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|---|---|---|
dima806/headgear_image_detection
|
Returns headgear type given an image.
See https://www.kaggle.com/code/dima806/headgear-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
BERET 1.0000 0.9565 0.9778 115
FEDORA 0.9913 1.0000 0.9956 114
SOMBERO 1.0000 1.0000 1.0000 115
HARD HAT 1.0000 1.0000 1.0000 115
FEZ 1.0000 0.9912 0.9956 114
ZUCCHETTO 1.0000 0.9912 0.9956 114
TOP HAT 1.0000 1.0000 1.0000 115
DEERSTALKER 0.9913 1.0000 0.9956 114
ASCOT CAP 0.9500 1.0000 0.9744 114
PORK PIE 0.9739 0.9825 0.9782 114
MILITARY HELMET 1.0000 1.0000 1.0000 115
BICORNE 1.0000 0.9912 0.9956 114
FOOTBALL HELMET 1.0000 1.0000 1.0000 115
MOTARBOARD 0.9913 1.0000 0.9956 114
BOATER 1.0000 1.0000 1.0000 115
PITH HELMET 0.9913 1.0000 0.9956 114
SOUTHWESTER 1.0000 0.9912 0.9956 114
BOWLER 0.9912 0.9825 0.9868 114
GARRISON CAP 1.0000 0.9912 0.9956 114
BASEBALL CAP 1.0000 1.0000 1.0000 115
accuracy 0.9939 2288
macro avg 0.9940 0.9939 0.9939 2288
weighted avg 0.9940 0.9939 0.9939 2288
```
|
[
"beret",
"fedora",
"sombero",
"hard hat",
"fez",
"zucchetto",
"top hat",
"deerstalker",
"ascot cap",
"pork pie",
"military helmet",
"bicorne",
"football helmet",
"motarboard",
"boater",
"pith helmet",
"southwester",
"bowler",
"garrison cap",
"baseball cap"
] |
galbitang/autotrain-jin0_sofa-94923146231
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 94923146231
- CO2 Emissions (in grams): 0.0667
## Validation Metrics
- Loss: 0.897
- Accuracy: 0.693
- Macro F1: 0.633
- Micro F1: 0.693
- Weighted F1: 0.686
- Macro Precision: 0.663
- Micro Precision: 0.693
- Weighted Precision: 0.693
- Macro Recall: 0.628
- Micro Recall: 0.693
- Weighted Recall: 0.693
|
[
"classicantique",
"frenchprovence",
"vintageretro",
"industrial",
"koreaaisa",
"lovelyromantic",
"minimalsimple",
"modern",
"natural",
"notherneurope",
"unique"
] |
galbitang/autotrain-jeongmi_bedframe-94918146232
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 94918146232
- CO2 Emissions (in grams): 0.0779
## Validation Metrics
- Loss: 0.544
- Accuracy: 0.824
- Macro F1: 0.829
- Micro F1: 0.824
- Weighted F1: 0.819
- Macro Precision: 0.859
- Micro Precision: 0.824
- Weighted Precision: 0.835
- Macro Recall: 0.816
- Micro Recall: 0.824
- Weighted Recall: 0.824
|
[
"classicantique",
"frenchprovence",
"vintageretro",
"industrial",
"koreaaisa",
"lovelyromantic",
"minimalsimple",
"modern",
"natural",
"notherneurope",
"unique"
] |
galbitang/autotrain-lamp_train_dataset-94947146236
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 94947146236
- CO2 Emissions (in grams): 0.0384
## Validation Metrics
- Loss: 1.766
- Accuracy: 0.450
- Macro F1: 0.318
- Micro F1: 0.450
- Weighted F1: 0.392
- Macro Precision: 0.321
- Micro Precision: 0.450
- Weighted Precision: 0.373
- Macro Recall: 0.355
- Micro Recall: 0.450
- Weighted Recall: 0.450
|
[
"classicantique",
"frenchprovence",
"vintageretro",
"industrial",
"koreaaisa",
"lovelyromantic",
"minimalsimple",
"modern",
"natural",
"notherneurope",
"unique"
] |
dima806/wild_cats_image_detection
|
Returns wild cat given an image.
