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asaaitika/vit-emotion
|
<!-- 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-emotion
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: 1.3899
- Accuracy: 0.55
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5061 | 1.0 | 40 | 1.5316 | 0.4875 |
| 0.4361 | 2.0 | 80 | 1.4641 | 0.5125 |
| 0.2775 | 3.0 | 120 | 1.4056 | 0.5375 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
Jesteban247/beit-base-patch16-224-pt22k-ft22k-finetuned-Chest_Xray_COVID19
|
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-finetuned-Chest_Xray_COVID19
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: 0.4037
- Accuracy: 0.8298
- Precision: 0.8176
- F1 Score: 0.8152
## 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 | Precision | F1 Score |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:--------:|
| 0.8633 | 1.0 | 26 | 0.4690 | 0.8175 | 0.8117 | 0.8117 |
| 0.4879 | 2.0 | 52 | 0.3828 | 0.8407 | 0.8323 | 0.8271 |
| 0.4464 | 2.9109 | 75 | 0.4037 | 0.8298 | 0.8176 | 0.8152 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"covid-19",
"normal",
"pneumonia-bacterial",
"pneumonia-viral"
] |
haneph033/vit-emotion-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. -->
# vit-emotion-results
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3155
- Accuracy: 0.5312
## 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.3518 | 0.4437 |
| 0.6084 | 2.0 | 80 | 1.3564 | 0.45 |
| 0.1864 | 3.0 | 120 | 1.3155 | 0.5312 |
| 0.0417 | 4.0 | 160 | 1.3422 | 0.4875 |
| 0.0255 | 5.0 | 200 | 1.3443 | 0.5312 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
AzedK/emotion-classifier-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. -->
# emotion-classifier-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.
## 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: 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
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
singgihkp/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 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
- 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: 3
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
Jesteban247/beit-base-patch16-224-pt22k-ft22k-finetuned-Melanoma_Cancer_Dataset
|
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-finetuned-Melanoma_Cancer_Dataset
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: 0.2378
- Accuracy: 0.9044
- Precision: 0.9044
- F1 Score: 0.9044
## 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 | Precision | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:--------:|
| 0.4436 | 1.0 | 38 | 0.2878 | 0.8790 | 0.8791 | 0.8790 |
| 0.3327 | 2.0 | 76 | 0.2664 | 0.8919 | 0.8936 | 0.8920 |
| 0.289 | 3.0 | 114 | 0.2378 | 0.9044 | 0.9044 | 0.9044 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"benign",
"malignant"
] |
yosha10/vit-emotion-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. -->
# vit-emotion-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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4992
- Accuracy: 0.4375
## 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8937 | 1.0 | 40 | 1.8321 | 0.425 |
| 1.579 | 2.0 | 80 | 1.5817 | 0.4188 |
| 1.4508 | 3.0 | 120 | 1.4802 | 0.475 |
| 1.3382 | 4.0 | 160 | 1.4247 | 0.4688 |
| 1.2473 | 5.0 | 200 | 1.4056 | 0.4375 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
isan78/trainer_output
|
<!-- 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. -->
# trainer_output
This model is a fine-tuned version of [aditira/emotion_classification](https://huggingface.co/aditira/emotion_classification) 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
- 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: 3.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
shingguy1/food-calorie-effnet
|
# 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]
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## 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]
|
[
"pizza",
"hamburger",
"sushi",
"salad",
"pasta",
"ice_cream",
"fried_rice",
"tacos",
"steak",
"chocolate_cake"
] |
Ferdii/trainer_output
|
<!-- 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. -->
# trainer_output
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2803
- 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: 5e-05
- 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: 3.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7"
] |
AlaaHussien/weather_classifier_model2
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AlaaHussien/weather_classifier_model2
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:
- Train Loss: 0.0700
- Validation Loss: 0.2853
- Train Accuracy: 0.9250
- Train Precision: 0.9267
- Train Recall: 0.9250
- Train F1: 0.9248
- Epoch: 19
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 109780, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:---------------:|:--------------:|:---------------:|:------------:|:--------:|:-----:|
| 1.2545 | 0.6449 | 0.8944 | 0.9010 | 0.8944 | 0.8907 | 0 |
| 0.5392 | 0.3960 | 0.9177 | 0.9191 | 0.9177 | 0.9181 | 1 |
| 0.3498 | 0.3183 | 0.9264 | 0.9271 | 0.9264 | 0.9263 | 2 |
| 0.2787 | 0.2797 | 0.9323 | 0.9331 | 0.9323 | 0.9323 | 3 |
| 0.2155 | 0.2695 | 0.9257 | 0.9279 | 0.9257 | 0.9260 | 4 |
| 0.1899 | 0.3079 | 0.9126 | 0.9165 | 0.9126 | 0.9121 | 5 |
| 0.1599 | 0.2749 | 0.9235 | 0.9259 | 0.9235 | 0.9241 | 6 |
| 0.1470 | 0.2709 | 0.9243 | 0.9257 | 0.9243 | 0.9237 | 7 |
| 0.1305 | 0.2566 | 0.9257 | 0.9263 | 0.9257 | 0.9259 | 8 |
| 0.1131 | 0.2699 | 0.9257 | 0.9271 | 0.9257 | 0.9260 | 9 |
| 0.1042 | 0.2719 | 0.9228 | 0.9240 | 0.9228 | 0.9229 | 10 |
| 0.0977 | 0.2857 | 0.9199 | 0.9208 | 0.9199 | 0.9197 | 11 |
| 0.0816 | 0.2592 | 0.9279 | 0.9294 | 0.9279 | 0.9281 | 12 |
| 0.0894 | 0.2888 | 0.9228 | 0.9256 | 0.9228 | 0.9226 | 13 |
| 0.1011 | 0.2978 | 0.9264 | 0.9287 | 0.9264 | 0.9264 | 14 |
| 0.0756 | 0.2590 | 0.9330 | 0.9344 | 0.9330 | 0.9330 | 15 |
| 0.0668 | 0.2863 | 0.9228 | 0.9255 | 0.9228 | 0.9235 | 16 |
| 0.0650 | 0.2871 | 0.9264 | 0.9282 | 0.9264 | 0.9262 | 17 |
| 0.0644 | 0.3003 | 0.9257 | 0.9279 | 0.9257 | 0.9261 | 18 |
| 0.0700 | 0.2853 | 0.9250 | 0.9267 | 0.9250 | 0.9248 | 19 |
### Framework versions
- Transformers 4.51.3
- TensorFlow 2.18.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"dew",
"fogsmog",
"frost",
"glaze",
"hail",
"lightning",
"rain",
"rainbow",
"rime",
"sandstorm",
"snow"
] |
Zulhaqimie/vit-emotion-recognition
|
<!-- 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-emotion-recognition
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.
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- 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
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"boring",
"engage",
"neutral",
"sleepy",
"surprise"
] |
izeah01/emotion-recognition
|
<!-- 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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1355
- Accuracy: 0.9984
## 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: 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
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
xcx0902/vit_mnist
|
# vit_mnist
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9"
] |
maceythm/vit-90-animals
|
___
# vit-90-animals
___
## Model description
This model is a fine-tuned Vision Transformer version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the [animal image dataset](https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals) from kaggle - trained to classify images into 90 different animal species. It achieves high accuracy on unseen data and was trained using supervised learning. The model can be used for general-purpose image classification in the animal domain and serves as a comparison baseline for zero-shot classification models such as CLIP.
The model achieves the following results on the evaluation set:
- Loss: 0.0840
- Accuracy: 0.9796
## Intended uses & limitations
### Intended uses
- Animal image classification (educational, demo, prototyping)
- Benchmarking against zero-shot classification models
- Use in Gradio interfaces or image analysis tools
### Limitations
- The model is limited to the 90 animal classes it was trained on
- It may not generalize well to image domains outside of its training distribution
- Performance can degrade with poor image quality or occlusions
## Training and evaluation data
The model was trained on a dataset containing 5,400 animal images categorized into 90 distinct classes. The dataset was obtained from Kaggle and according to the creator originally sourced from Google Images. The training/validation/test split was 80/10/10, and the label distribution is relatively balanced across classes.
Evaluation was conducted on the test split and compared to results from a zero-shot model (*openai/clip-vit-large-patch14*) using the same label set.
## Training procedure
- Base model: *google/vit-base-patch16-224*
- Fine-tuning method: Supervised training using the Hugging Face Trainer class
- Data augmentation: Applied during training (e.g., RandomHorizontalFlip, ColorJitter)
- Training time: ~5 epochs with and without augmentation
- Optimizer: AdamW (default settings)
- Evaluation metrics: Accuracy, precision, and recall
- Best performance (no augmentation): 98.3% test accuracy
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2021 | 1.0 | 270 | 0.3500 | 0.9611 |
| 0.2978 | 2.0 | 540 | 0.1766 | 0.9685 |
| 0.1886 | 3.0 | 810 | 0.1500 | 0.9685 |
| 0.1706 | 4.0 | 1080 | 0.1409 | 0.9685 |
| 0.1678 | 5.0 | 1350 | 0.1373 | 0.9667 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
[
"antelope",
"badger",
"bat",
"bear",
"bee",
"beetle",
"bison",
"boar",
"butterfly",
"cat",
"caterpillar",
"chimpanzee",
"cockroach",
"cow",
"coyote",
"crab",
"crow",
"deer",
"dog",
"dolphin",
"donkey",
"dragonfly",
"duck",
"eagle",
"elephant",
"flamingo",
"fly",
"fox",
"goat",
"goldfish",
"goose",
"gorilla",
"grasshopper",
"hamster",
"hare",
"hedgehog",
"hippopotamus",
"hornbill",
"horse",
"hummingbird",
"hyena",
"jellyfish",
"kangaroo",
"koala",
"ladybugs",
"leopard",
"lion",
"lizard",
"lobster",
"mosquito",
"moth",
"mouse",
"octopus",
"okapi",
"orangutan",
"otter",
"owl",
"ox",
"oyster",
"panda",
"parrot",
"pelecaniformes",
"penguin",
"pig",
"pigeon",
"porcupine",
"possum",
"raccoon",
"rat",
"reindeer",
"rhinoceros",
"sandpiper",
"seahorse",
"seal",
"shark",
"sheep",
"snake",
"sparrow",
"squid",
"squirrel",
"starfish",
"swan",
"tiger",
"turkey",
"turtle",
"whale",
"wolf",
"wombat",
"woodpecker",
"zebra"
] |
ccclllwww/smoker_cls_base_V2
|
<!-- 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. -->
# smoker_cls_base_V2
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: 0.2869
- Accuracy: 0.8916
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5144 | 1.0 | 33 | 0.4679 | 0.8118 |
| 0.3806 | 2.0 | 66 | 0.3606 | 0.8536 |
| 0.2982 | 3.0 | 99 | 0.2733 | 0.9163 |
| 0.2613 | 4.0 | 132 | 0.2611 | 0.8992 |
| 0.2083 | 5.0 | 165 | 0.2699 | 0.8802 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_large_V2
|
<!-- 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. -->
# smoker_cls_large_V2
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2527
- Accuracy: 0.9030
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3209 | 1.0 | 33 | 0.3349 | 0.8840 |
| 0.2436 | 2.0 | 66 | 0.2695 | 0.9049 |
| 0.164 | 3.0 | 99 | 0.2523 | 0.8916 |
| 0.133 | 4.0 | 132 | 0.2861 | 0.9011 |
| 0.084 | 5.0 | 165 | 0.3039 | 0.8954 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_huge_V2
|
<!-- 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. -->
# smoker_cls_huge_V2
This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3036
- Accuracy: 0.8802
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5506 | 1.0 | 33 | 0.5174 | 0.7034 |
| 0.4364 | 2.0 | 66 | 0.4017 | 0.8384 |
| 0.3492 | 3.0 | 99 | 0.3343 | 0.8745 |
| 0.3252 | 4.0 | 132 | 0.3222 | 0.8764 |
| 0.2921 | 5.0 | 165 | 0.3249 | 0.8574 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
AlaaHussien/final_weather
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AlaaHussien/final_weather
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:
- Train Loss: 0.1179
- Validation Loss: 0.2876
- Train Accuracy: 0.9235
- Train Precision: 0.9249
- Train Recall: 0.9235
- Train F1: 0.9237
- Epoch: 4
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 27445, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:---------------:|:--------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.1824 | 0.3373 | 0.9039 | 0.9071 | 0.9039 | 0.9041 | 0 |
| 0.1671 | 0.2880 | 0.9133 | 0.9159 | 0.9133 | 0.9132 | 1 |
| 0.1320 | 0.3227 | 0.9126 | 0.9149 | 0.9126 | 0.9127 | 2 |
| 0.1241 | 0.2833 | 0.9199 | 0.9223 | 0.9199 | 0.9200 | 3 |
| 0.1179 | 0.2876 | 0.9235 | 0.9249 | 0.9235 | 0.9237 | 4 |
### Framework versions
- Transformers 4.51.3
- TensorFlow 2.18.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"dew",
"fogsmog",
"frost",
"glaze",
"hail",
"lightning",
"rain",
"rainbow",
"rime",
"sandstorm",
"snow"
] |
juju5u5yp/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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4056
- Accuracy: 0.8339
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.119 | 1.0 | 20 | 0.9685 | 0.5018 |
| 0.6339 | 2.0 | 40 | 1.0183 | 0.6236 |
| 0.4452 | 3.0 | 60 | 0.4056 | 0.8339 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
[
"tumor",
"unlabeled",
"cyst",
"normal",
"stone"
] |
ccclllwww/smoker_cls_base_V3
|
<!-- 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. -->
# smoker_cls_base_V3
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: 0.4069
- Accuracy: 0.8707
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6292 | 1.0 | 17 | 0.5127 | 0.7072 |
| 0.4602 | 2.0 | 34 | 0.4405 | 0.8631 |
| 0.414 | 2.8485 | 48 | 0.4076 | 0.8764 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_large_V3
|
<!-- 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. -->
# smoker_cls_large_V3
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1317
- Accuracy: 0.9307
## 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 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5211 | 1.0 | 15 | 0.2070 | 0.9208 |
| 0.1693 | 2.0 | 30 | 0.1180 | 0.9703 |
| 0.1058 | 2.8421 | 42 | 0.1554 | 0.9505 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_base_V4
|
<!-- 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. -->
# smoker_cls_base_V4
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: 0.3615
- Accuracy: 0.8812
## 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 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6591 | 1.0 | 15 | 0.5081 | 0.8911 |
| 0.3677 | 2.0 | 30 | 0.3530 | 0.9010 |
| 0.3273 | 2.8421 | 42 | 0.3459 | 0.8911 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_base_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. -->
# smoker_cls_base_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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2048
- Accuracy: 0.9406
