \| 0.001 \| 8.8312 \| 680 \| 0.4477 \| 0.8846 \| 0.8342 \| \| 0.0015 \| 9.0909 \| 700 \| 0.4509 \| 0.8846 \| 0.8342 \| \| 0.001 \| 9.3506 \| 720 \| 0.4529 \| 0.8846 \| 0.8342 \| \| 0.0009 \| 9.6104 \| 740 \| 0.4569 \| 0.8846 \| 0.8342 \| \| 0.001 \| 9.8701 \| 760 \| 0.4563 \| 0.8846 \| 0.8342 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "malignant", "normal, benign" ]
KiViDrag/ViT_breastmnist_std_0	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_breastmnist_std_0 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3272 - Accuracy: 0.8718 - F1: 0.8371 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.3533 \| 0.2597 \| 20 \| 0.3035 \| 0.8846 \| 0.8406 \| \| 0.1354 \| 0.5195 \| 40 \| 0.2280 \| 0.8974 \| 0.8655 \| \| 0.0247 \| 0.7792 \| 60 \| 0.2669 \| 0.9231 \| 0.8956 \| \| 0.0089 \| 1.0390 \| 80 \| 0.2742 \| 0.9231 \| 0.8956 \| \| 0.003 \| 1.2987 \| 100 \| 0.3404 \| 0.9103 \| 0.8803 \| \| 0.0018 \| 1.5584 \| 120 \| 0.3583 \| 0.9231 \| 0.8956 \| \| 0.0013 \| 1.8182 \| 140 \| 0.3720 \| 0.9231 \| 0.8956 \| \| 0.0009 \| 2.0779 \| 160 \| 0.3892 \| 0.9231 \| 0.8956 \| \| 0.0007 \| 2.3377 \| 180 \| 0.3933 \| 0.9231 \| 0.8956 \| \| 0.0006 \| 2.5974 \| 200 \| 0.3948 \| 0.9231 \| 0.8956 \| \| 0.0005 \| 2.8571 \| 220 \| 0.4038 \| 0.9231 \| 0.8956 \| \| 0.0005 \| 3.1169 \| 240 \| 0.4145 \| 0.9231 \| 0.8956 \| \| 0.0004 \| 3.3766 \| 260 \| 0.4176 \| 0.9231 \| 0.8956 \| \| 0.0004 \| 3.6364 \| 280 \| 0.4230 \| 0.9231 \| 0.8956 \| \| 0.0003 \| 3.8961 \| 300 \| 0.4274 \| 0.9103 \| 0.8803 \| \| 0.0003 \| 4.1558 \| 320 \| 0.4344 \| 0.9231 \| 0.8956 \| \| 0.0003 \| 4.4156 \| 340 \| 0.4380 \| 0.9231 \| 0.8956 \| \| 0.0003 \| 4.6753 \| 360 \| 0.4406 \| 0.9103 \| 0.8803 \| \| 0.0003 \| 4.9351 \| 380 \| 0.4459 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 5.1948 \| 400 \| 0.4472 \| 0.9103 \| 0.8803 \| \| 0.0002 \| 5.4545 \| 420 \| 0.4514 \| 0.9103 \| 0.8803 \| \| 0.0002 \| 5.7143 \| 440 \| 0.4550 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 5.9740 \| 460 \| 0.4579 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 6.2338 \| 480 \| 0.4600 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 6.4935 \| 500 \| 0.4614 \| 0.9103 \| 0.8803 \| \| 0.0002 \| 6.7532 \| 520 \| 0.4637 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 7.0130 \| 540 \| 0.4660 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 7.2727 \| 560 \| 0.4684 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 7.5325 \| 580 \| 0.4695 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 7.7922 \| 600 \| 0.4710 \| 0.9103 \| 0.8803 \| \| 0.0001 \| 8.0519 \| 620 \| 0.4719 \| 0.9103 \| 0.8803 \| \| 0.0001 \| 8.3117 \| 640 \| 0.4726 \| 0.9103 \| 0.8803 \| \| 0.0001 \| 8.5714 \| 660 \| 0.4742 \| 0.9103 \| 0.8803 \| \| 0.0001 \| 8.8312 \| 680 \| 0.4754 \| 0.9231 \| 0.8956 \| \| 0.0002 \| 9.0909 \| 700 \| 0.4757 \| 0.9231 \| 0.8956 \| \| 0.0001 \| 9.3506 \| 720 \| 0.4759 \| 0.9231 \| 0.8956 \| \| 0.0001 \| 9.6104 \| 740 \| 0.4763 \| 0.9231 \| 0.8956 \| \| 0.0001 \| 9.8701 \| 760 \| 0.4765 \| 0.9231 \| 0.8956 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "malignant", "normal, benign" ]
KiViDrag/ViT_breastmnist_std_15	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_breastmnist_std_15 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4504 - Accuracy: 0.7885 - F1: 0.6551 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4628 \| 0.2597 \| 20 \| 0.4724 \| 0.7821 \| 0.5951 \| \| 0.3645 \| 0.5195 \| 40 \| 0.3994 \| 0.8590 \| 0.7786 \| \| 0.2744 \| 0.7792 \| 60 \| 0.4429 \| 0.8462 \| 0.7524 \| \| 0.3004 \| 1.0390 \| 80 \| 0.3893 \| 0.8590 \| 0.7886 \| \| 0.2153 \| 1.2987 \| 100 \| 0.4120 \| 0.8462 \| 0.7641 \| \| 0.1593 \| 1.5584 \| 120 \| 0.4542 \| 0.8590 \| 0.7786 \| \| 0.1189 \| 1.8182 \| 140 \| 0.3911 \| 0.8718 \| 0.8120 \| \| 0.1139 \| 2.0779 \| 160 \| 0.4154 \| 0.8590 \| 0.7886 \| \| 0.0707 \| 2.3377 \| 180 \| 0.4517 \| 0.8590 \| 0.7886 \| \| 0.0482 \| 2.5974 \| 200 \| 0.4824 \| 0.8718 \| 0.8034 \| \| 0.0499 \| 2.8571 \| 220 \| 0.4408 \| 0.8462 \| 0.7743 \| \| 0.0195 \| 3.1169 \| 240 \| 0.4874 \| 0.8462 \| 0.7743 \| \| 0.0146 \| 3.3766 \| 260 \| 0.4723 \| 0.8718 \| 0.8120 \| \| 0.0141 \| 3.6364 \| 280 \| 0.5117 \| 0.8590 \| 0.7886 \| \| 0.017 \| 3.8961 \| 300 \| 0.6032 \| 0.8462 \| 0.7743 \| \| 0.0052 \| 4.1558 \| 320 \| 0.5948 \| 0.8590 \| 0.7886 \| \| 0.005 \| 4.4156 \| 340 \| 0.5897 \| 0.8590 \| 0.7886 \| \| 0.0039 \| 4.6753 \| 360 \| 0.5729 \| 0.8462 \| 0.7743 \| \| 0.0088 \| 4.9351 \| 380 \| 0.5623 \| 0.8462 \| 0.7743 \| \| 0.0104 \| 5.1948 \| 400 \| 0.4814 \| 0.8718 \| 0.8194 \| \| 0.0012 \| 5.4545 \| 420 \| 0.5039 \| 0.8718 \| 0.8194 \| \| 0.001 \| 5.7143 \| 440 \| 0.5268 \| 0.8718 \| 0.8120 \| \| 0.001 \| 5.9740 \| 460 \| 0.5435 \| 0.8590 \| 0.7886 \| \| 0.0007 \| 6.2338 \| 480 \| 0.5435 \| 0.8462 \| 0.7743 \| \| 0.0007 \| 6.4935 \| 500 \| 0.5373 \| 0.8590 \| 0.7974 \| \| 0.0006 \| 6.7532 \| 520 \| 0.5745 \| 0.8590 \| 0.7886 \| \| 0.0007 \| 7.0130 \| 540 \| 0.5674 \| 0.8462 \| 0.7743 \| \| 0.0004 \| 7.2727 \| 560 \| 0.5826 \| 0.8462 \| 0.7743 \| \| 0.0006 \| 7.5325 \| 580 \| 0.5663 \| 0.8462 \| 0.7743 \| \| 0.0006 \| 7.7922 \| 600 \| 0.5751 \| 0.8462 \| 0.7743 \| \| 0.0005 \| 8.0519 \| 620 \| 0.5851 \| 0.8462 \| 0.7743 \| \| 0.0004 \| 8.3117 \| 640 \| 0.5782 \| 0.8462 \| 0.7743 \| \| 0.0004 \| 8.5714 \| 660 \| 0.5875 \| 0.8462 \| 0.7743 \| \| 0.0004 \| 8.8312 \| 680 \| 0.5939 \| 0.8462 \| 0.7743 \| \| 0.0004 \| 9.0909 \| 700 \| 0.5934 \| 0.8462 \| 0.7743 \| \| 0.0004 \| 9.3506 \| 720 \| 0.5925 \| 0.8462 \| 0.7743 \| \| 0.0004 \| 9.6104 \| 740 \| 0.5930 \| 0.8462 \| 0.7743 \| \| 0.0004 \| 9.8701 \| 760 \| 0.5945 \| 0.8462 \| 0.7743 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "malignant", "normal, benign" ]
KiViDrag/ViT_breastmnist_std_45	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_breastmnist_std_45 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4752 - Accuracy: 0.7821 - F1: 0.6733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5115 \| 0.2597 \| 20 \| 0.5292 \| 0.7308 \| 0.4222 \| \| 0.4949 \| 0.5195 \| 40 \| 0.5229 \| 0.7436 \| 0.4708 \| \| 0.4099 \| 0.7792 \| 60 \| 0.4728 \| 0.7692 \| 0.5568 \| \| 0.4461 \| 1.0390 \| 80 \| 0.4428 \| 0.8333 \| 0.7247 \| \| 0.4201 \| 1.2987 \| 100 \| 0.4311 \| 0.8718 \| 0.8120 \| \| 0.3532 \| 1.5584 \| 120 \| 0.4206 \| 0.8590 \| 0.7886 \| \| 0.3586 \| 1.8182 \| 140 \| 0.4292 \| 0.8590 \| 0.7886 \| \| 0.3412 \| 2.0779 \| 160 \| 0.4541 \| 0.8333 \| 0.7247 \| \| 0.2945 \| 2.3377 \| 180 \| 0.4179 \| 0.8333 \| 0.7606 \| \| 0.2555 \| 2.5974 \| 200 \| 0.4331 \| 0.8590 \| 0.7886 \| \| 0.2753 \| 2.8571 \| 220 \| 0.4310 \| 0.8205 \| 0.7367 \| \| 0.2079 \| 3.1169 \| 240 \| 0.4152 \| 0.8462 \| 0.7833 \| \| 0.217 \| 3.3766 \| 260 \| 0.4157 \| 0.8718 \| 0.8260 \| \| 0.167 \| 3.6364 \| 280 \| 0.4259 \| 0.8590 \| 0.8051 \| \| 0.1976 \| 3.8961 \| 300 \| 0.4346 \| 0.8462 \| 0.7913 \| \| 0.1376 \| 4.1558 \| 320 \| 0.4341 \| 0.8462 \| 0.7913 \| \| 0.1301 \| 4.4156 \| 340 \| 0.4418 \| 0.8462 \| 0.7983 \| \| 0.1503 \| 4.6753 \| 360 \| 0.4375 \| 0.8590 \| 0.8120 \| \| 0.126 \| 4.9351 \| 380 \| 0.4376 \| 0.8590 \| 0.8120 \| \| 0.098 \| 5.1948 \| 400 \| 0.4310 \| 0.8462 \| 0.7983 \| \| 0.0675 \| 5.4545 \| 420 \| 0.4545 \| 0.8333 \| 0.7849 \| \| 0.0618 \| 5.7143 \| 440 \| 0.4587 \| 0.8333 \| 0.7849 \| \| 0.0572 \| 5.9740 \| 460 \| 0.4629 \| 0.8462 \| 0.7983 \| \| 0.0283 \| 6.2338 \| 480 \| 0.4778 \| 0.8333 \| 0.7849 \| \| 0.0337 \| 6.4935 \| 500 \| 0.4820 \| 0.8462 \| 0.7983 \| \| 0.0416 \| 6.7532 \| 520 \| 0.4794 \| 0.8462 \| 0.8045 \| \| 0.0535 \| 7.0130 \| 540 \| 0.4811 \| 0.8333 \| 0.7849 \| \| 0.0146 \| 7.2727 \| 560 \| 0.4780 \| 0.8462 \| 0.7983 \| \| 0.0205 \| 7.5325 \| 580 \| 0.4889 \| 0.8333 \| 0.7849 \| \| 0.0118 \| 7.7922 \| 600 \| 0.5004 \| 0.8333 \| 0.7913 \| \| 0.0148 \| 8.0519 \| 620 \| 0.4974 \| 0.8333 \| 0.7849 \| \| 0.0078 \| 8.3117 \| 640 \| 0.5009 \| 0.8205 \| 0.7719 \| \| 0.0101 \| 8.5714 \| 660 \| 0.5079 \| 0.8205 \| 0.7719 \| \| 0.0042 \| 8.8312 \| 680 \| 0.5178 \| 0.8205 \| 0.7719 \| \| 0.0047 \| 9.0909 \| 700 \| 0.5186 \| 0.8205 \| 0.7719 \| \| 0.0029 \| 9.3506 \| 720 \| 0.5217 \| 0.8205 \| 0.7719 \| \| 0.0042 \| 9.6104 \| 740 \| 0.5238 \| 0.8077 \| 0.7592 \| \| 0.0038 \| 9.8701 \| 760 \| 0.5246 \| 0.8205 \| 0.7719 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "malignant", "normal, benign" ]
griffio/vit-base-patch16-224-rotated-dungeons-v101	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-rotated-dungeons-v101 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.6993 - Accuracy: 0.8333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.799 \| 0.8333 \| 10 \| 1.9456 \| 0.1667 \| \| 1.2417 \| 1.6667 \| 20 \| 1.7680 \| 0.3333 \| \| 1.1543 \| 2.5 \| 30 \| 1.5470 \| 0.5 \| \| 0.8829 \| 3.3333 \| 40 \| 1.4117 \| 0.5 \| \| 0.8725 \| 4.1667 \| 50 \| 1.2967 \| 0.5 \| \| 0.5514 \| 5.0 \| 60 \| 1.1901 \| 0.5 \| \| 0.4565 \| 5.8333 \| 70 \| 1.1867 \| 0.5 \| \| 0.3663 \| 6.6667 \| 80 \| 1.0417 \| 0.5 \| \| 0.3448 \| 7.5 \| 90 \| 0.8938 \| 0.6667 \| \| 0.2525 \| 8.3333 \| 100 \| 0.8804 \| 0.75 \| \| 0.1729 \| 9.1667 \| 110 \| 0.9067 \| 0.6667 \| \| 0.1848 \| 10.0 \| 120 \| 0.8441 \| 0.6667 \| \| 0.1847 \| 10.8333 \| 130 \| 0.7352 \| 0.8333 \| \| 0.0916 \| 11.6667 \| 140 \| 0.7336 \| 0.8333 \| \| 0.1771 \| 12.5 \| 150 \| 0.7012 \| 0.8333 \| \| 0.105 \| 13.3333 \| 160 \| 0.7030 \| 0.8333 \| \| 0.1008 \| 14.1667 \| 170 \| 0.7004 \| 0.8333 \| \| 0.1127 \| 15.0 \| 180 \| 0.6993 \| 0.8333 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-base-patch16-224-rotated-dungeons-v103	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-rotated-dungeons-v103 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.8291 - Accuracy: 0.8333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.522 \| 3.3333 \| 20 \| 0.8489 \| 0.6667 \| \| 0.0346 \| 6.6667 \| 40 \| 2.3103 \| 0.6667 \| \| 0.019 \| 10.0 \| 60 \| 1.4623 \| 0.75 \| \| 0.017 \| 13.3333 \| 80 \| 0.8291 \| 0.8333 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
nemik/frost-vision-v2-google_vit-base-patch16-224-v2024-11-09	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # frost-vision-v2-google_vit-base-patch16-224-v2024-11-09 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1716 - Accuracy: 0.9412 - F1: 0.8486 - Precision: 0.8540 - Recall: 0.8432 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.2398 \| 1.4085 \| 100 \| 0.2096 \| 0.9215 \| 0.7833 \| 0.8502 \| 0.7261 \| \| 0.1746 \| 2.8169 \| 200 \| 0.1676 \| 0.9370 \| 0.8362 \| 0.8494 \| 0.8234 \| \| 0.1316 \| 4.2254 \| 300 \| 0.1750 \| 0.9282 \| 0.8125 \| 0.8293 \| 0.7964 \| \| 0.1305 \| 5.6338 \| 400 \| 0.1671 \| 0.9342 \| 0.8270 \| 0.8498 \| 0.8054 \| \| 0.1119 \| 7.0423 \| 500 \| 0.1747 \| 0.9317 \| 0.8240 \| 0.8300 \| 0.8180 \| \| 0.0913 \| 8.4507 \| 600 \| 0.1515 \| 0.9415 \| 0.8505 \| 0.8505 \| 0.8505 \| \| 0.0964 \| 9.8592 \| 700 \| 0.1680 \| 0.9377 \| 0.8418 \| 0.8351 \| 0.8486 \| \| 0.0659 \| 11.2676 \| 800 \| 0.1891 \| 0.9275 \| 0.8144 \| 0.8144 \| 0.8144 \| \| 0.0706 \| 12.6761 \| 900 \| 0.1788 \| 0.9320 \| 0.8234 \| 0.8364 \| 0.8108 \| \| 0.069 \| 14.0845 \| 1000 \| 0.1716 \| 0.9412 \| 0.8486 \| 0.8540 \| 0.8432 \| \| 0.0543 \| 15.4930 \| 1100 \| 0.1847 \| 0.9363 \| 0.8341 \| 0.8489 \| 0.8198 \| \| 0.0515 \| 16.9014 \| 1200 \| 0.1741 \| 0.9408 \| 0.8470 \| 0.8564 \| 0.8378 \| \| 0.0489 \| 18.3099 \| 1300 \| 0.1793 \| 0.9461 \| 0.8620 \| 0.8628 \| 0.8613 \| \| 0.0339 \| 19.7183 \| 1400 \| 0.1806 \| 0.9444 \| 0.8569 \| 0.8616 \| 0.8523 \| \| 0.0409 \| 21.1268 \| 1500 \| 0.1784 \| 0.9440 \| 0.8569 \| 0.8561 \| 0.8577 \| \| 0.0275 \| 22.5352 \| 1600 \| 0.1839 \| 0.9437 \| 0.8548 \| 0.8611 \| 0.8486 \| \| 0.0231 \| 23.9437 \| 1700 \| 0.1865 \| 0.9415 \| 0.8480 \| 0.8622 \| 0.8342 \| \| 0.0204 \| 25.3521 \| 1800 \| 0.1884 \| 0.9405 \| 0.8482 \| 0.8459 \| 0.8505 \| \| 0.0245 \| 26.7606 \| 1900 \| 0.1935 \| 0.9377 \| 0.8410 \| 0.8387 \| 0.8432 \| \| 0.0202 \| 28.1690 \| 2000 \| 0.1888 \| 0.9394 \| 0.8456 \| 0.8426 \| 0.8486 \| \| 0.0187 \| 29.5775 \| 2100 \| 0.1914 \| 0.9415 \| 0.8502 \| 0.8517 \| 0.8486 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "snowing", "raining", "sunny", "cloudy", "night", "snow_on_road", "partial_snow_on_road", "clear_pavement", "wet_pavement", "iced_lens" ]
ricardoSLabs/pre_CIDAUTv3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pre_CIDAUTv3 This model is a fine-tuned version of [ricardoSLabs/pre_CIDAUTv2](https://huggingface.co/ricardoSLabs/pre_CIDAUTv2) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1209 - Accuracy: 0.9841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8889 \| 4 \| 0.5177 \| 0.7937 \| \| No log \| 2.0 \| 9 \| 0.3085 \| 0.8889 \| \| 1.0669 \| 2.8889 \| 13 \| 0.2461 \| 0.8889 \| \| 1.0669 \| 4.0 \| 18 \| 0.1346 \| 0.9683 \| \| 0.2558 \| 4.4444 \| 20 \| 0.1209 \| 0.9841 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "editada", "real" ]
ricardoSLabs/pre_CIDAUTv4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pre_CIDAUTv4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0177 - Accuracy: 0.9918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8889 \| 4 \| 0.6742 \| 0.5679 \| \| No log \| 2.0 \| 9 \| 0.3347 \| 0.9218 \| \| 0.6003 \| 2.8889 \| 13 \| 0.1238 \| 0.9753 \| \| 0.6003 \| 4.0 \| 18 \| 0.1298 \| 0.9465 \| \| 0.199 \| 4.8889 \| 22 \| 0.0360 \| 0.9877 \| \| 0.199 \| 6.0 \| 27 \| 0.1049 \| 0.9671 \| \| 0.0832 \| 6.8889 \| 31 \| 0.0058 \| 1.0 \| \| 0.0832 \| 8.0 \| 36 \| 0.0138 \| 0.9918 \| \| 0.0438 \| 8.8889 \| 40 \| 0.0177 \| 0.9918 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "editada", "real" ]
ricardoSLabs/pre_CIDAUTv5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pre_CIDAUTv5 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0190 - Accuracy: 0.9938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.9524 \| 5 \| 0.6238 \| 0.6460 \| \| 0.5991 \| 1.9048 \| 10 \| 0.2637 \| 0.9814 \| \| 0.5991 \| 2.8571 \| 15 \| 0.0767 \| 0.9938 \| \| 0.1441 \| 4.0 \| 21 \| 0.0365 \| 0.9876 \| \| 0.1441 \| 4.9524 \| 26 \| 0.0399 \| 0.9876 \| \| 0.075 \| 5.9048 \| 31 \| 0.0216 \| 0.9938 \| \| 0.075 \| 6.8571 \| 36 \| 0.0126 \| 1.0 \| \| 0.0581 \| 7.6190 \| 40 \| 0.0190 \| 0.9938 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0	[ "editada", "real" ]
nemik/frost-vision-v2-google_vit-base-patch16-384-v2024-11-10	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # frost-vision-v2-google_vit-base-patch16-384-v2024-11-10 This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the webdataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1847 - Accuracy: 0.9275 - F1: 0.8187 - Precision: 0.8172 - Recall: 0.8201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.2243 \| 1.4085 \| 100 \| 0.2088 \| 0.9243 \| 0.7981 \| 0.8534 \| 0.7496 \| \| 0.2438 \| 2.8169 \| 200 \| 0.1819 \| 0.9299 \| 0.8103 \| 0.8817 \| 0.7496 \| \| 0.1338 \| 4.2254 \| 300 \| 0.1608 \| 0.9377 \| 0.8449 \| 0.8397 \| 0.8501 \| \| 0.1224 \| 5.6338 \| 400 \| 0.1735 \| 0.9271 \| 0.8179 \| 0.8158 \| 0.8201 \| \| 0.1065 \| 7.0423 \| 500 \| 0.1847 \| 0.9275 \| 0.8187 \| 0.8172 \| 0.8201 \| \| 0.1008 \| 8.4507 \| 600 \| 0.1710 \| 0.9405 \| 0.8506 \| 0.8528 \| 0.8483 \| \| 0.1005 \| 9.8592 \| 700 \| 0.1823 \| 0.9384 \| 0.8405 \| 0.8698 \| 0.8131 \| \| 0.0756 \| 11.2676 \| 800 \| 0.1771 \| 0.9415 \| 0.8520 \| 0.8613 \| 0.8430 \| \| 0.0653 \| 12.6761 \| 900 \| 0.1971 \| 0.9324 \| 0.8310 \| 0.8295 \| 0.8325 \| \| 0.0367 \| 14.0845 \| 1000 \| 0.2123 \| 0.9296 \| 0.8221 \| 0.8294 \| 0.8148 \| \| 0.0459 \| 15.4930 \| 1100 \| 0.2006 \| 0.9335 \| 0.832 \| 0.8387 \| 0.8254 \| \| 0.0559 \| 16.9014 \| 1200 \| 0.2097 \| 0.9313 \| 0.8232 \| 0.8470 \| 0.8007 \| \| 0.0382 \| 18.3099 \| 1300 \| 0.2055 \| 0.9352 \| 0.8372 \| 0.8401 \| 0.8342 \| \| 0.0361 \| 19.7183 \| 1400 \| 0.2070 \| 0.9335 \| 0.8305 \| 0.8449 \| 0.8166 \| \| 0.0358 \| 21.1268 \| 1500 \| 0.1959 \| 0.9398 \| 0.8458 \| 0.8653 \| 0.8272 \| \| 0.0382 \| 22.5352 \| 1600 \| 0.2097 \| 0.9320 \| 0.8269 \| 0.8412 \| 0.8131 \| \| 0.0285 \| 23.9437 \| 1700 \| 0.2016 \| 0.9415 \| 0.8515 \| 0.8639 \| 0.8395 \| \| 0.0141 \| 25.3521 \| 1800 \| 0.2161 \| 0.9366 \| 0.8384 \| 0.8537 \| 0.8236 \| \| 0.0179 \| 26.7606 \| 1900 \| 0.2073 \| 0.9377 \| 0.8427 \| 0.8495 \| 0.8360 \| \| 0.0263 \| 28.1690 \| 2000 \| 0.2097 \| 0.9391 \| 0.8457 \| 0.8556 \| 0.8360 \| \| 0.0191 \| 29.5775 \| 2100 \| 0.2101 \| 0.9377 \| 0.8415 \| 0.8545 \| 0.8289 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "snowing", "raining", "sunny", "cloudy", "night", "snow_on_road", "partial_snow_on_road", "clear_pavement", "wet_pavement", "iced_lens" ]
CGscorpion/convnext-tiny-224-finetuned-eurosat-albumentations	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2746 - Accuracy: 0.9589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.0418 \| 0.9362 \| 11 \| 0.2497 \| 0.9041 \| \| 0.0349 \| 1.9574 \| 23 \| 0.2377 \| 0.9041 \| \| 0.0231 \| 2.9787 \| 35 \| 0.2695 \| 0.8904 \| \| 0.0154 \| 4.0 \| 47 \| 0.2185 \| 0.9041 \| \| 0.0061 \| 4.9362 \| 58 \| 0.2810 \| 0.9178 \| \| 0.0023 \| 5.9574 \| 70 \| 0.2905 \| 0.9315 \| \| 0.0029 \| 6.9787 \| 82 \| 0.2052 \| 0.9315 \| \| 0.0013 \| 8.0 \| 94 \| 0.2333 \| 0.9178 \| \| 0.0009 \| 8.9362 \| 105 \| 0.2262 \| 0.9315 \| \| 0.008 \| 9.9574 \| 117 \| 0.2247 \| 0.9178 \| \| 0.0014 \| 10.9787 \| 129 \| 0.3200 \| 0.9041 \| \| 0.0005 \| 12.0 \| 141 \| 0.2643 \| 0.9178 \| \| 0.0006 \| 12.9362 \| 152 \| 0.2911 \| 0.9178 \| \| 0.0007 \| 13.9574 \| 164 \| 0.2567 \| 0.9178 \| \| 0.0009 \| 14.9787 \| 176 \| 0.3170 \| 0.9178 \| \| 0.0052 \| 16.0 \| 188 \| 0.2435 \| 0.9315 \| \| 0.0005 \| 16.9362 \| 199 \| 0.2746 \| 0.9589 \| \| 0.0004 \| 17.9574 \| 211 \| 0.2347 \| 0.9315 \| \| 0.002 \| 18.9787 \| 223 \| 0.2999 \| 0.9178 \| \| 0.0021 \| 20.0 \| 235 \| 0.2648 \| 0.9178 \| \| 0.0003 \| 20.9362 \| 246 \| 0.2609 \| 0.9178 \| \| 0.0002 \| 21.9574 \| 258 \| 0.2709 \| 0.9315 \| \| 0.0004 \| 22.9787 \| 270 \| 0.2359 \| 0.9315 \| \| 0.005 \| 24.0 \| 282 \| 0.2484 \| 0.9178 \| \| 0.0011 \| 24.9362 \| 293 \| 0.3019 \| 0.9178 \| \| 0.0012 \| 25.9574 \| 305 \| 0.2715 \| 0.9178 \| \| 0.0003 \| 26.9787 \| 317 \| 0.2486 \| 0.9315 \| \| 0.0009 \| 28.0 \| 329 \| 0.2472 \| 0.9315 \| \| 0.0002 \| 28.9362 \| 340 \| 0.2449 \| 0.9315 \| \| 0.0002 \| 29.9574 \| 352 \| 0.2480 \| 0.9315 \| \| 0.0002 \| 30.9787 \| 364 \| 0.2520 \| 0.9315 \| \| 0.0002 \| 32.0 \| 376 \| 0.2528 \| 0.9315 \| \| 0.0001 \| 32.9362 \| 387 \| 0.2520 \| 0.9315 \| \| 0.0002 \| 33.9574 \| 399 \| 0.2503 \| 0.9315 \| \| 0.0001 \| 34.9787 \| 411 \| 0.2508 \| 0.9315 \| \| 0.0001 \| 36.0 \| 423 \| 0.2493 \| 0.9315 \| \| 0.0008 \| 36.9362 \| 434 \| 0.2558 \| 0.9315 \| \| 0.0001 \| 37.9574 \| 446 \| 0.2616 \| 0.9315 \| \| 0.0001 \| 38.9787 \| 458 \| 0.2623 \| 0.9315 \| \| 0.0011 \| 40.0 \| 470 \| 0.2617 \| 0.9315 \| \| 0.0002 \| 40.9362 \| 481 \| 0.2532 \| 0.9315 \| \| 0.0001 \| 41.9574 \| 493 \| 0.2495 \| 0.9315 \| \| 0.0001 \| 42.9787 \| 505 \| 0.2478 \| 0.9315 \| \| 0.0001 \| 44.0 \| 517 \| 0.2479 \| 0.9315 \| \| 0.0001 \| 44.9362 \| 528 \| 0.2481 \| 0.9315 \| \| 0.0001 \| 45.9574 \| 540 \| 0.2481 \| 0.9315 \| \| 0.002 \| 46.8085 \| 550 \| 0.2475 \| 0.9315 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1	[ "answer", "delete_line" ]
griffio/resnet-18-dungeons-001	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-18-dungeons-001 This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1886 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 85 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.0594 \| 6.6667 \| 10 \| 1.3086 \| 0.5833 \| \| 0.0973 \| 13.3333 \| 20 \| 1.3523 \| 0.5 \| \| 0.068 \| 20.0 \| 30 \| 1.2428 \| 0.5833 \| \| 0.0671 \| 26.6667 \| 40 \| 1.2280 \| 0.5833 \| \| 0.0527 \| 33.3333 \| 50 \| 1.2677 \| 0.5833 \| \| 0.0592 \| 40.0 \| 60 \| 1.2846 \| 0.5833 \| \| 0.0446 \| 46.6667 \| 70 \| 1.2210 \| 0.5833 \| \| 0.0565 \| 53.3333 \| 80 \| 1.1886 \| 0.5 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
griffio/vit-large-patch16-224-dungeons-001	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-large-patch16-224-dungeons-001 This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6325 - Accuracy: 0.75 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 85 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.8218 \| 6.6667 \| 10 \| 1.8564 \| 0.1667 \| \| 1.4325 \| 13.3333 \| 20 \| 1.7325 \| 0.3333 \| \| 0.8869 \| 20.0 \| 30 \| 1.5186 \| 0.4167 \| \| 0.3717 \| 26.6667 \| 40 \| 1.1131 \| 0.6667 \| \| 0.0945 \| 33.3333 \| 50 \| 0.8408 \| 0.75 \| \| 0.0175 \| 40.0 \| 60 \| 0.7224 \| 0.75 \| \| 0.0051 \| 46.6667 \| 70 \| 0.6674 \| 0.75 \| \| 0.0024 \| 53.3333 \| 80 \| 0.6325 \| 0.75 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "five", "four", "one", "three", "twelve", "two", "zero" ]
nemik/frost-vision-v2-google_vit-base-patch16-224-v2024-11-11	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # frost-vision-v2-google_vit-base-patch16-224-v2024-11-11 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Accuracy: 0.9320 - F1: 0.8224 - Precision: 0.8172 - Recall: 0.8278 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.3127 \| 1.4085 \| 100 \| 0.2932 \| 0.8940 \| 0.6725 \| 0.8153 \| 0.5722 \| \| 0.193 \| 2.8169 \| 200 \| 0.2136 \| 0.9190 \| 0.7834 \| 0.7969 \| 0.7704 \| \| 0.1503 \| 4.2254 \| 300 \| 0.1815 \| 0.9278 \| 0.8100 \| 0.8108 \| 0.8093 \| \| 0.1313 \| 5.6338 \| 400 \| 0.1623 \| 0.9327 \| 0.8183 \| 0.8415 \| 0.7963 \| \| 0.1166 \| 7.0423 \| 500 \| 0.1658 \| 0.9320 \| 0.8224 \| 0.8172 \| 0.8278 \| \| 0.093 \| 8.4507 \| 600 \| 0.1606 \| 0.9384 \| 0.8405 \| 0.8276 \| 0.8537 \| \| 0.0931 \| 9.8592 \| 700 \| 0.1625 \| 0.9366 \| 0.8370 \| 0.8191 \| 0.8556 \| \| 0.0733 \| 11.2676 \| 800 \| 0.1714 \| 0.9356 \| 0.8310 \| 0.8287 \| 0.8333 \| \| 0.0693 \| 12.6761 \| 900 \| 0.1568 \| 0.9398 \| 0.8403 \| 0.8475 \| 0.8333 \| \| 0.0615 \| 14.0845 \| 1000 \| 0.1666 \| 0.9342 \| 0.8270 \| 0.8262 \| 0.8278 \| \| 0.0562 \| 15.4930 \| 1100 \| 0.1636 \| 0.9394 \| 0.8404 \| 0.8420 \| 0.8389 \| \| 0.0507 \| 16.9014 \| 1200 \| 0.1613 \| 0.9401 \| 0.8435 \| 0.8388 \| 0.8481 \| \| 0.0552 \| 18.3099 \| 1300 \| 0.1590 \| 0.9412 \| 0.8455 \| 0.8447 \| 0.8463 \| \| 0.0439 \| 19.7183 \| 1400 \| 0.1704 \| 0.9394 \| 0.8425 \| 0.8333 \| 0.8519 \| \| 0.0367 \| 21.1268 \| 1500 \| 0.1702 \| 0.9426 \| 0.8484 \| 0.8523 \| 0.8444 \| \| 0.0424 \| 22.5352 \| 1600 \| 0.1685 \| 0.9394 \| 0.8419 \| 0.8358 \| 0.8481 \| \| 0.0306 \| 23.9437 \| 1700 \| 0.1771 \| 0.9380 \| 0.8397 \| 0.8262 \| 0.8537 \| \| 0.0352 \| 25.3521 \| 1800 \| 0.1691 \| 0.9401 \| 0.8440 \| 0.8364 \| 0.8519 \| \| 0.0323 \| 26.7606 \| 1900 \| 0.1687 \| 0.9426 \| 0.8509 \| 0.8409 \| 0.8611 \| \| 0.0297 \| 28.1690 \| 2000 \| 0.1732 \| 0.9401 \| 0.8455 \| 0.8304 \| 0.8611 \| \| 0.0229 \| 29.5775 \| 2100 \| 0.1712 \| 0.9412 \| 0.8475 \| 0.8360 \| 0.8593 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "snowing", "raining", "sunny", "cloudy", "night", "snow_on_road", "partial_snow_on_road", "clear_pavement", "wet_pavement", "iced_lens" ]
mikedata/vit-base-oxford-iiit-pets	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-oxford-iiit-pets This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1786 - Accuracy: 0.9405 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.381 \| 1.0 \| 370 \| 0.3122 \| 0.9269 \| \| 0.2349 \| 2.0 \| 740 \| 0.2465 \| 0.9242 \| \| 0.1579 \| 3.0 \| 1110 \| 0.2329 \| 0.9296 \| \| 0.1497 \| 4.0 \| 1480 \| 0.2171 \| 0.9310 \| \| 0.1252 \| 5.0 \| 1850 \| 0.2167 \| 0.9323 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "siamese", "birman", "shiba inu", "staffordshire bull terrier", "basset hound", "bombay", "japanese chin", "chihuahua", "german shorthaired", "pomeranian", "beagle", "english cocker spaniel", "american pit bull terrier", "ragdoll", "persian", "egyptian mau", "miniature pinscher", "sphynx", "maine coon", "keeshond", "yorkshire terrier", "havanese", "leonberger", "wheaten terrier", "american bulldog", "english setter", "boxer", "newfoundland", "bengal", "samoyed", "british shorthair", "great pyrenees", "abyssinian", "pug", "saint bernard", "russian blue", "scottish terrier" ]
Binaryy/test-trainer	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test-trainer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Chess dataset. It achieves the following results on the evaluation set: - Loss: 0.7291 - Accuracy: 0.9107 - F1: 0.9122 - Precision: 0.9172 - Recall: 0.9107 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 4 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 50 \| 1.6720 \| 0.4821 \| 0.4134 \| 0.3870 \| 0.4821 \| \| No log \| 2.0 \| 100 \| 1.4652 \| 0.6429 \| 0.6126 \| 0.7414 \| 0.6429 \| \| No log \| 3.0 \| 150 \| 1.1742 \| 0.7321 \| 0.7210 \| 0.7792 \| 0.7321 \| \| No log \| 4.0 \| 200 \| 0.9813 \| 0.8393 \| 0.8433 \| 0.8589 \| 0.8393 \| \| No log \| 5.0 \| 250 \| 0.8312 \| 0.8214 \| 0.8164 \| 0.8516 \| 0.8214 \| \| No log \| 6.0 \| 300 \| 0.7291 \| 0.9107 \| 0.9122 \| 0.9172 \| 0.9107 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.2.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "bishop", "king", "knight", "pawn", "queen", "rook" ]
mgarci14/resnet-50-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 3.2958 - Accuracy: 0.1952 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.3941 \| 1.0 \| 31 \| 3.3754 \| 0.1646 \| \| 3.3386 \| 2.0 \| 62 \| 3.3178 \| 0.2054 \| \| 3.3084 \| 3.0 \| 93 \| 3.2958 \| 0.1952 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "bangus", "big head carp", "black spotted barb", "catfish", "climbing perch", "fourfinger threadfin", "freshwater eel", "glass perchlet", "goby", "gold fish", "gourami", "grass carp", "green spotted puffer", "indian carp", "indo-pacific tarpon", "jaguar gapote", "janitor fish", "knifefish", "long-snouted pipefish", "mosquito fish", "mudfish", "mullet", "pangasius", "perch", "scat fish", "silver barb", "silver carp", "silver perch", "snakehead", "tenpounder", "tilapia" ]
MichaelHu03/CS4220-KOProject	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
ziyuyuyuyu1/results	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4006 - Accuracy: 0.7643 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.5045 \| 0.9998 \| 3237 \| 0.4944 \| 0.7125 \| \| 0.4578 \| 1.9998 \| 6475 \| 0.4348 \| 0.7457 \| \| 0.3922 \| 2.9993 \| 9711 \| 0.4006 \| 0.7643 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.4.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "pos", "neg" ]
DrRasha/rasha	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
e1010101/vit-384-large-patch-tongue-image-segmented	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segmented-augmented This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6965 - Precision: 0.8085 - Recall: 0.8837 - F1: 0.8444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.0857 \| 1.0 \| 327 \| 0.4359 \| 0.8176 \| 0.8040 \| 0.8107 \| \| 0.0164 \| 2.0 \| 654 \| 0.5654 \| 0.8043 \| 0.8605 \| 0.8315 \| \| 0.0056 \| 3.0 \| 981 \| 0.6437 \| 0.8182 \| 0.8671 \| 0.8419 \| \| 0.002 \| 4.0 \| 1308 \| 0.6739 \| 0.8055 \| 0.8804 \| 0.8413 \| \| 0.003 \| 5.0 \| 1635 \| 0.6965 \| 0.8085 \| 0.8837 \| 0.8444 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1	[ "crack", "red-dots", "toothmark" ]
griffio/vit-large-patch16-224-dungeon-geo-morphs-001	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-large-patch16-224-dungeon-geo-morphs-001 This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset. It achieves the following results on the evaluation set: - Loss: 0.3882 - Accuracy: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.9239 \| 6.6667 \| 10 \| 0.3882 \| 0.9667 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "three", "two", "zero" ]
griffio/vit-large-patch16-224-dungeon-geo-morphs-002	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-large-patch16-224-dungeon-geo-morphs-002 This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset. It achieves the following results on the evaluation set: - Loss: 0.0332 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.056 \| 6.6667 \| 10 \| 0.0332 \| 1.0 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1	[ "three", "two", "zero" ]
griffio/vit-large-patch16-224-dungeon-geo-morphs-003	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-large-patch16-224-dungeon-geo-morphs-003 This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset. It achieves the following results on the evaluation set: - Loss: 0.0149 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.8585 \| 6.6667 \| 10 \| 0.2204 \| 1.0 \| \| 0.2115 \| 13.3333 \| 20 \| 0.0530 \| 1.0 \| \| 0.0562 \| 20.0 \| 30 \| 0.0149 \| 1.0 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Tokenizers 0.20.3	[ "three", "two", "zero" ]
LRPxxx/fine-tuned-image-model	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Needed] ## Model Card Contact [More Information Needed]	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
bongbongbong/vit-base-beans-demo-v5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0167 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.0934 \| 1.5385 \| 100 \| 0.1081 \| 0.9699 \| \| 0.0204 \| 3.0769 \| 200 \| 0.0167 \| 1.0 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1	[ "angular_leaf_spot", "bean_rust", "healthy" ]
dkwjd/vit-base-beans-demo-v5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0254 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.0887 \| 1.5385 \| 100 \| 0.0401 \| 0.9925 \| \| 0.0115 \| 3.0769 \| 200 \| 0.0254 \| 0.9925 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "angular_leaf_spot", "bean_rust", "healthy" ]
wlfls0305/vit-base-beans-demo-v5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0586 - Accuracy: 0.9699 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.065 \| 1.5385 \| 100 \| 0.0755 \| 0.9850 \| \| 0.0183 \| 3.0769 \| 200 \| 0.0586 \| 0.9699 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "angular_leaf_spot", "bean_rust", "healthy" ]
DeardeermeloD/vit-base-beans-demo-v5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0144 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.1333 \| 1.5385 \| 100 \| 0.0851 \| 0.9774 \| \| 0.0399 \| 3.0769 \| 200 \| 0.0144 \| 1.0 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Tokenizers 0.20.3	[ "angular_leaf_spot", "bean_rust", "healthy" ]
rlatlswls/vit-base-beans-demo-v5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "angular_leaf_spot", "bean_rust", "healthy" ]
hohit/vit-base-beans-demo-v5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3	[ "angular_leaf_spot", "bean_rust", "healthy" ]
crocutacrocuto/convnext-base-224-MEGbis-5	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "aardvark", "bird", "black-and-white colobus", "blue duiker", "blue monkey", "buffalo", "bushbuck", "bushpig", "cattle", "chimpanzee", "civet_genet", "elephant", "galago_potto", "goat", "golden cat", "gorilla", "guineafowl", "leopard", "lhoests monkey", "mandrill", "mongoose", "monkey", "olive baboon", "pangolin", "porcupine", "red colobus_red-capped mangabey", "red duiker", "rodent", "serval", "spotted hyena", "squirrel", "water chevrotain", "yellow-backed duiker" ]