See https://www.kaggle.com/code/dima806/wild-cats-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
LIONS 1.0000 1.0000 1.0000 99
CARACAL 1.0000 1.0000 1.0000 99
AFRICAN LEOPARD 0.9897 0.9697 0.9796 99
CHEETAH 0.9899 0.9899 0.9899 99
SNOW LEOPARD 0.9900 0.9900 0.9900 100
TIGER 1.0000 1.0000 1.0000 99
OCELOT 0.9899 0.9899 0.9899 99
JAGUAR 0.9802 1.0000 0.9900 99
PUMA 1.0000 1.0000 1.0000 100
CLOUDED LEOPARD 0.9899 0.9899 0.9899 99
accuracy 0.9929 992
macro avg 0.9930 0.9929 0.9929 992
weighted avg 0.9930 0.9929 0.9929 992
```
|
[
"lions",
"caracal",
"african leopard",
"cheetah",
"snow leopard",
"tiger",
"ocelot",
"jaguar",
"puma",
"clouded leopard"
] |
zkdeng/convnextv2-tiny-22k-384-finetuned-Spiders
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnextv2-tiny-22k-384-finetuned-Spiders
This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-384](https://huggingface.co/facebook/convnextv2-tiny-22k-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1945
- eval_accuracy: 0.915
- eval_precision: 0.8899
- eval_recall: 0.9510
- eval_f1: 0.9194
- eval_runtime: 9.0512
- eval_samples_per_second: 22.097
- eval_steps_per_second: 1.436
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"lactrodectus_hesperus",
"parasteatoda_tepidariorum"
] |
galbitang/autotrain-jinvit_sofa_base-94978146242
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 94978146242
- CO2 Emissions (in grams): 0.0588
## Validation Metrics
- Loss: 0.725
- Accuracy: 0.750
- Macro F1: 0.678
- Micro F1: 0.750
- Weighted F1: 0.737
- Macro Precision: 0.746
- Micro Precision: 0.750
- Weighted Precision: 0.752
- Macro Recall: 0.654
- Micro Recall: 0.750
- Weighted Recall: 0.750
|
[
"classicantique",
"frenchprovence",
"industrial",
"koreaaisa",
"lovelyromantic",
"modern",
"natural",
"simple",
"unique",
"vintageretro"
] |
Leeyuyu/swin-tiny-patch4-window7-224-finetunedo
|
<!-- 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-finetunedo
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: 0.3710
- Roc Auc: 0.8606
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 3 | 0.5066 | 0.7647 |
| No log | 2.0 | 6 | 0.4204 | 0.7941 |
| No log | 3.0 | 9 | 0.4298 | 0.7353 |
| 0.4868 | 4.0 | 12 | 0.4040 | 0.8018 |
| 0.4868 | 5.0 | 15 | 0.3925 | 0.7724 |
| 0.4868 | 6.0 | 18 | 0.3674 | 0.8235 |
| 0.4096 | 7.0 | 21 | 0.3673 | 0.8606 |
| 0.4096 | 8.0 | 24 | 0.3710 | 0.8606 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"m",
"nonm"
] |
FoamoftheSea/pvt_v2_b0
|
# PVTv2
This is the Hugging Face PyTorch implementation of the [PVTv2](https://arxiv.org/abs/2106.13797) model.
## Model Description
The Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks.
Vision models using PVTv2 for a backbone:
1. [Segformer](https://arxiv.org/abs/2105.15203) for Semantic Segmentation.
2. [GLPN](https://arxiv.org/abs/2201.07436) for Monocular Depth.
3. [Deformable DETR](https://arxiv.org/abs/2010.04159) for 2D Object Detection.
4. [Panoptic Segformer](https://arxiv.org/abs/2109.03814) for Panoptic Segmentation.
|
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hilmansw/resnet18-food-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. -->
# Model description
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on an [custom](https://www.kaggle.com/datasets/faldoae/padangfood) dataset. This model was built using the "Padang Cuisine (Indonesian Food Image Classification)" dataset obtained from Kaggle. During the model building process, this was done using the Pytorch framework with pre-trained Resnet-18. The method used during the process of building this classification model is fine-tuning with the dataset.
## Training results
| Epoch | Accuracy |
|:-----:|:--------:|
| 1.0 | 0.6030 |
| 2.0 | 0.8342 |
| 3.0 | 0.8442 |
| 4.0 | 0.8191 |
| 5.0 | 0.8693 |
| 6.0 | 0.8643 |
| 7.0 | 0.8744 |
| 8.0 | 0.8643 |
| 9.0 | 0.8744 |
| 10.0 | 0.8744 |
| 11.0 | 0.8794 |
| 12.0 | 0.8744 |
| 13.0 | 0.8894 |
| 14.0 | 0.8794 |
| 15.0 | 0.8945 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- loss_function = CrossEntropyLoss
- optimizer = AdamW
- learning_rate: 0.00001
- batch_size: 16
- num_epochs: 15
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"ayam_goreng",
"ayam_pop",
"daging_rendang",
"dendeng_batokok",
"gulai_ikan",
"gulai_tambusu",
"gulai_tunjang",
"telur_balado",
"telur_dadar"
] |
dima806/deepfake_vs_real_image_detection
|
Checks whether an image is real or fake (AI-generated).
**Note to users who want to use this model in production**
Beware that this model is trained on a dataset collected about 3 years ago.
Since then, there is a remarkable progress in generating deepfake images with common AI tools, resulting in a significant concept drift.
To mitigate that, I urge you to retrain the model using the latest available labeled data.
As a quick-fix approach, simple reducing the threshold (say from default 0.5 to 0.1 or even 0.01) of labelling image as a fake may suffice.
However, you will do that at your own risk, and retraining the model is the better way of handling the concept drift.
See https://www.kaggle.com/code/dima806/deepfake-vs-real-faces-detection-vit for more details.