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6908 | 1.0 | 15 | 0.5521 | 0.8416 |
| 0.3849 | 2.0 | 30 | 0.3459 | 0.8812 |
| 0.3025 | 3.0 | 45 | 0.2673 | 0.9109 |
| 0.2061 | 4.0 | 60 | 0.1941 | 0.9505 |
| 0.2003 | 4.7018 | 70 | 0.2054 | 0.9406 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_large_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. -->
# smoker_cls_large_V5
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1352
- Accuracy: 0.9505
## 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 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5132 | 1.0 | 15 | 0.2900 | 0.8515 |
| 0.1768 | 2.0 | 30 | 0.1087 | 0.9703 |
| 0.1168 | 2.8421 | 42 | 0.1495 | 0.9406 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_base_MS_V1
|
<!-- 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. -->
# smoker_cls_base_MS_V1
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: 0.6667
- Accuracy: 0.7624
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6939 | 1.0 | 15 | 0.6830 | 0.6436 |
| 0.633 | 2.0 | 30 | 0.6732 | 0.7030 |
| 0.6766 | 3.0 | 45 | 0.6678 | 0.7030 |
| 0.6208 | 4.0 | 60 | 0.6704 | 0.7129 |
| 0.6698 | 4.7018 | 70 | 0.6696 | 0.6733 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
yagizkilicarslan/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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1989
- 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3785 | 1.0 | 370 | 0.2845 | 0.9310 |
| 0.2124 | 2.0 | 740 | 0.2249 | 0.9296 |
| 0.163 | 3.0 | 1110 | 0.2004 | 0.9432 |
| 0.1401 | 4.0 | 1480 | 0.1967 | 0.9418 |
| 0.138 | 5.0 | 1850 | 0.1937 | 0.9445 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"british shorthair",
"beagle",
"scottish terrier",
"persian",
"birman",
"siamese",
"basset hound",
"pug",
"staffordshire bull terrier",
"chihuahua",
"sphynx",
"ragdoll",
"wheaten terrier",
"keeshond",
"egyptian mau",
"havanese",
"miniature pinscher",
"german shorthaired",
"american bulldog",
"shiba inu",
"english cocker spaniel",
"pomeranian",
"yorkshire terrier",
"american pit bull terrier",
"saint bernard",
"english setter",
"russian blue",
"japanese chin",
"great pyrenees",
"maine coon",
"bombay",
"newfoundland",
"samoyed",
"boxer",
"leonberger",
"bengal",
"abyssinian"
] |
ccclllwww/smoker_cls_base_V6
|
<!-- 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. -->
# smoker_cls_base_V6
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: 0.1984
- Accuracy: 0.9505
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.669 | 1.0 | 15 | 0.5135 | 0.9109 |
| 0.3698 | 2.0 | 30 | 0.3192 | 0.9208 |
| 0.2882 | 3.0 | 45 | 0.2819 | 0.8911 |
| 0.1957 | 4.0 | 60 | 0.1932 | 0.9406 |
| 0.2011 | 4.7018 | 70 | 0.2157 | 0.9208 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_base_MS_V2
|
<!-- 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. -->
# smoker_cls_base_MS_V2
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: 0.6767
- Accuracy: 0.6832
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6949 | 1.0 | 15 | 0.6926 | 0.5248 |
| 0.6357 | 2.0 | 30 | 0.6824 | 0.6436 |
| 0.6817 | 3.0 | 45 | 0.6812 | 0.6733 |
| 0.6273 | 4.0 | 60 | 0.6806 | 0.6436 |
| 0.6787 | 4.7018 | 70 | 0.6759 | 0.7228 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_base_V7
|
<!-- 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. -->
# smoker_cls_base_V7
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: 0.2327
- Accuracy: 0.9109
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6845 | 1.0 | 15 | 0.5545 | 0.8515 |
| 0.3824 | 2.0 | 30 | 0.3374 | 0.8911 |
| 0.2909 | 3.0 | 45 | 0.2966 | 0.8713 |
| 0.2165 | 4.0 | 60 | 0.2239 | 0.9208 |
| 0.2055 | 4.7018 | 70 | 0.2405 | 0.9208 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_base_MS_V3
|
<!-- 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. -->
# smoker_cls_base_MS_V3
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: 0.6777
- Accuracy: 0.6931
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6949 | 1.0 | 15 | 0.6924 | 0.5248 |
| 0.6355 | 2.0 | 30 | 0.6834 | 0.6436 |
| 0.682 | 3.0 | 45 | 0.6813 | 0.6535 |
| 0.6271 | 4.0 | 60 | 0.6814 | 0.6436 |
| 0.6788 | 4.7018 | 70 | 0.6767 | 0.7129 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_large_V7
|
<!-- 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. -->
# smoker_cls_large_V7
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1285
- Accuracy: 0.9604
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4729 | 1.0 | 15 | 0.2574 | 0.8812 |
| 0.1518 | 2.0 | 30 | 0.1074 | 0.9703 |
| 0.1084 | 3.0 | 45 | 0.2192 | 0.9010 |
| 0.069 | 4.0 | 60 | 0.1042 | 0.9505 |
| 0.0617 | 4.7018 | 70 | 0.1612 | 0.9010 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
siyoulee/finetuned-lora-food101
|
# 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]
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare",
"waffles"
] |
ccclllwww/smoker_cls_base_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. -->
# smoker_cls_base_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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2000
- Accuracy: 0.9208
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6548 | 1.0 | 15 | 0.4615 | 0.9406 |
| 0.3297 | 2.0 | 30 | 0.2549 | 0.9307 |
| 0.249 | 3.0 | 45 | 0.2352 | 0.9208 |
| 0.175 | 4.0 | 60 | 0.1806 | 0.9505 |
| 0.1756 | 4.7018 | 70 | 0.2199 | 0.9307 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
ccclllwww/smoker_cls_base_MS_V4
|
<!-- 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. -->
# smoker_cls_base_MS_V4
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: 0.6776
- Accuracy: 0.7129
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6949 | 1.0 | 15 | 0.6937 | 0.5149 |
| 0.6349 | 2.0 | 30 | 0.6832 | 0.6139 |
| 0.6812 | 3.0 | 45 | 0.6810 | 0.6733 |
| 0.6266 | 4.0 | 60 | 0.6807 | 0.6733 |
| 0.6781 | 4.7018 | 70 | 0.6758 | 0.7426 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
shingguy1/fine_tuned_convnext
|
# 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]
|
[
"pizza",
"hamburger",
"sushi",
"caesar_salad",
"spaghetti_bolognese",
"ice_cream",
"fried_rice",
"tacos",
"steak",
"chocolate_cake"
] |
shingguy1/fine_tuned_vit
|
# 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
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[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]
|
[
"pizza",
"hamburger",
"sushi",
"caesar_salad",
"spaghetti_bolognese",
"ice_cream",
"fried_rice",
"tacos",
"steak",
"chocolate_cake"
] |
prithivMLmods/open-deepfake-detection
|
# open-deepfake-detection
> open-deepfake-detection is a vision-language encoder model fine-tuned from `siglip2-base-patch16-512` for binary image classification. It is trained to detect whether an image is fake or real using the *OpenDeepfake-Preview* dataset. The model uses the `SiglipForImageClassification` architecture.
> \[!note]
> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
> \[!important]
Experimental Model
```py
Classification Report:
precision recall f1-score support
Fake 0.9718 0.9155 0.9428 10000
Real 0.9201 0.9734 0.9460 9999
accuracy 0.9444 19999
macro avg 0.9459 0.9444 0.9444 19999
weighted avg 0.9459 0.9444 0.9444 19999
```
---
## Label Space: 2 Classes
The model classifies an image as either:
```
Class 0: Fake
Class 1: Real
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/open-deepfake-detection" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "Fake",
"1": "Real"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="Deepfake Detection"),
title="open-deepfake-detection",
description="Upload an image to detect whether it is AI-generated (Fake) or a real photograph (Real), using the OpenDeepfake-Preview dataset."
)
if __name__ == "__main__":
iface.launch()
```
---
## Demo Inference
> [!warning]
real
> [!warning]
fake
## Intended Use
`open-deepfake-detection` is designed for:
* **Deepfake Detection** – Identify AI-generated or manipulated images.
* **Content Moderation** – Flag synthetic or fake visual content.
* **Dataset Curation** – Remove synthetic samples from mixed datasets.
* **Visual Authenticity Verification** – Check the integrity of visual media.
* **Digital Forensics** – Support image source verification and traceability.
|
[
"fake",
"real"
] |
juju5u5yp/swin-small-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-small-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-small-patch4-window7-224](https://huggingface.co/microsoft/swin-small-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9061
- Accuracy: 0.7232
## 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: 32
- eval_batch_size: 32
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1265 | 1.0 | 77 | 1.1283 | 0.6531 |
| 1.0442 | 2.0 | 154 | 1.0860 | 0.6568 |
| 1.0043 | 3.0 | 231 | 1.0477 | 0.6605 |
| 0.9986 | 4.0 | 308 | 1.0174 | 0.6900 |
| 1.0202 | 5.0 | 385 | 0.9889 | 0.6863 |
| 0.9499 | 6.0 | 462 | 0.9659 | 0.7011 |
| 0.9138 | 7.0 | 539 | 0.9453 | 0.7122 |
| 0.9211 | 8.0 | 616 | 0.9305 | 0.7122 |
| 0.9263 | 9.0 | 693 | 0.9194 | 0.7196 |
| 0.8678 | 10.0 | 770 | 0.9114 | 0.7232 |
| 0.9109 | 11.0 | 847 | 0.9071 | 0.7196 |
| 0.9316 | 12.0 | 924 | 0.9061 | 0.7232 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"unlabeled",
"cyst",
"normal",
"stone",
"tumor"
] |
Tharsana/vit-brain-tumor
|
<!-- 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-brain-tumor
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 custom/brain-tumor dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4552
- 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: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6298 | 1.0 | 1478 | 0.6281 | 0.7495 |
| 0.5911 | 2.0 | 2956 | 0.5900 | 0.7920 |
| 0.5705 | 3.0 | 4434 | 0.5593 | 0.8103 |
| 0.5091 | 4.0 | 5912 | 0.5358 | 0.8237 |
| 0.5124 | 5.0 | 7390 | 0.5169 | 0.8343 |
| 0.4631 | 6.0 | 8868 | 0.5029 | 0.8467 |
| 0.4834 | 7.0 | 10346 | 0.4907 | 0.8515 |
| 0.4724 | 8.0 | 11824 | 0.4804 | 0.8541 |
| 0.4702 | 9.0 | 13302 | 0.4715 | 0.8549 |
| 0.4162 | 10.0 | 14780 | 0.4653 | 0.8570 |
| 0.4664 | 11.0 | 16258 | 0.4603 | 0.8602 |
| 0.469 | 12.0 | 17736 | 0.4565 | 0.8610 |
| 0.4299 | 13.0 | 19214 | 0.4539 | 0.8621 |
| 0.4191 | 14.0 | 20692 | 0.4522 | 0.8621 |
| 0.4652 | 15.0 | 22170 | 0.4517 | 0.8623 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
[
"no",
"yes"
] |
prithivMLmods/siglip2-mini-explicit-content
|
# **siglip2-mini-explicit-content**
> **siglip2-mini-explicit-content** is an image classification vision-language encoder model fine-tuned from **`siglip2-base-patch16-512`** for a single-label classification task. It is designed to classify images into categories related to explicit, sensual, or safe-for-work content using the **SiglipForImageClassification** architecture.
> \[!Note]
> This model is intended to promote positive, safe, and respectful digital environments. Misuse is strongly discouraged and may violate platform or regional guidelines. As a classification model, it does not generate unsafe content and is suitable for moderation purposes.
> [!note]
*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786
> [!Important]
Note: Explicit, sensual, and pornographic content may appear in the results; however, all of them are considered not safe for work.
```py
Classification Report:
precision recall f1-score support
Anime Picture 0.8897 0.8296 0.8586 5600
Extincing & Sensual 0.8984 0.9477 0.9224 5618
Hentai 0.8993 0.9118 0.9055 5600
Pornography 0.9527 0.9285 0.9404 5970
Safe for Work 0.8957 0.9172 0.9063 6000
accuracy 0.9074 28788
macro avg 0.9072 0.9069 0.9066 28788
weighted avg 0.9076 0.9074 0.9071 28788
```
---
The model categorizes images into five classes:
* **Class 0:** Anime Picture
* **Class 1:** Extincing & Sensual
* **Class 2:** Hentai
* **Class 3:** Pornography
* **Class 4:** Safe for Work
---
# **Run with Transformers**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/siglip2-mini-explicit-content"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated labels
labels = {
"0": "Anime Picture",
"1": "Extincing & Sensual",
"2": "Hentai",
"3": "Pornography",
"4": "Safe for Work"
}
def detect_explicit_content(image):
"""Predicts content category in an uploaded image."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Gradio Interface
iface = gr.Interface(
fn=detect_explicit_content,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="siglip2-mini-explicit-content",
description="Upload an image to classify it as Anime, Hentai, Sensual, Pornographic, or Safe for Work."
)
if __name__ == "__main__":
iface.launch()
```
---
# **Guidelines for Use of siglip2-mini-explicit-content**
This model is designed for responsible content moderation and filtering. It is especially tuned for anime, hentai, and adult content. Use it ethically, with the following guidelines:
### **Recommended Use Cases**
* Content Moderation in social media and forums
* Parental Controls for safer browsing environments
* Dataset Curation for removing NSFW images from training data
* Safe Search Filtering for engines and discovery systems
* Workplace Image Scanning for compliance
### **Prohibited or Discouraged Use**
* Harassment, exposure, or targeting of individuals
* Use on private content without consent
* Illegal or unethical surveillance
* Sole reliance for legal or reputational decisions
* Deceptive manipulation of moderation results
---
# **Important Notes**
* Optimized for **anime and adult content detection**. Not suitable for detecting violence, drugs, or hate symbols.
* Probabilistic outputs — always **verify** with human review where needed.
* This model's predictions are **not legal classifications**.
---
## Demo Inference
> [!warning]
Anime Picture
> [!warning]
Extincing & Sensual
> [!warning]
Hentai
> [!warning]
Pornography
> [!warning]
Safe for Work
---
## **Ethical Reminder**
This tool was created to **enhance digital safety**. Do not use it to harm, surveil, or exploit individuals or communities. By using this model, you commit to ethical and privacy-respecting practices.
|
[
"anime picture",
"extincing & sensual",
"hentai",
"pornography",
"safe for work"
] |
prithivMLmods/vit-mini-explicit-content
|
# **vit-mini-explicit-content**
> **vit-mini-explicit-content** is an image classification vision-language model fine-tuned from **vit-base-patch16-224-in21k** for a single-label classification task. It categorizes images based on their explicitness using the **ViTForImageClassification** architecture.
> \[!Note]
> This model is designed to promote safe, respectful, and responsible online spaces. It does **not** generate explicit content; it only classifies images. Misuse may violate platform or regional policies and is strongly discouraged.
> [!Note]
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale : https://arxiv.org/abs/2010.11929, Visual Transformers: Token-based Image Representation and Processing for Computer Vision: https://arxiv.org/pdf/2006.03677
> [!Important]
Note: Explicit, sensual, and pornographic content may appear in the results; however, all of them are considered not safe for work.