qubvel-hf/vit-base-beans

# vit-base-beans This model is a fine-tuned version of [timm/resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.4913 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.07 | 1.0 | 130 | 1.0683 | 0.4135 | | 1.0523 | 2.0 | 260 | 1.0356 | 0.6241 | | 1.0439 | 3.0 | 390 | 1.0045 | 0.6617 | | 1.0056 | 4.0 | 520 | 0.9671 | 0.7293 | | 0.9853 | 5.0 | 650 | 0.9245 | 0.7895 | | 0.9581 | 6.0 | 780 | 0.8744 | 0.7820 | | 0.9044 | 7.0 | 910 | 0.8172 | 0.7820 | | 0.869 | 8.0 | 1040 | 0.7737 | 0.8271 | | 0.8804 | 9.0 | 1170 | 0.7098 | 0.8271 | | 0.7757 | 10.0 | 1300 | 0.6705 | 0.8120 | | 0.7694 | 11.0 | 1430 | 0.6382 | 0.8571 | | 0.7966 | 12.0 | 1560 | 0.6088 | 0.7895 | | 0.7425 | 13.0 | 1690 | 0.5724 | 0.8496 | | 0.7698 | 14.0 | 1820 | 0.5665 | 0.8195 | | 0.6632 | 15.0 | 1950 | 0.5308 | 0.8571 | | 0.6162 | 16.0 | 2080 | 0.5262 | 0.8346 | | 0.6128 | 17.0 | 2210 | 0.5081 | 0.8421 | | 0.685 | 18.0 | 2340 | 0.4913 | 0.8571 | | 0.6614 | 19.0 | 2470 | 0.4937 | 0.8496 | | 0.6934 | 20.0 | 2600 | 0.5027 | 0.8571 | ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.5.0+cu118 - Datasets 2.21.0 - Tokenizers 0.20.0