```
Classification report:
precision recall f1-score support
Real 0.9921 0.9933 0.9927 38080
Fake 0.9933 0.9921 0.9927 38081
accuracy 0.9927 76161
macro avg 0.9927 0.9927 0.9927 76161
weighted avg 0.9927 0.9927 0.9927 76161
```
|
[
"real",
"fake"
] |
galbitang/autotrain-sofa_1015-95167146296
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95167146296
- CO2 Emissions (in grams): 3.2484
## Validation Metrics
- Loss: 0.860
- Accuracy: 0.698
- Macro F1: 0.628
- Micro F1: 0.698
- Weighted F1: 0.694
- Macro Precision: 0.646
- Micro Precision: 0.698
- Weighted Precision: 0.699
- Macro Recall: 0.625
- Micro Recall: 0.698
- Weighted Recall: 0.698
|
[
"classicantique",
"frenchprovence",
"vintageretro",
"industrial",
"koreaaisa",
"lovelyromantic",
"minimalsimple",
"modern",
"natural",
"notherneurope",
"unique"
] |
Akshay0706/Plant-Diseases-Classification-Training-Arguments
|
<!-- 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. -->
# Plant-Diseases-Classification-Training-Arguments
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 26 | 0.4907 | 0.9524 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"0",
"1"
] |
galbitang/autotrain-ijeongmi_lamp_final-95169146297
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95169146297
- CO2 Emissions (in grams): 2.3970
## Validation Metrics
- Loss: 1.042
- Accuracy: 0.655
- Macro F1: 0.563
- Micro F1: 0.655
- Weighted F1: 0.646
- Macro Precision: 0.602
- Micro Precision: 0.655
- Weighted Precision: 0.652
- Macro Recall: 0.552
- Micro Recall: 0.655
- Weighted Recall: 0.655
|
[
"frenchprovence",
"industrial",
"koreaaisa",
"lovelyromantic",
"modern",
"natural",
"notherneurope",
"unique",
"vintageretro"
] |
galbitang/autotrain-jeongmi_lamp_fffinal-95171146298
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95171146298
- CO2 Emissions (in grams): 2.2590
## Validation Metrics
- Loss: 1.174
- Accuracy: 0.632
- Macro F1: 0.485
- Micro F1: 0.632
- Weighted F1: 0.606
- Macro Precision: 0.617
- Micro Precision: 0.632
- Weighted Precision: 0.630
- Macro Recall: 0.482
- Micro Recall: 0.632
- Weighted Recall: 0.632
|
[
"classicantique",
"frenchprovence",
"vintageretro",
"industrial",
"koreaaisa",
"lovelyromantic",
"minimalsimple",
"modern",
"natural",
"notherneurope",
"unique"
] |
galbitang/autotrain-table_1015-95170146299
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95170146299
- CO2 Emissions (in grams): 0.0626
## Validation Metrics
- Loss: 0.851
- Accuracy: 0.751
- Macro F1: 0.694
- Micro F1: 0.751
- Weighted F1: 0.744
- Macro Precision: 0.728
- Micro Precision: 0.751
- Weighted Precision: 0.747
- Macro Recall: 0.679
- Micro Recall: 0.751
- Weighted Recall: 0.751
|
[
"classicantique",
"frenchprovence",
"vintageretro",
"industrial",
"koreaaisa",
"lovelyromantic",
"minimalsimple",
"modern",
"natural",
"notherneurope",
"unique"
] |
fahmindra/padang_cuisine_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. -->
# padang_cuisine_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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8549
- Accuracy: 0.9509
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1256 | 0.98 | 10 | 2.0189 | 0.6012 |
| 1.839 | 1.95 | 20 | 1.6867 | 0.8834 |
| 1.5149 | 2.93 | 30 | 1.3800 | 0.9080 |
| 1.2405 | 4.0 | 41 | 1.1324 | 0.9141 |
| 1.0359 | 4.98 | 51 | 0.9649 | 0.9387 |
| 0.874 | 5.95 | 61 | 0.8402 | 0.9448 |
| 0.766 | 6.93 | 71 | 0.7901 | 0.9387 |
| 0.7065 | 8.0 | 82 | 0.7175 | 0.9448 |
| 0.6558 | 8.98 | 92 | 0.7112 | 0.9387 |
| 0.6537 | 9.76 | 100 | 0.7114 | 0.9325 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"ayam_goreng",
"ayam_pop",
"daging_rendang",
"dendeng_batokok",
"gulai_ikan",
"gulai_tambusu",
"gulai_tunjang",
"telur_balado",
"telur_dadar"
] |
dima806/133_dog_breeds_image_detection
|
Returns dog breed given an image.
See https://www.kaggle.com/code/dima806/133-dog-breed-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
Norwich_terrier 0.8750 0.8974 0.8861 39
Bichon_frise 0.8125 1.0000 0.8966 39
Entlebucher_mountain_dog 0.8889 0.6316 0.7385 38
Briard 1.0000 1.0000 1.0000 39
Norwegian_elkhound 0.9487 0.9487 0.9487 39
Field_spaniel 0.6731 0.9211 0.7778 38
Gordon_setter 0.9500 1.0000 0.9744 38
Cocker_spaniel 0.8378 0.8158 0.8267 38