```py
Classification Report:
precision recall f1-score support
Anime Picture 0.9077 0.7937 0.8469 5600
Extincing & Sensual 0.9245 0.9717 0.9475 5618
Hentai 0.8680 0.9391 0.9021 5600
Pornography 0.9614 0.9544 0.9579 5970
Safe for Work 0.9235 0.9235 0.9235 6000
accuracy 0.9171 28788
macro avg 0.9170 0.9165 0.9156 28788
weighted avg 0.9177 0.9171 0.9163 28788
```
---
The model categorizes images into five classes:
* **Class 0:** Anime Picture
* **Class 1:** Enticing & Sensual
* **Class 2:** Hentai
* **Class 3:** Pornography
* **Class 4:** Safe for Work
# **Run with Transformers**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/vit-mini-explicit-content" # Updated model path
model = ViTForImageClassification.from_pretrained(model_name)
processor = ViTImageProcessor.from_pretrained(model_name)
# Updated label mapping
labels = {
"0": "Anime Picture",
"1": "Enticing & Sensual",
"2": "Hentai",
"3": "Pornography",
"4": "Safe for Work"
}
def explicit_content_detection(image):
"""Predicts the type of content in the image."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=explicit_content_detection,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="vit-mini-explicit-content",
description="Upload an image to classify whether it is anime, enticing & sensual, hentai, pornographic, or safe for work."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
---
## Demo Inference
> [!warning]
Anime Picture
> [!warning]
Extincing & Sensual
> [!warning]
Hentai
> [!warning]
Pornography
> [!warning]
Safe for Work
---
# **Recommended Use Cases**
* Image moderation pipelines
* Parental and institutional content filters
* Dataset cleansing before training
* Online safety and well-being platforms
* Enhancing search engine filtering
# **Discouraged / Prohibited Use**
* Non-consensual or malicious monitoring
* Automated judgments without human review
* Misrepresentation of moderation systems
* Use in unlawful or unethical surveillance
* Harassment, exploitation, or shaming
|
[
"anime picture",
"extincing & sensual",
"hentai",
"pornography",
"safe for work"
] |
Nech-C/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 [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- 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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 51 | 4.5099 | 0.0422 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
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"33",
"34",
"35",
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"38",
"39",
"40",
"41",
"42",
"43",
"44",
"45",
"46",
"47",
"48",
"49",
"50",
"51",
"52",
"53",
"54",
"55",
"56",
"57",
"58",
"59",
"60",
"61",
"62",
"63",
"64",
"65",
"66",
"67",
"68",
"69",
"70",
"71",
"72",
"73",
"74",
"75",
"76",
"77",
"78",
"79",
"80",
"81",
"82",
"83",
"84",
"85",
"86",
"87",
"88",
"89",
"90",
"91",
"92",
"93",
"94",
"95",
"96",
"97"
] |
Amoros/test_dino-large-2025_05_19_65652-bs16_freeze
|
<!-- 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_dino-large-2025_05_19_65652-bs16_freeze
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0573
- F1 Micro: 0.6898
- F1 Macro: 0.1356
- Accuracy: 0.5959
- Learning Rate: 0.0005
## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:------:|
| 0.0771 | 1.0 | 1081 | 0.0632 | 0.6761 | 0.1357 | 0.5803 | 0.0005 |
| 0.0671 | 2.0 | 2162 | 0.0594 | 0.6917 | 0.1381 | 0.6018 | 0.0005 |
| 0.0664 | 3.0 | 3243 | 0.0612 | 0.6820 | 0.1381 | 0.6087 | 0.0005 |
| 0.0636 | 4.0 | 4324 | 0.0574 | 0.6980 | 0.1413 | 0.6080 | 0.0005 |
| 0.063 | 5.0 | 5405 | 0.0581 | 0.6880 | 0.1375 | 0.6032 | 0.0005 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu118
- Datasets 3.0.2
- Tokenizers 0.21.1
|
[
"algae",
"acr",
"acr_br",
"anem",
"cca",
"ech",
"fts",
"gal",
"gon",
"h_oval",
"h_uni",
"mtp",
"p",
"poc",
"por",
"r",
"rdc",
"s",
"sg",
"sarg",
"ser",
"slt",
"sp",
"turf",
"unk"
] |
Gabriel2502/finetuned-pneumonia-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. -->
# finetuned-pneumonia-classification
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: 0.1026
- Accuracy: 0.9663
## 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: 32
- eval_batch_size: 16
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.284 | 0.7634 | 100 | 0.3032 | 0.8830 |
| 0.1528 | 1.5267 | 200 | 0.2668 | 0.9119 |
| 0.1592 | 2.2901 | 300 | 0.1441 | 0.9487 |
| 0.0885 | 3.0534 | 400 | 0.1037 | 0.9663 |
| 0.098 | 3.8168 | 500 | 0.1026 | 0.9663 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"normal",
"pneumonia"
] |
jjj123123/autotrain-agj3j-koite
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.2980284094810486
f1: 0.8873239436619719
precision: 0.9
recall: 0.875
auc: 0.9592592592592593
accuracy: 0.8632478632478633
|
[
"a",
"f"
] |
jjj123123/autotrain-il0fd-y2jyo
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.30006498098373413
f1: 0.9261744966442953
precision: 0.8961038961038961
recall: 0.9583333333333334
auc: 0.9422839506172839
accuracy: 0.905982905982906
|
[
"a",
"f"
] |
ricardoSLabs/gender_mozilla_mel_spec_Vit_swin-tiny-patch4-window7-224_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. -->
# gender_mozilla_mel_spec_Vit_swin-tiny-patch4-window7-224_1
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: 0.1463
- Accuracy: 0.9533
## 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 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6887 | 1.0 | 11 | 0.5200 | 0.8367 |
| 0.3893 | 2.0 | 22 | 0.2368 | 0.92 |
| 0.2526 | 3.0 | 33 | 0.1906 | 0.94 |
| 0.1812 | 4.0 | 44 | 0.1990 | 0.93 |
| 0.158 | 5.0 | 55 | 0.2096 | 0.92 |
| 0.1333 | 6.0 | 66 | 0.1821 | 0.9333 |
| 0.1249 | 7.0 | 77 | 0.1546 | 0.95 |
| 0.0678 | 8.0 | 88 | 0.1535 | 0.9567 |
| 0.0524 | 9.0 | 99 | 0.1599 | 0.9433 |
| 0.0359 | 10.0 | 110 | 0.1463 | 0.9533 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
[
"female",
"male"
] |
ricardoSLabs/gender_mozilla_mel_spec_Vit_vit-tiny-patch16-224_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. -->
# gender_mozilla_mel_spec_Vit_vit-tiny-patch16-224_1
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3420
- Accuracy: 0.9367
## 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 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6724 | 1.0 | 11 | 0.3985 | 0.87 |
| 0.3071 | 2.0 | 22 | 0.2514 | 0.92 |
| 0.1861 | 3.0 | 33 | 0.2074 | 0.93 |
| 0.1192 | 4.0 | 44 | 0.2194 | 0.94 |
| 0.0655 | 5.0 | 55 | 0.2362 | 0.9367 |
| 0.0268 | 6.0 | 66 | 0.2645 | 0.9333 |
| 0.0239 | 7.0 | 77 | 0.3006 | 0.9333 |
| 0.0049 | 8.0 | 88 | 0.3445 | 0.9333 |
| 0.007 | 9.0 | 99 | 0.3609 | 0.93 |
| 0.0005 | 10.0 | 110 | 0.3420 | 0.9367 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
[
"female",
"male"
] |
ricardoSLabs/gender_mozilla_mel_spec_Vit_swin-tiny-patch4-window7-224_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. -->
# gender_mozilla_mel_spec_Vit_swin-tiny-patch4-window7-224_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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1979
- Accuracy: 0.9433
## 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 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.67 | 1.0 | 11 | 0.4961 | 0.8433 |
| 0.3765 | 2.0 | 22 | 0.2358 | 0.9267 |
| 0.2019 | 3.0 | 33 | 0.2070 | 0.92 |
| 0.1496 | 4.0 | 44 | 0.2166 | 0.9367 |
| 0.1091 | 5.0 | 55 | 0.1860 | 0.93 |
| 0.0807 | 6.0 | 66 | 0.1865 | 0.9533 |
| 0.058 | 7.0 | 77 | 0.1981 | 0.9467 |
| 0.0398 | 8.0 | 88 | 0.1964 | 0.9467 |
| 0.0308 | 9.0 | 99 | 0.2150 | 0.9433 |
| 0.019 | 10.0 | 110 | 0.1979 | 0.9433 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
[
"female",
"male"
] |
ricardoSLabs/gender_mozilla_mel_spec_Vit_vit-tiny-patch16-224_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. -->
# gender_mozilla_mel_spec_Vit_vit-tiny-patch16-224_2
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3420
- Accuracy: 0.9367
## 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 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6724 | 1.0 | 11 | 0.3985 | 0.87 |
| 0.3071 | 2.0 | 22 | 0.2514 | 0.92 |
| 0.1861 | 3.0 | 33 | 0.2074 | 0.93 |
| 0.1192 | 4.0 | 44 | 0.2194 | 0.94 |
| 0.0655 | 5.0 | 55 | 0.2362 | 0.9367 |
| 0.0268 | 6.0 | 66 | 0.2645 | 0.9333 |
| 0.0239 | 7.0 | 77 | 0.3006 | 0.9333 |
| 0.0049 | 8.0 | 88 | 0.3445 | 0.9333 |
| 0.007 | 9.0 | 99 | 0.3609 | 0.93 |
| 0.0005 | 10.0 | 110 | 0.3420 | 0.9367 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
[
"female",
"male"
] |
ricardoSLabs/id_ravdess_mel_spec_Vit_swin-tiny-patch4-window7-224_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. -->
# id_ravdess_mel_spec_Vit_swin-tiny-patch4-window7-224_1
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: 0.3684
- Accuracy: 0.8935
## 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 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 3.1637 | 0.0509 |
| 3.1954 | 2.0 | 16 | 3.0952 | 0.1343 |
| 3.1006 | 3.0 | 24 | 2.9019 | 0.2083 |
| 2.8528 | 4.0 | 32 | 2.5496 | 0.3611 |
| 2.3055 | 5.0 | 40 | 1.9709 | 0.4583 |
| 2.3055 | 6.0 | 48 | 1.4990 | 0.5880 |
| 1.5571 | 7.0 | 56 | 1.2140 | 0.6898 |
| 1.0455 | 8.0 | 64 | 1.0802 | 0.6574 |
| 0.7066 | 9.0 | 72 | 0.8690 | 0.75 |
| 0.4544 | 10.0 | 80 | 0.6908 | 0.7778 |
| 0.4544 | 11.0 | 88 | 0.5625 | 0.8102 |
| 0.2632 | 12.0 | 96 | 0.5499 | 0.8194 |
| 0.1741 | 13.0 | 104 | 0.5504 | 0.8056 |
| 0.1183 | 14.0 | 112 | 0.5433 | 0.8056 |
| 0.0809 | 15.0 | 120 | 0.5394 | 0.8194 |
| 0.0809 | 16.0 | 128 | 0.4215 | 0.8565 |
| 0.0663 | 17.0 | 136 | 0.4608 | 0.8704 |
| 0.0487 | 18.0 | 144 | 0.4469 | 0.8426 |
| 0.0433 | 19.0 | 152 | 0.4972 | 0.8519 |
| 0.0366 | 20.0 | 160 | 0.3546 | 0.8796 |
| 0.0366 | 21.0 | 168 | 0.4419 | 0.8657 |
| 0.0193 | 22.0 | 176 | 0.5580 | 0.8519 |
| 0.0192 | 23.0 | 184 | 0.5105 | 0.8426 |
| 0.0234 | 24.0 | 192 | 0.3844 | 0.8611 |
| 0.017 | 25.0 | 200 | 0.4979 | 0.875 |
| 0.017 | 26.0 | 208 | 0.4034 | 0.8657 |
| 0.0194 | 27.0 | 216 | 0.4212 | 0.8889 |
| 0.0168 | 28.0 | 224 | 0.4298 | 0.8843 |
| 0.0217 | 29.0 | 232 | 0.3586 | 0.8981 |
| 0.0097 | 30.0 | 240 | 0.4900 | 0.8796 |
| 0.0097 | 31.0 | 248 | 0.4183 | 0.8565 |
| 0.0057 | 32.0 | 256 | 0.7694 | 0.8380 |
| 0.0133 | 33.0 | 264 | 0.4215 | 0.8657 |
| 0.0198 | 34.0 | 272 | 0.5039 | 0.8657 |
| 0.0113 | 35.0 | 280 | 0.4496 | 0.8796 |
| 0.0113 | 36.0 | 288 | 0.4230 | 0.8889 |
| 0.0026 | 37.0 | 296 | 0.5184 | 0.8565 |
| 0.0095 | 38.0 | 304 | 0.4038 | 0.8889 |
| 0.0059 | 39.0 | 312 | 0.4309 | 0.875 |
| 0.0097 | 40.0 | 320 | 0.6481 | 0.8148 |
| 0.0097 | 41.0 | 328 | 0.3568 | 0.8935 |
| 0.0058 | 42.0 | 336 | 0.3418 | 0.9259 |
| 0.0035 | 43.0 | 344 | 0.3504 | 0.9213 |
| 0.0042 | 44.0 | 352 | 0.3305 | 0.9167 |
| 0.0018 | 45.0 | 360 | 0.3697 | 0.9028 |
| 0.0018 | 46.0 | 368 | 0.3399 | 0.9028 |
| 0.0049 | 47.0 | 376 | 0.3395 | 0.9028 |
| 0.002 | 48.0 | 384 | 0.3574 | 0.9028 |
| 0.0031 | 49.0 | 392 | 0.3696 | 0.8981 |
| 0.0011 | 50.0 | 400 | 0.3684 | 0.8935 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
[
"actor_01",
"actor_02",
"actor_11",
"actor_12",
"actor_13",
"actor_14",
"actor_15",
"actor_16",
"actor_17",
"actor_18",
"actor_19",
"actor_20",
"actor_03",
"actor_21",
"actor_22",
"actor_23",
"actor_24",
"actor_04",
"actor_05",
"actor_06",
"actor_07",
"actor_08",
"actor_09",
"actor_10"
] |
ricardoSLabs/id_ravdess_mel_spec_Vit_vit-tiny-patch16-224_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. -->
# id_ravdess_mel_spec_Vit_vit-tiny-patch16-224_1
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8036
- 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 3.5290 | 0.0231 |
| 3.7813 | 2.0 | 16 | 3.1577 | 0.0880 |
| 3.1834 | 3.0 | 24 | 2.9675 | 0.1435 |
| 2.8849 | 4.0 | 32 | 2.6636 | 0.2037 |
| 2.3044 | 5.0 | 40 | 2.2016 | 0.3565 |
| 2.3044 | 6.0 | 48 | 1.8205 | 0.4444 |
| 1.5789 | 7.0 | 56 | 1.5203 | 0.5417 |
| 1.0355 | 8.0 | 64 | 1.3309 | 0.6481 |
| 0.5941 | 9.0 | 72 | 1.2203 | 0.6528 |
| 0.3301 | 10.0 | 80 | 1.1015 | 0.6806 |
| 0.3301 | 11.0 | 88 | 1.0155 | 0.7176 |
| 0.1632 | 12.0 | 96 | 0.9678 | 0.7454 |
| 0.0791 | 13.0 | 104 | 0.9265 | 0.7454 |
| 0.0347 | 14.0 | 112 | 0.8884 | 0.75 |
| 0.0165 | 15.0 | 120 | 0.8745 | 0.7454 |
| 0.0165 | 16.0 | 128 | 0.8517 | 0.7824 |
| 0.007 | 17.0 | 136 | 0.8396 | 0.7593 |
| 0.0033 | 18.0 | 144 | 0.8265 | 0.7917 |
| 0.0019 | 19.0 | 152 | 0.8109 | 0.7685 |
| 0.0013 | 20.0 | 160 | 0.8015 | 0.7917 |
| 0.0013 | 21.0 | 168 | 0.8209 | 0.7917 |
| 0.001 | 22.0 | 176 | 0.7898 | 0.7917 |
| 0.0008 | 23.0 | 184 | 0.8016 | 0.7824 |
| 0.0007 | 24.0 | 192 | 0.8024 | 0.7824 |
| 0.0006 | 25.0 | 200 | 0.8014 | 0.7917 |
| 0.0006 | 26.0 | 208 | 0.7987 | 0.7824 |
| 0.0006 | 27.0 | 216 | 0.8025 | 0.7870 |
| 0.0006 | 28.0 | 224 | 0.8009 | 0.7824 |
| 0.0005 | 29.0 | 232 | 0.8001 | 0.7824 |
| 0.0005 | 30.0 | 240 | 0.8014 | 0.7870 |
| 0.0005 | 31.0 | 248 | 0.8025 | 0.7824 |
| 0.0005 | 32.0 | 256 | 0.8013 | 0.7824 |
| 0.0004 | 33.0 | 264 | 0.8026 | 0.7824 |
| 0.0004 | 34.0 | 272 | 0.8004 | 0.7824 |
| 0.0004 | 35.0 | 280 | 0.8027 | 0.7870 |
| 0.0004 | 36.0 | 288 | 0.8011 | 0.7824 |
| 0.0004 | 37.0 | 296 | 0.8036 | 0.7917 |
| 0.0004 | 38.0 | 304 | 0.8027 | 0.7824 |
| 0.0004 | 39.0 | 312 | 0.8028 | 0.7870 |
| 0.0004 | 40.0 | 320 | 0.8031 | 0.7824 |
| 0.0004 | 41.0 | 328 | 0.8037 | 0.7824 |
| 0.0004 | 42.0 | 336 | 0.8028 | 0.7870 |
| 0.0003 | 43.0 | 344 | 0.8027 | 0.7870 |
| 0.0003 | 44.0 | 352 | 0.8028 | 0.7824 |
| 0.0003 | 45.0 | 360 | 0.8034 | 0.7870 |
| 0.0003 | 46.0 | 368 | 0.8032 | 0.7870 |
| 0.0003 | 47.0 | 376 | 0.8031 | 0.7917 |
| 0.0003 | 48.0 | 384 | 0.8034 | 0.7870 |
| 0.0003 | 49.0 | 392 | 0.8037 | 0.7917 |
| 0.0003 | 50.0 | 400 | 0.8036 | 0.7917 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
[
"actor_01",
"actor_02",
"actor_11",
"actor_12",
"actor_13",
"actor_14",
"actor_15",
"actor_16",
"actor_17",
"actor_18",
"actor_19",
"actor_20",
"actor_03",
"actor_21",
"actor_22",
"actor_23",
"actor_24",
"actor_04",
"actor_05",
"actor_06",
"actor_07",
"actor_08",
"actor_09",
"actor_10"
] |
shingguy1/fine_tuned_efficientnet_b0
|
# 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.
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## Uses
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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).
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|
[
"pizza",
"hamburger",
"sushi",
"caesar_salad",
"spaghetti_bolognese",
"ice_cream",
"fried_rice",
"tacos",
"steak",
"chocolate_cake"
] |
prithivMLmods/open-age-detection
|
# open-age-detection
> `open-age-detection` is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to classify the estimated age group of a person from an image. The model uses the `SiglipForImageClassification` architecture.