[ "angular_leaf_spot", "bean_rust", "healthy" ]

alyzbane/convnextv2-tiny-1k-224-finetuned-barkley

# convnextv2-tiny-1k-224-finetuned-barkley This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0083 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 - Top1 Accuracy: 1.0 - Error Rate: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Top1 Accuracy | Error Rate | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:| | 1.4696 | 1.0 | 38 | 1.1807 | 0.7273 | 0.6513 | 0.6180 | 0.6768 | 0.6513 | 0.3232 | | 0.7197 | 2.0 | 76 | 0.3719 | 0.9439 | 0.9408 | 0.9404 | 0.9434 | 0.9474 | 0.0566 | | 0.2388 | 3.0 | 114 | 0.1489 | 0.9688 | 0.9671 | 0.9671 | 0.9716 | 0.9671 | 0.0284 | | 0.1048 | 4.0 | 152 | 0.0730 | 0.9868 | 0.9868 | 0.9868 | 0.9878 | 0.9868 | 0.0122 | | 0.1103 | 5.0 | 190 | 0.0288 | 0.9868 | 0.9868 | 0.9868 | 0.9878 | 0.9868 | 0.0122 | | 0.072 | 6.0 | 228 | 0.0537 | 0.9877 | 0.9868 | 0.9869 | 0.9868 | 0.9868 | 0.0132 | | 0.0248 | 7.0 | 266 | 0.0083 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | | 0.0371 | 8.0 | 304 | 0.0653 | 0.9819 | 0.9803 | 0.9802 | 0.9800 | 0.9803 | 0.0200 | | 0.0626 | 9.0 | 342 | 0.2271 | 0.9545 | 0.9408 | 0.9404 | 0.95 | 0.9408 | 0.0500 | | 0.07 | 10.0 | 380 | 0.0304 | 0.9936 | 0.9934 | 0.9934 | 0.9933 | 0.9934 | 0.0067 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.19.1

[ "iinstia bijuga", "mangifera indica", "pterocarpus indicus", "roystonea regia", "tabebuia" ]

bombshelll/swin-brain-abnormalities-classification

# swin-brain-abnormalities-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2060 - Accuracy: 0.9531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.8774 | 0.9892 | 23 | 0.5766 | 0.7688 | | 0.4681 | 1.9785 | 46 | 0.2226 | 0.9363 | | 0.2733 | 2.9677 | 69 | 0.1654 | 0.9296 | | 0.2105 | 4.0 | 93 | 0.1851 | 0.9414 | | 0.1705 | 4.9892 | 116 | 0.2025 | 0.9397 | | 0.1716 | 5.9785 | 139 | 0.1505 | 0.9497 | | 0.1528 | 6.9677 | 162 | 0.2124 | 0.9430 | | 0.1377 | 8.0 | 186 | 0.1841 | 0.9464 | | 0.1275 | 8.9892 | 209 | 0.1919 | 0.9481 | | 0.115 | 9.9785 | 232 | 0.1570 | 0.9548 | | 0.1076 | 10.9677 | 255 | 0.2035 | 0.9481 | | 0.1001 | 12.0 | 279 | 0.1805 | 0.9514 | | 0.0932 | 12.9892 | 302 | 0.2067 | 0.9497 | | 0.0814 | 13.9785 | 325 | 0.2107 | 0.9481 | | 0.0767 | 14.8387 | 345 | 0.2060 | 0.9531 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "lesion", "normal", "tumor" ]

bombshelll/swin-brain-plane-classification

# swin-brain-plane-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0556 - Accuracy: 0.9778 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0606 | 1.0 | 5 | 0.7887 | 0.8741 | | 0.7256 | 2.0 | 10 | 0.3949 | 0.9111 | | 0.4331 | 3.0 | 15 | 0.1731 | 0.9704 | | 0.3165 | 4.0 | 20 | 0.1250 | 0.9630 | | 0.1827 | 5.0 | 25 | 0.0847 | 0.9704 | | 0.1488 | 6.0 | 30 | 0.0919 | 0.9630 | | 0.1425 | 7.0 | 35 | 0.0604 | 0.9778 | | 0.1487 | 8.0 | 40 | 0.0718 | 0.9778 | | 0.118 | 9.0 | 45 | 0.0578 | 0.9778 | | 0.1278 | 10.0 | 50 | 0.0556 | 0.9778 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "axial", "coronal", "sagittal" ]

bombshelll/swin-brain-modality-classification

# swin-brain-modality-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0197 - Accuracy: 0.9956 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7116 | 1.0 | 16 | 0.1466 | 0.9333 | | 0.0955 | 2.0 | 32 | 0.0323 | 0.9911 | | 0.0464 | 3.0 | 48 | 0.0197 | 0.9956 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "ct", "mri t1", "mri t2", "mri t2 flair" ]

kiranshivaraju/convnext2-tiny-finetuned-aug-pcb_data

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "bad", "good" ]

neuralhaven/mobilevit-xx-small-FireRisk

# mobilevit-xx-small-FireRisk This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9814 - eval_accuracy: 0.6146 - eval_precision: 0.5070 - eval_recall: 0.4671 - eval_f1: 0.4675 - eval_runtime: 164.589 - eval_samples_per_second: 130.878 - eval_steps_per_second: 1.027 - epoch: 1.0 - step: 550 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "very low", "low", "moderate", "high", "very high", "non-burnable", "water" ]

AnthoneoJ/ms6-train

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "food", "guru", "inside", "menu", "outside", "people" ]

bombshelll/swin-brain-tumor-type-classification

# swin-brain-tumor-type-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2978 - Accuracy: 0.9081 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.4824 | 1.0 | 21 | 2.2558 | 0.2420 | | 2.1213 | 2.0 | 42 | 1.8165 | 0.4170 | | 1.6613 | 3.0 | 63 | 1.3678 | 0.5671 | | 1.3237 | 4.0 | 84 | 1.1940 | 0.6060 | | 1.1543 | 5.0 | 105 | 0.9205 | 0.7049 | | 0.9317 | 6.0 | 126 | 0.8121 | 0.7314 | | 0.7891 | 7.0 | 147 | 0.6553 | 0.7986 | | 0.6812 | 8.0 | 168 | 0.5720 | 0.8180 | | 0.6348 | 9.0 | 189 | 0.5364 | 0.8180 | | 0.5488 | 10.0 | 210 | 0.4780 | 0.8428 | | 0.505 | 11.0 | 231 | 0.4540 | 0.8569 | | 0.4758 | 12.0 | 252 | 0.3992 | 0.8852 | | 0.4306 | 13.0 | 273 | 0.4280 | 0.8675 | | 0.3952 | 14.0 | 294 | 0.4019 | 0.8781 | | 0.3726 | 15.0 | 315 | 0.3794 | 0.8763 | | 0.3191 | 16.0 | 336 | 0.3482 | 0.8958 | | 0.3014 | 17.0 | 357 | 0.3372 | 0.8940 | | 0.2785 | 18.0 | 378 | 0.3472 | 0.8993 | | 0.2948 | 19.0 | 399 | 0.3246 | 0.9064 | | 0.2618 | 20.0 | 420 | 0.3060 | 0.9081 | | 0.2705 | 21.0 | 441 | 0.3122 | 0.9046 | | 0.2479 | 22.0 | 462 | 0.3061 | 0.9028 | | 0.2411 | 23.0 | 483 | 0.3040 | 0.9099 | | 0.2556 | 24.0 | 504 | 0.2990 | 0.9099 | | 0.2413 | 25.0 | 525 | 0.2978 | 0.9081 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "astrocitoma", "carcinoma", "ependimoma", "ganglioglioma", "germinoma", "glioblastoma", "granuloma", "meduloblastoma", "meningioma", "neurocitoma", "oligodendroglioma", "papiloma", "schwannoma", "tuberculoma" ]

neuralhaven/deit-tiny-patch16-224-FireRisk

# deit-tiny-patch16-224-FireRisk This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9478 - Accuracy: 0.6253 - Precision: 0.5221 - Recall: 0.5179 - F1: 0.5190 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 512 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9714 | 1.0 | 138 | 0.9923 | 0.6114 | 0.5167 | 0.5118 | 0.5070 | | 0.9482 | 2.0 | 276 | 0.9760 | 0.6094 | 0.4985 | 0.5138 | 0.5030 | | 0.9263 | 3.0 | 414 | 0.9398 | 0.6314 | 0.5226 | 0.5202 | 0.5180 | | 0.903 | 4.0 | 552 | 0.9683 | 0.6183 | 0.5141 | 0.5021 | 0.5039 | | 0.8536 | 5.0 | 690 | 0.9478 | 0.6253 | 0.5221 | 0.5179 | 0.5190 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "very low", "low", "moderate", "high", "very high", "non-burnable", "water" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v9

# vit-base-patch16-224-in21k-rotated-dungeons-v9 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.3109 - Accuracy: 0.625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 22 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.014 | 8.3333 | 100 | 1.5028 | 0.5 | | 0.6464 | 16.6667 | 200 | 1.3109 | 0.625 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v10

# vit-base-patch16-224-in21k-rotated-dungeons-v10 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.9495 - Accuracy: 0.875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.3924 | 8.3333 | 100 | 1.0648 | 0.75 | | 0.2925 | 16.6667 | 200 | 0.9745 | 0.875 | | 0.2696 | 25.0 | 300 | 0.9495 | 0.875 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v11

# vit-base-patch16-224-in21k-rotated-dungeons-v11 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.7050 - Accuracy: 0.875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 27 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.1396 | 8.3333 | 100 | 0.7253 | 0.875 | | 0.1215 | 16.6667 | 200 | 0.7134 | 0.875 | | 0.1175 | 25.0 | 300 | 0.7050 | 0.875 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v12

# vit-base-patch16-224-in21k-rotated-dungeons-v12 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.4316 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 27 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.0811 | 8.3333 | 100 | 1.6611 | 0.4167 | | 0.6509 | 16.6667 | 200 | 1.4987 | 0.5 | | 0.5656 | 25.0 | 300 | 1.4316 | 0.6667 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

AbdoulayeDIOP/lettuce-npk-vit

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/abdoulaye-diop/lettuce-npk-deficiency-prediction/runs/zjp8gyck) # lettuce-npk-vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1492 - Accuracy: 0.9524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.1633 | 0.992 | 31 | 1.1239 | 0.8571 | | 0.4802 | 1.984 | 62 | 0.4213 | 0.9048 | | 0.1386 | 2.976 | 93 | 0.2501 | 0.9524 | | 0.1003 | 4.0 | 125 | 0.1879 | 0.9524 | | 0.0871 | 4.992 | 156 | 0.3482 | 0.8571 | | 0.0702 | 5.984 | 187 | 0.2048 | 0.9524 | | 0.0594 | 6.976 | 218 | 0.2824 | 0.9048 | | 0.0425 | 8.0 | 250 | 0.2567 | 0.9524 | | 0.0398 | 8.992 | 281 | 0.3363 | 0.8571 | | 0.0348 | 9.984 | 312 | 0.2518 | 0.9524 | | 0.0411 | 10.9760 | 343 | 0.0369 | 1.0 | | 0.0445 | 12.0 | 375 | 0.2288 | 0.9524 | | 0.0353 | 12.992 | 406 | 0.2364 | 0.8571 | | 0.0384 | 13.984 | 437 | 0.2255 | 0.9524 | | 0.0331 | 14.9760 | 468 | 0.0572 | 1.0 | | 0.0252 | 16.0 | 500 | 0.2103 | 0.9524 | | 0.0337 | 16.992 | 531 | 0.0295 | 1.0 | | 0.0302 | 17.984 | 562 | 0.2805 | 0.9048 | | 0.0328 | 18.976 | 593 | 0.2127 | 0.9524 | | 0.0315 | 19.84 | 620 | 0.1492 | 0.9524 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "-k", "-n", "-p", "fn" ]

Encore02/vit-weldclassifyv4

# vit-weldclassifyv4 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3301 - Accuracy: 0.9209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.8207 | 0.6410 | 100 | 1.0336 | 0.5647 | | 0.6506 | 1.2821 | 200 | 1.1982 | 0.5791 | | 0.5324 | 1.9231 | 300 | 0.6060 | 0.7770 | | 0.2486 | 2.5641 | 400 | 0.7294 | 0.7518 | | 0.1366 | 3.2051 | 500 | 0.4832 | 0.8417 | | 0.3124 | 3.8462 | 600 | 0.8676 | 0.7626 | | 0.0296 | 4.4872 | 700 | 0.4233 | 0.8885 | | 0.0723 | 5.1282 | 800 | 0.4470 | 0.8849 | | 0.0342 | 5.7692 | 900 | 0.3406 | 0.9173 | | 0.0055 | 6.4103 | 1000 | 0.3301 | 0.9209 | | 0.0048 | 7.0513 | 1100 | 0.3471 | 0.9173 | | 0.0036 | 7.6923 | 1200 | 0.3346 | 0.9137 | | 0.003 | 8.3333 | 1300 | 0.3498 | 0.9137 | | 0.003 | 8.9744 | 1400 | 0.3549 | 0.9101 | | 0.0027 | 9.6154 | 1500 | 0.3569 | 0.9137 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "a", "b", "c", "d" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v14

# vit-base-patch16-224-in21k-rotated-dungeons-v14 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.2003 - Accuracy: 0.8125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 22 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.1165 | 8.3333 | 100 | 1.4525 | 0.5 | | 0.7716 | 16.6667 | 200 | 1.2092 | 0.8125 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v15

# vit-base-patch16-224-in21k-rotated-dungeons-v15 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.1946 - Accuracy: 0.7917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 22 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.1388 | 8.3333 | 100 | 1.4610 | 0.5 | | 0.8121 | 16.6667 | 200 | 1.2161 | 0.7917 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

DouglasBraga/swin-tiny-patch4-window7-224-finetuned-leukemia.v2.2

# swin-tiny-patch4-window7-224-finetuned-leukemia.v2.2 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5507 - Accuracy: 0.7638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.2349 | 0.9984 | 312 | 0.5575 | 0.7698 | | 0.2191 | 2.0 | 625 | 0.5572 | 0.7618 | | 0.2124 | 2.9984 | 937 | 0.5580 | 0.769 | | 0.2207 | 4.0 | 1250 | 0.5500 | 0.763 | | 0.2143 | 4.9984 | 1562 | 0.5575 | 0.7652 | | 0.2191 | 6.0 | 1875 | 0.5486 | 0.7728 | | 0.2063 | 6.9984 | 2187 | 0.5594 | 0.7615 | | 0.207 | 8.0 | 2500 | 0.5405 | 0.7695 | | 0.2273 | 8.9984 | 2812 | 0.5568 | 0.7672 | | 0.2136 | 10.0 | 3125 | 0.5483 | 0.7728 | | 0.2184 | 10.9984 | 3437 | 0.5606 | 0.7665 | | 0.212 | 12.0 | 3750 | 0.5578 | 0.761 | | 0.1903 | 12.9984 | 4062 | 0.5371 | 0.769 | | 0.2487 | 14.0 | 4375 | 0.5582 | 0.7645 | | 0.2025 | 14.9984 | 4687 | 0.5414 | 0.7778 | | 0.2207 | 16.0 | 5000 | 0.5376 | 0.7685 | | 0.2012 | 16.9984 | 5312 | 0.5489 | 0.7702 | | 0.2198 | 18.0 | 5625 | 0.5560 | 0.7752 | | 0.2171 | 18.9984 | 5937 | 0.5570 | 0.7725 | | 0.2116 | 20.0 | 6250 | 0.5622 | 0.7625 | | 0.2162 | 20.9984 | 6562 | 0.5587 | 0.7668 | | 0.224 | 22.0 | 6875 | 0.5456 | 0.7712 | | 0.212 | 22.9984 | 7187 | 0.5647 | 0.7652 | | 0.2084 | 24.0 | 7500 | 0.5533 | 0.7672 | | 0.2226 | 24.9984 | 7812 | 0.5434 | 0.7705 | | 0.2173 | 26.0 | 8125 | 0.5738 | 0.7675 | | 0.2216 | 26.9984 | 8437 | 0.5557 | 0.7672 | | 0.1918 | 28.0 | 8750 | 0.5502 | 0.7705 | | 0.199 | 28.9984 | 9062 | 0.5456 | 0.7675 | | 0.21 | 29.9520 | 9360 | 0.5483 | 0.7715 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1

[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

DeepTrader/vit-finetuned-1

# vit-finetuned-1 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2750 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.1575 | 1.0 | | No log | 2.0 | 2 | 0.1166 | 1.0 | | No log | 3.0 | 3 | 0.2496 | 1.0 | | No log | 4.0 | 4 | 0.4093 | 0.5 | | No log | 5.0 | 5 | 0.6732 | 0.5 | | No log | 6.0 | 6 | 0.9200 | 0.5 | | No log | 7.0 | 7 | 1.0925 | 0.5 | | No log | 8.0 | 8 | 1.1963 | 0.5 | | No log | 9.0 | 9 | 1.2521 | 0.5 | | No log | 10.0 | 10 | 1.2750 | 0.5 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cpu - Datasets 3.1.0 - Tokenizers 0.20.1

[ "ironed_tshirts", "not_ironed_tshirts" ]

gabriletomasin/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0740 - Accuracy: 0.9736 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0835 | 1.0 | 152 | 0.1241 | 0.9616 | | 0.8216 | 2.0 | 304 | 0.0918 | 0.9685 | | 0.6378 | 3.0 | 456 | 0.0740 | 0.9736 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1

[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v16

# vit-base-patch16-224-in21k-rotated-dungeons-v16 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.4158 - Accuracy: 0.6429 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 22 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.2148 | 8.3333 | 100 | 1.6360 | 0.3214 | | 0.8693 | 16.6667 | 200 | 1.4230 | 0.6429 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v17

# vit-base-patch16-224-in21k-rotated-dungeons-v17 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.3167 - Accuracy: 0.7857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.2481 | 8.3333 | 100 | 1.6308 | 0.2857 | | 0.8449 | 16.6667 | 200 | 1.4119 | 0.5714 | | 0.7993 | 25.0 | 300 | 1.3354 | 0.7143 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

kiranshivaraju/convnext-tiny-finetuned-aug-pcb_data

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "bad", "good" ]

Razertory/font-identifier

# font-identifier This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0010 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 3.2404 | 0.9524 | 15 | 3.1135 | 0.06 | | 2.7846 | 1.9683 | 31 | 2.4576 | 0.33 | | 2.3956 | 2.9841 | 47 | 1.7152 | 0.58 | | 1.6171 | 4.0 | 63 | 1.0931 | 0.775 | | 1.2882 | 4.9524 | 78 | 0.6347 | 0.85 | | 0.7191 | 5.9683 | 94 | 0.3957 | 0.94 | | 0.5196 | 6.9841 | 110 | 0.2080 | 0.965 | | 0.3999 | 8.0 | 126 | 0.1480 | 0.965 | | 0.2476 | 8.9524 | 141 | 0.0934 | 0.985 | | 0.2176 | 9.9683 | 157 | 0.0768 | 0.99 | | 0.194 | 10.9841 | 173 | 0.0365 | 0.995 | | 0.1572 | 12.0 | 189 | 0.0616 | 0.985 | | 0.1381 | 12.9524 | 204 | 0.0640 | 0.985 | | 0.1291 | 13.9683 | 220 | 0.0522 | 0.985 | | 0.094 | 14.9841 | 236 | 0.0442 | 0.99 | | 0.1037 | 16.0 | 252 | 0.0492 | 0.99 | | 0.1067 | 16.9524 | 267 | 0.0629 | 0.985 | | 0.0912 | 17.9683 | 283 | 0.0486 | 0.985 | | 0.0702 | 18.9841 | 299 | 0.0344 | 0.99 | | 0.0677 | 20.0 | 315 | 0.0242 | 0.995 | | 0.0566 | 20.9524 | 330 | 0.0295 | 0.99 | | 0.0742 | 21.9683 | 346 | 0.0300 | 0.99 | | 0.0675 | 22.9841 | 362 | 0.0159 | 1.0 | | 0.0501 | 24.0 | 378 | 0.0105 | 0.995 | | 0.0651 | 24.9524 | 393 | 0.0362 | 0.995 | | 0.0665 | 25.9683 | 409 | 0.0335 | 0.985 | | 0.0533 | 26.9841 | 425 | 0.0369 | 0.99 | | 0.0487 | 28.0 | 441 | 0.0296 | 0.99 | | 0.0384 | 28.9524 | 456 | 0.0177 | 0.995 | | 0.038 | 29.9683 | 472 | 0.0176 | 0.995 | | 0.0342 | 30.9841 | 488 | 0.0165 | 0.995 | | 0.055 | 32.0 | 504 | 0.0199 | 0.995 | | 0.0418 | 32.9524 | 519 | 0.0022 | 1.0 | | 0.0447 | 33.9683 | 535 | 0.0071 | 0.995 | | 0.0436 | 34.9841 | 551 | 0.0587 | 0.98 | | 0.0307 | 36.0 | 567 | 0.0244 | 0.995 | | 0.0413 | 36.9524 | 582 | 0.0227 | 0.99 | | 0.0351 | 37.9683 | 598 | 0.0323 | 0.99 | | 0.0267 | 38.9841 | 614 | 0.0510 | 0.985 | | 0.0259 | 40.0 | 630 | 0.0009 | 1.0 | | 0.0245 | 40.9524 | 645 | 0.0017 | 1.0 | | 0.0227 | 41.9683 | 661 | 0.0208 | 0.995 | | 0.0458 | 42.9841 | 677 | 0.0445 | 0.99 | | 0.0263 | 44.0 | 693 | 0.0339 | 0.99 | | 0.0458 | 44.9524 | 708 | 0.0124 | 0.995 | | 0.0374 | 45.9683 | 724 | 0.0253 | 0.995 | | 0.0413 | 46.9841 | 740 | 0.0025 | 1.0 | | 0.0413 | 47.6190 | 750 | 0.0010 | 1.0 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.0 - Datasets 3.1.0 - Tokenizers 0.20.1