Irish_setter 1.0000 0.9231 0.9600 39
Wirehaired_pointing_griffon 0.7600 0.9744 0.8539 39
Giant_schnauzer 1.0000 0.9737 0.9867 38
Maltese 0.7755 1.0000 0.8736 38
English_springer_spaniel 0.8571 0.9474 0.9000 38
Bernese_mountain_dog 1.0000 0.9231 0.9600 39
Alaskan_malamute 1.0000 1.0000 1.0000 38
American_eskimo_dog 0.9500 1.0000 0.9744 38
Havanese 0.0000 0.0000 0.0000 38
Icelandic_sheepdog 0.9412 0.8421 0.8889 38
Manchester_terrier 0.8298 1.0000 0.9070 39
Dogue_de_bordeaux 0.9048 0.9744 0.9383 39
Cardigan_welsh_corgi 0.9231 0.6154 0.7385 39
Norfolk_terrier 0.9487 0.9487 0.9487 39
Canaan_dog 0.8800 0.5789 0.6984 38
Clumber_spaniel 0.9737 0.9737 0.9737 38
Black_russian_terrier 0.9286 1.0000 0.9630 39
German_shepherd_dog 0.8780 0.9474 0.9114 38
Affenpinscher 0.8837 0.9744 0.9268 39
Bearded_collie 0.9697 0.8421 0.9014 38
Chinese_shar-pei 0.9677 0.7692 0.8571 39
Labrador_retriever 0.9333 0.3684 0.5283 38
Irish_terrier 0.9714 0.8947 0.9315 38
Chinese_crested 1.0000 0.8421 0.9143 38
Anatolian_shepherd_dog 1.0000 0.8947 0.9444 38
Brittany 1.0000 0.8947 0.9444 38
Norwegian_buhund 0.8372 0.9474 0.8889 38
Miniature_schnauzer 0.9512 1.0000 0.9750 39
Xoloitzcuintli 0.9750 1.0000 0.9873 39
Dalmatian 0.8667 1.0000 0.9286 39
Greyhound 0.8750 0.9211 0.8974 38
Leonberger 1.0000 1.0000 1.0000 39
Ibizan_hound 1.0000 0.9487 0.9737 39
Bloodhound 1.0000 1.0000 1.0000 38
Bluetick_coonhound 1.0000 1.0000 1.0000 39
English_setter 1.0000 1.0000 1.0000 38
Neapolitan_mastiff 0.8864 1.0000 0.9398 39
Parson_russell_terrier 0.9167 0.8462 0.8800 39
Brussels_griffon 0.9714 0.8947 0.9315 38
Bulldog 0.9268 1.0000 0.9620 38
Bullmastiff 0.7857 0.5641 0.6567 39
Borzoi 1.0000 1.0000 1.0000 38
Poodle 1.0000 0.8421 0.9143 38
Kuvasz 0.8500 0.8947 0.8718 38
Plott 0.8810 0.9737 0.9250 38
Belgian_malinois 0.9722 0.9211 0.9459 38
Japanese_chin 0.9286 1.0000 0.9630 39
Smooth_fox_terrier 0.9024 0.9737 0.9367 38
Flat-coated_retriever 0.8298 1.0000 0.9070 39
Pointer 1.0000 0.6316 0.7742 38
Otterhound 0.9487 0.9737 0.9610 38
Pomeranian 0.9167 0.8684 0.8919 38
Lhasa_apso 0.8444 0.9744 0.9048 39
Bouvier_des_flandres 0.9737 0.9737 0.9737 38
Irish_water_spaniel 0.9730 0.9474 0.9600 38
Old_english_sheepdog 0.8837 0.9744 0.9268 39
Basset_hound 1.0000 0.9744 0.9870 39
American_water_spaniel 0.8571 0.9474 0.9000 38
Airedale_terrier 0.7308 1.0000 0.8444 38
Border_terrier 0.9730 0.9474 0.9600 38
Irish_wolfhound 1.0000 1.0000 1.0000 39
Yorkshire_terrier 0.7037 1.0000 0.8261 38
Papillon 0.9048 1.0000 0.9500 38
Dachshund 1.0000 0.7895 0.8824 38
Cavalier_king_charles_spaniel 0.8140 0.9211 0.8642 38
Tibetan_mastiff 1.0000 0.9487 0.9737 39
Pekingese 1.0000 0.9211 0.9589 38
German_wirehaired_pointer 1.0000 0.6316 0.7742 38
Doberman_pinscher 0.6102 0.9474 0.7423 38
Keeshond 1.0000 1.0000 1.0000 39
Dandie_dinmont_terrier 1.0000 0.9737 0.9867 38
American_staffordshire_terrier 0.8718 0.8947 0.8831 38
Cairn_terrier 1.0000 0.9744 0.9870 39
Portuguese_water_dog 0.9722 0.8974 0.9333 39
Golden_retriever 0.9000 0.9474 0.9231 38
Basenji 0.8125 1.0000 0.8966 39
Bedlington_terrier 1.0000 0.9737 0.9867 38
Newfoundland 0.9737 0.9737 0.9737 38
Boxer 0.8444 0.9744 0.9048 39
Pembroke_welsh_corgi 0.6923 0.9474 0.8000 38
German_pinscher 1.0000 0.3846 0.5556 39
Chesapeake_bay_retriever 1.0000 0.9474 0.9730 38
Chow_chow 1.0000 1.0000 1.0000 38
Collie 0.9500 1.0000 0.9744 38
Komondor 1.0000 1.0000 1.0000 38
Boston_terrier 1.0000 1.0000 1.0000 39
Glen_of_imaal_terrier 0.9231 0.9231 0.9231 39
Beauceron 0.9429 0.8462 0.8919 39
Belgian_sheepdog 1.0000 1.0000 1.0000 38
Bull_terrier 1.0000 0.9737 0.9867 38
German_shorthaired_pointer 0.7917 1.0000 0.8837 38
Silky_terrier 0.9545 0.5526 0.7000 38
Great_dane 0.9630 0.6667 0.7879 39
French_bulldog 1.0000 0.9474 0.9730 38
Welsh_springer_spaniel 0.7600 1.0000 0.8636 38
Curly-coated_retriever 0.8810 0.9487 0.9136 39
Cane_corso 0.8250 0.8462 0.8354 39
Italian_greyhound 0.8780 0.9231 0.9000 39
Australian_terrier 0.9487 0.9487 0.9487 39
Australian_shepherd 0.9722 0.9211 0.9459 38
Belgian_tervuren 0.9500 0.9744 0.9620 39
Lakeland_terrier 1.0000 0.5263 0.6897 38
Finnish_spitz 0.9000 0.9474 0.9231 38
English_toy_spaniel 0.9375 0.7895 0.8571 38
Boykin_spaniel 0.8750 0.5526 0.6774 38
Pharaoh_hound 0.9024 0.9737 0.9367 38