> \[!note]
> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
```py
Classification Report:
precision recall f1-score support
Child 0-12 0.9827 0.9859 0.9843 2193
Teenager 13-20 0.9663 0.8713 0.9163 1779
Adult 21-44 0.9669 0.9884 0.9775 9999
Middle Age 45-64 0.9665 0.9538 0.9601 3785
Aged 65+ 0.9737 0.9706 0.9722 1260
accuracy 0.9691 19016
macro avg 0.9713 0.9540 0.9621 19016
weighted avg 0.9691 0.9691 0.9688 19016
```
---
## Label Space: 5 Age Groups
```
Class 0: Child 0–12
Class 1: Teenager 13–20
Class 2: Adult 21–44
Class 3: Middle Age 45–64
Class 4: Aged 65+
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/open-age-detection" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "Child 0-12",
"1": "Teenager 13-20",
"2": "Adult 21-44",
"3": "Middle Age 45-64",
"4": "Aged 65+"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=5, label="Age Group Detection"),
title="open-age-detection",
description="Upload a facial image to estimate the age group: Child, Teenager, Adult, Middle Age, or Aged."
)
if __name__ == "__main__":
iface.launch()
```
---
## Demo Inference
---
## Intended Use
`open-age-detection` is designed for:
* **Demographic Analysis** – Estimate age groups for statistical or analytical applications.
* **Smart Personalization** – Age-based content or product recommendation.
* **Access Control** – Assist systems requiring age verification.
* **Social Research** – Study age-related trends in image datasets.
* **Surveillance and Security** – Profile age ranges in monitored environments.
|
[
"child 0-12",
"teenager 13-20",
"adult 21-44",
"middle age 45-64",
"aged 65+"
] |
Nbfit/smoker_cls_base_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. -->
# smoker_cls_base_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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2012
- Accuracy: 0.9109
## 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 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6548 | 1.0 | 15 | 0.4618 | 0.9307 |
| 0.3297 | 2.0 | 30 | 0.2515 | 0.9307 |
| 0.247 | 3.0 | 45 | 0.2294 | 0.9307 |
| 0.1701 | 4.0 | 60 | 0.1895 | 0.9505 |
| 0.173 | 4.7018 | 70 | 0.2165 | 0.9208 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_smoking",
"smoking"
] |
Haaaaaaaaaax/dinov2-Base-finetuned-food101
|
<!-- 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. -->
# dinov2-Base-finetuned-food101
This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0924
- Accuracy: 0.98
- F1: 0.9809
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.675 | 1.0 | 625 | 0.1778 | 0.938 | 0.9399 |
| 0.5284 | 2.0 | 1250 | 0.1248 | 0.9613 | 0.9615 |
| 0.3455 | 3.0 | 1875 | 0.1548 | 0.9513 | 0.9523 |
| 0.1915 | 4.0 | 2500 | 0.1321 | 0.9567 | 0.9586 |
| 0.1953 | 5.0 | 3125 | 0.0772 | 0.978 | 0.9784 |
| 0.183 | 6.0 | 3750 | 0.0829 | 0.9773 | 0.9781 |
| 0.1513 | 7.0 | 4375 | 0.1134 | 0.972 | 0.9735 |
| 0.1222 | 8.0 | 5000 | 0.1164 | 0.972 | 0.9729 |
| 0.2001 | 9.0 | 5625 | 0.0865 | 0.9773 | 0.9783 |
| 0.0618 | 10.0 | 6250 | 0.0924 | 0.98 | 0.9809 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare",
"waffles"
] |
Haaaaaaaaaax/google-vit-Base-finetuned-food101
|
<!-- 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. -->
# google-vit-Base-finetuned-food101
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: 0.5531
- Accuracy: 0.9727
- F1: 0.9740
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 3.4867 | 1.0 | 157 | 3.2502 | 0.8793 | 0.8778 |
| 2.0982 | 2.0 | 314 | 1.9278 | 0.9353 | 0.9355 |
| 1.5644 | 3.0 | 471 | 1.3466 | 0.9633 | 0.9639 |
| 1.1841 | 4.0 | 628 | 1.0414 | 0.9653 | 0.9662 |
| 1.0235 | 5.0 | 785 | 0.8482 | 0.9687 | 0.9696 |
| 0.8153 | 6.0 | 942 | 0.7218 | 0.9673 | 0.9688 |
| 0.7418 | 7.0 | 1099 | 0.6439 | 0.9687 | 0.9700 |
| 0.6897 | 8.0 | 1256 | 0.5831 | 0.972 | 0.9730 |
| 0.6453 | 9.0 | 1413 | 0.5531 | 0.9727 | 0.9740 |
| 0.6199 | 9.9408 | 1560 | 0.5457 | 0.9707 | 0.9720 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare",
"waffles"
] |
Amoros/Amoros_Beaugosse_test-large-2025_05_20_57422-bs16_freeze
|
<!-- 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. -->
# Amoros_Beaugosse_test-large-2025_05_20_57422-bs16_freeze
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0987
- F1 Micro: 0.5482
- F1 Macro: 0.3454
- Accuracy: 0.4313
- Learning Rate: 1e-05
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.8014 | 0.1708 | 0.1167 | 0.1568 | 0.001 |
| No log | 2.0 | 390 | 0.1238 | 0.3956 | 0.1496 | 0.2677 | 0.001 |
| 0.2531 | 3.0 | 585 | 0.1158 | 0.4517 | 0.1890 | 0.3270 | 0.001 |
| 0.2531 | 4.0 | 780 | 0.1146 | 0.4248 | 0.1657 | 0.2945 | 0.001 |
| 0.2531 | 5.0 | 975 | 0.1105 | 0.4866 | 0.2335 | 0.3652 | 0.001 |
| 0.1272 | 6.0 | 1170 | 0.1105 | 0.4681 | 0.2026 | 0.3442 | 0.001 |
| 0.1272 | 7.0 | 1365 | 0.1122 | 0.4576 | 0.2113 | 0.3308 | 0.001 |
| 0.1158 | 8.0 | 1560 | 0.1062 | 0.4813 | 0.2474 | 0.3461 | 0.001 |
| 0.1158 | 9.0 | 1755 | 0.1058 | 0.4863 | 0.2428 | 0.3537 | 0.001 |
| 0.1158 | 10.0 | 1950 | 0.1075 | 0.4813 | 0.2409 | 0.3480 | 0.001 |
| 0.1126 | 11.0 | 2145 | 0.1058 | 0.4942 | 0.2747 | 0.3595 | 0.001 |
| 0.1126 | 12.0 | 2340 | 0.1065 | 0.4944 | 0.2533 | 0.3738 | 0.001 |
| 0.1098 | 13.0 | 2535 | 0.1071 | 0.4823 | 0.2584 | 0.3614 | 0.001 |
| 0.1098 | 14.0 | 2730 | 0.1086 | 0.5022 | 0.2807 | 0.3776 | 0.001 |
| 0.1098 | 15.0 | 2925 | 0.1056 | 0.5032 | 0.2649 | 0.3767 | 0.001 |
| 0.108 | 16.0 | 3120 | 0.1077 | 0.5106 | 0.2838 | 0.3958 | 0.001 |
| 0.108 | 17.0 | 3315 | 0.1072 | 0.4892 | 0.2847 | 0.3709 | 0.001 |
| 0.1064 | 18.0 | 3510 | 0.1057 | 0.4962 | 0.2752 | 0.3709 | 0.001 |
| 0.1064 | 19.0 | 3705 | 0.1053 | 0.4917 | 0.2690 | 0.3671 | 0.001 |
| 0.1064 | 20.0 | 3900 | 0.1052 | 0.5016 | 0.2853 | 0.3738 | 0.001 |
| 0.1047 | 21.0 | 4095 | 0.1064 | 0.5085 | 0.2684 | 0.3728 | 0.001 |
| 0.1047 | 22.0 | 4290 | 0.1057 | 0.5067 | 0.3060 | 0.3767 | 0.001 |
| 0.1047 | 23.0 | 4485 | 0.1059 | 0.5094 | 0.3023 | 0.3862 | 0.001 |
| 0.1049 | 24.0 | 4680 | 0.1055 | 0.5167 | 0.3091 | 0.3910 | 0.001 |
| 0.1049 | 25.0 | 4875 | 0.1059 | 0.4856 | 0.3083 | 0.3614 | 0.001 |
| 0.1037 | 26.0 | 5070 | 0.1073 | 0.4931 | 0.2853 | 0.3738 | 0.001 |
| 0.1037 | 27.0 | 5265 | 0.1006 | 0.5214 | 0.3116 | 0.3948 | 0.0001 |
| 0.1037 | 28.0 | 5460 | 0.1003 | 0.5369 | 0.3350 | 0.4092 | 0.0001 |
| 0.0968 | 29.0 | 5655 | 0.1000 | 0.5425 | 0.3217 | 0.4197 | 0.0001 |
| 0.0968 | 30.0 | 5850 | 0.0993 | 0.5555 | 0.3540 | 0.4331 | 0.0001 |
| 0.0932 | 31.0 | 6045 | 0.0993 | 0.5562 | 0.3749 | 0.4359 | 0.0001 |
| 0.0932 | 32.0 | 6240 | 0.0990 | 0.5597 | 0.3724 | 0.4369 | 0.0001 |
| 0.0932 | 33.0 | 6435 | 0.0991 | 0.5521 | 0.3543 | 0.4293 | 0.0001 |
| 0.0899 | 34.0 | 6630 | 0.0986 | 0.5636 | 0.3742 | 0.4446 | 0.0001 |
| 0.0899 | 35.0 | 6825 | 0.0990 | 0.5634 | 0.3743 | 0.4455 | 0.0001 |
| 0.0882 | 36.0 | 7020 | 0.0997 | 0.5573 | 0.3655 | 0.4407 | 0.0001 |
| 0.0882 | 37.0 | 7215 | 0.1018 | 0.5635 | 0.3725 | 0.4493 | 0.0001 |
| 0.0882 | 38.0 | 7410 | 0.0988 | 0.5694 | 0.3782 | 0.4541 | 0.0001 |
| 0.0846 | 39.0 | 7605 | 0.0991 | 0.5684 | 0.3795 | 0.4541 | 0.0001 |
| 0.0846 | 40.0 | 7800 | 0.0994 | 0.5692 | 0.3688 | 0.4570 | 0.0001 |
| 0.0846 | 41.0 | 7995 | 0.0995 | 0.5711 | 0.3758 | 0.4551 | 1e-05 |
| 0.0832 | 42.0 | 8190 | 0.0991 | 0.5714 | 0.3770 | 0.4608 | 1e-05 |
| 0.0832 | 43.0 | 8385 | 0.0993 | 0.5753 | 0.3807 | 0.4598 | 1e-05 |
| 0.0824 | 44.0 | 8580 | 0.0988 | 0.5640 | 0.3753 | 0.4503 | 1e-05 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu118
- Datasets 3.0.2
- Tokenizers 0.21.1
|
[
"algae",
"acr",
"anem",
"cca",
"ech",
"fts",
"gal",
"gon",
"mtp",
"p",
"poc",
"por",
"r",
"rdc",
"s",
"sg",
"ser",
"slt",
"sp",
"unk"
] |
Haaaaaaaaaax/efficientnet-b3-finetuned-food101
|
<!-- 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. -->
# efficientnet-b3-finetuned-food101
This model is a fine-tuned version of [google/efficientnet-b3](https://huggingface.co/google/efficientnet-b3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5278
- Accuracy: 0.9
- F1: 0.9042
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 4.1358 | 1.0 | 157 | 3.9867 | 0.328 | 0.3446 |
| 2.5658 | 2.0 | 314 | 2.4812 | 0.71 | 0.7205 |
| 1.5019 | 3.0 | 471 | 1.4903 | 0.806 | 0.8127 |
| 1.1238 | 4.0 | 628 | 0.9173 | 0.834 | 0.8377 |
| 1.0664 | 5.0 | 785 | 0.6943 | 0.8793 | 0.8803 |
| 0.8955 | 6.0 | 942 | 0.6340 | 0.8847 | 0.8874 |
| 0.8461 | 7.0 | 1099 | 0.5398 | 0.8907 | 0.8934 |
| 0.841 | 8.0 | 1256 | 0.5054 | 0.88 | 0.8863 |
| 0.8 | 9.0 | 1413 | 0.5278 | 0.9 | 0.9042 |
| 0.7614 | 9.9408 | 1560 | 0.5160 | 0.896 | 0.9002 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare",
"waffles"
] |
debbysa/vit-emotion-output
|
<!-- 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-emotion-output
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3454
- Accuracy: 0.4875
## 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6596 | 1.0 | 40 | 1.6180 | 0.3187 |
| 0.9457 | 2.0 | 80 | 1.5013 | 0.3375 |
| 0.498 | 3.0 | 120 | 1.3814 | 0.4437 |
| 0.2755 | 4.0 | 160 | 1.3854 | 0.4688 |
| 0.1628 | 5.0 | 200 | 1.3454 | 0.4875 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7"
] |
ricardoSLabs/id_ravdess_mel_spec_Vit_swin-tiny-patch4-window7-224_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. -->
# id_ravdess_mel_spec_Vit_swin-tiny-patch4-window7-224_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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2989
- Accuracy: 0.8981
## 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: 43
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 3.1629 | 0.0463 |
| 3.2006 | 2.0 | 16 | 3.0994 | 0.1343 |
| 3.1171 | 3.0 | 24 | 2.9243 | 0.25 |
| 2.8825 | 4.0 | 32 | 2.5593 | 0.3287 |
| 2.3542 | 5.0 | 40 | 2.0665 | 0.4907 |
| 2.3542 | 6.0 | 48 | 1.6166 | 0.5741 |
| 1.6626 | 7.0 | 56 | 1.2288 | 0.6852 |
| 1.1051 | 8.0 | 64 | 1.0197 | 0.7130 |
| 0.7312 | 9.0 | 72 | 0.8346 | 0.75 |
| 0.4713 | 10.0 | 80 | 0.7557 | 0.7593 |
| 0.4713 | 11.0 | 88 | 0.6095 | 0.8241 |
| 0.2823 | 12.0 | 96 | 0.5716 | 0.8056 |
| 0.1572 | 13.0 | 104 | 0.4956 | 0.8333 |
| 0.1104 | 14.0 | 112 | 0.4822 | 0.8380 |
| 0.0772 | 15.0 | 120 | 0.4910 | 0.8519 |
| 0.0772 | 16.0 | 128 | 0.4082 | 0.8935 |
| 0.0575 | 17.0 | 136 | 0.3986 | 0.875 |
| 0.0366 | 18.0 | 144 | 0.4707 | 0.8241 |
| 0.0287 | 19.0 | 152 | 0.4791 | 0.8426 |
| 0.0252 | 20.0 | 160 | 0.4041 | 0.8704 |
| 0.0252 | 21.0 | 168 | 0.4406 | 0.8519 |
| 0.036 | 22.0 | 176 | 0.3919 | 0.8796 |
| 0.0251 | 23.0 | 184 | 0.4180 | 0.8704 |
| 0.0176 | 24.0 | 192 | 0.3936 | 0.8657 |
| 0.0142 | 25.0 | 200 | 0.3191 | 0.8981 |
| 0.0142 | 26.0 | 208 | 0.3062 | 0.8889 |
| 0.0091 | 27.0 | 216 | 0.3444 | 0.8796 |
| 0.0135 | 28.0 | 224 | 0.4647 | 0.8519 |
| 0.0147 | 29.0 | 232 | 0.3634 | 0.875 |
| 0.0172 | 30.0 | 240 | 0.4332 | 0.875 |
| 0.0172 | 31.0 | 248 | 0.3612 | 0.8704 |
| 0.0111 | 32.0 | 256 | 0.3731 | 0.875 |
| 0.0076 | 33.0 | 264 | 0.4858 | 0.8657 |
| 0.0139 | 34.0 | 272 | 0.3653 | 0.8843 |
| 0.0063 | 35.0 | 280 | 0.4072 | 0.8704 |
| 0.0063 | 36.0 | 288 | 0.3097 | 0.8796 |
| 0.0073 | 37.0 | 296 | 0.3541 | 0.8704 |