[ "abeezee", "aclonica", "baloo2", "bangers", "barlowcondensed", "deftonestylus", "kanit", "onean", "worksans", "dintalksans", "kingsbridgeexlt", "steelfishoutline", "adamina", "adventpro", "akronim", "aladin", "alef", "aleo", "alexbrush", "amaranth" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v18

# vit-base-patch16-224-in21k-rotated-dungeons-v18 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.5308 - Accuracy: 0.3571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 6 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2464 | 12.5 | 100 | 1.6494 | 0.2857 | | 0.9502 | 25.0 | 200 | 1.5193 | 0.3929 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v19

# vit-base-patch16-224-in21k-rotated-dungeons-v19 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.2440 - Accuracy: 0.75 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v20

# vit-base-patch16-224-in21k-rotated-dungeons-v20 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.6906 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 18 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.3392 | 8.3333 | 100 | 0.8138 | 0.8571 | | 0.2148 | 16.6667 | 200 | 0.7027 | 0.8571 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v21

# vit-base-patch16-224-in21k-rotated-dungeons-v21 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.5714 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.1615 | 8.3333 | 100 | 0.6069 | 0.8571 | | 0.1215 | 16.6667 | 200 | 0.5686 | 0.8571 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v22

# vit-base-patch16-224-in21k-rotated-dungeons-v22 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.5265 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.1127 | 4.3478 | 100 | 0.5795 | 0.8571 | | 0.0985 | 8.6957 | 200 | 0.5159 | 0.8571 | | 0.0799 | 13.0435 | 300 | 0.5685 | 0.8571 | | 0.078 | 17.3913 | 400 | 0.5435 | 0.8571 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

tiendoan/finetune-clip-vit-large-patch14

# finetune-clip-vit-large-patch14 This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6545 - F1: 0.6242 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:------:|:----:|:---------------:|:------:| | 0.7084 | 0.3690 | 100 | 0.7173 | 0.5405 | | 0.6818 | 0.7380 | 200 | 0.7269 | 0.5683 | | 0.7169 | 1.1070 | 300 | 0.6949 | 0.5683 | | 0.6957 | 1.4760 | 400 | 0.6799 | 0.5650 | | 0.6218 | 1.8450 | 500 | 0.7344 | 0.5766 | | 0.6406 | 2.2140 | 600 | 0.6600 | 0.6118 | | 0.6645 | 2.5830 | 700 | 0.6581 | 0.6113 | | 0.6546 | 2.9520 | 800 | 0.6549 | 0.6192 | | 0.6068 | 3.3210 | 900 | 0.6542 | 0.6224 | | 0.6351 | 3.6900 | 1000 | 0.6545 | 0.6242 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "not-sarcasm", "sarcasm" ]

jix0727/vit-base-beans

# vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0670 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2797 | 1.0 | 130 | 0.2151 | 0.9624 | | 0.1301 | 2.0 | 260 | 0.1259 | 0.9774 | | 0.1385 | 3.0 | 390 | 0.0962 | 0.9774 | | 0.0764 | 4.0 | 520 | 0.0670 | 0.9925 | | 0.1154 | 5.0 | 650 | 0.0809 | 0.9774 | ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

limsoft0401/restnet-18

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v001

# vit-base-patch16-224-in21k-rotated-dungeons-v001 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.1914 - Accuracy: 0.8214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.4924 | 0.5 | | No log | 2.0 | 12 | 1.4798 | 0.5 | | No log | 3.0 | 18 | 1.4518 | 0.5357 | | No log | 4.0 | 24 | 1.4402 | 0.5357 | | No log | 5.0 | 30 | 1.4052 | 0.5357 | | No log | 6.0 | 36 | 1.3828 | 0.6786 | | No log | 7.0 | 42 | 1.3588 | 0.6786 | | No log | 8.0 | 48 | 1.3295 | 0.6786 | | No log | 9.0 | 54 | 1.3263 | 0.7143 | | No log | 10.0 | 60 | 1.3072 | 0.75 | | No log | 11.0 | 66 | 1.2918 | 0.7143 | | No log | 12.0 | 72 | 1.2718 | 0.8214 | | No log | 13.0 | 78 | 1.2728 | 0.7857 | | No log | 14.0 | 84 | 1.2628 | 0.75 | | No log | 15.0 | 90 | 1.2333 | 0.7857 | | No log | 16.0 | 96 | 1.2253 | 0.7857 | | No log | 17.0 | 102 | 1.2240 | 0.7857 | | No log | 18.0 | 108 | 1.2249 | 0.7857 | | No log | 19.0 | 114 | 1.2177 | 0.7857 | | No log | 20.0 | 120 | 1.2098 | 0.7857 | | No log | 21.0 | 126 | 1.2029 | 0.8214 | | No log | 22.0 | 132 | 1.1875 | 0.8571 | | No log | 23.0 | 138 | 1.1873 | 0.8571 | | No log | 24.0 | 144 | 1.2051 | 0.7857 | | No log | 25.0 | 150 | 1.1914 | 0.8214 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v002

# vit-base-patch16-224-in21k-rotated-dungeons-v002 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 1.0084 - Accuracy: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 12 | 1.1281 | 0.8571 | | No log | 2.0 | 24 | 1.1248 | 0.8571 | | No log | 3.0 | 36 | 1.0930 | 0.8571 | | No log | 4.0 | 48 | 1.1040 | 0.8214 | | No log | 5.0 | 60 | 1.0646 | 0.8214 | | No log | 6.0 | 72 | 1.0540 | 0.8214 | | No log | 7.0 | 84 | 1.0309 | 0.8571 | | No log | 8.0 | 96 | 1.0274 | 0.8571 | | No log | 9.0 | 108 | 1.0155 | 0.8571 | | No log | 10.0 | 120 | 1.0079 | 0.8571 | | No log | 11.0 | 132 | 1.0175 | 0.8571 | | No log | 12.0 | 144 | 1.0029 | 0.8571 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-in21k-rotated-dungeons-v003

# vit-base-patch16-224-in21k-rotated-dungeons-v003 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.5927 - F1: 0.8548 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 14 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 12 | 1.6942 | 0.0095 | | No log | 2.0 | 24 | 1.4463 | 0.3217 | | No log | 3.0 | 36 | 1.2808 | 0.3690 | | No log | 4.0 | 48 | 0.9816 | 0.5798 | | No log | 5.0 | 60 | 0.6291 | 0.8548 | | No log | 6.0 | 72 | 0.9176 | 0.7226 | | No log | 7.0 | 84 | 0.7163 | 0.8548 | | No log | 8.0 | 96 | 0.6404 | 0.8548 | | No log | 9.0 | 108 | 0.6627 | 0.8548 | | No log | 10.0 | 120 | 0.7034 | 0.8177 | | No log | 11.0 | 132 | 0.5796 | 0.8548 | | No log | 12.0 | 144 | 0.5746 | 0.8548 | | No log | 13.0 | 156 | 0.5902 | 0.8548 | | No log | 14.0 | 168 | 0.6146 | 0.8548 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

gustavomalkomes/vit-base-patch16-224-in21k

# vit-base-patch16-224-in21k This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the chainyo/rvl-cdip dataset. It achieves the following results on the evaluation set: - Loss: 0.4223 - Accuracy: 0.8788 - Memory Allocated (gb): 1.49 - Max Memory Allocated (gb): 2.1 - Total Memory Available (gb): 126.62 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.0a0+git12138a8 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "advertisement", "budget", "presentation", "questionnaire", "resume", "scientific publication", "scientific report", "specification", "email", "file folder", "form", "handwritten", "invoice", "letter", "memo", "news article" ]

hungryhunglee/my_awesome_food_model

# my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

osada-pku/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0688 - Accuracy: 0.9748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2593 | 1.0 | 190 | 0.1323 | 0.9541 | | 0.1607 | 2.0 | 380 | 0.0793 | 0.9730 | | 0.1287 | 3.0 | 570 | 0.0688 | 0.9748 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 2.11.0 - Tokenizers 0.19.1

[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]

Danx15/vit-msn-small-finetuned-custom-logo

# vit-msn-small-finetuned-custom-logo This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset. It achieves the following results on the evaluation set: - eval_loss: 6.3923 - eval_model_preparation_time: 0.0035 - eval_accuracy: 0.0031 - eval_runtime: 9.5833 - eval_samples_per_second: 170.087 - eval_steps_per_second: 2.713 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Framework versions - Transformers 4.46.2 - Pytorch 2.4.1+cu121 - Datasets 2.20.0 - Tokenizers 0.20.3

[ "8ballpool - origin", "abinbev - origin", "acluofflorida - origin", "afkarena - draconic resurgence", "afkarena - original", "activision - origin", "acura - origin", "adroll - origin", "age-of-methology - age-of-methology-titans", "age-of-methology - age-of-methology-retold", "age-of-methology - origin", "aion - origin", "akčnítarifygomobil - origin", "amgen - origin", "ancestry - origin", "applestore - origin", "arknights - origin", "assasin's-creed-shadows - origin", "audicenterguatemala - origin", "aura kingdom - link of hearts(japan)", "aura kingdom - r(hongkong)", "aura kingdom - r(taiwan)", "aura kingdom - s", "aura kingdom - anotherfate", "aura kingdom - english", "aura kingdom - taiwan", "axetrader - axetraderfunding", "axetrader - axetraderhub", "axetrader - original", "azurlane - chinese", "azurlane - cross wave (en)", "azurlane - cross wave (jp)", "azurlane - korean", "azurlane - original", "bmw - 1917", "bmw - 1933", "bmw - 1954", "bmw - 1974", "bmw - 1979", "bmw - 2007", "bmw - present", "bmwgroupinösterreich - origin", "barilla - 1924-1936", "barilla - 1936-1949", "barilla - 1949-1952", "barilla - 1952-1954", "barilla - 1954-1969", "barilla - 2003-2015", "barilla - 2015-2022", "barilla - 2022-present", "battlenight - origin", "bayerbrasil - origin", "bestbuy - origin", "bingoblitz - app logo", "bingoblitz - option1", "bingoblitz - option2", "bitlife - bitlifede-lebenssimulation", "bitlife - bitlifeespañol-simuladordevida", "bitlife - option1", "bitlife - option2", "bitlife - option3", "bitlife - option4", "bitlife - option5", "bitlife - option6", "blackclover - english", "blackclover - japan", "blackdesert - mobile", "blackdesert - online", "blackdesert - online resmastered", "blackdesert - remastered", "blackdesert - standard", "blacklivesmatter - origin", "bleach - original", "bleach - thousand-year blood war", "bmbvalve - origin", "byronhollisterfordoñaanacountysheriff2022 - origin", "cdc - cdc-health-care-public", "cdc - original", "cdc - round", "callofduty - black-ops-6", "callofduty - mobilevn", "callofduty - warzonemobile", "caltrate - origin", "centrummedycznebody&mind - origin", "cerave - origin", "chainsaw-man - origin", "coinbeach - origin", "colgate - colgate - chinese", "colgate - colgate 1897", "colgate - colgate 1946", "colgate - colgate 1963", "colgate - colgate 1980", "colgate - colgate 2001", "colgate - colgate 2004", "colgate - colgate 2009", "colgate - colgate 2016", "colgate - colgate 2018", "com2us - origin", "converse - all star", "converse - converse 1963", "converse - converse 1977", "converse - converse 2003", "converse - converse 2007", "converse - converse 2011", "converse - converse 2017", "cricketwireless - origin", "deadpool-&-wolverine - 1st logo", "deadpool-&-wolverine - 2nd logo", "deadpool-&-wolverine - 3nd logo", "death note - 2003-2006", "death note - 2006-present", "death note - into the dark", "dell technologies - emc", "dell technologies - horizontal", "dell technologies - hybrid", "dell technologies - vertical", "demon slayer - english", "demon slayer - japanese", "diablo-vessel-of-hatred - hybrid", "diablo-vessel-of-hatred - picture logo", "dinsko - origin", "doom-the-dark-ages - origin", "dove - 1955", "dove - 1969", "dove - 2004-present", "dove - picture logo", "dragon age - dragon age 2", "dragon age - origins", "dragon age - origins ultimate edition", "dragon nest - dragon nest 1", "dragon nest - dragon nest 2 evolution", "dragon nest - world of dragon nest", "dragon raja - english", "dragon raja - origins", "dragon raja - the blazing dawn", "dragonballz - budokai tenkaichi", "dragonballz - horizontal", "dragonballz - vertical", "dream destination canada - version1", "dream destination canada - version2", "dream destination canada - version3", "dreamwork animation - 1998-2004", "dreamwork animation - 2004-2006", "dreamwork animation - 2007-2018", "dreamwork animation - 2014-2017", "dreamwork animation - 2016-now", "dreamwork animation - 2016-now picture logo", "dreamworkstation - spirit", "dreamworksanimation - origin", "dresslily - version2(horizontal)", "dresslily - version1", "dresslily - version3(vertical)", "dresslily - version4", "dunkin - dunkin donuts 1950", "dunkin - dunkin donuts 1956", "dunkin - dunkin donuts 1960", "dunkin - dunkin donuts 1961", "dunkin - dunkin donuts 1970", "dunkin - dunkin donuts 1976", "dunkin - dunkin donuts 1980", "dunkin - dunkin donuts 2002", "dunkin - dunkin donuts 2007", "dunkin - dunkin donuts 2019-present", "dunkin - dunkin coffee", "dunkin - dunkin donuts", "ecco - version1", "ecco - version2", "ecco - version3", "ecco - version4", "ecco - version5", "ecco - version6", "edhanesfornc - origin", "elden ring - shadow of the erdtree", "elden ring - wordmark logo", "eldenring - icon", "eterniumrudeboy - origin", "fallout - fallout 1", "fallout - fallout 2", "fallout - fallout 3", "fallout - fallout tatics", "fireemblemheroes - origin", "flintlock - version1", "flintlock - version2", "fortnite - fortnite original", "fortnite - fortnite stw 2017", "fortnite - wordmark logo", "fragpunk - origin", "furgonazos-recargados - origin", "gameofsultans - origin", "gap - origin", "gardenscapeslandscapecentre - origin", "gear-of-war-e-day - origin", "genshinimpact - origin", "gintama - english", "gintama - japanese", "glad2glowindonesia - origin", "gmail - 2004 version1", "gmail - 2004 version2", "gmail - 2004-2010", "gmail - 2010-2013", "gmail - 2013-2020", "gmail - 2020-present", "googleplay - origin", "grammarly - origin", "groupon - 2008", "groupon - 2012", "hcahealthcare - hybrid logo", "hcahealthcare - picture logo", "harrypotter - hogwartsmystery", "harrypotter - puzzles&spells", "hellmann's - 1913", "hellmann's - 1929-1945", "hellmann's - 1945-1988", "hellmann's - 1988-2001", "hellmann's - 2001-2004", "hellmann's - 2004-2005", "hellmann's - 2015-2017", "hellmann's - 2017-present", "hertz - origin", "honda - honda car 1961-1969", "honda - honda car 1969-1981", "honda - honda car 1981-2000", "honda - honda car 2000-2024", "honda - honda car 2024-present", "honda - honda car picture logo", "honda - honda motor 1947-1948", "honda - honda motor 1948-1953", "honda - honda motor 1953-1968", "honda - honda motor 1968", "honda - honda motor 1968-1973", "honda - honda motor 1973-1985", "honda - honda motor 1985-1988", "honda - honda motor 1988-2000", "honda - honda motor 2000-present", "hoyoverse - origin", "huawei - app gallery", "huawei - original", "huntexhunter - 1998", "hunterxhunter - 2011", "huyndai - origin", "hyundai - origin", "ikea - origin", "idleheroes - origin", "indeedbrasil - origin", "indiana-johns - origin", "industrywest - origin", "intel - 1968", "intel - 2006", "intel - 2020", "intuitturbotax - origin", "inversionesgrupojif - origin", "johnson'sbaby - origin", "kfc - 1956", "kfc - 1959", "kfc - 1978", "kfc - 1991", "kfc - 1997", "kfc - 2006", "kfc - 2014-2018", "kfc - 2018-present", "kariteinonderbroek - origin", "kia - 1953", "kia - 1964", "kia - 1986", "kia - 1994", "kia - 2012", "kia - 2021", "kinto-one - origin", "kraft - 1926", "kraft - 2012-present", "kraft - 1960-2012", "la-colombe-coffee-roasters - origin", "lastshelter-survival - origin", "lay's - 1932", "lay's - 1965", "lay's - 1986", "lay's - 1997", "lay's - 2003", "lay's - 2007", "lay's - 2019", "lexus - origin", "lifeisstrange - origin", "lineage2 - origin", "lysolus - origin", "madly-madagascar - origin", "mafiacity - version1", "mafiacity - version2", "mark-miller-subaru - origin", "mars - origin", "marvel - loki picture logo", "marvel - loki wordmark", "marvel - marvel studio", "marvel - origin", "marvelcontestofchampions - origin", "marvelstrikeforce - origin", "matchingtonmansion - origin", "mcdonald's - 1940", "mcdonald's - 1948", "mcdonald's - 1955", "mcdonald's - 1961", "mcdonald's - 1968", "mcdonald's - 1975", "mcdonald's - 1992", "mcdonald's - 1999", "mcdonald's - 2000", "mcdonald's - 2006", "mechabreak - origin", "microsoft - origin", "microsoft-flight-simulator - origin", "mobilelegends adversionenture - version1", "mobilelegends adversionenture - version2", "mobilelegends-bangbang - origin", "motorcity - origin", "my hero academy - 2014", "my hero academy - chinese", "my hero academy - japanese", "mythicsummon-idlerpg - origin", "nashvillejusticeleague - origin", "national night out - 2019", "national night out - 2023", "national night out - 2024", "national night out - version1", "national night out - version5", "nike - origin", "nintendo - origin", "nintendoswitch - origin", "novartiscareers - origin", "nộithấtxbox - origin", "o'reillyautoparts - origin", "oasis - origin", "one-punch-man - origin", "onepiece - origin", "optivista - origin", "papajohns - origin", "papermario - origin", "paw-patrol-the-mighty-movie - origin", "pepsi - origin", "perfect dark - 2025", "perfect dark - initiative", "perfect dark - before 2010", "perfectworldm-godswarcis - origin", "perfectworldmobile - origin", "pfizer - new version", "pfizer - old version", "photoshop - origin", "pizza hut - 1999", "pizza hut - 2008", "pizza hut - 2010", "pizza hut - 2014", "pizza hut - 2019", "pokemon - origin", "pringles - origin", "projectmakeover - origin", "purple - origin", "quaker - 1877", "quaker - 1946", "quaker - 1957", "quaker - 1970", "quaker - 1972", "quaker - 2007", "quaker - 2010", "quaker - 2011", "quaker - 2012", "quaker - 2017", "ragnarokonlinerpg - origin", "ragnarokx-nextgeneration - origin", "raid-shadowlegends - origin", "retailmenot - picture logo", "retailmenot - wordmark", "riseofkingdoms - origin", "rosewe - origin", "rotita - origin", "samsung - origin", "sanofibelgium - origin", "sanofiexpertos - origin", "sea of thieves - alternate logo", "sea of thieves - deluxe edition", "sea of thieves - picture logo", "sea of thieves - standard logo", "searchlightpictures - origin", "seresto - origin", "seven knights ii - horizontal", "seven knights ii - vertical", "simcitybuildit - origin", "snickersworkweardeutschland - origin", "spinmaster - origin", "st.judechildren'sresearchhospital-scienceandmedicine - origin", "stalker2 - origin", "starfield - origin", "starbucks - origin", "stateofdecay-3 - hybrid", "stateofdecay-3 - picture", "stateofdecay-3 - workmark", "steam - origin", "stellaartois - origin", "summonerswar - origin", "tresemmépolska - origin", "talesofwind - origin", "teckdesk - origin", "theants-undergroundkingdom - origin", "thehomedepotfoundation - origin", "thesevendeadlysins-idleadventure - origin", "theu.s.concealedcarryassociationforsavinglives - origin", "theuniversityofnorthcarolinaatchapelhill - origin", "thextishell - origin", "tommyhanes - origin", "toyota - origin", "trolls - origin", "tynenol - origin", "ugc - 1982-1988", "ugc - 1988-2001", "ugc - 2001-2011", "ugc - 2011-2018", "ugc - 2018-present", "unicorn-academy - origin", "unilever-food-solutions - origin", "universityofvirginia - origin", "vnggames - origin", "vikingcareers - origin", "visa - origin", "volkswagengroupservices - origin", "wwesupercard - season1+2", "wwesupercard - season3", "wwesupercard - season4", "wwesupercard - season5", "wwesupercard - season6", "wwesupercard - season7", "walgreens - picture logo", "walgreens - wordmark", "walmartcostarica - origin", "war and margic - version1", "war and margic - version2", "war and margic - version3", "war and margic - version4", "wellsfargo - origin", "westgame - origin", "winter-burrow - origin", "women's-health-beauty-award - origin", "world of warcraft - battle for azeroth", "world of warcraft - cataclysm", "world of warcraft - dragonflight", "world of warcraft - legion", "world of warcraft - mists of pandaria", "world of warcraft - shadowlands", "world of warcraft - the burning crusade", "world of warcraft - the war within", "world of warcraft - warlords of draenor", "world of warcraft - wrath of the lich king", "xbox - origin", "xenoblade chronicles - xenoblade chronicles definitive edition", "xenoblade chronicles - xenoblade chronicles 2 torna – the golden country", "xenoblade chronicles - xenoblade chronicles 3 future redeemed", "xenoblade chronicles - xenoblade chronicles 3d", "xenoblade chronicles - xenoblade chronicles x", "zalando - picture logo", "zalando - wordmark", "acer - origin", "adidas - 1950-1971", "adidas - 1967-present", "adidas - 1971-present", "adidas - 1991-present", "adidas - 2002-present", "adidas - 2005-present", "adidas - 2022-present", "alibaba - alibaba cloud hybrid", "alibaba - alibaba cloud picture logo", "alibaba - alibaba group", "alibaba - alibaba picture logo", "alibaba - alibaba.com", "atomfall - origin", "audible - hybrid", "audible - icon", "avowed - origin", "black-rifle-coffee - origin", "cement-australia - origin", "esteelauder - origin", "green-mountain-coffee - origin", "killer-of-the-flower-moon - origin", "lazada - origin", "naruto - origin", "netflix - origin", "nvidia - origin", "punk-bunny-coffee - origin", "south-of-midnight - origin", "the-witcher-wild-hunt - origin" ]