Afghan_hound 0.9250 0.9487 0.9367 39
American_foxhound 0.9355 0.7436 0.8286 39
Lowchen 0.5965 0.8718 0.7083 39
Mastiff 0.7500 0.9474 0.8372 38
Petit_basset_griffon_vendeen 0.9070 1.0000 0.9512 39
Kerry_blue_terrier 0.8478 1.0000 0.9176 39
Irish_red_and_white_setter 0.8919 0.8462 0.8684 39
Australian_cattle_dog 1.0000 0.9474 0.9730 38
Beagle 0.7551 0.9737 0.8506 38
Great_pyrenees 0.7805 0.8421 0.8101 38
Border_collie 0.9744 1.0000 0.9870 38
Saint_bernard 1.0000 1.0000 1.0000 38
Akita 0.8182 0.7105 0.7606 38
Norwegian_lundehund 0.8261 1.0000 0.9048 38
Nova_scotia_duck_tolling_retriever 0.9211 0.9211 0.9211 38
Greater_swiss_mountain_dog 0.6667 0.9231 0.7742 39
Chihuahua 1.0000 0.9487 0.9737 39
Black_and_tan_coonhound 0.8667 1.0000 0.9286 39
English_cocker_spaniel 0.8710 0.7105 0.7826 38
accuracy 0.9017 5108
macro avg 0.9061 0.9015 0.8955 5108
weighted avg 0.9061 0.9017 0.8957 5108
```
|
[
"norwich_terrier",
"bichon_frise",
"entlebucher_mountain_dog",
"briard",
"norwegian_elkhound",
"field_spaniel",
"gordon_setter",
"cocker_spaniel",
"irish_setter",
"wirehaired_pointing_griffon",
"giant_schnauzer",
"maltese",
"english_springer_spaniel",
"bernese_mountain_dog",
"alaskan_malamute",
"american_eskimo_dog",
"havanese",
"icelandic_sheepdog",
"manchester_terrier",
"dogue_de_bordeaux",
"cardigan_welsh_corgi",
"norfolk_terrier",
"canaan_dog",
"clumber_spaniel",
"black_russian_terrier",
"german_shepherd_dog",
"affenpinscher",
"bearded_collie",
"chinese_shar-pei",
"labrador_retriever",
"irish_terrier",
"chinese_crested",
"anatolian_shepherd_dog",
"brittany",
"norwegian_buhund",
"miniature_schnauzer",
"xoloitzcuintli",
"dalmatian",
"greyhound",
"leonberger",
"ibizan_hound",
"bloodhound",
"bluetick_coonhound",
"english_setter",
"neapolitan_mastiff",
"parson_russell_terrier",
"brussels_griffon",
"bulldog",
"bullmastiff",
"borzoi",
"poodle",
"kuvasz",
"plott",
"belgian_malinois",
"japanese_chin",
"smooth_fox_terrier",
"flat-coated_retriever",
"pointer",
"otterhound",
"pomeranian",
"lhasa_apso",
"bouvier_des_flandres",
"irish_water_spaniel",
"old_english_sheepdog",
"basset_hound",
"american_water_spaniel",
"airedale_terrier",
"border_terrier",
"irish_wolfhound",
"yorkshire_terrier",
"papillon",
"dachshund",
"cavalier_king_charles_spaniel",
"tibetan_mastiff",
"pekingese",
"german_wirehaired_pointer",
"doberman_pinscher",
"keeshond",
"dandie_dinmont_terrier",
"american_staffordshire_terrier",
"cairn_terrier",
"portuguese_water_dog",
"golden_retriever",
"basenji",
"bedlington_terrier",
"newfoundland",
"boxer",
"pembroke_welsh_corgi",
"german_pinscher",
"chesapeake_bay_retriever",
"chow_chow",
"collie",
"komondor",
"boston_terrier",
"glen_of_imaal_terrier",
"beauceron",
"belgian_sheepdog",
"bull_terrier",
"german_shorthaired_pointer",
"silky_terrier",
"great_dane",
"french_bulldog",
"welsh_springer_spaniel",
"curly-coated_retriever",
"cane_corso",
"italian_greyhound",
"australian_terrier",
"australian_shepherd",
"belgian_tervuren",
"lakeland_terrier",
"finnish_spitz",
"english_toy_spaniel",
"boykin_spaniel",
"pharaoh_hound",
"afghan_hound",
"american_foxhound",
"lowchen",
"mastiff",
"petit_basset_griffon_vendeen",
"kerry_blue_terrier",
"irish_red_and_white_setter",
"australian_cattle_dog",
"beagle",
"great_pyrenees",
"border_collie",
"saint_bernard",
"akita",
"norwegian_lundehund",
"nova_scotia_duck_tolling_retriever",
"greater_swiss_mountain_dog",
"chihuahua",
"black_and_tan_coonhound",
"english_cocker_spaniel"
] |
galbitang/autotrain-lamp_1015-95249146314
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95249146314
- CO2 Emissions (in grams): 0.0513
## Validation Metrics
- Loss: 1.035
- Accuracy: 0.660
- Macro F1: 0.478
- Micro F1: 0.660
- Weighted F1: 0.624
- Macro Precision: 0.525
- Micro Precision: 0.660
- Weighted Precision: 0.614
- Macro Recall: 0.490
- Micro Recall: 0.660
- Weighted Recall: 0.660
|
[
"classicantique",
"frenchprovence",
"vintageretro",
"industrial",
"koreaasia",
"lovelyromantic",
"minimalsimple",
"modern",
"natural",
"notherneurope",
"unique"
] |
galbitang/autotrain-bed_frame_merge_vit-95266146325
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95266146325
- CO2 Emissions (in grams): 0.0627
## Validation Metrics
- Loss: 0.409