| 0.0079 | 38.0 | 304 | 0.3692 | 0.8889 |
| 0.0064 | 39.0 | 312 | 0.3086 | 0.9074 |
| 0.0057 | 40.0 | 320 | 0.3198 | 0.8935 |
| 0.0057 | 41.0 | 328 | 0.3424 | 0.9028 |
| 0.0045 | 42.0 | 336 | 0.2878 | 0.9028 |
| 0.0038 | 43.0 | 344 | 0.2910 | 0.9074 |
| 0.0026 | 44.0 | 352 | 0.2817 | 0.8981 |
| 0.0051 | 45.0 | 360 | 0.2945 | 0.8796 |
| 0.0051 | 46.0 | 368 | 0.2729 | 0.9028 |
| 0.0036 | 47.0 | 376 | 0.2742 | 0.9028 |
| 0.006 | 48.0 | 384 | 0.3017 | 0.8981 |
| 0.0011 | 49.0 | 392 | 0.3064 | 0.8889 |
| 0.0021 | 50.0 | 400 | 0.2989 | 0.8981 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
[
"actor_01",
"actor_02",
"actor_11",
"actor_12",
"actor_13",
"actor_14",
"actor_15",
"actor_16",
"actor_17",
"actor_18",
"actor_19",
"actor_20",
"actor_03",
"actor_21",
"actor_22",
"actor_23",
"actor_24",
"actor_04",
"actor_05",
"actor_06",
"actor_07",
"actor_08",
"actor_09",
"actor_10"
] |
ricardoSLabs/id_ravdess_mel_spec_Vit_vit-tiny-patch16-224_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. -->
# id_ravdess_mel_spec_Vit_vit-tiny-patch16-224_2
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8514
- Accuracy: 0.7870
## 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: 43
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 3.4190 | 0.0324 |
| 3.577 | 2.0 | 16 | 3.1245 | 0.0972 |
| 3.124 | 3.0 | 24 | 2.9168 | 0.1806 |
| 2.7828 | 4.0 | 32 | 2.5031 | 0.2454 |
| 2.1565 | 5.0 | 40 | 2.0137 | 0.3796 |
| 2.1565 | 6.0 | 48 | 1.6643 | 0.5463 |
| 1.4039 | 7.0 | 56 | 1.4283 | 0.5880 |
| 0.8467 | 8.0 | 64 | 1.2302 | 0.6620 |
| 0.471 | 9.0 | 72 | 1.1420 | 0.6574 |
| 0.245 | 10.0 | 80 | 1.0551 | 0.7130 |
| 0.245 | 11.0 | 88 | 1.0008 | 0.7361 |
| 0.1111 | 12.0 | 96 | 0.9660 | 0.75 |
| 0.0515 | 13.0 | 104 | 0.9158 | 0.7407 |
| 0.0227 | 14.0 | 112 | 0.9132 | 0.7407 |
| 0.0106 | 15.0 | 120 | 0.8355 | 0.7685 |
| 0.0106 | 16.0 | 128 | 0.8486 | 0.7639 |
| 0.0042 | 17.0 | 136 | 0.8263 | 0.7778 |
| 0.0021 | 18.0 | 144 | 0.8304 | 0.7731 |
| 0.0013 | 19.0 | 152 | 0.8260 | 0.7824 |
| 0.0009 | 20.0 | 160 | 0.8407 | 0.7731 |
| 0.0009 | 21.0 | 168 | 0.8337 | 0.7824 |
| 0.0008 | 22.0 | 176 | 0.8311 | 0.7824 |
| 0.0006 | 23.0 | 184 | 0.8370 | 0.7778 |
| 0.0006 | 24.0 | 192 | 0.8371 | 0.7778 |
| 0.0005 | 25.0 | 200 | 0.8373 | 0.7870 |
| 0.0005 | 26.0 | 208 | 0.8399 | 0.7870 |
| 0.0005 | 27.0 | 216 | 0.8394 | 0.7870 |
| 0.0005 | 28.0 | 224 | 0.8412 | 0.7824 |
| 0.0004 | 29.0 | 232 | 0.8416 | 0.7870 |
| 0.0004 | 30.0 | 240 | 0.8431 | 0.7870 |
| 0.0004 | 31.0 | 248 | 0.8450 | 0.7824 |
| 0.0004 | 32.0 | 256 | 0.8430 | 0.7870 |
| 0.0004 | 33.0 | 264 | 0.8458 | 0.7824 |
| 0.0003 | 34.0 | 272 | 0.8466 | 0.7870 |
| 0.0003 | 35.0 | 280 | 0.8454 | 0.7870 |
| 0.0003 | 36.0 | 288 | 0.8468 | 0.7824 |
| 0.0003 | 37.0 | 296 | 0.8484 | 0.7870 |
| 0.0003 | 38.0 | 304 | 0.8484 | 0.7870 |
| 0.0003 | 39.0 | 312 | 0.8492 | 0.7824 |
| 0.0003 | 40.0 | 320 | 0.8498 | 0.7870 |
| 0.0003 | 41.0 | 328 | 0.8492 | 0.7870 |
| 0.0003 | 42.0 | 336 | 0.8491 | 0.7870 |
| 0.0003 | 43.0 | 344 | 0.8505 | 0.7870 |
| 0.0003 | 44.0 | 352 | 0.8509 | 0.7870 |
| 0.0003 | 45.0 | 360 | 0.8505 | 0.7870 |
| 0.0003 | 46.0 | 368 | 0.8509 | 0.7870 |
| 0.0003 | 47.0 | 376 | 0.8510 | 0.7870 |
| 0.0003 | 48.0 | 384 | 0.8511 | 0.7870 |
| 0.0003 | 49.0 | 392 | 0.8514 | 0.7870 |
| 0.0003 | 50.0 | 400 | 0.8514 | 0.7870 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
[
"actor_01",
"actor_02",
"actor_11",
"actor_12",
"actor_13",
"actor_14",
"actor_15",
"actor_16",
"actor_17",
"actor_18",
"actor_19",
"actor_20",
"actor_03",
"actor_21",
"actor_22",
"actor_23",
"actor_24",
"actor_04",
"actor_05",
"actor_06",
"actor_07",
"actor_08",
"actor_09",
"actor_10"
] |
mbiarreta/swin-camdeboo-loc
|
<!-- 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-camdeboo-loc
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: 1.7931
- Accuracy: 0.6781
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.706 | 0.0633 | 100 | 1.7448 | 0.4844 |
| 1.3052 | 0.1267 | 200 | 1.9530 | 0.4844 |
| 1.1569 | 0.1900 | 300 | 1.6721 | 0.5363 |
| 1.4451 | 0.2533 | 400 | 1.6504 | 0.5812 |
| 0.9833 | 0.3167 | 500 | 2.2411 | 0.4998 |
| 1.1427 | 0.3800 | 600 | 1.5587 | 0.5720 |
| 1.0228 | 0.4433 | 700 | 1.9815 | 0.5817 |
| 0.8635 | 0.5066 | 800 | 1.6364 | 0.5953 |
| 0.8243 | 0.5700 | 900 | 1.6178 | 0.5834 |
| 0.6203 | 0.6333 | 1000 | 1.5926 | 0.5997 |
| 0.7647 | 0.6966 | 1100 | 1.9691 | 0.5491 |
| 0.6963 | 0.7600 | 1200 | 1.7098 | 0.6055 |
| 0.7896 | 0.8233 | 1300 | 1.6605 | 0.5962 |
| 0.8549 | 0.8866 | 1400 | 1.4914 | 0.6231 |
| 0.6353 | 0.9500 | 1500 | 2.0022 | 0.5495 |
| 0.5124 | 1.0133 | 1600 | 1.8427 | 0.5914 |
| 0.297 | 1.0766 | 1700 | 1.9349 | 0.6090 |
| 0.5861 | 1.1400 | 1800 | 1.7115 | 0.6667 |
| 0.7606 | 1.2033 | 1900 | 2.1216 | 0.6200 |
| 0.1236 | 1.2666 | 2000 | 2.1265 | 0.6196 |
| 0.3057 | 1.3300 | 2100 | 1.7538 | 0.6332 |
| 0.3497 | 1.3933 | 2200 | 2.1055 | 0.6072 |
| 0.5086 | 1.4566 | 2300 | 2.0850 | 0.6433 |
| 0.3447 | 1.5199 | 2400 | 1.8278 | 0.6583 |
| 0.6164 | 1.5833 | 2500 | 1.9431 | 0.6160 |
| 0.3085 | 1.6466 | 2600 | 1.5871 | 0.6874 |
| 0.5517 | 1.7099 | 2700 | 1.7441 | 0.6535 |
| 0.3404 | 1.7733 | 2800 | 1.6698 | 0.6742 |
| 0.1559 | 1.8366 | 2900 | 1.8379 | 0.6706 |
| 0.3857 | 1.8999 | 3000 | 1.8784 | 0.6583 |
| 0.2708 | 1.9633 | 3100 | 1.7931 | 0.6781 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"aardwolf",
"baboon",
"genetcommonsmallspotted",
"harescrub",
"hartebeestred",
"jackalblackbacked",
"klipspringer",
"kudu",
"meerkatsuricate",
"mongooseegyptianlargegrey",
"monkeyvervet",
"ostrich",
"birdofprey",
"porcupine",
"reedbuckmountain",
"rhebokgrey",
"springbok",
"steenbok",
"tortoise",
"wildebeestblack",
"zebramountain",
"birdother",
"blesbok",
"bustardkori",
"duikercommongrey",
"eland",
"foxbateared",
"gemsbokoryx"
] |
Amoros/Amoros_Beaugosse_test-large-2025_05_21_36883-bs16_freeze
|
<!-- 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. -->
# Amoros_Beaugosse_test-large-2025_05_21_36883-bs16_freeze
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0853
- F1 Micro: 0.6248
- F1 Macro: 0.4593
- Accuracy: 0.5213
- Learning Rate: 0.0000
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Rate |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:--------:|:--------:|:------:|
| 0.1213 | 1.0 | 1953 | 0.1046 | 0.4612 | 0.2564 | 0.3234 | 0.001 |
| 0.1152 | 2.0 | 3906 | 0.1038 | 0.4901 | 0.2770 | 0.3573 | 0.001 |
| 0.1147 | 3.0 | 5859 | 0.1009 | 0.4919 | 0.2707 | 0.3565 | 0.001 |
| 0.1141 | 4.0 | 7812 | 0.1008 | 0.4977 | 0.2798 | 0.3596 | 0.001 |
| 0.1127 | 5.0 | 9765 | 0.1019 | 0.5050 | 0.2555 | 0.3705 | 0.001 |
| 0.1131 | 6.0 | 11718 | 0.1001 | 0.5039 | 0.2749 | 0.3700 | 0.001 |
| 0.1133 | 7.0 | 13671 | 0.1004 | 0.5146 | 0.3024 | 0.3790 | 0.001 |
| 0.1128 | 8.0 | 15624 | 0.1004 | 0.5112 | 0.2921 | 0.3798 | 0.001 |
| 0.1119 | 9.0 | 17577 | 0.0984 | 0.5345 | 0.2850 | 0.4081 | 0.001 |
| 0.1145 | 10.0 | 19530 | 0.1012 | 0.5173 | 0.3044 | 0.3820 | 0.001 |
| 0.1128 | 11.0 | 21483 | 0.0998 | 0.5087 | 0.2539 | 0.3724 | 0.001 |
| 0.1131 | 12.0 | 23436 | 0.0987 | 0.5197 | 0.2886 | 0.3841 | 0.001 |
| 0.1109 | 13.0 | 25389 | 0.0980 | 0.5060 | 0.2898 | 0.3651 | 0.001 |
| 0.1122 | 14.0 | 27342 | 0.0994 | 0.5217 | 0.2700 | 0.3878 | 0.001 |
| 0.113 | 15.0 | 29295 | 0.1008 | 0.5238 | 0.2805 | 0.3908 | 0.001 |
| 0.1135 | 16.0 | 31248 | 0.0981 | 0.5208 | 0.2764 | 0.3859 | 0.001 |
| 0.1137 | 17.0 | 33201 | 0.0978 | 0.5259 | 0.2954 | 0.3905 | 0.001 |
| 0.114 | 18.0 | 35154 | 0.0994 | 0.5162 | 0.2774 | 0.3836 | 0.001 |
| 0.1143 | 19.0 | 37107 | 0.0976 | 0.5134 | 0.2685 | 0.3742 | 0.001 |
| 0.1144 | 20.0 | 39060 | 0.0979 | 0.5194 | 0.2862 | 0.3801 | 0.001 |
| 0.1154 | 21.0 | 41013 | 0.1040 | 0.5174 | 0.2748 | 0.3874 | 0.001 |
| 0.1133 | 22.0 | 42966 | 0.0971 | 0.5323 | 0.3003 | 0.4011 | 0.001 |
| 0.114 | 23.0 | 44919 | 0.0982 | 0.5241 | 0.2923 | 0.3912 | 0.001 |
| 0.1113 | 24.0 | 46872 | 0.0985 | 0.5161 | 0.2783 | 0.3793 | 0.001 |
| 0.1132 | 25.0 | 48825 | 0.0996 | 0.5160 | 0.2651 | 0.3792 | 0.001 |
| 0.1127 | 26.0 | 50778 | 0.0987 | 0.5367 | 0.2911 | 0.4103 | 0.001 |
| 0.1135 | 27.0 | 52731 | 0.0987 | 0.5277 | 0.2785 | 0.3952 | 0.001 |
| 0.1133 | 28.0 | 54684 | 0.1018 | 0.5285 | 0.2633 | 0.4042 | 0.001 |
| 0.1064 | 29.0 | 56637 | 0.0944 | 0.5590 | 0.3221 | 0.4321 | 0.0001 |
| 0.1063 | 30.0 | 58590 | 0.0929 | 0.5714 | 0.3412 | 0.4493 | 0.0001 |
| 0.1042 | 31.0 | 60543 | 0.0918 | 0.5832 | 0.3729 | 0.4619 | 0.0001 |
| 0.1038 | 32.0 | 62496 | 0.0912 | 0.5768 | 0.3565 | 0.4528 | 0.0001 |
| 0.103 | 33.0 | 64449 | 0.0904 | 0.5873 | 0.3712 | 0.4670 | 0.0001 |
| 0.1036 | 34.0 | 66402 | 0.0903 | 0.5843 | 0.3731 | 0.4594 | 0.0001 |
| 0.1047 | 35.0 | 68355 | 0.0903 | 0.5869 | 0.3827 | 0.4621 | 0.0001 |
| 0.1026 | 36.0 | 70308 | 0.0897 | 0.5943 | 0.3954 | 0.4729 | 0.0001 |
| 0.1028 | 37.0 | 72261 | 0.0893 | 0.5983 | 0.3983 | 0.4794 | 0.0001 |
| 0.1022 | 38.0 | 74214 | 0.0892 | 0.5997 | 0.4080 | 0.4820 | 0.0001 |
| 0.1012 | 39.0 | 76167 | 0.0889 | 0.6008 | 0.3995 | 0.4822 | 0.0001 |
| 0.1022 | 40.0 | 78120 | 0.0884 | 0.6030 | 0.4085 | 0.4843 | 0.0001 |
| 0.1002 | 41.0 | 80073 | 0.0888 | 0.5940 | 0.3896 | 0.4700 | 0.0001 |
| 0.1008 | 42.0 | 82026 | 0.0880 | 0.6024 | 0.4174 | 0.4807 | 0.0001 |
| 0.0991 | 43.0 | 83979 | 0.0882 | 0.6065 | 0.4184 | 0.4895 | 0.0001 |
| 0.1005 | 44.0 | 85932 | 0.0881 | 0.6047 | 0.4126 | 0.4860 | 0.0001 |
| 0.1001 | 45.0 | 87885 | 0.0878 | 0.6090 | 0.4236 | 0.4940 | 0.0001 |
| 0.0989 | 46.0 | 89838 | 0.0875 | 0.6053 | 0.4205 | 0.4862 | 0.0001 |
| 0.0996 | 47.0 | 91791 | 0.0873 | 0.6069 | 0.4215 | 0.4880 | 0.0001 |
| 0.0986 | 48.0 | 93744 | 0.0869 | 0.6110 | 0.4260 | 0.4938 | 0.0001 |
| 0.0988 | 49.0 | 95697 | 0.0872 | 0.6114 | 0.4155 | 0.4966 | 0.0001 |
| 0.0994 | 50.0 | 97650 | 0.0871 | 0.6136 | 0.4336 | 0.4974 | 0.0001 |
| 0.0998 | 51.0 | 99603 | 0.0872 | 0.6104 | 0.4271 | 0.4947 | 0.0001 |
| 0.0992 | 52.0 | 101556 | 0.0871 | 0.6109 | 0.4289 | 0.4951 | 0.0001 |
| 0.0988 | 53.0 | 103509 | 0.0871 | 0.6137 | 0.4268 | 0.4990 | 0.0001 |
| 0.0977 | 54.0 | 105462 | 0.0867 | 0.6147 | 0.4405 | 0.5006 | 0.0001 |
| 0.0987 | 55.0 | 107415 | 0.0872 | 0.6131 | 0.4344 | 0.4986 | 0.0001 |
| 0.0985 | 56.0 | 109368 | 0.0865 | 0.6122 | 0.4268 | 0.4959 | 0.0001 |
| 0.0982 | 57.0 | 111321 | 0.0864 | 0.6177 | 0.4405 | 0.5041 | 0.0001 |
| 0.0997 | 58.0 | 113274 | 0.0865 | 0.6104 | 0.4243 | 0.4962 | 0.0001 |
| 0.0982 | 59.0 | 115227 | 0.0865 | 0.6165 | 0.4318 | 0.5044 | 0.0001 |
| 0.0973 | 60.0 | 117180 | 0.0868 | 0.6140 | 0.4458 | 0.4993 | 0.0001 |
| 0.0974 | 61.0 | 119133 | 0.0872 | 0.6115 | 0.4204 | 0.4940 | 0.0001 |
| 0.0976 | 62.0 | 121086 | 0.0862 | 0.6121 | 0.4359 | 0.4944 | 0.0001 |
| 0.0977 | 63.0 | 123039 | 0.0865 | 0.6087 | 0.4275 | 0.4900 | 0.0001 |
| 0.0968 | 64.0 | 124992 | 0.0862 | 0.6156 | 0.4383 | 0.5010 | 0.0001 |
| 0.0971 | 65.0 | 126945 | 0.0864 | 0.6151 | 0.4484 | 0.5009 | 0.0001 |
| 0.0968 | 66.0 | 128898 | 0.0857 | 0.6216 | 0.4437 | 0.5099 | 0.0001 |
| 0.0962 | 67.0 | 130851 | 0.0859 | 0.6186 | 0.4409 | 0.5051 | 0.0001 |
| 0.0977 | 68.0 | 132804 | 0.0859 | 0.6186 | 0.4427 | 0.5049 | 0.0001 |