kiranshivaraju/convnext-large-finetuned-aug-pcb_data

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "bad", "good" ]

kiranshivaraju/swin-tiny-patch4-window7-224-finetuned-aug-pcb

# swin-tiny-patch4-window7-224-finetuned-aug-pcb This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5192 - Accuracy: 0.7465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5945 | 1.0 | 15 | 0.5192 | 0.7465 | | 0.5435 | 2.0 | 30 | 0.4960 | 0.7230 | | 0.5187 | 3.0 | 45 | 0.4820 | 0.7418 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "bad", "good" ]

kiranshivaraju/resnet-50-pac_aug

# resnet-50-pac_aug This model is a fine-tuned version of [kiranshivaraju/resnet-50-pac_aug](https://huggingface.co/kiranshivaraju/resnet-50-pac_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6055 - Recall: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Recall | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6335 | 1.0 | 15 | 0.6352 | 0.0 | | 0.643 | 2.0 | 30 | 0.6276 | 0.0 | | 0.6434 | 3.0 | 45 | 0.6228 | 0.0 | | 0.6305 | 4.0 | 60 | 0.6156 | 0.0 | | 0.6339 | 5.0 | 75 | 0.6167 | 0.0 | | 0.6346 | 6.0 | 90 | 0.6112 | 0.0 | | 0.6235 | 7.0 | 105 | 0.6095 | 0.0 | | 0.6279 | 8.0 | 120 | 0.6082 | 0.0 | | 0.6241 | 9.0 | 135 | 0.6073 | 0.0 | | 0.6233 | 10.0 | 150 | 0.6055 | 0.0 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "bad", "good" ]

ricardoSLabs/CIDAUT

# CIDAUT This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0808 - Accuracy: 0.9861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 0.5989 | 0.6759 | | No log | 2.0 | 8 | 0.3383 | 0.9444 | | 0.5387 | 3.0 | 12 | 0.1813 | 0.9769 | | 0.5387 | 4.0 | 16 | 0.1283 | 0.9676 | | 0.1494 | 5.0 | 20 | 0.0808 | 0.9861 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "editada", "real" ]

ricardoSLabs/CIDAUTv2

# CIDAUTv2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2719 - Accuracy: 0.9259 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 0.7322 | 0.5880 | | No log | 2.0 | 8 | 0.6585 | 0.5972 | | 0.7438 | 3.0 | 12 | 0.6115 | 0.7222 | | 0.7438 | 4.0 | 16 | 0.5726 | 0.7546 | | 0.5781 | 5.0 | 20 | 0.4803 | 0.7824 | | 0.5781 | 6.0 | 24 | 0.4627 | 0.8333 | | 0.5781 | 7.0 | 28 | 0.4060 | 0.8056 | | 0.4511 | 8.0 | 32 | 0.3512 | 0.8796 | | 0.4511 | 9.0 | 36 | 0.2725 | 0.9028 | | 0.296 | 10.0 | 40 | 0.2719 | 0.9259 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "editada", "real" ]

kiranshivaraju/convnext-large-224-finetuned-dog-vs-cat

# convnext-large-224-finetuned-dog-vs-cat This model is a fine-tuned version of [facebook/convnext-large-224](https://huggingface.co/facebook/convnext-large-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3586 - Recall: 0.9783 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Recall | |:-------------:|:------:|:----:|:---------------:|:------:| | 0.496 | 0.9954 | 164 | 0.4792 | 0.9201 | | 0.3996 | 1.9970 | 329 | 0.3836 | 0.9765 | | 0.373 | 2.9863 | 492 | 0.3586 | 0.9783 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "cat", "dog" ]

tiendoan/finetune-vit-base-patch16-224

# finetune-vit-base-patch16-224 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "image-sarcasm", "multi-sarcasm", "not-sarcasm", "text-sarcasm" ]

e1010101/vit-384-tongue-image-segmented-augmented

# segmented-augmented This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5815 - Precision: 0.8308 - Recall: 0.9136 - F1: 0.8703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.081 | 1.0 | 327 | 0.3693 | 0.8464 | 0.8970 | 0.8710 | | 0.0151 | 2.0 | 654 | 0.4906 | 0.8171 | 0.8904 | 0.8521 | | 0.0066 | 3.0 | 981 | 0.5194 | 0.8416 | 0.9003 | 0.8700 | | 0.0029 | 4.0 | 1308 | 0.5671 | 0.8308 | 0.9136 | 0.8703 | | 0.0026 | 5.0 | 1635 | 0.5815 | 0.8308 | 0.9136 | 0.8703 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1

[ "crack", "red-dots", "toothmark" ]

obivine/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0156 - Accuracy: 0.9972 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.5582 | 0.9697 | 16 | 0.0498 | 0.9903 | | 0.0905 | 2.0 | 33 | 0.0156 | 0.9972 | | 0.0762 | 2.9091 | 48 | 0.0167 | 0.9972 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

[ "cat", "dog" ]

soplac/test2

# test2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8384 - Accuracy: 0.75 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 1.1029 | 0.125 | | No log | 2.0 | 2 | 1.0384 | 0.625 | | No log | 3.0 | 3 | 0.9989 | 0.75 | | No log | 4.0 | 4 | 0.9557 | 0.75 | | No log | 5.0 | 5 | 0.8941 | 0.75 | | No log | 6.0 | 6 | 0.8952 | 0.75 | | No log | 7.0 | 7 | 0.8669 | 0.75 | | No log | 8.0 | 8 | 0.8705 | 0.75 | | No log | 9.0 | 9 | 0.8357 | 0.75 | | 0.4538 | 10.0 | 10 | 0.8384 | 0.75 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "pastel colors runway fashion", "runway fashion outfit", "stripes runway fashion" ]

ricardoSLabs/drive_scene_time_day

# drive_scene_time_day This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0484 - Accuracy: 0.9891 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0701 | 0.9855 | 34 | 0.0746 | 0.9818 | | 0.0666 | 2.0 | 69 | 0.0632 | 0.9836 | | 0.0565 | 2.9855 | 103 | 0.0556 | 0.9873 | | 0.0412 | 4.0 | 138 | 0.0602 | 0.9864 | | 0.0397 | 4.9275 | 170 | 0.0484 | 0.9891 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "daytime", "night" ]

tiendoan/finetune_vit_base_patch16_224_1epoch

# finetune_vit_base_patch16_224_1epoch This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "image-sarcasm", "multi-sarcasm", "not-sarcasm", "text-sarcasm" ]

matinbaig43/swinv2-tiny-patch4-window16-256-finetuned-plantdisease

# swinv2-tiny-patch4-window16-256-finetuned-plantdisease This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "pepper__bell___bacterial_spot", "pepper__bell___healthy", "potato___early_blight", "potato___late_blight", "potato___healthy", "tomato_bacterial_spot", "tomato_early_blight", "tomato_late_blight", "tomato_leaf_mold", "tomato_septoria_leaf_spot", "tomato_spider_mites_two_spotted_spider_mite", "tomato__target_spot", "tomato__tomato_yellowleaf__curl_virus", "tomato__tomato_mosaic_virus", "tomato_healthy" ]

pku-sasahara/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Accuracy: 0.9781 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2614 | 1.0 | 190 | 0.1177 | 0.9644 | | 0.1862 | 2.0 | 380 | 0.0753 | 0.9748 | | 0.1673 | 3.0 | 570 | 0.0623 | 0.9781 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 2.11.0 - Tokenizers 0.19.1

[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]

ricardoSLabs/pre_CIDAUTv1

# pre_CIDAUTv1 This model is a fine-tuned version of [ricardoSLabs/drive_scene_time_day](https://huggingface.co/ricardoSLabs/drive_scene_time_day) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6832 - Accuracy: 0.5741 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 1.0035 | 0.5741 | | No log | 2.0 | 8 | 0.7069 | 0.4583 | | 1.3203 | 3.0 | 12 | 0.6761 | 0.5741 | | 1.3203 | 4.0 | 16 | 0.6869 | 0.5741 | | 0.6943 | 5.0 | 20 | 0.6832 | 0.5741 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "editada", "real" ]

srusti03/swinv2-tiny-patch4-window16-256-finetuned-plantdisease

# swinv2-tiny-patch4-window16-256-finetuned-plantdisease This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0636 - Accuracy: 0.9777 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.103 | 1.0 | 516 | 0.0636 | 0.9777 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "pepper__bell___bacterial_spot", "pepper__bell___healthy", "potato___early_blight", "potato___late_blight", "potato___healthy", "tomato_bacterial_spot", "tomato_early_blight", "tomato_late_blight", "tomato_leaf_mold", "tomato_septoria_leaf_spot", "tomato_spider_mites_two_spotted_spider_mite", "tomato__target_spot", "tomato__tomato_yellowleaf__curl_virus", "tomato__tomato_mosaic_virus", "tomato_healthy" ]

soplac/stripes

# stripes This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2177 - Accuracy: 0.9194 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | No log | 0.9032 | 7 | 0.6456 | 0.7742 | | 0.6684 | 1.9355 | 15 | 0.4995 | 0.8629 | | 0.4818 | 2.9677 | 23 | 0.3788 | 0.8629 | | 0.3026 | 4.0 | 31 | 0.2881 | 0.9113 | | 0.3026 | 4.9032 | 38 | 0.2530 | 0.9032 | | 0.1806 | 5.9355 | 46 | 0.2359 | 0.9194 | | 0.1161 | 6.9677 | 54 | 0.2288 | 0.9194 | | 0.0894 | 8.0 | 62 | 0.2043 | 0.9435 | | 0.0894 | 8.9032 | 69 | 0.2042 | 0.9355 | | 0.0723 | 9.0323 | 70 | 0.2177 | 0.9194 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "not_stripes", "stripes" ]

ricardoSLabs/pre_CIDAUTv2

# pre_CIDAUTv2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0031 - Accuracy: 0.9991 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.3603 | 0.9639 | 20 | 0.2950 | 0.8633 | | 0.068 | 1.9759 | 41 | 0.0205 | 0.9921 | | 0.0484 | 2.9880 | 62 | 0.0384 | 0.9885 | | 0.0211 | 4.0 | 83 | 0.0082 | 0.9982 | | 0.0145 | 4.8193 | 100 | 0.0031 | 0.9991 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "editada", "real" ]

cvmil/swin-base-patch4-window7-224-augmented

# swin-base-patch4-window7-224_11092024 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5135 - Accuracy: 0.8337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0366 | 1.0 | 400 | 0.9471 | 0.72 | | 0.8257 | 2.0 | 800 | 0.7889 | 0.7538 | | 0.7119 | 3.0 | 1200 | 0.7232 | 0.7775 | | 0.6969 | 4.0 | 1600 | 0.6739 | 0.7837 | | 0.6487 | 5.0 | 2000 | 0.6371 | 0.7863 | | 0.5956 | 6.0 | 2400 | 0.6198 | 0.7887 | | 0.5604 | 7.0 | 2800 | 0.5941 | 0.8025 | | 0.5732 | 8.0 | 3200 | 0.5867 | 0.795 | | 0.5578 | 9.0 | 3600 | 0.5705 | 0.8025 | | 0.5449 | 10.0 | 4000 | 0.5575 | 0.8113 | | 0.5419 | 11.0 | 4400 | 0.5505 | 0.8213 | | 0.5086 | 12.0 | 4800 | 0.5385 | 0.8213 | | 0.4929 | 13.0 | 5200 | 0.5340 | 0.8213 | | 0.4701 | 14.0 | 5600 | 0.5297 | 0.8187 | | 0.4803 | 15.0 | 6000 | 0.5240 | 0.8225 | | 0.4988 | 16.0 | 6400 | 0.5197 | 0.83 | | 0.4842 | 17.0 | 6800 | 0.5165 | 0.8313 | | 0.4917 | 18.0 | 7200 | 0.5148 | 0.8313 | | 0.4734 | 19.0 | 7600 | 0.5140 | 0.8325 | | 0.4714 | 20.0 | 8000 | 0.5135 | 0.8337 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.1+cpu - Datasets 3.0.1 - Tokenizers 0.20.1

[ "bacterial leaf blight", "brown spot", "healthy rice leaf", "leaf blast", "leaf scald", "narrow brown leaf spot", "rice hispa", "sheath blight" ]

ozair23/mobilenet_v2_1.0_224-finetuned-plantdisease

# mobilenet_v2_1.0_224-finetuned-plantdisease This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0702 - Accuracy: 0.9777 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:| | 0.3974 | 0.9996 | 1145 | 0.3599 | 0.8979 | | 0.2155 | 2.0 | 2291 | 0.1525 | 0.9603 | | 0.2058 | 2.9996 | 3436 | 0.1492 | 0.9559 | | 0.1524 | 4.0 | 4582 | 0.1025 | 0.9694 | | 0.1274 | 4.9996 | 5727 | 0.0928 | 0.9706 | | 0.1141 | 6.0 | 6873 | 0.0874 | 0.9723 | | 0.1275 | 6.9996 | 8018 | 0.1226 | 0.9620 | | 0.1323 | 8.0 | 9164 | 0.0702 | 0.9777 | | 0.1212 | 8.9996 | 10309 | 0.1257 | 0.9607 | | 0.0981 | 9.9956 | 11450 | 0.0750 | 0.9751 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "apple___apple_scab", "apple___black_rot", "apple___cedar_apple_rust", "apple___healthy", "blueberry___healthy", "cherry_(including_sour)___powdery_mildew", "cherry_(including_sour)___healthy", "corn_(maize)___cercospora_leaf_spot gray_leaf_spot", "corn_(maize)___common_rust_", "corn_(maize)___northern_leaf_blight", "corn_(maize)___healthy", "grape___black_rot", "grape___esca_(black_measles)", "grape___leaf_blight_(isariopsis_leaf_spot)", "grape___healthy", "orange___haunglongbing_(citrus_greening)", "peach___bacterial_spot", "peach___healthy", "pepper,_bell___bacterial_spot", "pepper,_bell___healthy", "potato___early_blight", "potato___late_blight", "potato___healthy", "raspberry___healthy", "soybean___healthy", "squash___powdery_mildew", "strawberry___leaf_scorch", "strawberry___healthy", "tomato___bacterial_spot", "tomato___early_blight", "tomato___late_blight", "tomato___leaf_mold", "tomato___septoria_leaf_spot", "tomato___spider_mites two-spotted_spider_mite", "tomato___target_spot", "tomato___tomato_yellow_leaf_curl_virus", "tomato___tomato_mosaic_virus", "tomato___healthy" ]

KiViDrag/ViT_bloodmnist_std_60

# ViT_bloodmnist_std_60 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3387 - Accuracy: 0.8913 - F1: 0.8681 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:| | 0.7924 | 0.0595 | 200 | 1.7254 | 0.4106 | 0.3373 | | 0.4527 | 0.1189 | 400 | 1.2129 | 0.6641 | 0.5830 | | 0.4004 | 0.1784 | 600 | 0.9461 | 0.7547 | 0.6592 | | 0.3657 | 0.2378 | 800 | 0.7647 | 0.8084 | 0.7425 | | 0.3506 | 0.2973 | 1000 | 0.6377 | 0.8043 | 0.7888 | | 0.3081 | 0.3567 | 1200 | 0.6453 | 0.8055 | 0.7728 | | 0.2848 | 0.4162 | 1400 | 0.6006 | 0.8195 | 0.7385 | | 0.28 | 0.4756 | 1600 | 0.8017 | 0.7097 | 0.6680 | | 0.3041 | 0.5351 | 1800 | 0.4496 | 0.8586 | 0.8187 | | 0.272 | 0.5945 | 2000 | 0.7200 | 0.7541 | 0.7126 | | 0.259 | 0.6540 | 2200 | 0.5110 | 0.8131 | 0.7867 | | 0.2524 | 0.7134 | 2400 | 0.4057 | 0.8633 | 0.8343 | | 0.2439 | 0.7729 | 2600 | 0.4060 | 0.8604 | 0.8288 | | 0.2422 | 0.8323 | 2800 | 0.4496 | 0.8627 | 0.8229 | | 0.2332 | 0.8918 | 3000 | 0.4147 | 0.8586 | 0.8157 | | 0.2192 | 0.9512 | 3200 | 0.3414 | 0.8756 | 0.8578 | | 0.212 | 1.0107 | 3400 | 0.4139 | 0.8464 | 0.8048 | | 0.1738 | 1.0702 | 3600 | 0.5111 | 0.8213 | 0.7703 | | 0.1718 | 1.1296 | 3800 | 0.3725 | 0.8674 | 0.8398 | | 0.1679 | 1.1891 | 4000 | 0.4632 | 0.8400 | 0.8283 | | 0.1706 | 1.2485 | 4200 | 0.4331 | 0.8511 | 0.8216 | | 0.1602 | 1.3080 | 4400 | 0.4359 | 0.8382 | 0.8094 | | 0.1502 | 1.3674 | 4600 | 0.5608 | 0.7903 | 0.7278 | | 0.1713 | 1.4269 | 4800 | 0.3495 | 0.8762 | 0.8555 | | 0.1544 | 1.4863 | 5000 | 0.5389 | 0.8072 | 0.7830 | | 0.1477 | 1.5458 | 5200 | 0.3790 | 0.8645 | 0.8318 | | 0.1515 | 1.6052 | 5400 | 0.4332 | 0.8300 | 0.7977 | | 0.1465 | 1.6647 | 5600 | 0.5368 | 0.8230 | 0.7546 | | 0.1409 | 1.7241 | 5800 | 0.4630 | 0.8493 | 0.8004 | | 0.1294 | 1.7836 | 6000 | 0.3530 | 0.8803 | 0.8396 | | 0.1252 | 1.8430 | 6200 | 0.3822 | 0.875 | 0.8410 | | 0.1273 | 1.9025 | 6400 | 0.2833 | 0.9042 | 0.8802 | | 0.1196 | 1.9620 | 6600 | 0.3610 | 0.8791 | 0.8407 | | 0.1018 | 2.0214 | 6800 | 0.3968 | 0.8581 | 0.8354 | | 0.0692 | 2.0809 | 7000 | 0.4695 | 0.8458 | 0.8122 | | 0.0674 | 2.1403 | 7200 | 0.4450 | 0.8534 | 0.8136 | | 0.0615 | 2.1998 | 7400 | 0.3819 | 0.8721 | 0.8483 | | 0.0574 | 2.2592 | 7600 | 0.3725 | 0.875 | 0.8468 | | 0.067 | 2.3187 | 7800 | 0.4728 | 0.8481 | 0.8078 | | 0.0684 | 2.3781 | 8000 | 0.3483 | 0.8873 | 0.8590 | | 0.066 | 2.4376 | 8200 | 0.3763 | 0.8797 | 0.8514 | | 0.0521 | 2.4970 | 8400 | 0.4029 | 0.8657 | 0.8377 | | 0.0553 | 2.5565 | 8600 | 0.4100 | 0.8697 | 0.8382 | | 0.0534 | 2.6159 | 8800 | 0.3810 | 0.8762 | 0.8469 | | 0.0475 | 2.6754 | 9000 | 0.4043 | 0.8703 | 0.8416 | | 0.054 | 2.7348 | 9200 | 0.4014 | 0.8762 | 0.8460 | | 0.0526 | 2.7943 | 9400 | 0.4015 | 0.875 | 0.8439 | | 0.0481 | 2.8537 | 9600 | 0.4047 | 0.8779 | 0.8455 | | 0.0442 | 2.9132 | 9800 | 0.3997 | 0.8773 | 0.8449 | | 0.0372 | 2.9727 | 10000 | 0.4131 | 0.8762 | 0.8433 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]