- Accuracy: 0.872
- Macro F1: 0.868
- Micro F1: 0.872
- Weighted F1: 0.872
- Macro Precision: 0.879
- Micro Precision: 0.872
- Weighted Precision: 0.873
- Macro Recall: 0.860
- Micro Recall: 0.872
- Weighted Recall: 0.872
|
[
"classicantique",
"frenchprovence",
"industrial",
"koreaasia",
"lovelyromantic",
"modern",
"natural",
"simple",
"unique",
"vintageretro"
] |
galbitang/autotrain-chair_merge_vit-95268146326
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95268146326
- CO2 Emissions (in grams): 2.8301
## Validation Metrics
- Loss: 0.607
- Accuracy: 0.814
- Macro F1: 0.674
- Micro F1: 0.814
- Weighted F1: 0.801
- Macro Precision: 0.682
- Micro Precision: 0.814
- Weighted Precision: 0.797
- Macro Recall: 0.676
- Micro Recall: 0.814
- Weighted Recall: 0.814
|
[
"classsicantique",
"frenchprovence",
"industrial",
"koreaasia",
"lovelyromantic",
"modern",
"natural",
"simple",
"unique",
"vintageretro"
] |
galbitang/autotrain-sofa_merge_vit-95267146327
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95267146327
- CO2 Emissions (in grams): 3.5112
## Validation Metrics
- Loss: 0.678
- Accuracy: 0.784
- Macro F1: 0.740
- Micro F1: 0.784
- Weighted F1: 0.778
- Macro Precision: 0.767
- Micro Precision: 0.784
- Weighted Precision: 0.786
- Macro Recall: 0.739
- Micro Recall: 0.784
- Weighted Recall: 0.784
|
[
"classicantique",
"frenchprovence",
"industrial",
"koreaasia",
"lovelyromantic",
"modern",
"natural",
"simple",
"unique",
"vintageretro"
] |
galbitang/autotrain-table_merge_vit_2-95271146330
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95271146330
- CO2 Emissions (in grams): 0.0864
## Validation Metrics
- Loss: 0.690
- Accuracy: 0.810
- Macro F1: 0.788
- Micro F1: 0.810
- Weighted F1: 0.807
- Macro Precision: 0.815
- Micro Precision: 0.810
- Weighted Precision: 0.813
- Macro Recall: 0.776
- Micro Recall: 0.810
- Weighted Recall: 0.810
|
[
"classicantique",
"frenchprovence",
"industrial",
"koreaasia",
"lovelyromantic",
"modern",
"natural",
"simple",
"unique",
"vintageretro"
] |
dima806/ai_vs_real_image_detection
|
Checks whether the image is real or fake (AI-generated).
**Note to users who want to use this model in production:**
Beware that this model is trained on a dataset collected about 2 years ago. Since then, there is a remarkable progress in generating deepfake images with common AI tools, resulting in a significant concept drift. To mitigate that, I urge you to retrain the model using the latest available labeled data. As a quick-fix approach, simple reducing the threshold (say from default 0.5 to 0.1 or even 0.01) of labelling image as a fake may suffice. However, you will do that at your own risk, and retraining the model is the better way of handling the concept drift.
See https://www.kaggle.com/code/dima806/cifake-ai-generated-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
REAL 0.9868 0.9780 0.9824 24000
FAKE 0.9782 0.9870 0.9826 24000
accuracy 0.9825 48000
macro avg 0.9825 0.9825 0.9825 48000
weighted avg 0.9825 0.9825 0.9825 48000
```
|
[
"real",
"fake"
] |
hchcsuim/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0670
- Accuracy: 0.9748
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2374 | 1.0 | 190 | 0.1074 | 0.9615 |
| 0.1797 | 2.0 | 380 | 0.0838 | 0.9674 |
| 0.111 | 3.0 | 570 | 0.0670 | 0.9748 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
Abhiram4/AnimeCharacterClassifierMark1
|
<!-- 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. -->
# AnimeCharacterClassifierMark1
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6720
- Accuracy: 0.8655
## Model description
More information needed
## Intended uses & 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 42
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0145 | 0.99 | 17 | 4.9303 | 0.0092 |
| 4.8416 | 1.97 | 34 | 4.7487 | 0.0287 |
| 4.4383 | 2.96 | 51 | 4.3597 | 0.1170 |
| 4.0762 | 4.0 | 69 | 3.6419 | 0.3224 |
| 3.108 | 4.99 | 86 | 2.8574 | 0.5246 |
| 2.1571 | 5.97 | 103 | 2.2129 | 0.6653 |
| 1.4685 | 6.96 | 120 | 1.7290 | 0.7495 |
| 1.1649 | 8.0 | 138 | 1.3862 | 0.7977 |
| 0.7905 | 8.99 | 155 | 1.1589 | 0.8214 |
| 0.5549 | 9.97 | 172 | 1.0263 | 0.8296 |
| 0.4577 | 10.96 | 189 | 0.8994 | 0.8368 |