| 0.0969 | 69.0 | 134757 | 0.0863 | 0.6197 | 0.4344 | 0.5109 | 0.0001 |
| 0.0972 | 70.0 | 136710 | 0.0858 | 0.6242 | 0.4471 | 0.5158 | 0.0001 |
| 0.0961 | 71.0 | 138663 | 0.0858 | 0.6219 | 0.4529 | 0.5104 | 0.0001 |
| 0.0972 | 72.0 | 140616 | 0.0858 | 0.6215 | 0.4392 | 0.5131 | 0.0001 |
| 0.0948 | 73.0 | 142569 | 0.0852 | 0.6232 | 0.4489 | 0.5112 | 1e-05 |
| 0.0956 | 74.0 | 144522 | 0.0850 | 0.6263 | 0.4569 | 0.5188 | 1e-05 |
| 0.0928 | 75.0 | 146475 | 0.0848 | 0.6273 | 0.4543 | 0.5189 | 1e-05 |
| 0.0963 | 76.0 | 148428 | 0.0847 | 0.6299 | 0.4604 | 0.5244 | 1e-05 |
| 0.0946 | 77.0 | 150381 | 0.0846 | 0.6272 | 0.4473 | 0.5187 | 1e-05 |
| 0.0947 | 78.0 | 152334 | 0.0845 | 0.6325 | 0.4540 | 0.5271 | 1e-05 |
| 0.0939 | 79.0 | 154287 | 0.0846 | 0.6245 | 0.4471 | 0.5143 | 1e-05 |
| 0.0938 | 80.0 | 156240 | 0.0846 | 0.6264 | 0.4489 | 0.5169 | 1e-05 |
| 0.0946 | 81.0 | 158193 | 0.0844 | 0.6299 | 0.4637 | 0.5227 | 1e-05 |
| 0.0937 | 82.0 | 160146 | 0.0844 | 0.6307 | 0.4596 | 0.5253 | 1e-05 |
| 0.094 | 83.0 | 162099 | 0.0844 | 0.6289 | 0.4588 | 0.5218 | 1e-05 |
| 0.0957 | 84.0 | 164052 | 0.0841 | 0.6286 | 0.4608 | 0.5221 | 1e-05 |
| 0.0934 | 85.0 | 166005 | 0.0844 | 0.6270 | 0.4572 | 0.5195 | 1e-05 |
| 0.0932 | 86.0 | 167958 | 0.0841 | 0.6310 | 0.4637 | 0.5262 | 1e-05 |
| 0.0938 | 87.0 | 169911 | 0.0844 | 0.6282 | 0.4543 | 0.5218 | 1e-05 |
| 0.0926 | 88.0 | 171864 | 0.0844 | 0.6318 | 0.4613 | 0.5267 | 1e-05 |
| 0.0937 | 89.0 | 173817 | 0.0843 | 0.6260 | 0.4494 | 0.5152 | 1e-05 |
| 0.0925 | 90.0 | 175770 | 0.0844 | 0.6243 | 0.4515 | 0.5140 | 1e-05 |
| 0.0937 | 91.0 | 177723 | 0.0841 | 0.6327 | 0.4606 | 0.5276 | 1e-05 |
| 0.0924 | 92.0 | 179676 | 0.0841 | 0.6301 | 0.4568 | 0.5226 | 1e-05 |
| 0.0923 | 93.0 | 181629 | 0.0839 | 0.6317 | 0.4593 | 0.5272 | 0.0000 |
| 0.0929 | 94.0 | 183582 | 0.0841 | 0.6301 | 0.4572 | 0.5234 | 0.0000 |
| 0.0924 | 95.0 | 185535 | 0.0844 | 0.6308 | 0.4614 | 0.5248 | 0.0000 |
| 0.0937 | 96.0 | 187488 | 0.0839 | 0.6314 | 0.4611 | 0.5251 | 0.0000 |
| 0.0926 | 97.0 | 189441 | 0.0843 | 0.6296 | 0.4546 | 0.5227 | 0.0000 |
| 0.0951 | 98.0 | 191394 | 0.0842 | 0.6294 | 0.4556 | 0.5217 | 0.0000 |
| 0.0928 | 99.0 | 193347 | 0.0840 | 0.6301 | 0.4615 | 0.5236 | 0.0000 |
| 0.093 | 100.0 | 195300 | 0.0842 | 0.6319 | 0.4612 | 0.5263 | 0.0000 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu118
- Datasets 3.0.2
- Tokenizers 0.21.1
|
[
"algae",
"acr",
"anem",
"cca",
"ech",
"fts",
"gal",
"gon",
"mtp",
"p",
"poc",
"por",
"r",
"rdc",
"s",
"sg",
"ser",
"slt",
"sp",
"unk"
] |
binjn/vit-finetuned
|
<!-- 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
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: 2.2256
- Accuracy: 0.43
## 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: 128
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 211 | 2.9776 | 0.252 |
| No log | 2.0 | 422 | 2.6746 | 0.326 |
| 2.9731 | 3.0 | 633 | 2.4969 | 0.362 |
| 2.9731 | 4.0 | 844 | 2.2917 | 0.434 |
| 2.1058 | 5.0 | 1055 | 2.2256 | 0.43 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"label_23",
"label_24",
"label_25",
"label_26",
"label_27",
"label_28",
"label_29",
"label_30",
"label_31",
"label_32",
"label_33",
"label_34",
"label_35",
"label_36",
"label_37",
"label_38",
"label_39",
"label_40",
"label_41",
"label_42",
"label_43",
"label_44",
"label_45",
"label_46",
"label_47",
"label_48",
"label_49",
"label_50",
"label_51",
"label_52",
"label_53",
"label_54",
"label_55",
"label_56",
"label_57",
"label_58",
"label_59",
"label_60",
"label_61",
"label_62",
"label_63",
"label_64",
"label_65",
"label_66",
"label_67",
"label_68",
"label_69",
"label_70",
"label_71",
"label_72",
"label_73",
"label_74",
"label_75",
"label_76",
"label_77",
"label_78",
"label_79",
"label_80",
"label_81",
"label_82",
"label_83",
"label_84",
"label_85",
"label_86",
"label_87",
"label_88",
"label_89",
"label_90",
"label_91",
"label_92",
"label_93",
"label_94",
"label_95",
"label_96",
"label_97",
"label_98",
"label_99"
] |
binjn/vit-finetuned2
|
<!-- 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-finetuned2
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8828
- Accuracy: 0.746
## 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: 64
- eval_batch_size: 128
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 211 | 3.2058 | 0.2 |
| No log | 2.0 | 422 | 2.7863 | 0.27 |
| 3.5109 | 3.0 | 633 | 2.6225 | 0.306 |
| 3.5109 | 4.0 | 844 | 2.3383 | 0.392 |
| 2.6956 | 5.0 | 1055 | 2.1045 | 0.456 |
| 2.6956 | 6.0 | 1266 | 1.8551 | 0.504 |
| 2.6956 | 7.0 | 1477 | 1.6949 | 0.54 |
| 2.213 | 8.0 | 1688 | 1.5866 | 0.576 |
| 2.213 | 9.0 | 1899 | 1.3373 | 0.646 |
| 1.8406 | 10.0 | 2110 | 1.2958 | 0.64 |
| 1.8406 | 11.0 | 2321 | 1.3066 | 0.652 |
| 1.5618 | 12.0 | 2532 | 1.1972 | 0.664 |
| 1.5618 | 13.0 | 2743 | 1.1654 | 0.67 |
| 1.5618 | 14.0 | 2954 | 1.0900 | 0.7 |
| 1.3308 | 15.0 | 3165 | 1.0244 | 0.704 |
| 1.3308 | 16.0 | 3376 | 1.0534 | 0.706 |
| 1.1426 | 17.0 | 3587 | 0.9758 | 0.732 |
| 1.1426 | 18.0 | 3798 | 0.9583 | 0.716 |
| 1.0085 | 19.0 | 4009 | 0.9191 | 0.732 |
| 1.0085 | 20.0 | 4220 | 0.8828 | 0.746 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"label_23",
"label_24",
"label_25",
"label_26",
"label_27",
"label_28",
"label_29",
"label_30",
"label_31",
"label_32",
"label_33",
"label_34",
"label_35",
"label_36",
"label_37",
"label_38",
"label_39",
"label_40",
"label_41",
"label_42",
"label_43",
"label_44",
"label_45",
"label_46",
"label_47",
"label_48",
"label_49",
"label_50",
"label_51",
"label_52",
"label_53",
"label_54",
"label_55",
"label_56",
"label_57",
"label_58",
"label_59",
"label_60",
"label_61",
"label_62",
"label_63",
"label_64",
"label_65",
"label_66",
"label_67",
"label_68",
"label_69",
"label_70",
"label_71",
"label_72",
"label_73",
"label_74",
"label_75",
"label_76",
"label_77",
"label_78",
"label_79",
"label_80",
"label_81",
"label_82",
"label_83",
"label_84",
"label_85",
"label_86",
"label_87",
"label_88",
"label_89",
"label_90",
"label_91",
"label_92",
"label_93",
"label_94",
"label_95",
"label_96",
"label_97",
"label_98",
"label_99"
] |
mbiarreta/swin-camdeboo
|
<!-- 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-camdeboo
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 camdeboo dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7505
- Accuracy: 0.5693
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6714 | 0.0647 | 100 | 2.1798 | 0.3780 |
| 1.3024 | 0.1294 | 200 | 2.0776 | 0.4628 |
| 1.2379 | 0.1942 | 300 | 2.1343 | 0.4699 |
| 0.921 | 0.2589 | 400 | 2.0805 | 0.4909 |
| 1.2239 | 0.3236 | 500 | 1.8752 | 0.5048 |
| 1.1477 | 0.3883 | 600 | 2.1270 | 0.4671 |
| 1.0999 | 0.4531 | 700 | 1.8343 | 0.5404 |
| 0.8614 | 0.5178 | 800 | 2.1993 | 0.4814 |
| 1.4409 | 0.5825 | 900 | 2.0452 | 0.5436 |
| 1.1298 | 0.6472 | 1000 | 2.0431 | 0.5234 |
| 0.6623 | 0.7120 | 1100 | 1.8523 | 0.5626 |
| 0.5556 | 0.7767 | 1200 | 1.9374 | 0.5745 |
| 0.6044 | 0.8414 | 1300 | 2.1097 | 0.5293 |
| 0.7965 | 0.9061 | 1400 | 1.7505 | 0.5693 |
| 0.6878 | 0.9709 | 1500 | 2.0018 | 0.5590 |
| 0.763 | 1.0356 | 1600 | 2.1517 | 0.5527 |
| 0.3674 | 1.1003 | 1700 | 2.5025 | 0.5642 |
| 0.6865 | 1.1650 | 1800 | 2.0337 | 0.5654 |
| 0.3911 | 1.2298 | 1900 | 2.1313 | 0.5852 |
| 0.6468 | 1.2945 | 2000 | 2.4708 | 0.5689 |
| 0.4501 | 1.3592 | 2100 | 2.2926 | 0.5911 |
| 0.2603 | 1.4239 | 2200 | 2.3958 | 0.6026 |
| 0.327 | 1.4887 | 2300 | 2.0308 | 0.5963 |
| 0.5728 | 1.5534 | 2400 | 2.5120 | 0.5860 |
| 0.3978 | 1.6181 | 2500 | 2.2434 | 0.6086 |
| 0.3691 | 1.6828 | 2600 | 2.2805 | 0.6208 |
| 0.443 | 1.7476 | 2700 | 2.2768 | 0.6097 |
| 0.5276 | 1.8123 | 2800 | 2.2888 | 0.6193 |
| 0.3785 | 1.8770 | 2900 | 2.3886 | 0.5983 |
| 0.5051 | 1.9417 | 3000 | 2.3341 | 0.5975 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"aardwolf",
"baboon",
"genetcommonsmallspotted",
"harescrub",
"hartebeestred",
"jackalblackbacked",
"klipspringer",
"kudu",
"meerkatsuricate",
"mongooseegyptianlargegrey",
"monkeyvervet",
"ostrich",
"birdofprey",
"porcupine",
"reedbuckmountain",
"rhebokgrey",
"springbok",
"steenbok",
"tortoise",
"wildebeestblack",
"zebramountain",
"birdother",
"blesbok",
"bustardkori",
"duikercommongrey",
"eland",
"foxbateared",
"gemsbokoryx"
] |
detorcla/cifar10-resnet18
|
# vit-base-cifar10-augmented
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) using data augmentation.
It achieves the following results on the evaluation set:
- **Loss:** 0.0445
- **Accuracy:** 95.54%
## 🧠 Model Description
The base model is a Vision Transformer (ViT) originally trained on ImageNet-21k. This version has been fine-tuned on CIFAR-10, a standard image classification benchmark, using PyTorch and Hugging Face Transformers.
Training was done using extensive **data augmentation**, including random crops, flips, rotations, and color jitter to improve generalization on small input images (32×32, resized to 224×224).
## ✅ Intended Uses & Limitations
### Intended uses
- Educational and research use on small image classification tasks
- Benchmarking transfer learning for ViT on CIFAR-10
- Demonstrating the impact of data augmentation on fine-tuning performance
### Limitations
- Not optimized for real-time inference
- Fine-tuned only on CIFAR-10; not suitable for general-purpose image classification
- Requires resized input (224×224)
## 📦 Training and Evaluation Data
- **Dataset**: [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Size**: 60,000 images (10 classes)
- **Split**: 75% training, 25% test
All images were resized to 224×224 and normalized using ViT’s original mean/std values.
## ⚙️ Training Procedure
### Hyperparameters
- Learning rate: `1e-4`
- Optimizer: `Adam`
- Batch size: `8`
- Epochs: `10`
- Scheduler: `ReduceLROnPlateau`
### Data Augmentation Used
- `RandomResizedCrop(224)`
- `RandomHorizontalFlip()`
- `RandomRotation(10)`
- `ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1)`
### Training Results
| Epoch | Training Loss | Test Accuracy |
|-------|---------------|---------------|
| 1 | 0.1969 | 94.62% |
| 2 | 0.1189 | 95.05% |
| 3 | 0.0899 | **95.54%** |
| 4 | 0.0720 | 94.68% |
| 5 | 0.0650 | 94.84% |
| 6 | 0.0576 | 94.76% |
| 7 | 0.0560 | 95.33% |
| 8 | 0.0488 | 94.31% |
| 9 | 0.0499 | 95.42% |
| 10 | 0.0445 | 94.33% |
## 🧪 Framework Versions
- `transformers`: 4.50.0
- `torch`: 2.6.0+cu124
- `datasets`: 3.4.1
- `tokenizers`: 0.21.1
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
Fadri/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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0583
- Accuracy: 0.9865
## Model details
- **Modellname:** ViT-Base (Vision Transformer) Feintuning
- **Version:** 1.0
- **Autoren:** Fadri
- **Datum:** 2025-05-21
- **Framework:** PyTorch, Transformers (Hugging Face)
- **Referenz:** https://huggingface.co/Fadri/results
## Model description
Dieses Modell ist ein Vision Transformer (ViT-Base), der auf das CIFAR-10-Datenset feingetunt wurde. CIFAR-10 umfasst 10 Klassen von Farb-Bildern (Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck).