KiViDrag/ViT_bloodmnist_std_45

# ViT_bloodmnist_std_45 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2659 - Accuracy: 0.9065 - F1: 0.8909 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:| | 0.6113 | 0.0595 | 200 | 0.8908 | 0.6846 | 0.5917 | | 0.3578 | 0.1189 | 400 | 0.5958 | 0.7956 | 0.7548 | | 0.3118 | 0.1784 | 600 | 0.5688 | 0.7810 | 0.7132 | | 0.2815 | 0.2378 | 800 | 0.5227 | 0.7961 | 0.7645 | | 0.266 | 0.2973 | 1000 | 0.6554 | 0.7687 | 0.7229 | | 0.2353 | 0.3567 | 1200 | 0.3328 | 0.8838 | 0.8615 | | 0.2297 | 0.4162 | 1400 | 0.4696 | 0.8592 | 0.7990 | | 0.2267 | 0.4756 | 1600 | 0.4362 | 0.8493 | 0.8117 | | 0.2266 | 0.5351 | 1800 | 0.3286 | 0.8838 | 0.8407 | | 0.2047 | 0.5945 | 2000 | 0.3614 | 0.8697 | 0.8382 | | 0.1948 | 0.6540 | 2200 | 0.3144 | 0.8843 | 0.8546 | | 0.1953 | 0.7134 | 2400 | 0.3805 | 0.8657 | 0.8180 | | 0.1728 | 0.7729 | 2600 | 0.3364 | 0.8820 | 0.8339 | | 0.1658 | 0.8323 | 2800 | 0.2873 | 0.8978 | 0.8743 | | 0.1594 | 0.8918 | 3000 | 0.3062 | 0.8914 | 0.8580 | | 0.1649 | 0.9512 | 3200 | 0.3313 | 0.8867 | 0.8577 | | 0.1508 | 1.0107 | 3400 | 0.2117 | 0.9217 | 0.9133 | | 0.1062 | 1.0702 | 3600 | 0.2978 | 0.8919 | 0.8756 | | 0.1091 | 1.1296 | 3800 | 0.2832 | 0.9019 | 0.8831 | | 0.0993 | 1.1891 | 4000 | 0.3275 | 0.8943 | 0.8718 | | 0.1001 | 1.2485 | 4200 | 0.3420 | 0.8896 | 0.8568 | | 0.1092 | 1.3080 | 4400 | 0.2594 | 0.9130 | 0.8909 | | 0.092 | 1.3674 | 4600 | 0.3181 | 0.8966 | 0.8753 | | 0.1036 | 1.4269 | 4800 | 0.2721 | 0.9048 | 0.8852 | | 0.0896 | 1.4863 | 5000 | 0.3795 | 0.8820 | 0.8617 | | 0.0904 | 1.5458 | 5200 | 0.2382 | 0.9171 | 0.8980 | | 0.0864 | 1.6052 | 5400 | 0.3845 | 0.8814 | 0.8499 | | 0.0809 | 1.6647 | 5600 | 0.3189 | 0.8984 | 0.8758 | | 0.0764 | 1.7241 | 5800 | 0.3952 | 0.8843 | 0.8522 | | 0.0796 | 1.7836 | 6000 | 0.3656 | 0.8867 | 0.8460 | | 0.0695 | 1.8430 | 6200 | 0.3266 | 0.8925 | 0.8597 | | 0.0682 | 1.9025 | 6400 | 0.3247 | 0.8960 | 0.8647 | | 0.06 | 1.9620 | 6600 | 0.2349 | 0.9223 | 0.9055 | | 0.0498 | 2.0214 | 6800 | 0.2578 | 0.9176 | 0.8952 | | 0.0296 | 2.0809 | 7000 | 0.2592 | 0.9211 | 0.9070 | | 0.0251 | 2.1403 | 7200 | 0.3249 | 0.9048 | 0.8797 | | 0.02 | 2.1998 | 7400 | 0.2977 | 0.9165 | 0.8973 | | 0.0274 | 2.2592 | 7600 | 0.3411 | 0.9013 | 0.8730 | | 0.0241 | 2.3187 | 7800 | 0.3916 | 0.9013 | 0.8752 | | 0.0253 | 2.3781 | 8000 | 0.2919 | 0.9136 | 0.8939 | | 0.0197 | 2.4376 | 8200 | 0.3294 | 0.9077 | 0.8835 | | 0.0209 | 2.4970 | 8400 | 0.3709 | 0.8966 | 0.8652 | | 0.0175 | 2.5565 | 8600 | 0.3639 | 0.9001 | 0.8733 | | 0.0191 | 2.6159 | 8800 | 0.3706 | 0.9048 | 0.8790 | | 0.0167 | 2.6754 | 9000 | 0.3120 | 0.9171 | 0.8993 | | 0.0224 | 2.7348 | 9200 | 0.3493 | 0.9048 | 0.8799 | | 0.015 | 2.7943 | 9400 | 0.3398 | 0.9130 | 0.8889 | | 0.0155 | 2.8537 | 9600 | 0.3707 | 0.9036 | 0.8758 | | 0.0129 | 2.9132 | 9800 | 0.3467 | 0.9118 | 0.8909 | | 0.0126 | 2.9727 | 10000 | 0.3470 | 0.9095 | 0.8874 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]

KiViDrag/ViT_breastmnist_std_60

# ViT_breastmnist_std_60 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5157 - Accuracy: 0.7756 - F1: 0.6137 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | 0.5179 | 0.2597 | 20 | 0.5240 | 0.7436 | 0.5385 | | 0.4306 | 0.5195 | 40 | 0.4807 | 0.7949 | 0.6855 | | 0.4258 | 0.7792 | 60 | 0.4812 | 0.7949 | 0.6518 | | 0.4613 | 1.0390 | 80 | 0.4491 | 0.8333 | 0.7247 | | 0.4194 | 1.2987 | 100 | 0.4573 | 0.8333 | 0.7247 | | 0.3693 | 1.5584 | 120 | 0.4665 | 0.8205 | 0.6953 | | 0.3825 | 1.8182 | 140 | 0.4695 | 0.7821 | 0.6733 | | 0.387 | 2.0779 | 160 | 0.4410 | 0.8205 | 0.7248 | | 0.3341 | 2.3377 | 180 | 0.4422 | 0.8205 | 0.7367 | | 0.3192 | 2.5974 | 200 | 0.4457 | 0.8205 | 0.7111 | | 0.3062 | 2.8571 | 220 | 0.4575 | 0.8205 | 0.7111 | | 0.2485 | 3.1169 | 240 | 0.4526 | 0.8333 | 0.7383 | | 0.2415 | 3.3766 | 260 | 0.4430 | 0.8462 | 0.7641 | | 0.2377 | 3.6364 | 280 | 0.4529 | 0.8333 | 0.7247 | | 0.2417 | 3.8961 | 300 | 0.4386 | 0.8205 | 0.7111 | | 0.1783 | 4.1558 | 320 | 0.4467 | 0.8333 | 0.7383 | | 0.193 | 4.4156 | 340 | 0.4724 | 0.8077 | 0.6823 | | 0.1736 | 4.6753 | 360 | 0.4757 | 0.8333 | 0.7383 | | 0.1656 | 4.9351 | 380 | 0.4677 | 0.8333 | 0.7383 | | 0.1214 | 5.1948 | 400 | 0.4747 | 0.8077 | 0.6981 | | 0.0851 | 5.4545 | 420 | 0.4782 | 0.7949 | 0.6698 | | 0.0893 | 5.7143 | 440 | 0.4842 | 0.8077 | 0.6823 | | 0.0978 | 5.9740 | 460 | 0.4883 | 0.8077 | 0.6823 | | 0.0518 | 6.2338 | 480 | 0.4861 | 0.8077 | 0.6981 | | 0.0662 | 6.4935 | 500 | 0.5017 | 0.8077 | 0.6981 | | 0.058 | 6.7532 | 520 | 0.5092 | 0.7949 | 0.6518 | | 0.0511 | 7.0130 | 540 | 0.5003 | 0.8205 | 0.7111 | | 0.0235 | 7.2727 | 560 | 0.5041 | 0.8077 | 0.6823 | | 0.0204 | 7.5325 | 580 | 0.5140 | 0.8205 | 0.7111 | | 0.0196 | 7.7922 | 600 | 0.5122 | 0.8205 | 0.7111 | | 0.0108 | 8.0519 | 620 | 0.5186 | 0.8205 | 0.7111 | | 0.012 | 8.3117 | 640 | 0.5315 | 0.8333 | 0.7247 | | 0.0077 | 8.5714 | 660 | 0.5319 | 0.8205 | 0.7111 | | 0.0187 | 8.8312 | 680 | 0.5279 | 0.8205 | 0.7111 | | 0.0063 | 9.0909 | 700 | 0.5304 | 0.8205 | 0.7111 | | 0.004 | 9.3506 | 720 | 0.5312 | 0.8205 | 0.7111 | | 0.0044 | 9.6104 | 740 | 0.5310 | 0.8205 | 0.7111 | | 0.0076 | 9.8701 | 760 | 0.5323 | 0.8205 | 0.7111 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "malignant", "normal, benign" ]

Tianmu28/vit_google_vehicle_classification_model

# vehicle_classification This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0269 - Accuracy: 0.9917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0356 | 1.0 | 245 | 0.0432 | 0.9869 | | 0.0036 | 2.0 | 490 | 0.0403 | 0.9869 | | 0.0004 | 3.0 | 735 | 0.0275 | 0.9905 | | 0.0002 | 4.0 | 980 | 0.0260 | 0.9917 | | 0.0002 | 5.0 | 1225 | 0.0261 | 0.9917 | | 0.0001 | 6.0 | 1470 | 0.0264 | 0.9917 | | 0.0001 | 7.0 | 1715 | 0.0267 | 0.9917 | | 0.0001 | 8.0 | 1960 | 0.0269 | 0.9917 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6" ]

KiViDrag/ViT_bloodmnist_std_15

# ViT_bloodmnist_std_15 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1123 - Accuracy: 0.9699 - F1: 0.9662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:| | 0.4602 | 0.0595 | 200 | 0.2846 | 0.9019 | 0.8995 | | 0.19 | 0.1189 | 400 | 0.2421 | 0.9118 | 0.9056 | | 0.1612 | 0.1784 | 600 | 0.1811 | 0.9299 | 0.9222 | | 0.1443 | 0.2378 | 800 | 0.1166 | 0.9556 | 0.9491 | | 0.1105 | 0.2973 | 1000 | 0.1091 | 0.9603 | 0.9560 | | 0.0996 | 0.3567 | 1200 | 0.1631 | 0.9433 | 0.9292 | | 0.0913 | 0.4162 | 1400 | 0.1724 | 0.9393 | 0.9285 | | 0.0708 | 0.4756 | 1600 | 0.1206 | 0.9591 | 0.9540 | | 0.0829 | 0.5351 | 1800 | 0.0888 | 0.9685 | 0.9625 | | 0.0624 | 0.5945 | 2000 | 0.1379 | 0.9579 | 0.9519 | | 0.0652 | 0.6540 | 2200 | 0.1158 | 0.9685 | 0.9667 | | 0.0495 | 0.7134 | 2400 | 0.1169 | 0.9655 | 0.9642 | | 0.0425 | 0.7729 | 2600 | 0.0944 | 0.9679 | 0.9643 | | 0.0405 | 0.8323 | 2800 | 0.1280 | 0.9650 | 0.9605 | | 0.0382 | 0.8918 | 3000 | 0.0762 | 0.9778 | 0.9755 | | 0.0336 | 0.9512 | 3200 | 0.1064 | 0.9708 | 0.9697 | | 0.0318 | 1.0107 | 3400 | 0.1001 | 0.9720 | 0.9682 | | 0.0162 | 1.0702 | 3600 | 0.1018 | 0.9737 | 0.9720 | | 0.0165 | 1.1296 | 3800 | 0.1431 | 0.9614 | 0.9537 | | 0.0133 | 1.1891 | 4000 | 0.0808 | 0.9766 | 0.9736 | | 0.0146 | 1.2485 | 4200 | 0.0912 | 0.9737 | 0.9707 | | 0.0091 | 1.3080 | 4400 | 0.1006 | 0.9761 | 0.9747 | | 0.0074 | 1.3674 | 4600 | 0.1114 | 0.9702 | 0.9680 | | 0.0134 | 1.4269 | 4800 | 0.1200 | 0.9725 | 0.9705 | | 0.012 | 1.4863 | 5000 | 0.1063 | 0.9720 | 0.9694 | | 0.0099 | 1.5458 | 5200 | 0.1239 | 0.9690 | 0.9667 | | 0.006 | 1.6052 | 5400 | 0.1308 | 0.9731 | 0.9677 | | 0.0057 | 1.6647 | 5600 | 0.1479 | 0.9702 | 0.9682 | | 0.0107 | 1.7241 | 5800 | 0.1194 | 0.9720 | 0.9684 | | 0.0122 | 1.7836 | 6000 | 0.1083 | 0.9708 | 0.9691 | | 0.0081 | 1.8430 | 6200 | 0.1087 | 0.9725 | 0.9690 | | 0.0055 | 1.9025 | 6400 | 0.1063 | 0.9766 | 0.9731 | | 0.0039 | 1.9620 | 6600 | 0.1530 | 0.9679 | 0.9631 | | 0.0075 | 2.0214 | 6800 | 0.1052 | 0.9778 | 0.9764 | | 0.0022 | 2.0809 | 7000 | 0.1340 | 0.9673 | 0.9628 | | 0.0024 | 2.1403 | 7200 | 0.1034 | 0.9761 | 0.9742 | | 0.0014 | 2.1998 | 7400 | 0.1039 | 0.9772 | 0.9751 | | 0.0007 | 2.2592 | 7600 | 0.1032 | 0.9801 | 0.9792 | | 0.0008 | 2.3187 | 7800 | 0.0984 | 0.9807 | 0.9797 | | 0.0013 | 2.3781 | 8000 | 0.1034 | 0.9766 | 0.9752 | | 0.0013 | 2.4376 | 8200 | 0.1049 | 0.9766 | 0.9749 | | 0.0013 | 2.4970 | 8400 | 0.1006 | 0.9772 | 0.9756 | | 0.0018 | 2.5565 | 8600 | 0.1157 | 0.9749 | 0.9703 | | 0.0011 | 2.6159 | 8800 | 0.1049 | 0.9784 | 0.9779 | | 0.0007 | 2.6754 | 9000 | 0.1167 | 0.9755 | 0.9721 | | 0.0003 | 2.7348 | 9200 | 0.1058 | 0.9772 | 0.9746 | | 0.0008 | 2.7943 | 9400 | 0.1049 | 0.9796 | 0.9767 | | 0.0009 | 2.8537 | 9600 | 0.1084 | 0.9807 | 0.9787 | | 0.0005 | 2.9132 | 9800 | 0.0999 | 0.9807 | 0.9787 | | 0.0001 | 2.9727 | 10000 | 0.1001 | 0.9813 | 0.9796 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]

KiViDrag/ViT_bloodmnist_std_30

# ViT_bloodmnist_std_30 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1697 - Accuracy: 0.9430 - F1: 0.9339 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:| | 0.5658 | 0.0595 | 200 | 1.2306 | 0.5076 | 0.4526 | | 0.2887 | 0.1189 | 400 | 0.6368 | 0.7751 | 0.7410 | | 0.2406 | 0.1784 | 600 | 0.6641 | 0.7827 | 0.7050 | | 0.2229 | 0.2378 | 800 | 0.4808 | 0.8072 | 0.7832 | | 0.1955 | 0.2973 | 1000 | 0.4868 | 0.8002 | 0.7827 | | 0.1654 | 0.3567 | 1200 | 0.3306 | 0.8657 | 0.8466 | | 0.1627 | 0.4162 | 1400 | 0.3754 | 0.8732 | 0.8367 | | 0.1479 | 0.4756 | 1600 | 0.2421 | 0.9118 | 0.8949 | | 0.1501 | 0.5351 | 1800 | 0.2125 | 0.9235 | 0.9076 | | 0.1372 | 0.5945 | 2000 | 0.3706 | 0.8616 | 0.8337 | | 0.1194 | 0.6540 | 2200 | 0.1552 | 0.9451 | 0.9370 | | 0.1194 | 0.7134 | 2400 | 0.2345 | 0.9194 | 0.8992 | | 0.1135 | 0.7729 | 2600 | 0.2121 | 0.9287 | 0.9113 | | 0.1032 | 0.8323 | 2800 | 0.2023 | 0.9299 | 0.9152 | | 0.1006 | 0.8918 | 3000 | 0.1784 | 0.9451 | 0.9376 | | 0.0814 | 0.9512 | 3200 | 0.1273 | 0.9533 | 0.9484 | | 0.0842 | 1.0107 | 3400 | 0.2012 | 0.9363 | 0.9240 | | 0.0426 | 1.0702 | 3600 | 0.2221 | 0.9340 | 0.9280 | | 0.06 | 1.1296 | 3800 | 0.2641 | 0.9100 | 0.9037 | | 0.0632 | 1.1891 | 4000 | 0.1796 | 0.9433 | 0.9339 | | 0.0506 | 1.2485 | 4200 | 0.2771 | 0.8989 | 0.8838 | | 0.0467 | 1.3080 | 4400 | 0.1939 | 0.9393 | 0.9265 | | 0.0469 | 1.3674 | 4600 | 0.1896 | 0.9410 | 0.9322 | | 0.0457 | 1.4269 | 4800 | 0.1477 | 0.9509 | 0.9479 | | 0.0416 | 1.4863 | 5000 | 0.2789 | 0.9206 | 0.9086 | | 0.043 | 1.5458 | 5200 | 0.1832 | 0.9463 | 0.9389 | | 0.0412 | 1.6052 | 5400 | 0.2100 | 0.9404 | 0.9337 | | 0.0358 | 1.6647 | 5600 | 0.2368 | 0.9287 | 0.9135 | | 0.0376 | 1.7241 | 5800 | 0.2668 | 0.9252 | 0.9096 | | 0.0385 | 1.7836 | 6000 | 0.2145 | 0.9398 | 0.9291 | | 0.0273 | 1.8430 | 6200 | 0.1995 | 0.9433 | 0.9302 | | 0.0251 | 1.9025 | 6400 | 0.1900 | 0.9486 | 0.9395 | | 0.0298 | 1.9620 | 6600 | 0.1617 | 0.9597 | 0.9526 | | 0.02 | 2.0214 | 6800 | 0.1984 | 0.9463 | 0.9343 | | 0.0083 | 2.0809 | 7000 | 0.1899 | 0.9498 | 0.9377 | | 0.0068 | 2.1403 | 7200 | 0.2592 | 0.9340 | 0.9199 | | 0.0059 | 2.1998 | 7400 | 0.2101 | 0.9428 | 0.9335 | | 0.0066 | 2.2592 | 7600 | 0.2247 | 0.9422 | 0.9259 | | 0.0062 | 2.3187 | 7800 | 0.2370 | 0.9439 | 0.9348 | | 0.0084 | 2.3781 | 8000 | 0.2266 | 0.9474 | 0.9390 | | 0.0049 | 2.4376 | 8200 | 0.2343 | 0.9480 | 0.9354 | | 0.0075 | 2.4970 | 8400 | 0.2032 | 0.9486 | 0.9378 | | 0.0025 | 2.5565 | 8600 | 0.1916 | 0.9515 | 0.9436 | | 0.0064 | 2.6159 | 8800 | 0.2066 | 0.9533 | 0.9436 | | 0.004 | 2.6754 | 9000 | 0.2404 | 0.9445 | 0.9321 | | 0.0029 | 2.7348 | 9200 | 0.2402 | 0.9439 | 0.9322 | | 0.0008 | 2.7943 | 9400 | 0.2256 | 0.9468 | 0.9365 | | 0.003 | 2.8537 | 9600 | 0.2265 | 0.9492 | 0.9408 | | 0.002 | 2.9132 | 9800 | 0.2278 | 0.9515 | 0.9419 | | 0.0013 | 2.9727 | 10000 | 0.2175 | 0.9504 | 0.9422 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]

KiViDrag/ViT_bloodmnist_std_0

# ViT_bloodmnist_std_0 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1010 - Accuracy: 0.9690 - F1: 0.9644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:| | 0.3571 | 0.0595 | 200 | 0.1183 | 0.9597 | 0.9577 | | 0.1349 | 0.1189 | 400 | 0.1324 | 0.9568 | 0.9521 | | 0.093 | 0.1784 | 600 | 0.1167 | 0.9609 | 0.9587 | | 0.0777 | 0.2378 | 800 | 0.0855 | 0.9755 | 0.9715 | | 0.0559 | 0.2973 | 1000 | 0.1004 | 0.9667 | 0.9649 | | 0.0473 | 0.3567 | 1200 | 0.1123 | 0.9696 | 0.9668 | | 0.0395 | 0.4162 | 1400 | 0.1074 | 0.9690 | 0.9676 | | 0.0338 | 0.4756 | 1600 | 0.1189 | 0.9632 | 0.9608 | | 0.027 | 0.5351 | 1800 | 0.1097 | 0.9772 | 0.9755 | | 0.0176 | 0.5945 | 2000 | 0.0958 | 0.9784 | 0.9766 | | 0.0105 | 0.6540 | 2200 | 0.1423 | 0.9720 | 0.9692 | | 0.0161 | 0.7134 | 2400 | 0.1725 | 0.9650 | 0.9625 | | 0.0113 | 0.7729 | 2600 | 0.1278 | 0.9708 | 0.9675 | | 0.0077 | 0.8323 | 2800 | 0.1132 | 0.9766 | 0.9743 | | 0.0078 | 0.8918 | 3000 | 0.1646 | 0.9690 | 0.9679 | | 0.007 | 0.9512 | 3200 | 0.1128 | 0.9737 | 0.9718 | | 0.0036 | 1.0107 | 3400 | 0.1489 | 0.9725 | 0.9735 | | 0.0047 | 1.0702 | 3600 | 0.1232 | 0.9796 | 0.9787 | | 0.0158 | 1.1296 | 3800 | 0.1597 | 0.9673 | 0.9615 | | 0.0082 | 1.1891 | 4000 | 0.1633 | 0.9731 | 0.9731 | | 0.0029 | 1.2485 | 4200 | 0.1312 | 0.9784 | 0.9770 | | 0.0029 | 1.3080 | 4400 | 0.1311 | 0.9778 | 0.9760 | | 0.0005 | 1.3674 | 4600 | 0.1121 | 0.9825 | 0.9818 | | 0.0039 | 1.4269 | 4800 | 0.2170 | 0.9626 | 0.9587 | | 0.0097 | 1.4863 | 5000 | 0.1750 | 0.9690 | 0.9693 | | 0.0065 | 1.5458 | 5200 | 0.1327 | 0.9778 | 0.9768 | | 0.0047 | 1.6052 | 5400 | 0.1401 | 0.9761 | 0.9744 | | 0.0035 | 1.6647 | 5600 | 0.1273 | 0.9801 | 0.9803 | | 0.0001 | 1.7241 | 5800 | 0.1269 | 0.9784 | 0.9777 | | 0.0 | 1.7836 | 6000 | 0.1601 | 0.9737 | 0.9723 | | 0.0 | 1.8430 | 6200 | 0.1328 | 0.9772 | 0.9765 | | 0.0 | 1.9025 | 6400 | 0.1326 | 0.9772 | 0.9765 | | 0.0 | 1.9620 | 6600 | 0.1333 | 0.9772 | 0.9765 | | 0.0022 | 2.0214 | 6800 | 0.1839 | 0.9755 | 0.9749 | | 0.0008 | 2.0809 | 7000 | 0.1914 | 0.9702 | 0.9683 | | 0.0008 | 2.1403 | 7200 | 0.1954 | 0.9731 | 0.9725 | | 0.0008 | 2.1998 | 7400 | 0.1592 | 0.9743 | 0.9737 | | 0.0 | 2.2592 | 7600 | 0.1653 | 0.9755 | 0.9750 | | 0.0 | 2.3187 | 7800 | 0.1649 | 0.9749 | 0.9747 | | 0.0 | 2.3781 | 8000 | 0.1654 | 0.9755 | 0.9756 | | 0.0 | 2.4376 | 8200 | 0.1646 | 0.9755 | 0.9756 | | 0.0 | 2.4970 | 8400 | 0.1643 | 0.9755 | 0.9756 | | 0.0 | 2.5565 | 8600 | 0.1713 | 0.9749 | 0.9747 | | 0.0 | 2.6159 | 8800 | 0.1698 | 0.9755 | 0.9756 | | 0.0 | 2.6754 | 9000 | 0.1698 | 0.9755 | 0.9756 | | 0.0 | 2.7348 | 9200 | 0.1696 | 0.9755 | 0.9756 | | 0.0 | 2.7943 | 9400 | 0.1696 | 0.9755 | 0.9756 | | 0.0 | 2.8537 | 9600 | 0.1696 | 0.9755 | 0.9756 | | 0.0 | 2.9132 | 9800 | 0.1697 | 0.9755 | 0.9756 | | 0.0 | 2.9727 | 10000 | 0.1698 | 0.9755 | 0.9756 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "basophil", "eosinophil", "erythroblast", "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)", "lymphocyte", "monocyte", "neutrophil", "platelet" ]