| 0.2964 | 12.0 | 207 | 0.8086 | 0.8552 |
| 0.194 | 12.99 | 224 | 0.7446 | 0.8583 |
| 0.1358 | 13.97 | 241 | 0.7064 | 0.8573 |
| 0.1116 | 14.96 | 258 | 0.6720 | 0.8655 |
| 0.0811 | 16.0 | 276 | 0.6515 | 0.8645 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
[
"abigail_williams_(fate)",
"aegis_(persona)",
"aisaka_taiga",
"albedo",
"anastasia_(idolmaster)",
"aqua_(konosuba)",
"arcueid_brunestud",
"asia_argento",
"astolfo_(fate)",
"asuna_(sao)",
"atago_(azur_lane)",
"ayanami_rei",
"belfast_(azur_lane)",
"bremerton_(azur_lane)",
"c.c",
"chitanda_eru",
"chloe_von_einzbern",
"cleveland_(azur_lane)",
"d.va_(overwatch)",
"dido_(azur_lane)",
"emilia_rezero",
"enterprise_(azur_lane)",
"formidable_(azur_lane)",
"fubuki_(one-punch_man)",
"fujibayashi_kyou",
"fujiwara_chika",
"furukawa_nagisa",
"gawr_gura",
"gilgamesh",
"giorno_giovanna",
"hanekawa_tsubasa",
"hatsune_miku",
"hayasaka_ai",
"hirasawa_yui",
"hyuuga_hinata",
"ichigo_(darling_in_the_franxx)",
"illyasviel_von_einzbern",
"irisviel_von_einzbern",
"ishtar_(fate_grand_order)",
"isshiki_iroha",
"jonathan_joestar",
"kamado_nezuko",
"kaname_madoka",
"kanbaru_suruga",
"karin_(blue_archive)",
"karna_(fate)",
"katsuragi_misato",
"keqing_(genshin_impact)",
"kirito",
"kiryu_coco",
"kizuna_ai",
"kochou_shinobu",
"komi_shouko",
"laffey_(azur_lane)",
"lancer",
"makise_kurisu",
"mash_kyrielight",
"matou_sakura",
"megumin",
"mei_(pokemon)",
"meltlilith",
"minato_aqua",
"misaka_mikoto",
"miyazono_kawori",
"mori_calliope",
"nagato_yuki",
"nakano_azusa",
"nakano_itsuki",
"nakano_miku",
"nakano_nino",
"nakano_yotsuba",
"nami_(one_piece)",
"nekomata_okayu",
"nico_robin",
"ninomae_ina'nis",
"nishikino_maki",
"okita_souji_(fate)",
"ookami_mio",
"oshino_ougi",
"oshino_shinobu",
"ouro_kronii",
"paimon_(genshin_impact)",
"platelet_(hataraku_saibou)",
"ram_rezero",
"raphtalia",
"rem_rezero",
"rias_gremory",
"rider",
"ryougi_shiki",
"sakura_futaba",
"sakurajima_mai",
"sakurauchi_riko",
"satonaka_chie",
"semiramis_(fate)",
"sengoku_nadeko",
"senjougahara_hitagi",
"shidare_hotaru",
"shinomiya_kaguya",
"shirakami_fubuki",
"shirogane_naoto",
"shirogane_noel",
"shishiro_botan",
"shuten_douji_(fate)",
"sinon",
"souryuu_asuka_langley",
"st_ar-15_(girls_frontline)",
"super_sonico",
"suzuhara_lulu",
"suzumiya_haruhi",
"taihou_(azur_lane)",
"takagi-san",
"takamaki_anne",
"takanashi_rikka",
"takao_(azur_lane)",
"takarada_rikka",
"takimoto_hifumi",
"tokoyami_towa",
"toosaka_rin",
"toujou_nozomi",
"tsushima_yoshiko",
"unicorn_(azur_lane)",
"usada_pekora",
"utsumi_erise",
"watson_amelia",
"waver_velvet",
"xenovia_(high_school_dxd)",
"yui_(angel_beats!)",
"yuigahama_yui",
"yukinoshita_yukino",
"zero_two_(darling_in_the_franxx)"
] |
LucyintheSky/model-prediction
|
<!-- 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. -->
# Fashion Model Prediction
## Model description
This model predicts the name of the fashion model in the image. It is trained on [Lucy in the Sky](https://www.lucyinthesky.com/shop) images.
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
## Training and evaluation data
It achieves the following results on the evaluation set:
- Loss: 0.4297
- Accuracy: 0.9435
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"anna",
"bianca",
"mila",
"natasha",
"tailine",
"cat",
"ellie",
"gabby",
"genevive",
"jessica",
"kiele",
"lisa",
"melanie"
] |
seige-ml/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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0961
- Accuracy: 0.3333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.84 | 4 | 1.1132 | 0.32 |
| No log | 1.89 | 9 | 1.0985 | 0.3267 |
| 1.1116 | 2.53 | 12 | 1.0961 | 0.3333 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"0",
"1",
"2"
] |
dima806/faces_age_detection
|
Returns age group with about 91% accuracy based on facial image.
See https://www.kaggle.com/code/dima806/age-group-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
MIDDLE 0.8316 0.9278 0.8771 4321
YOUNG 0.9598 0.8563 0.9051 4322
OLD 0.9552 0.9477 0.9515 4322
accuracy 0.9106 12965
macro avg 0.9155 0.9106 0.9112 12965
weighted avg 0.9155 0.9106 0.9112 12965
```
|
[
"middle",
"young",
"old"
] |
dima806/farm_insects_image_detection
|
Returns farm insect type given an image with about 91% accuracy.