Der Feintuning-Prozess nutzte augmentierte Trainingsbilder, um Robustheit gegenüber Varianz in Beleuchtung, Rotation und Skalierung zu erhöhen.
## Training & Evaluierung
- **Datensatz:** CIFAR-10 (50.000 Trainings-, 10.000 Test-Bilder)
- **Datenquelle & Lizenz:**
- Download von der offiziellen Website der Canadian Institute for Advanced Research (CIFAR).
- Lizenz: MIT (frei verfügbar für Forschung und Lehre).
- **Datenaufteilung:** 45.000 Trainings-, 5.000 Validierungs-, 10.000 Test-Bilder
- **Augmentation:**
- Zufällige horizontale Spiegelung
- Rotation ±15°
- Skalierung 0.8–1.2
- Farb- und Kontrast-Jitter
- **Hyperparameter:**
- Lernrate: 3e-5 mit Warmup (5 % Steps) und linearem Decay
- Batch-Size: 64 (Train), 128 (Validation)
- Optimizer: AdamW (β₁=0.9, β₂=0.999, ε=1e-8)
- Epochs: 20
- **Hardware:** NVIDIA Tesla V100, 16 GB VRAM
- **Ergebnisse:**
- Trainingsverlust: 0.0583
- Validierungs-Accuracy: 98.65 %
- Test-Accuracy: 98.45 %
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0388 | 1.0 | 1563 | 0.0732 | 0.9815 |
| 0.017 | 2.0 | 3126 | 0.0621 | 0.9847 |
| 0.0028 | 3.0 | 4689 | 0.0583 | 0.9865 |
### Framework versions
- Transformers 4.52.1
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
## Zero-Shot-Baseline
Zur Einordnung der Feintuning-Leistung wurde ein CLIP-ResNet50-Modell (OpenAI CLIP) ohne zusätzliche Feineinstellung auf dem CIFAR-10-Testset evaluiert:
- **Zero-Shot-Test-Accuracy:** 76.2 %
| Modell | Test-Accuracy |
|---------------------------|---------------|
| CLIP-ResNet50 (Zero-Shot) | 76.2 % |
| ViT-Base (Feintuning) | 98.45 % |
## Intended Uses
- Klassifikation von kleinen Farb-Bildern in 10 Kategorien (z. B. in Lehr- und Forschungsumgebungen).
- Demonstration von Feintuning-Prozessen für Transformer-Modelle im Computer-Vision-Bereich.
## Limitations
- Nur für Bildgrößen 32×32 px optimiert – nicht direkt auf größere Auflösungen übertragbar ohne zusätzliche Anpassung.
- CIFAR-10 ist relativ klein und künstlich; Ergebnisse auf realen, größeren Datensätzen können abweichen.
- Fehlklassifizierungen treten bei ähnlichen Klassen (z. B. Katze vs. Hund) auf.
## Training Data
- **Quelle:** https://www.cs.toronto.edu/~kriz/cifar.html
- **Split:** 45k Train / 5k Val / 10k Test
- **Augmentation:**siehe oben
- **Vorverarbeitung:**
- Normalisierung auf den Mittelwert und die Standardabweichung von CIFAR-10
- Resizing auf 224×224 px (Input-Requirement von ViT)
## Evaluation Data
- Unverändertes CIFAR-10-Testset (10 k Bilder), gleiche Vorverarbeitung wie Training.
## Ethical Considerations
- Kein sensibler oder personenbezogener Inhalt.
- Lizenzkonformität mit MIT-Lizenz.
- Modell nicht für kritische Anwendungen (z. B. medizinische Diagnostik) geeignet.
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
h3ckno/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.
It achieves the following results on the evaluation set:
- Loss: 2.4959
- Accuracy: 0.87
## 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 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.3148 | 1.0 | 13 | 3.4177 | 0.8 |
| 3.161 | 2.0 | 26 | 2.6511 | 0.82 |
| 2.6086 | 2.8 | 36 | 2.4959 | 0.87 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"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"
] |
aipglu/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.
It achieves the following results on the evaluation set:
- Loss: 1.6199
- Accuracy: 0.892
## 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 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7531 | 1.0 | 63 | 2.5553 | 0.829 |
| 1.8465 | 2.0 | 126 | 1.7735 | 0.879 |
| 1.6267 | 2.96 | 186 | 1.6199 | 0.892 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"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"
] |
songthienll/mobilevit_ai_real_classifier
|
<!-- 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_ai_real_classifier
This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1279
- Accuracy: 0.9567
## 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: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2617 | 1.0 | 219 | 0.2093 | 0.918 |
| 0.2115 | 2.0 | 438 | 0.1867 | 0.921 |
| 0.1832 | 3.0 | 657 | 0.2291 | 0.9135 |
| 0.1516 | 4.0 | 876 | 0.1993 | 0.9175 |
| 0.1303 | 5.0 | 1095 | 0.1567 | 0.945 |
| 0.1113 | 6.0 | 1314 | 0.1590 | 0.947 |
| 0.0992 | 7.0 | 1533 | 0.1634 | 0.94 |
| 0.0794 | 8.0 | 1752 | 0.1702 | 0.9445 |
| 0.0819 | 9.0 | 1971 | 0.1566 | 0.949 |
| 0.0725 | 10.0 | 2190 | 0.1620 | 0.949 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"fake",
"real"
] |
TalentoTechIA/Hamilton_20_05_25
|
<!-- 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. -->
# Hamilton_20_05_25
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: 0.0252
- 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1456 | 3.8462 | 500 | 0.0252 | 0.9925 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV70
|
<!-- 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-window8-256-dmae-humeda-DAV70
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3655
- Accuracy: 0.9029
## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 45
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0983 | 1.0 | 15 | 1.0283 | 0.5371 |
| 0.8777 | 2.0 | 30 | 0.7419 | 0.7371 |
| 0.69 | 3.0 | 45 | 0.4895 | 0.8 |
| 0.4806 | 4.0 | 60 | 0.4128 | 0.8114 |
| 0.436 | 5.0 | 75 | 0.3188 | 0.8743 |
| 0.3499 | 6.0 | 90 | 0.3870 | 0.8286 |
| 0.3754 | 7.0 | 105 | 0.3523 | 0.8857 |
| 0.2954 | 8.0 | 120 | 0.3168 | 0.8686 |
| 0.3075 | 9.0 | 135 | 0.3359 | 0.8743 |
| 0.2405 | 10.0 | 150 | 0.3676 | 0.8514 |
| 0.2716 | 11.0 | 165 | 0.3715 | 0.8343 |
| 0.2414 | 12.0 | 180 | 0.3665 | 0.8971 |
| 0.2116 | 13.0 | 195 | 0.3554 | 0.88 |
| 0.2134 | 14.0 | 210 | 0.4033 | 0.8686 |
| 0.2058 | 15.0 | 225 | 0.4590 | 0.84 |
| 0.1925 | 16.0 | 240 | 0.4088 | 0.8457 |
| 0.2004 | 17.0 | 255 | 0.4399 | 0.8743 |
| 0.1541 | 18.0 | 270 | 0.3550 | 0.8686 |
| 0.2124 | 19.0 | 285 | 0.3603 | 0.8914 |
| 0.1975 | 20.0 | 300 | 0.3655 | 0.9029 |
| 0.15 | 21.0 | 315 | 0.3863 | 0.8971 |
| 0.1796 | 22.0 | 330 | 0.3288 | 0.9029 |
| 0.1307 | 23.0 | 345 | 0.3781 | 0.8857 |
| 0.1721 | 24.0 | 360 | 0.3938 | 0.9029 |
| 0.1161 | 25.0 | 375 | 0.4481 | 0.88 |
| 0.1096 | 26.0 | 390 | 0.4351 | 0.8686 |
| 0.1203 | 27.0 | 405 | 0.4337 | 0.8686 |
| 0.1481 | 28.0 | 420 | 0.5295 | 0.8457 |
| 0.1295 | 29.0 | 435 | 0.4443 | 0.8857 |
| 0.1234 | 30.0 | 450 | 0.4490 | 0.8914 |
| 0.1368 | 31.0 | 465 | 0.5945 | 0.8629 |
| 0.1024 | 32.0 | 480 | 0.5224 | 0.88 |
| 0.1255 | 33.0 | 495 | 0.4385 | 0.9029 |
| 0.1285 | 34.0 | 510 | 0.5272 | 0.8971 |
| 0.1316 | 35.0 | 525 | 0.4936 | 0.8971 |
| 0.1042 | 36.0 | 540 | 0.5399 | 0.8971 |
| 0.1028 | 37.0 | 555 | 0.5199 | 0.8857 |
| 0.0923 | 38.0 | 570 | 0.4813 | 0.9029 |
| 0.1 | 39.0 | 585 | 0.5702 | 0.8914 |
| 0.0943 | 40.0 | 600 | 0.5477 | 0.8971 |
| 0.1135 | 41.0 | 615 | 0.5281 | 0.8971 |
| 0.1021 | 42.0 | 630 | 0.5353 | 0.8971 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.21.1
|
[
"avanzada",
"avanzada humeda",
"no dmae"
] |
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV71
|
<!-- 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-window8-256-dmae-humeda-DAV71
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5977
- Accuracy: 0.9029
## 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: 6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1246 | 1.0 | 15 | 1.0559 | 0.4171 |
| 0.8728 | 2.0 | 30 | 0.7460 | 0.6971 |
| 0.6663 | 3.0 | 45 | 0.4563 | 0.8057 |
| 0.4632 | 4.0 | 60 | 0.4076 | 0.8286 |
| 0.4278 | 5.0 | 75 | 0.3670 | 0.84 |
| 0.361 | 6.0 | 90 | 0.3624 | 0.8457 |
| 0.3742 | 7.0 | 105 | 0.3504 | 0.8629 |
| 0.3313 | 8.0 | 120 | 0.2962 | 0.8629 |
| 0.2977 | 9.0 | 135 | 0.3321 | 0.8686 |
| 0.2589 | 10.0 | 150 | 0.3425 | 0.8629 |
| 0.2477 | 11.0 | 165 | 0.3982 | 0.8457 |
| 0.2187 | 12.0 | 180 | 0.5954 | 0.8514 |
| 0.2342 | 13.0 | 195 | 0.3745 | 0.8514 |
| 0.2444 | 14.0 | 210 | 0.5220 | 0.8629 |
| 0.2067 | 15.0 | 225 | 0.4433 | 0.8457 |
| 0.1882 | 16.0 | 240 | 0.3937 | 0.8629 |
| 0.199 | 17.0 | 255 | 0.5103 | 0.8629 |
| 0.1565 | 18.0 | 270 | 0.3608 | 0.8857 |
| 0.2068 | 19.0 | 285 | 0.3679 | 0.88 |
| 0.194 | 20.0 | 300 | 0.5581 | 0.8457 |
| 0.1654 | 21.0 | 315 | 0.5074 | 0.8686 |
| 0.1986 | 22.0 | 330 | 0.4395 | 0.88 |
| 0.1257 | 23.0 | 345 | 0.4293 | 0.8686 |
| 0.1976 | 24.0 | 360 | 0.4932 | 0.8571 |
| 0.1563 | 25.0 | 375 | 0.4254 | 0.8857 |
| 0.0985 | 26.0 | 390 | 0.5097 | 0.8686 |
| 0.1238 | 27.0 | 405 | 0.7264 | 0.8514 |
| 0.1577 | 28.0 | 420 | 0.4827 | 0.8571 |
| 0.1271 | 29.0 | 435 | 0.5305 | 0.8686 |
| 0.1002 | 30.0 | 450 | 0.5888 | 0.8629 |
| 0.1268 | 31.0 | 465 | 0.6433 | 0.8571 |
| 0.1153 | 32.0 | 480 | 0.8394 | 0.8343 |
| 0.1191 | 33.0 | 495 | 0.7475 | 0.84 |
| 0.1184 | 34.0 | 510 | 0.4884 | 0.8743 |
| 0.1332 | 35.0 | 525 | 0.5834 | 0.8857 |
| 0.1071 | 36.0 | 540 | 0.6279 | 0.8571 |
| 0.0886 | 37.0 | 555 | 0.6999 | 0.8629 |
| 0.0744 | 38.0 | 570 | 0.7295 | 0.8686 |
| 0.1274 | 39.0 | 585 | 0.6137 | 0.8914 |
| 0.0795 | 40.0 | 600 | 0.5706 | 0.8743 |
| 0.0962 | 41.0 | 615 | 0.6100 | 0.8857 |
| 0.094 | 42.0 | 630 | 0.6149 | 0.8743 |
| 0.0945 | 43.0 | 645 | 0.5689 | 0.88 |
| 0.0584 | 44.0 | 660 | 0.7019 | 0.8743 |
| 0.0676 | 45.0 | 675 | 0.6934 | 0.88 |
| 0.0763 | 46.0 | 690 | 0.6047 | 0.8914 |
| 0.0762 | 47.0 | 705 | 0.6064 | 0.88 |
| 0.0696 | 48.0 | 720 | 0.7336 | 0.8686 |
| 0.0555 | 49.0 | 735 | 0.6599 | 0.8743 |
| 0.0572 | 50.0 | 750 | 0.5977 | 0.9029 |
| 0.0648 | 51.0 | 765 | 0.6257 | 0.88 |
| 0.0705 | 52.0 | 780 | 0.6654 | 0.8857 |
| 0.0646 | 53.0 | 795 | 0.6813 | 0.8686 |
| 0.0795 | 54.0 | 810 | 0.6209 | 0.8743 |
| 0.0828 | 55.0 | 825 | 0.6457 | 0.8743 |
| 0.0674 | 56.0 | 840 | 0.6521 | 0.88 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.21.1
|
[
"avanzada",
"avanzada humeda",
"no dmae"
] |
thomas970804/Drinking-Recognition-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. -->
# Drinking-Recognition-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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- Accuracy: 0.9613
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6956 | 1.0 | 6 | 0.6315 | 0.6220 |
| 0.4884 | 2.0 | 12 | 0.3439 | 0.8839 |
| 0.295 | 3.0 | 18 | 0.2190 | 0.9315 |
| 0.208 | 4.0 | 24 | 0.1723 | 0.9375 |
| 0.1873 | 5.0 | 30 | 0.1499 | 0.9435 |
| 0.1481 | 6.0 | 36 | 0.1475 | 0.9435 |
| 0.1367 | 7.0 | 42 | 0.1341 | 0.9554 |
| 0.1223 | 8.0 | 48 | 0.1486 | 0.9464 |
| 0.119 | 9.0 | 54 | 0.1377 | 0.9583 |
| 0.1019 | 10.0 | 60 | 0.1365 | 0.9613 |
| 0.1102 | 11.0 | 66 | 0.1326 | 0.9554 |
| 0.11 | 12.0 | 72 | 0.1330 | 0.9524 |
| 0.0948 | 13.0 | 78 | 0.1484 | 0.9375 |
| 0.0933 | 14.0 | 84 | 0.1348 | 0.9435 |
| 0.0874 | 15.0 | 90 | 0.1336 | 0.9524 |
| 0.0854 | 16.0 | 96 | 0.1331 | 0.9524 |
| 0.0962 | 17.0 | 102 | 0.1337 | 0.9494 |
| 0.0724 | 18.0 | 108 | 0.1282 | 0.9613 |
| 0.0887 | 19.0 | 114 | 0.1278 | 0.9583 |
| 0.0866 | 20.0 | 120 | 0.1281 | 0.9583 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"not_drinking",
"drinking"
] |
balitop/emotion-classifier-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. -->
# emotion-classifier-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.
## 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: 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
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
BeckerAnas/convnext-tiny-224-finetuned-alzheimer-MRI
|
<!-- 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-alzheimer-MRI
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8995
- Accuracy: 0.5602
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0151 | 1.0 | 36 | 0.9395 | 0.5391 |
| 0.9132 | 2.0 | 72 | 0.8595 | 0.5508 |
| 0.876 | 3.0 | 108 | 0.8437 | 0.5488 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
BeckerAnas/convnext-base-384-finetuned-alzheimer-MRI
|
<!-- 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-base-384-finetuned-alzheimer-MRI
This model is a fine-tuned version of [facebook/convnext-base-384](https://huggingface.co/facebook/convnext-base-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8576
- Accuracy: 0.5805
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9877 | 1.0 | 36 | 0.9405 | 0.5625 |
| 0.8723 | 2.0 | 72 | 0.8801 | 0.5957 |
| 0.8327 | 3.0 | 108 | 0.8639 | 0.5977 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
BeckerAnas/jolly-river-194
|
<!-- 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. -->
# jolly-river-194
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: 0.8782
- Accuracy: 0.5859
## 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: 256
- eval_batch_size: 256
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0947 | 1.0 | 18 | 0.9551 | 0.5312 |
| 0.9423 | 2.0 | 36 | 0.9010 | 0.5703 |
| 0.9059 | 3.0 | 54 | 0.8782 | 0.5859 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
aevalone/vit-base-patch16-224-finetuned-forgery
|
<!-- 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-finetuned-forgery
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0659
- Accuracy: 0.9762
## Model description
More information needed
## Intended uses & limitations
To use, combine known genuine signature with questioned signature into a single image, then run inference.