KiViDrag/ViT_breastmnist_std_30

# ViT_breastmnist_std_30 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3936 - Accuracy: 0.8269 - F1: 0.7315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | 0.5034 | 0.2597 | 20 | 0.4719 | 0.7436 | 0.4708 | | 0.4414 | 0.5195 | 40 | 0.4457 | 0.7821 | 0.6400 | | 0.3762 | 0.7792 | 60 | 0.4212 | 0.8205 | 0.7248 | | 0.4059 | 1.0390 | 80 | 0.3988 | 0.8462 | 0.7641 | | 0.3249 | 1.2987 | 100 | 0.3829 | 0.8333 | 0.7606 | | 0.2991 | 1.5584 | 120 | 0.4080 | 0.8462 | 0.7743 | | 0.2948 | 1.8182 | 140 | 0.3932 | 0.8462 | 0.7833 | | 0.2667 | 2.0779 | 160 | 0.4388 | 0.8333 | 0.7502 | | 0.2049 | 2.3377 | 180 | 0.4047 | 0.8333 | 0.7606 | | 0.1639 | 2.5974 | 200 | 0.4301 | 0.8333 | 0.7502 | | 0.1732 | 2.8571 | 220 | 0.4028 | 0.8333 | 0.7606 | | 0.1138 | 3.1169 | 240 | 0.3755 | 0.8718 | 0.8194 | | 0.1099 | 3.3766 | 260 | 0.4019 | 0.8590 | 0.7886 | | 0.1285 | 3.6364 | 280 | 0.3739 | 0.8590 | 0.7974 | | 0.1265 | 3.8961 | 300 | 0.3714 | 0.8590 | 0.8051 | | 0.0735 | 4.1558 | 320 | 0.3820 | 0.8718 | 0.8194 | | 0.0515 | 4.4156 | 340 | 0.3910 | 0.8462 | 0.7833 | | 0.0577 | 4.6753 | 360 | 0.3984 | 0.8462 | 0.7833 | | 0.0584 | 4.9351 | 380 | 0.4314 | 0.8590 | 0.7974 | | 0.0241 | 5.1948 | 400 | 0.4040 | 0.8718 | 0.8194 | | 0.015 | 5.4545 | 420 | 0.4201 | 0.8718 | 0.8194 | | 0.023 | 5.7143 | 440 | 0.4276 | 0.8718 | 0.8194 | | 0.0254 | 5.9740 | 460 | 0.4271 | 0.8846 | 0.8342 | | 0.0086 | 6.2338 | 480 | 0.4149 | 0.8718 | 0.8194 | | 0.012 | 6.4935 | 500 | 0.4738 | 0.8718 | 0.8120 | | 0.0052 | 6.7532 | 520 | 0.4314 | 0.8846 | 0.8342 | | 0.0123 | 7.0130 | 540 | 0.4363 | 0.8718 | 0.8194 | | 0.0026 | 7.2727 | 560 | 0.4477 | 0.8846 | 0.8342 | | 0.0018 | 7.5325 | 580 | 0.4447 | 0.8718 | 0.8194 | | 0.0024 | 7.7922 | 600 | 0.4588 | 0.8718 | 0.8194 | | 0.0076 | 8.0519 | 620 | 0.4517 | 0.8718 | 0.8194 | | 0.0013 | 8.3117 | 640 | 0.4535 | 0.8718 | 0.8194 | | 0.0012 | 8.5714 | 660 | 0.4479 | 0.8846 | 0.8342 | | 0.001 | 8.8312 | 680 | 0.4477 | 0.8846 | 0.8342 | | 0.0015 | 9.0909 | 700 | 0.4509 | 0.8846 | 0.8342 | | 0.001 | 9.3506 | 720 | 0.4529 | 0.8846 | 0.8342 | | 0.0009 | 9.6104 | 740 | 0.4569 | 0.8846 | 0.8342 | | 0.001 | 9.8701 | 760 | 0.4563 | 0.8846 | 0.8342 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "malignant", "normal, benign" ]

KiViDrag/ViT_breastmnist_std_0

# ViT_breastmnist_std_0 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3272 - Accuracy: 0.8718 - F1: 0.8371 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | 0.3533 | 0.2597 | 20 | 0.3035 | 0.8846 | 0.8406 | | 0.1354 | 0.5195 | 40 | 0.2280 | 0.8974 | 0.8655 | | 0.0247 | 0.7792 | 60 | 0.2669 | 0.9231 | 0.8956 | | 0.0089 | 1.0390 | 80 | 0.2742 | 0.9231 | 0.8956 | | 0.003 | 1.2987 | 100 | 0.3404 | 0.9103 | 0.8803 | | 0.0018 | 1.5584 | 120 | 0.3583 | 0.9231 | 0.8956 | | 0.0013 | 1.8182 | 140 | 0.3720 | 0.9231 | 0.8956 | | 0.0009 | 2.0779 | 160 | 0.3892 | 0.9231 | 0.8956 | | 0.0007 | 2.3377 | 180 | 0.3933 | 0.9231 | 0.8956 | | 0.0006 | 2.5974 | 200 | 0.3948 | 0.9231 | 0.8956 | | 0.0005 | 2.8571 | 220 | 0.4038 | 0.9231 | 0.8956 | | 0.0005 | 3.1169 | 240 | 0.4145 | 0.9231 | 0.8956 | | 0.0004 | 3.3766 | 260 | 0.4176 | 0.9231 | 0.8956 | | 0.0004 | 3.6364 | 280 | 0.4230 | 0.9231 | 0.8956 | | 0.0003 | 3.8961 | 300 | 0.4274 | 0.9103 | 0.8803 | | 0.0003 | 4.1558 | 320 | 0.4344 | 0.9231 | 0.8956 | | 0.0003 | 4.4156 | 340 | 0.4380 | 0.9231 | 0.8956 | | 0.0003 | 4.6753 | 360 | 0.4406 | 0.9103 | 0.8803 | | 0.0003 | 4.9351 | 380 | 0.4459 | 0.9231 | 0.8956 | | 0.0002 | 5.1948 | 400 | 0.4472 | 0.9103 | 0.8803 | | 0.0002 | 5.4545 | 420 | 0.4514 | 0.9103 | 0.8803 | | 0.0002 | 5.7143 | 440 | 0.4550 | 0.9231 | 0.8956 | | 0.0002 | 5.9740 | 460 | 0.4579 | 0.9231 | 0.8956 | | 0.0002 | 6.2338 | 480 | 0.4600 | 0.9231 | 0.8956 | | 0.0002 | 6.4935 | 500 | 0.4614 | 0.9103 | 0.8803 | | 0.0002 | 6.7532 | 520 | 0.4637 | 0.9231 | 0.8956 | | 0.0002 | 7.0130 | 540 | 0.4660 | 0.9231 | 0.8956 | | 0.0002 | 7.2727 | 560 | 0.4684 | 0.9231 | 0.8956 | | 0.0002 | 7.5325 | 580 | 0.4695 | 0.9231 | 0.8956 | | 0.0002 | 7.7922 | 600 | 0.4710 | 0.9103 | 0.8803 | | 0.0001 | 8.0519 | 620 | 0.4719 | 0.9103 | 0.8803 | | 0.0001 | 8.3117 | 640 | 0.4726 | 0.9103 | 0.8803 | | 0.0001 | 8.5714 | 660 | 0.4742 | 0.9103 | 0.8803 | | 0.0001 | 8.8312 | 680 | 0.4754 | 0.9231 | 0.8956 | | 0.0002 | 9.0909 | 700 | 0.4757 | 0.9231 | 0.8956 | | 0.0001 | 9.3506 | 720 | 0.4759 | 0.9231 | 0.8956 | | 0.0001 | 9.6104 | 740 | 0.4763 | 0.9231 | 0.8956 | | 0.0001 | 9.8701 | 760 | 0.4765 | 0.9231 | 0.8956 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "malignant", "normal, benign" ]

KiViDrag/ViT_breastmnist_std_15

# ViT_breastmnist_std_15 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4504 - Accuracy: 0.7885 - F1: 0.6551 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | 0.4628 | 0.2597 | 20 | 0.4724 | 0.7821 | 0.5951 | | 0.3645 | 0.5195 | 40 | 0.3994 | 0.8590 | 0.7786 | | 0.2744 | 0.7792 | 60 | 0.4429 | 0.8462 | 0.7524 | | 0.3004 | 1.0390 | 80 | 0.3893 | 0.8590 | 0.7886 | | 0.2153 | 1.2987 | 100 | 0.4120 | 0.8462 | 0.7641 | | 0.1593 | 1.5584 | 120 | 0.4542 | 0.8590 | 0.7786 | | 0.1189 | 1.8182 | 140 | 0.3911 | 0.8718 | 0.8120 | | 0.1139 | 2.0779 | 160 | 0.4154 | 0.8590 | 0.7886 | | 0.0707 | 2.3377 | 180 | 0.4517 | 0.8590 | 0.7886 | | 0.0482 | 2.5974 | 200 | 0.4824 | 0.8718 | 0.8034 | | 0.0499 | 2.8571 | 220 | 0.4408 | 0.8462 | 0.7743 | | 0.0195 | 3.1169 | 240 | 0.4874 | 0.8462 | 0.7743 | | 0.0146 | 3.3766 | 260 | 0.4723 | 0.8718 | 0.8120 | | 0.0141 | 3.6364 | 280 | 0.5117 | 0.8590 | 0.7886 | | 0.017 | 3.8961 | 300 | 0.6032 | 0.8462 | 0.7743 | | 0.0052 | 4.1558 | 320 | 0.5948 | 0.8590 | 0.7886 | | 0.005 | 4.4156 | 340 | 0.5897 | 0.8590 | 0.7886 | | 0.0039 | 4.6753 | 360 | 0.5729 | 0.8462 | 0.7743 | | 0.0088 | 4.9351 | 380 | 0.5623 | 0.8462 | 0.7743 | | 0.0104 | 5.1948 | 400 | 0.4814 | 0.8718 | 0.8194 | | 0.0012 | 5.4545 | 420 | 0.5039 | 0.8718 | 0.8194 | | 0.001 | 5.7143 | 440 | 0.5268 | 0.8718 | 0.8120 | | 0.001 | 5.9740 | 460 | 0.5435 | 0.8590 | 0.7886 | | 0.0007 | 6.2338 | 480 | 0.5435 | 0.8462 | 0.7743 | | 0.0007 | 6.4935 | 500 | 0.5373 | 0.8590 | 0.7974 | | 0.0006 | 6.7532 | 520 | 0.5745 | 0.8590 | 0.7886 | | 0.0007 | 7.0130 | 540 | 0.5674 | 0.8462 | 0.7743 | | 0.0004 | 7.2727 | 560 | 0.5826 | 0.8462 | 0.7743 | | 0.0006 | 7.5325 | 580 | 0.5663 | 0.8462 | 0.7743 | | 0.0006 | 7.7922 | 600 | 0.5751 | 0.8462 | 0.7743 | | 0.0005 | 8.0519 | 620 | 0.5851 | 0.8462 | 0.7743 | | 0.0004 | 8.3117 | 640 | 0.5782 | 0.8462 | 0.7743 | | 0.0004 | 8.5714 | 660 | 0.5875 | 0.8462 | 0.7743 | | 0.0004 | 8.8312 | 680 | 0.5939 | 0.8462 | 0.7743 | | 0.0004 | 9.0909 | 700 | 0.5934 | 0.8462 | 0.7743 | | 0.0004 | 9.3506 | 720 | 0.5925 | 0.8462 | 0.7743 | | 0.0004 | 9.6104 | 740 | 0.5930 | 0.8462 | 0.7743 | | 0.0004 | 9.8701 | 760 | 0.5945 | 0.8462 | 0.7743 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "malignant", "normal, benign" ]

KiViDrag/ViT_breastmnist_std_45

# ViT_breastmnist_std_45 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4752 - Accuracy: 0.7821 - F1: 0.6733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | 0.5115 | 0.2597 | 20 | 0.5292 | 0.7308 | 0.4222 | | 0.4949 | 0.5195 | 40 | 0.5229 | 0.7436 | 0.4708 | | 0.4099 | 0.7792 | 60 | 0.4728 | 0.7692 | 0.5568 | | 0.4461 | 1.0390 | 80 | 0.4428 | 0.8333 | 0.7247 | | 0.4201 | 1.2987 | 100 | 0.4311 | 0.8718 | 0.8120 | | 0.3532 | 1.5584 | 120 | 0.4206 | 0.8590 | 0.7886 | | 0.3586 | 1.8182 | 140 | 0.4292 | 0.8590 | 0.7886 | | 0.3412 | 2.0779 | 160 | 0.4541 | 0.8333 | 0.7247 | | 0.2945 | 2.3377 | 180 | 0.4179 | 0.8333 | 0.7606 | | 0.2555 | 2.5974 | 200 | 0.4331 | 0.8590 | 0.7886 | | 0.2753 | 2.8571 | 220 | 0.4310 | 0.8205 | 0.7367 | | 0.2079 | 3.1169 | 240 | 0.4152 | 0.8462 | 0.7833 | | 0.217 | 3.3766 | 260 | 0.4157 | 0.8718 | 0.8260 | | 0.167 | 3.6364 | 280 | 0.4259 | 0.8590 | 0.8051 | | 0.1976 | 3.8961 | 300 | 0.4346 | 0.8462 | 0.7913 | | 0.1376 | 4.1558 | 320 | 0.4341 | 0.8462 | 0.7913 | | 0.1301 | 4.4156 | 340 | 0.4418 | 0.8462 | 0.7983 | | 0.1503 | 4.6753 | 360 | 0.4375 | 0.8590 | 0.8120 | | 0.126 | 4.9351 | 380 | 0.4376 | 0.8590 | 0.8120 | | 0.098 | 5.1948 | 400 | 0.4310 | 0.8462 | 0.7983 | | 0.0675 | 5.4545 | 420 | 0.4545 | 0.8333 | 0.7849 | | 0.0618 | 5.7143 | 440 | 0.4587 | 0.8333 | 0.7849 | | 0.0572 | 5.9740 | 460 | 0.4629 | 0.8462 | 0.7983 | | 0.0283 | 6.2338 | 480 | 0.4778 | 0.8333 | 0.7849 | | 0.0337 | 6.4935 | 500 | 0.4820 | 0.8462 | 0.7983 | | 0.0416 | 6.7532 | 520 | 0.4794 | 0.8462 | 0.8045 | | 0.0535 | 7.0130 | 540 | 0.4811 | 0.8333 | 0.7849 | | 0.0146 | 7.2727 | 560 | 0.4780 | 0.8462 | 0.7983 | | 0.0205 | 7.5325 | 580 | 0.4889 | 0.8333 | 0.7849 | | 0.0118 | 7.7922 | 600 | 0.5004 | 0.8333 | 0.7913 | | 0.0148 | 8.0519 | 620 | 0.4974 | 0.8333 | 0.7849 | | 0.0078 | 8.3117 | 640 | 0.5009 | 0.8205 | 0.7719 | | 0.0101 | 8.5714 | 660 | 0.5079 | 0.8205 | 0.7719 | | 0.0042 | 8.8312 | 680 | 0.5178 | 0.8205 | 0.7719 | | 0.0047 | 9.0909 | 700 | 0.5186 | 0.8205 | 0.7719 | | 0.0029 | 9.3506 | 720 | 0.5217 | 0.8205 | 0.7719 | | 0.0042 | 9.6104 | 740 | 0.5238 | 0.8077 | 0.7592 | | 0.0038 | 9.8701 | 760 | 0.5246 | 0.8205 | 0.7719 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "malignant", "normal, benign" ]

griffio/vit-base-patch16-224-rotated-dungeons-v101

# vit-base-patch16-224-rotated-dungeons-v101 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.6993 - Accuracy: 0.8333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.799 | 0.8333 | 10 | 1.9456 | 0.1667 | | 1.2417 | 1.6667 | 20 | 1.7680 | 0.3333 | | 1.1543 | 2.5 | 30 | 1.5470 | 0.5 | | 0.8829 | 3.3333 | 40 | 1.4117 | 0.5 | | 0.8725 | 4.1667 | 50 | 1.2967 | 0.5 | | 0.5514 | 5.0 | 60 | 1.1901 | 0.5 | | 0.4565 | 5.8333 | 70 | 1.1867 | 0.5 | | 0.3663 | 6.6667 | 80 | 1.0417 | 0.5 | | 0.3448 | 7.5 | 90 | 0.8938 | 0.6667 | | 0.2525 | 8.3333 | 100 | 0.8804 | 0.75 | | 0.1729 | 9.1667 | 110 | 0.9067 | 0.6667 | | 0.1848 | 10.0 | 120 | 0.8441 | 0.6667 | | 0.1847 | 10.8333 | 130 | 0.7352 | 0.8333 | | 0.0916 | 11.6667 | 140 | 0.7336 | 0.8333 | | 0.1771 | 12.5 | 150 | 0.7012 | 0.8333 | | 0.105 | 13.3333 | 160 | 0.7030 | 0.8333 | | 0.1008 | 14.1667 | 170 | 0.7004 | 0.8333 | | 0.1127 | 15.0 | 180 | 0.6993 | 0.8333 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-base-patch16-224-rotated-dungeons-v103

# vit-base-patch16-224-rotated-dungeons-v103 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the rotated_maps dataset. It achieves the following results on the evaluation set: - Loss: 0.8291 - Accuracy: 0.8333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.522 | 3.3333 | 20 | 0.8489 | 0.6667 | | 0.0346 | 6.6667 | 40 | 2.3103 | 0.6667 | | 0.019 | 10.0 | 60 | 1.4623 | 0.75 | | 0.017 | 13.3333 | 80 | 0.8291 | 0.8333 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

nemik/frost-vision-v2-google_vit-base-patch16-224-v2024-11-09

# frost-vision-v2-google_vit-base-patch16-224-v2024-11-09 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1716 - Accuracy: 0.9412 - F1: 0.8486 - Precision: 0.8540 - Recall: 0.8432 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.2398 | 1.4085 | 100 | 0.2096 | 0.9215 | 0.7833 | 0.8502 | 0.7261 | | 0.1746 | 2.8169 | 200 | 0.1676 | 0.9370 | 0.8362 | 0.8494 | 0.8234 | | 0.1316 | 4.2254 | 300 | 0.1750 | 0.9282 | 0.8125 | 0.8293 | 0.7964 | | 0.1305 | 5.6338 | 400 | 0.1671 | 0.9342 | 0.8270 | 0.8498 | 0.8054 | | 0.1119 | 7.0423 | 500 | 0.1747 | 0.9317 | 0.8240 | 0.8300 | 0.8180 | | 0.0913 | 8.4507 | 600 | 0.1515 | 0.9415 | 0.8505 | 0.8505 | 0.8505 | | 0.0964 | 9.8592 | 700 | 0.1680 | 0.9377 | 0.8418 | 0.8351 | 0.8486 | | 0.0659 | 11.2676 | 800 | 0.1891 | 0.9275 | 0.8144 | 0.8144 | 0.8144 | | 0.0706 | 12.6761 | 900 | 0.1788 | 0.9320 | 0.8234 | 0.8364 | 0.8108 | | 0.069 | 14.0845 | 1000 | 0.1716 | 0.9412 | 0.8486 | 0.8540 | 0.8432 | | 0.0543 | 15.4930 | 1100 | 0.1847 | 0.9363 | 0.8341 | 0.8489 | 0.8198 | | 0.0515 | 16.9014 | 1200 | 0.1741 | 0.9408 | 0.8470 | 0.8564 | 0.8378 | | 0.0489 | 18.3099 | 1300 | 0.1793 | 0.9461 | 0.8620 | 0.8628 | 0.8613 | | 0.0339 | 19.7183 | 1400 | 0.1806 | 0.9444 | 0.8569 | 0.8616 | 0.8523 | | 0.0409 | 21.1268 | 1500 | 0.1784 | 0.9440 | 0.8569 | 0.8561 | 0.8577 | | 0.0275 | 22.5352 | 1600 | 0.1839 | 0.9437 | 0.8548 | 0.8611 | 0.8486 | | 0.0231 | 23.9437 | 1700 | 0.1865 | 0.9415 | 0.8480 | 0.8622 | 0.8342 | | 0.0204 | 25.3521 | 1800 | 0.1884 | 0.9405 | 0.8482 | 0.8459 | 0.8505 | | 0.0245 | 26.7606 | 1900 | 0.1935 | 0.9377 | 0.8410 | 0.8387 | 0.8432 | | 0.0202 | 28.1690 | 2000 | 0.1888 | 0.9394 | 0.8456 | 0.8426 | 0.8486 | | 0.0187 | 29.5775 | 2100 | 0.1914 | 0.9415 | 0.8502 | 0.8517 | 0.8486 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "snowing", "raining", "sunny", "cloudy", "night", "snow_on_road", "partial_snow_on_road", "clear_pavement", "wet_pavement", "iced_lens" ]

ricardoSLabs/pre_CIDAUTv3

# pre_CIDAUTv3 This model is a fine-tuned version of [ricardoSLabs/pre_CIDAUTv2](https://huggingface.co/ricardoSLabs/pre_CIDAUTv2) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1209 - Accuracy: 0.9841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | No log | 0.8889 | 4 | 0.5177 | 0.7937 | | No log | 2.0 | 9 | 0.3085 | 0.8889 | | 1.0669 | 2.8889 | 13 | 0.2461 | 0.8889 | | 1.0669 | 4.0 | 18 | 0.1346 | 0.9683 | | 0.2558 | 4.4444 | 20 | 0.1209 | 0.9841 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "editada", "real" ]

ricardoSLabs/pre_CIDAUTv4

# pre_CIDAUTv4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0177 - Accuracy: 0.9918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | No log | 0.8889 | 4 | 0.6742 | 0.5679 | | No log | 2.0 | 9 | 0.3347 | 0.9218 | | 0.6003 | 2.8889 | 13 | 0.1238 | 0.9753 | | 0.6003 | 4.0 | 18 | 0.1298 | 0.9465 | | 0.199 | 4.8889 | 22 | 0.0360 | 0.9877 | | 0.199 | 6.0 | 27 | 0.1049 | 0.9671 | | 0.0832 | 6.8889 | 31 | 0.0058 | 1.0 | | 0.0832 | 8.0 | 36 | 0.0138 | 0.9918 | | 0.0438 | 8.8889 | 40 | 0.0177 | 0.9918 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "editada", "real" ]

ricardoSLabs/pre_CIDAUTv5

# pre_CIDAUTv5 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0190 - Accuracy: 0.9938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | No log | 0.9524 | 5 | 0.6238 | 0.6460 | | 0.5991 | 1.9048 | 10 | 0.2637 | 0.9814 | | 0.5991 | 2.8571 | 15 | 0.0767 | 0.9938 | | 0.1441 | 4.0 | 21 | 0.0365 | 0.9876 | | 0.1441 | 4.9524 | 26 | 0.0399 | 0.9876 | | 0.075 | 5.9048 | 31 | 0.0216 | 0.9938 | | 0.075 | 6.8571 | 36 | 0.0126 | 1.0 | | 0.0581 | 7.6190 | 40 | 0.0190 | 0.9938 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0

[ "editada", "real" ]

nemik/frost-vision-v2-google_vit-base-patch16-384-v2024-11-10

# frost-vision-v2-google_vit-base-patch16-384-v2024-11-10 This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the webdataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1847 - Accuracy: 0.9275 - F1: 0.8187 - Precision: 0.8172 - Recall: 0.8201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.2243 | 1.4085 | 100 | 0.2088 | 0.9243 | 0.7981 | 0.8534 | 0.7496 | | 0.2438 | 2.8169 | 200 | 0.1819 | 0.9299 | 0.8103 | 0.8817 | 0.7496 | | 0.1338 | 4.2254 | 300 | 0.1608 | 0.9377 | 0.8449 | 0.8397 | 0.8501 | | 0.1224 | 5.6338 | 400 | 0.1735 | 0.9271 | 0.8179 | 0.8158 | 0.8201 | | 0.1065 | 7.0423 | 500 | 0.1847 | 0.9275 | 0.8187 | 0.8172 | 0.8201 | | 0.1008 | 8.4507 | 600 | 0.1710 | 0.9405 | 0.8506 | 0.8528 | 0.8483 | | 0.1005 | 9.8592 | 700 | 0.1823 | 0.9384 | 0.8405 | 0.8698 | 0.8131 | | 0.0756 | 11.2676 | 800 | 0.1771 | 0.9415 | 0.8520 | 0.8613 | 0.8430 | | 0.0653 | 12.6761 | 900 | 0.1971 | 0.9324 | 0.8310 | 0.8295 | 0.8325 | | 0.0367 | 14.0845 | 1000 | 0.2123 | 0.9296 | 0.8221 | 0.8294 | 0.8148 | | 0.0459 | 15.4930 | 1100 | 0.2006 | 0.9335 | 0.832 | 0.8387 | 0.8254 | | 0.0559 | 16.9014 | 1200 | 0.2097 | 0.9313 | 0.8232 | 0.8470 | 0.8007 | | 0.0382 | 18.3099 | 1300 | 0.2055 | 0.9352 | 0.8372 | 0.8401 | 0.8342 | | 0.0361 | 19.7183 | 1400 | 0.2070 | 0.9335 | 0.8305 | 0.8449 | 0.8166 | | 0.0358 | 21.1268 | 1500 | 0.1959 | 0.9398 | 0.8458 | 0.8653 | 0.8272 | | 0.0382 | 22.5352 | 1600 | 0.2097 | 0.9320 | 0.8269 | 0.8412 | 0.8131 | | 0.0285 | 23.9437 | 1700 | 0.2016 | 0.9415 | 0.8515 | 0.8639 | 0.8395 | | 0.0141 | 25.3521 | 1800 | 0.2161 | 0.9366 | 0.8384 | 0.8537 | 0.8236 | | 0.0179 | 26.7606 | 1900 | 0.2073 | 0.9377 | 0.8427 | 0.8495 | 0.8360 | | 0.0263 | 28.1690 | 2000 | 0.2097 | 0.9391 | 0.8457 | 0.8556 | 0.8360 | | 0.0191 | 29.5775 | 2100 | 0.2101 | 0.9377 | 0.8415 | 0.8545 | 0.8289 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "snowing", "raining", "sunny", "cloudy", "night", "snow_on_road", "partial_snow_on_road", "clear_pavement", "wet_pavement", "iced_lens" ]

CGscorpion/convnext-tiny-224-finetuned-eurosat-albumentations

# convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2746 - Accuracy: 0.9589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.0418 | 0.9362 | 11 | 0.2497 | 0.9041 | | 0.0349 | 1.9574 | 23 | 0.2377 | 0.9041 | | 0.0231 | 2.9787 | 35 | 0.2695 | 0.8904 | | 0.0154 | 4.0 | 47 | 0.2185 | 0.9041 | | 0.0061 | 4.9362 | 58 | 0.2810 | 0.9178 | | 0.0023 | 5.9574 | 70 | 0.2905 | 0.9315 | | 0.0029 | 6.9787 | 82 | 0.2052 | 0.9315 | | 0.0013 | 8.0 | 94 | 0.2333 | 0.9178 | | 0.0009 | 8.9362 | 105 | 0.2262 | 0.9315 | | 0.008 | 9.9574 | 117 | 0.2247 | 0.9178 | | 0.0014 | 10.9787 | 129 | 0.3200 | 0.9041 | | 0.0005 | 12.0 | 141 | 0.2643 | 0.9178 | | 0.0006 | 12.9362 | 152 | 0.2911 | 0.9178 | | 0.0007 | 13.9574 | 164 | 0.2567 | 0.9178 | | 0.0009 | 14.9787 | 176 | 0.3170 | 0.9178 | | 0.0052 | 16.0 | 188 | 0.2435 | 0.9315 | | 0.0005 | 16.9362 | 199 | 0.2746 | 0.9589 | | 0.0004 | 17.9574 | 211 | 0.2347 | 0.9315 | | 0.002 | 18.9787 | 223 | 0.2999 | 0.9178 | | 0.0021 | 20.0 | 235 | 0.2648 | 0.9178 | | 0.0003 | 20.9362 | 246 | 0.2609 | 0.9178 | | 0.0002 | 21.9574 | 258 | 0.2709 | 0.9315 | | 0.0004 | 22.9787 | 270 | 0.2359 | 0.9315 | | 0.005 | 24.0 | 282 | 0.2484 | 0.9178 | | 0.0011 | 24.9362 | 293 | 0.3019 | 0.9178 | | 0.0012 | 25.9574 | 305 | 0.2715 | 0.9178 | | 0.0003 | 26.9787 | 317 | 0.2486 | 0.9315 | | 0.0009 | 28.0 | 329 | 0.2472 | 0.9315 | | 0.0002 | 28.9362 | 340 | 0.2449 | 0.9315 | | 0.0002 | 29.9574 | 352 | 0.2480 | 0.9315 | | 0.0002 | 30.9787 | 364 | 0.2520 | 0.9315 | | 0.0002 | 32.0 | 376 | 0.2528 | 0.9315 | | 0.0001 | 32.9362 | 387 | 0.2520 | 0.9315 | | 0.0002 | 33.9574 | 399 | 0.2503 | 0.9315 | | 0.0001 | 34.9787 | 411 | 0.2508 | 0.9315 | | 0.0001 | 36.0 | 423 | 0.2493 | 0.9315 | | 0.0008 | 36.9362 | 434 | 0.2558 | 0.9315 | | 0.0001 | 37.9574 | 446 | 0.2616 | 0.9315 | | 0.0001 | 38.9787 | 458 | 0.2623 | 0.9315 | | 0.0011 | 40.0 | 470 | 0.2617 | 0.9315 | | 0.0002 | 40.9362 | 481 | 0.2532 | 0.9315 | | 0.0001 | 41.9574 | 493 | 0.2495 | 0.9315 | | 0.0001 | 42.9787 | 505 | 0.2478 | 0.9315 | | 0.0001 | 44.0 | 517 | 0.2479 | 0.9315 | | 0.0001 | 44.9362 | 528 | 0.2481 | 0.9315 | | 0.0001 | 45.9574 | 540 | 0.2481 | 0.9315 | | 0.002 | 46.8085 | 550 | 0.2475 | 0.9315 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

[ "answer", "delete_line" ]

griffio/resnet-18-dungeons-001

# resnet-18-dungeons-001 This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1886 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 85 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.0594 | 6.6667 | 10 | 1.3086 | 0.5833 | | 0.0973 | 13.3333 | 20 | 1.3523 | 0.5 | | 0.068 | 20.0 | 30 | 1.2428 | 0.5833 | | 0.0671 | 26.6667 | 40 | 1.2280 | 0.5833 | | 0.0527 | 33.3333 | 50 | 1.2677 | 0.5833 | | 0.0592 | 40.0 | 60 | 1.2846 | 0.5833 | | 0.0446 | 46.6667 | 70 | 1.2210 | 0.5833 | | 0.0565 | 53.3333 | 80 | 1.1886 | 0.5 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

griffio/vit-large-patch16-224-dungeons-001

# vit-large-patch16-224-dungeons-001 This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6325 - Accuracy: 0.75 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 85 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.8218 | 6.6667 | 10 | 1.8564 | 0.1667 | | 1.4325 | 13.3333 | 20 | 1.7325 | 0.3333 | | 0.8869 | 20.0 | 30 | 1.5186 | 0.4167 | | 0.3717 | 26.6667 | 40 | 1.1131 | 0.6667 | | 0.0945 | 33.3333 | 50 | 0.8408 | 0.75 | | 0.0175 | 40.0 | 60 | 0.7224 | 0.75 | | 0.0051 | 46.6667 | 70 | 0.6674 | 0.75 | | 0.0024 | 53.3333 | 80 | 0.6325 | 0.75 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "five", "four", "one", "three", "twelve", "two", "zero" ]

nemik/frost-vision-v2-google_vit-base-patch16-224-v2024-11-11

# frost-vision-v2-google_vit-base-patch16-224-v2024-11-11 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Accuracy: 0.9320 - F1: 0.8224 - Precision: 0.8172 - Recall: 0.8278 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.3127 | 1.4085 | 100 | 0.2932 | 0.8940 | 0.6725 | 0.8153 | 0.5722 | | 0.193 | 2.8169 | 200 | 0.2136 | 0.9190 | 0.7834 | 0.7969 | 0.7704 | | 0.1503 | 4.2254 | 300 | 0.1815 | 0.9278 | 0.8100 | 0.8108 | 0.8093 | | 0.1313 | 5.6338 | 400 | 0.1623 | 0.9327 | 0.8183 | 0.8415 | 0.7963 | | 0.1166 | 7.0423 | 500 | 0.1658 | 0.9320 | 0.8224 | 0.8172 | 0.8278 | | 0.093 | 8.4507 | 600 | 0.1606 | 0.9384 | 0.8405 | 0.8276 | 0.8537 | | 0.0931 | 9.8592 | 700 | 0.1625 | 0.9366 | 0.8370 | 0.8191 | 0.8556 | | 0.0733 | 11.2676 | 800 | 0.1714 | 0.9356 | 0.8310 | 0.8287 | 0.8333 | | 0.0693 | 12.6761 | 900 | 0.1568 | 0.9398 | 0.8403 | 0.8475 | 0.8333 | | 0.0615 | 14.0845 | 1000 | 0.1666 | 0.9342 | 0.8270 | 0.8262 | 0.8278 | | 0.0562 | 15.4930 | 1100 | 0.1636 | 0.9394 | 0.8404 | 0.8420 | 0.8389 | | 0.0507 | 16.9014 | 1200 | 0.1613 | 0.9401 | 0.8435 | 0.8388 | 0.8481 | | 0.0552 | 18.3099 | 1300 | 0.1590 | 0.9412 | 0.8455 | 0.8447 | 0.8463 | | 0.0439 | 19.7183 | 1400 | 0.1704 | 0.9394 | 0.8425 | 0.8333 | 0.8519 | | 0.0367 | 21.1268 | 1500 | 0.1702 | 0.9426 | 0.8484 | 0.8523 | 0.8444 | | 0.0424 | 22.5352 | 1600 | 0.1685 | 0.9394 | 0.8419 | 0.8358 | 0.8481 | | 0.0306 | 23.9437 | 1700 | 0.1771 | 0.9380 | 0.8397 | 0.8262 | 0.8537 | | 0.0352 | 25.3521 | 1800 | 0.1691 | 0.9401 | 0.8440 | 0.8364 | 0.8519 | | 0.0323 | 26.7606 | 1900 | 0.1687 | 0.9426 | 0.8509 | 0.8409 | 0.8611 | | 0.0297 | 28.1690 | 2000 | 0.1732 | 0.9401 | 0.8455 | 0.8304 | 0.8611 | | 0.0229 | 29.5775 | 2100 | 0.1712 | 0.9412 | 0.8475 | 0.8360 | 0.8593 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "snowing", "raining", "sunny", "cloudy", "night", "snow_on_road", "partial_snow_on_road", "clear_pavement", "wet_pavement", "iced_lens" ]

mikedata/vit-base-oxford-iiit-pets

# vit-base-oxford-iiit-pets This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1786 - Accuracy: 0.9405 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.381 | 1.0 | 370 | 0.3122 | 0.9269 | | 0.2349 | 2.0 | 740 | 0.2465 | 0.9242 | | 0.1579 | 3.0 | 1110 | 0.2329 | 0.9296 | | 0.1497 | 4.0 | 1480 | 0.2171 | 0.9310 | | 0.1252 | 5.0 | 1850 | 0.2167 | 0.9323 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "siamese", "birman", "shiba inu", "staffordshire bull terrier", "basset hound", "bombay", "japanese chin", "chihuahua", "german shorthaired", "pomeranian", "beagle", "english cocker spaniel", "american pit bull terrier", "ragdoll", "persian", "egyptian mau", "miniature pinscher", "sphynx", "maine coon", "keeshond", "yorkshire terrier", "havanese", "leonberger", "wheaten terrier", "american bulldog", "english setter", "boxer", "newfoundland", "bengal", "samoyed", "british shorthair", "great pyrenees", "abyssinian", "pug", "saint bernard", "russian blue", "scottish terrier" ]

Binaryy/test-trainer

# test-trainer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Chess dataset. It achieves the following results on the evaluation set: - Loss: 0.7291 - Accuracy: 0.9107 - F1: 0.9122 - Precision: 0.9172 - Recall: 0.9107 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 4 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 50 | 1.6720 | 0.4821 | 0.4134 | 0.3870 | 0.4821 | | No log | 2.0 | 100 | 1.4652 | 0.6429 | 0.6126 | 0.7414 | 0.6429 | | No log | 3.0 | 150 | 1.1742 | 0.7321 | 0.7210 | 0.7792 | 0.7321 | | No log | 4.0 | 200 | 0.9813 | 0.8393 | 0.8433 | 0.8589 | 0.8393 | | No log | 5.0 | 250 | 0.8312 | 0.8214 | 0.8164 | 0.8516 | 0.8214 | | No log | 6.0 | 300 | 0.7291 | 0.9107 | 0.9122 | 0.9172 | 0.9107 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.2.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "bishop", "king", "knight", "pawn", "queen", "rook" ]

mgarci14/resnet-50-finetuned-eurosat

# resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 3.2958 - Accuracy: 0.1952 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.3941 | 1.0 | 31 | 3.3754 | 0.1646 | | 3.3386 | 2.0 | 62 | 3.3178 | 0.2054 | | 3.3084 | 3.0 | 93 | 3.2958 | 0.1952 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "bangus", "big head carp", "black spotted barb", "catfish", "climbing perch", "fourfinger threadfin", "freshwater eel", "glass perchlet", "goby", "gold fish", "gourami", "grass carp", "green spotted puffer", "indian carp", "indo-pacific tarpon", "jaguar gapote", "janitor fish", "knifefish", "long-snouted pipefish", "mosquito fish", "mudfish", "mullet", "pangasius", "perch", "scat fish", "silver barb", "silver carp", "silver perch", "snakehead", "tenpounder", "tilapia" ]

MichaelHu03/CS4220-KOProject

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

ziyuyuyuyu1/results

# results This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4006 - Accuracy: 0.7643 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.5045 | 0.9998 | 3237 | 0.4944 | 0.7125 | | 0.4578 | 1.9998 | 6475 | 0.4348 | 0.7457 | | 0.3922 | 2.9993 | 9711 | 0.4006 | 0.7643 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.4.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "pos", "neg" ]

DrRasha/rasha

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[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

e1010101/vit-384-large-patch-tongue-image-segmented

# segmented-augmented This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6965 - Precision: 0.8085 - Recall: 0.8837 - F1: 0.8444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.0857 | 1.0 | 327 | 0.4359 | 0.8176 | 0.8040 | 0.8107 | | 0.0164 | 2.0 | 654 | 0.5654 | 0.8043 | 0.8605 | 0.8315 | | 0.0056 | 3.0 | 981 | 0.6437 | 0.8182 | 0.8671 | 0.8419 | | 0.002 | 4.0 | 1308 | 0.6739 | 0.8055 | 0.8804 | 0.8413 | | 0.003 | 5.0 | 1635 | 0.6965 | 0.8085 | 0.8837 | 0.8444 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1

[ "crack", "red-dots", "toothmark" ]

griffio/vit-large-patch16-224-dungeon-geo-morphs-001

# vit-large-patch16-224-dungeon-geo-morphs-001 This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset. It achieves the following results on the evaluation set: - Loss: 0.3882 - Accuracy: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.9239 | 6.6667 | 10 | 0.3882 | 0.9667 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "three", "two", "zero" ]

griffio/vit-large-patch16-224-dungeon-geo-morphs-002

# vit-large-patch16-224-dungeon-geo-morphs-002 This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset. It achieves the following results on the evaluation set: - Loss: 0.0332 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.056 | 6.6667 | 10 | 0.0332 | 1.0 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

[ "three", "two", "zero" ]

griffio/vit-large-patch16-224-dungeon-geo-morphs-003

# vit-large-patch16-224-dungeon-geo-morphs-003 This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset. It achieves the following results on the evaluation set: - Loss: 0.0149 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.8585 | 6.6667 | 10 | 0.2204 | 1.0 | | 0.2115 | 13.3333 | 20 | 0.0530 | 1.0 | | 0.0562 | 20.0 | 30 | 0.0149 | 1.0 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Tokenizers 0.20.3

[ "three", "two", "zero" ]

LRPxxx/fine-tuned-image-model

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

bongbongbong/vit-base-beans-demo-v5

# vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0167 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0934 | 1.5385 | 100 | 0.1081 | 0.9699 | | 0.0204 | 3.0769 | 200 | 0.0167 | 1.0 | ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

[ "angular_leaf_spot", "bean_rust", "healthy" ]

dkwjd/vit-base-beans-demo-v5

# vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0254 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0887 | 1.5385 | 100 | 0.0401 | 0.9925 | | 0.0115 | 3.0769 | 200 | 0.0254 | 0.9925 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "angular_leaf_spot", "bean_rust", "healthy" ]

wlfls0305/vit-base-beans-demo-v5

# vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0586 - Accuracy: 0.9699 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.065 | 1.5385 | 100 | 0.0755 | 0.9850 | | 0.0183 | 3.0769 | 200 | 0.0586 | 0.9699 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "angular_leaf_spot", "bean_rust", "healthy" ]

DeardeermeloD/vit-base-beans-demo-v5

# vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0144 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.1333 | 1.5385 | 100 | 0.0851 | 0.9774 | | 0.0399 | 3.0769 | 200 | 0.0144 | 1.0 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Tokenizers 0.20.3

[ "angular_leaf_spot", "bean_rust", "healthy" ]

rlatlswls/vit-base-beans-demo-v5

# vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "angular_leaf_spot", "bean_rust", "healthy" ]

hohit/vit-base-beans-demo-v5

# vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

[ "angular_leaf_spot", "bean_rust", "healthy" ]

crocutacrocuto/convnext-base-224-MEGbis-5

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "aardvark", "bird", "black-and-white colobus", "blue duiker", "blue monkey", "buffalo", "bushbuck", "bushpig", "cattle", "chimpanzee", "civet_genet", "elephant", "galago_potto", "goat", "golden cat", "gorilla", "guineafowl", "leopard", "lhoests monkey", "mandrill", "mongoose", "monkey", "olive baboon", "pangolin", "porcupine", "red colobus_red-capped mangabey", "red duiker", "rodent", "serval", "spotted hyena", "squirrel", "water chevrotain", "yellow-backed duiker" ]