```python
from PIL import Image
def create_comparison_image(img1_path, img2_path):
# Open images
img1 = Image.open(img1_path).convert("RGB")
img2 = Image.open(img2_path).convert("RGB")
# Resize to same height
height = max(img1.height, img2.height)
width1 = int(img1.width * (height / img1.height))
width2 = int(img2.width * (height / img2.height))
img1 = img1.resize((width1, height), Image.LANCZOS)
img2 = img2.resize((width2, height), Image.LANCZOS)
# Create new image with space for both images
total_width = width1 + width2
comparison = Image.new('RGB', (total_width, height))
# Paste images side by side
comparison.paste(img1, (0, 0))
comparison.paste(img2, (width1, 0))
return comparison
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.1687073595562957e-05
- train_batch_size: 29
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 145
- 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_steps: 1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3114 | 0.9991 | 470 | 0.1464 | 0.9477 |
| 0.2831 | 1.9991 | 940 | 0.0803 | 0.9697 |
| 0.2806 | 2.9991 | 1410 | 0.0727 | 0.9756 |
| 0.2779 | 3.9991 | 1880 | 0.0744 | 0.9758 |
| 0.2588 | 4.9991 | 2350 | 0.0659 | 0.9762 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"forgery",
"genuine"
] |
Jialuo21/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: 0.4185
- Accuracy: 0.7551
## 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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4891 | 1.0 | 810 | 0.5060 | 0.7054 |
| 0.4271 | 2.0 | 1620 | 0.4423 | 0.7433 |
| 0.4216 | 2.9969 | 2427 | 0.4185 | 0.7551 |
### Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"pos",
"neg"
] |
RobertoSonic/swinv2-tiny-patch4-window8-256-dmae-humeda-DAV72
|
<!-- 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-window8-256-dmae-humeda-DAV72
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7140
- Accuracy: 0.8743
## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.0851 | 1.0 | 15 | 1.0597 | 0.4857 |
| 0.9595 | 2.0 | 30 | 0.9427 | 0.64 |
| 0.8642 | 3.0 | 45 | 0.6913 | 0.7086 |
| 0.5836 | 4.0 | 60 | 0.5780 | 0.7257 |
| 0.5395 | 5.0 | 75 | 0.4822 | 0.7829 |
| 0.4215 | 6.0 | 90 | 0.4077 | 0.8229 |
| 0.4329 | 7.0 | 105 | 0.4352 | 0.8114 |
| 0.3695 | 8.0 | 120 | 0.3244 | 0.8743 |
| 0.3314 | 9.0 | 135 | 0.3186 | 0.8914 |
| 0.3176 | 10.0 | 150 | 0.3788 | 0.8514 |
| 0.3368 | 11.0 | 165 | 0.3458 | 0.8629 |
| 0.2558 | 12.0 | 180 | 0.4196 | 0.8457 |
| 0.2579 | 13.0 | 195 | 0.3485 | 0.8743 |
| 0.2413 | 14.0 | 210 | 0.4509 | 0.8629 |
| 0.2374 | 15.0 | 225 | 0.3904 | 0.8743 |
| 0.2214 | 16.0 | 240 | 0.3461 | 0.8514 |
| 0.2189 | 17.0 | 255 | 0.5986 | 0.8229 |
| 0.2458 | 18.0 | 270 | 0.3360 | 0.8914 |
| 0.2431 | 19.0 | 285 | 0.3475 | 0.8857 |
| 0.2136 | 20.0 | 300 | 0.3242 | 0.88 |
| 0.1871 | 21.0 | 315 | 0.4103 | 0.8857 |
| 0.1996 | 22.0 | 330 | 0.3606 | 0.9029 |
| 0.1367 | 23.0 | 345 | 0.4657 | 0.8629 |
| 0.1963 | 24.0 | 360 | 0.4267 | 0.8743 |
| 0.1519 | 25.0 | 375 | 0.4322 | 0.8686 |
| 0.1365 | 26.0 | 390 | 0.4214 | 0.88 |
| 0.1158 | 27.0 | 405 | 0.4472 | 0.8743 |
| 0.1621 | 28.0 | 420 | 0.4020 | 0.8743 |
| 0.1271 | 29.0 | 435 | 0.4054 | 0.8857 |
| 0.136 | 30.0 | 450 | 0.4286 | 0.9143 |
| 0.1386 | 31.0 | 465 | 0.5015 | 0.8857 |
| 0.1153 | 32.0 | 480 | 0.6675 | 0.8629 |
| 0.1139 | 33.0 | 495 | 0.5458 | 0.8971 |
| 0.144 | 34.0 | 510 | 0.5303 | 0.88 |
| 0.1542 | 35.0 | 525 | 0.5164 | 0.8914 |
| 0.1208 | 36.0 | 540 | 0.5690 | 0.88 |
| 0.1034 | 37.0 | 555 | 0.7427 | 0.8571 |
| 0.0889 | 38.0 | 570 | 0.9084 | 0.8286 |
| 0.1355 | 39.0 | 585 | 0.5977 | 0.8743 |
| 0.0895 | 40.0 | 600 | 0.5400 | 0.8914 |
| 0.1072 | 41.0 | 615 | 0.6018 | 0.8743 |
| 0.1356 | 42.0 | 630 | 0.5493 | 0.8743 |
| 0.0953 | 43.0 | 645 | 0.5350 | 0.8914 |
| 0.0781 | 44.0 | 660 | 0.5269 | 0.88 |
| 0.0854 | 45.0 | 675 | 0.5428 | 0.88 |
| 0.0983 | 46.0 | 690 | 0.4897 | 0.8857 |
| 0.0944 | 47.0 | 705 | 0.5177 | 0.8971 |
| 0.1152 | 48.0 | 720 | 0.6401 | 0.8629 |
| 0.0608 | 49.0 | 735 | 0.7380 | 0.8629 |
| 0.0898 | 50.0 | 750 | 0.4922 | 0.8971 |
| 0.0923 | 51.0 | 765 | 0.5427 | 0.8971 |
| 0.0743 | 52.0 | 780 | 0.9941 | 0.84 |
| 0.0753 | 53.0 | 795 | 0.5342 | 0.8857 |
| 0.0751 | 54.0 | 810 | 0.6452 | 0.88 |
| 0.1222 | 55.0 | 825 | 0.6297 | 0.8743 |
| 0.0786 | 56.0 | 840 | 0.6592 | 0.8629 |
| 0.134 | 57.0 | 855 | 0.6541 | 0.8686 |
| 0.092 | 58.0 | 870 | 0.6523 | 0.8571 |
| 0.1036 | 59.0 | 885 | 0.5562 | 0.8971 |
| 0.0825 | 60.0 | 900 | 0.6117 | 0.8743 |
| 0.0923 | 61.0 | 915 | 0.5778 | 0.8686 |
| 0.0909 | 62.0 | 930 | 0.5974 | 0.8686 |
| 0.0536 | 63.0 | 945 | 0.7557 | 0.8514 |
| 0.0572 | 64.0 | 960 | 0.6255 | 0.8857 |
| 0.0824 | 65.0 | 975 | 0.6768 | 0.8686 |
| 0.0773 | 66.0 | 990 | 0.5942 | 0.9029 |
| 0.0495 | 67.0 | 1005 | 0.7902 | 0.8571 |
| 0.0649 | 68.0 | 1020 | 0.6097 | 0.8914 |
| 0.0852 | 69.0 | 1035 | 0.6614 | 0.8914 |
| 0.0634 | 70.0 | 1050 | 0.6604 | 0.8914 |
| 0.0774 | 71.0 | 1065 | 0.7848 | 0.8514 |
| 0.0803 | 72.0 | 1080 | 0.6424 | 0.8914 |
| 0.0645 | 73.0 | 1095 | 0.7508 | 0.8857 |
| 0.0483 | 74.0 | 1110 | 0.7523 | 0.8629 |
| 0.0586 | 75.0 | 1125 | 0.8278 | 0.8629 |
| 0.1 | 76.0 | 1140 | 0.7503 | 0.8686 |
| 0.0434 | 77.0 | 1155 | 0.7820 | 0.8743 |
| 0.0792 | 78.0 | 1170 | 0.7016 | 0.88 |
| 0.055 | 79.0 | 1185 | 0.8635 | 0.8571 |
| 0.0666 | 80.0 | 1200 | 0.7208 | 0.8686 |
| 0.0563 | 81.0 | 1215 | 0.7606 | 0.8686 |
| 0.0535 | 82.0 | 1230 | 0.7329 | 0.88 |
| 0.0499 | 83.0 | 1245 | 0.7253 | 0.88 |
| 0.0418 | 84.0 | 1260 | 0.7429 | 0.8686 |
| 0.0736 | 85.0 | 1275 | 0.7621 | 0.8743 |
| 0.0593 | 86.0 | 1290 | 0.7970 | 0.8571 |
| 0.0658 | 87.0 | 1305 | 0.7211 | 0.8686 |
| 0.0531 | 88.0 | 1320 | 0.7420 | 0.8686 |
| 0.0604 | 89.0 | 1335 | 0.7151 | 0.8743 |
| 0.0661 | 90.0 | 1350 | 0.6881 | 0.8857 |
| 0.058 | 91.0 | 1365 | 0.7139 | 0.8686 |
| 0.0436 | 92.0 | 1380 | 0.7260 | 0.8686 |
| 0.0733 | 93.0 | 1395 | 0.7150 | 0.8743 |
| 0.0501 | 93.3390 | 1400 | 0.7140 | 0.8743 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.21.1
|
[
"avanzada",
"avanzada humeda",
"no dmae"
] |
KiViDrag/resnet34_AIvsHuman
|
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"label_79926",
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"label_79946",
"label_79947",
"label_79948",
"label_79949"
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kuchikihater/swin-skin-cancer
|
<!-- 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-data-augmentation-balanced-base-beans
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the HAM1000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5919
- Accuracy: 0.8158
## 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: 64
- 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: 15
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
[
"akiec",
"bcc",
"bkl",
"df",
"mel",
"nv",
"vasc"
] |
Tharsana/vit-base-brain-tumor
|
<!-- 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. -->
# clip-brain-tumor
This model uses the Zero-Shot model openai/clip-vit-large-patch14 to classify brain tumors in MRI scans — without any fine-tuning.
The classification is based on the following semantic labels:
“An MRI scan showing a brain tumor”
“An MRI scan showing a healthy brain”
The model was evaluated on an MRI dataset and achieved the following results:
- Accuracy: 0.8770
- Precision: 0.8853
- Recall: 0.8770
# vit-base-brain-tumor
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the your-username/brain-tumor dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3678
- Accuracy: 0.8699
## 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: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8264 | 1.0 | 135 | 0.6904 | 0.5709 |
| 0.6692 | 2.0 | 270 | 0.5918 | 0.7313 |
| 0.5584 | 3.0 | 405 | 0.5281 | 0.7836 |
| 0.4993 | 4.0 | 540 | 0.4851 | 0.8321 |
| 0.4554 | 5.0 | 675 | 0.4554 | 0.8321 |
| 0.4237 | 6.0 | 810 | 0.4345 | 0.8246 |
| 0.4035 | 7.0 | 945 | 0.4183 | 0.8246 |
| 0.3861 | 8.0 | 1080 | 0.4066 | 0.8321 |
| 0.3793 | 9.0 | 1215 | 0.3976 | 0.8433 |
| 0.3678 | 10.0 | 1350 | 0.3898 | 0.8433 |
| 0.3665 | 11.0 | 1485 | 0.3843 | 0.8433 |
| 0.3564 | 12.0 | 1620 | 0.3802 | 0.8433 |
| 0.3518 | 13.0 | 1755 | 0.3772 | 0.8470 |
| 0.3508 | 14.0 | 1890 | 0.3755 | 0.8470 |
| 0.3518 | 15.0 | 2025 | 0.3750 | 0.8470 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
[
"no",
"yes"
] |
BeckerAnas/vivid-silence-196
|
<!-- 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. -->
# vivid-silence-196
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: 0.8678
- Accuracy: 0.6055
## 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: 256
- eval_batch_size: 256
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0737 | 1.0 | 18 | 0.9622 | 0.5234 |
| 0.9317 | 2.0 | 36 | 0.8867 | 0.5879 |
| 0.8886 | 3.0 | 54 | 0.8678 | 0.6055 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
BeckerAnas/swept-brook-197
|
<!-- 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. -->
# swept-brook-197
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: 0.6066
- Accuracy: 0.7402
## 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: 256
- eval_batch_size: 256
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1424 | 1.0 | 18 | 0.9758 | 0.5527 |
| 0.937 | 2.0 | 36 | 0.8802 | 0.5488 |
| 0.9044 | 3.0 | 54 | 0.9013 | 0.5215 |
| 0.8604 | 4.0 | 72 | 0.8435 | 0.5781 |
| 0.8388 | 5.0 | 90 | 0.8415 | 0.5859 |
| 0.8287 | 6.0 | 108 | 0.9338 | 0.5098 |
| 0.8042 | 7.0 | 126 | 0.7766 | 0.6152 |
| 0.7452 | 8.0 | 144 | 0.7439 | 0.6328 |
| 0.701 | 9.0 | 162 | 0.7072 | 0.6543 |
| 0.6725 | 10.0 | 180 | 0.6605 | 0.7070 |
| 0.6326 | 11.0 | 198 | 0.6436 | 0.7012 |
| 0.6066 | 12.0 | 216 | 0.6437 | 0.7129 |
| 0.5878 | 13.0 | 234 | 0.6104 | 0.7461 |
| 0.5813 | 14.0 | 252 | 0.6086 | 0.7344 |
| 0.5753 | 15.0 | 270 | 0.6066 | 0.7402 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
remonemo/beans_no_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. -->
# beans_no_aug
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the nateraw/beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1538
- Accuracy: 0.9766
## 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: 16
- 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
### Training results
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/Hamilton_23_05_25
|
<!-- 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. -->
# Hamilton_23_05_25
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: 0.1270
- Accuracy: 0.9774
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0447 | 3.8462 | 500 | 0.1270 | 0.9774 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/alemadera_24_05_25
|
<!-- 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. -->
# alemadera_24_05_25
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: 0.1145
- Accuracy: 0.9774
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1522 | 3.8462 | 500 | 0.1145 | 0.9774 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/juanprueba
|
<!-- 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. -->
# juanprueba
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: 0.1207
- 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0022 | 3.8462 | 500 | 0.1207 | 0.9699 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/Marcela_24_05_25
|
<!-- 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. -->
# Marcela_24_05_25
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: 0.0162
- 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1187 | 3.8462 | 500 | 0.0162 | 0.9925 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/Danilo_24_05_25
|
<!-- 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. -->
# Danilo_24_05_25
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: 0.0532
- Accuracy: 0.9850
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1289 | 3.8462 | 500 | 0.0532 | 0.9850 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/Rcalixto_24_05_25
|
<!-- 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. -->
# Rcalixto_24_05_25
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: 0.0397
- Accuracy: 0.9850
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1406 | 3.8462 | 500 | 0.0397 | 0.9850 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/Melissa_24_05_25
|
<!-- 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. -->
# Melissa_24_05_25
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: 0.0057
- 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1199 | 3.8462 | 500 | 0.0057 | 1.0 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/Kevin_23_05_25
|
<!-- 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. -->
# Kevin_23_05_25
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: 0.0237
- 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1305 | 3.8462 | 500 | 0.0237 | 0.9925 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
sokis/Andres_24_05_25
|
<!-- 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. -->
# Andres_24_05_25
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: 0.0431
- 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1314 | 3.8462 | 500 | 0.0431 | 0.9925 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TalentoTechIA/MariaAvendano_24_05_25
|
<!-- 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. -->
# MariaAvendano_24_05_25
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: 0.0348
- 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.13 | 3.8462 | 500 | 0.0348 | 0.9925 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
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