See https://www.kaggle.com/code/dima806/farm-insects-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
Fall Armyworms 0.7895 0.3191 0.4545 47
Western Corn Rootworms 0.9787 0.9787 0.9787 47
Colorado Potato Beetles 1.0000 0.9792 0.9895 48
Thrips 0.9762 0.8723 0.9213 47
Corn Earworms 0.9070 0.8125 0.8571 48
Cabbage Loopers 0.9388 0.9583 0.9485 48
Armyworms 0.6143 0.9149 0.7350 47
Brown Marmorated Stink Bugs 1.0000 1.0000 1.0000 48
Tomato Hornworms 0.9792 1.0000 0.9895 47
Citrus Canker 0.9038 1.0000 0.9495 47
Aphids 0.9020 0.9583 0.9293 48
Corn Borers 0.8148 0.9167 0.8627 48
Fruit Flies 1.0000 1.0000 1.0000 48
Africanized Honey Bees (Killer Bees) 1.0000 1.0000 1.0000 48
Spider Mites 0.9167 0.9167 0.9167 48
accuracy 0.9090 714
macro avg 0.9147 0.9085 0.9022 714
weighted avg 0.9151 0.9090 0.9027 714
```
|
[
"fall armyworms",
"western corn rootworms",
"colorado potato beetles",
"thrips",
"corn earworms",
"cabbage loopers",
"armyworms",
"brown marmorated stink bugs",
"tomato hornworms",
"citrus canker",
"aphids",
"corn borers",
"fruit flies",
"africanized honey bees (killer bees)",
"spider mites"
] |
abelkrw/beans_image_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. -->
# beans_image_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 the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1072
- Accuracy: 0.96
## Model description
More information needed
## Intended uses & 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: 12
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.94 | 8 | 1.3666 | 0.66 |
| 0.3651 | 2.0 | 17 | 0.3823 | 0.84 |
| 0.5622 | 2.94 | 25 | 0.3333 | 0.86 |
| 0.3373 | 4.0 | 34 | 0.1274 | 0.97 |
| 0.2055 | 4.94 | 42 | 0.1882 | 0.93 |
| 0.1819 | 6.0 | 51 | 0.2265 | 0.9 |
| 0.1819 | 6.94 | 59 | 0.2395 | 0.91 |
| 0.2428 | 8.0 | 68 | 0.1451 | 0.97 |
| 0.1305 | 8.94 | 76 | 0.1554 | 0.94 |
| 0.1203 | 9.41 | 80 | 0.1705 | 0.92 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
abhirajeshbhai/weather_vit_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. -->
# weather_vit_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: 0.1100
- Accuracy: 0.9735
## Model description
More information needed
## Intended uses & 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 127 | 0.1199 | 0.9735 |
| No log | 2.0 | 254 | 0.1290 | 0.9646 |
| No log | 3.0 | 381 | 0.1100 | 0.9735 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"cloudy",
"rain",
"shine",
"sunrise"
] |
bryandts/garbage_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. -->
# garbage_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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0790
- Accuracy: 0.9707
## Model description
More information needed
## Intended uses & 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1259 | 1.0 | 1254 | 0.0790 | 0.9707 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"o",
"r"
] |
gianlab/swin-tiny-patch4-window7-224-finetuned-ecg-classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-ecg-classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
## Model description
This model was created by importing the dataset of the photos of ECG image into Google Colab from kaggle here: https://www.kaggle.com/datasets/erhmrai/ecg-image-data/data . I then used the image classification tutorial here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb
obtaining the following notebook:
https://colab.research.google.com/drive/1KC6twirtsc7N1kmlwY3IQKVUmSuK7zlh?usp=sharing
The possible classified data are:
<ul>
<li>N: Normal beat</li>
<li>S: Supraventricular premature beat</li>
<li>V: Premature ventricular contraction</li>
<li>F: Fusion of ventricular and normal beat</li>
<li>Q: Unclassifiable beat</li>
<li>M: myocardial infarction</li>
</ul>
### ECG example:
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0476 | 1.0 | 697 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"f",
"m",
"n",
"q",
"s",
"v"
] |
khleeloo/vit-base-skin
|
<!-- 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-skin
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: 0.6917
- Accuracy: 0.8549
- F1: 0.8552
- Precision: 0.8560
- Recall: 0.8549
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9322 | 0.16 | 100 | 0.8109 | 0.6943 | 0.6290 | 0.5939 | 0.6943 |
| 0.7518 | 0.32 | 200 | 0.6722 | 0.7409 | 0.6832 | 0.6945 | 0.7409 |
| 0.6616 | 0.48 | 300 | 0.7126 | 0.7358 | 0.7077 | 0.7039 | 0.7358 |
| 0.8264 | 0.64 | 400 | 0.6001 | 0.8135 | 0.8092 | 0.8178 | 0.8135 |
| 0.5767 | 0.8 | 500 | 0.6306 | 0.7772 | 0.7619 | 0.7945 | 0.7772 |
| 0.5939 | 0.96 | 600 | 0.4621 | 0.8290 | 0.7988 | 0.8397 | 0.8290 |
| 0.4351 | 1.12 | 700 | 0.5544 | 0.7979 | 0.7894 | 0.8410 | 0.7979 |
| 0.4737 | 1.28 | 800 | 0.5151 | 0.8238 | 0.8334 | 0.8708 | 0.8238 |
| 0.428 | 1.44 | 900 | 0.4980 | 0.8238 | 0.8170 | 0.8299 | 0.8238 |
| 0.4596 | 1.6 | 1000 | 0.5650 | 0.7927 | 0.8032 | 0.8428 | 0.7927 |
| 0.4096 | 1.76 | 1100 | 0.4544 | 0.8342 | 0.8178 | 0.8567 | 0.8342 |
| 0.4328 | 1.92 | 1200 | 0.4524 | 0.8290 | 0.8294 | 0.8482 | 0.8290 |
| 0.2272 | 2.08 | 1300 | 0.4808 | 0.8290 | 0.8304 | 0.8409 | 0.8290 |
| 0.2415 | 2.24 | 1400 | 0.5585 | 0.7927 | 0.7916 | 0.8057 | 0.7927 |
| 0.2743 | 2.4 | 1500 | 0.4144 | 0.8497 | 0.8484 | 0.8497 | 0.8497 |
| 0.1943 | 2.56 | 1600 | 0.3977 | 0.8705 | 0.8722 | 0.8761 | 0.8705 |
| 0.1839 | 2.72 | 1700 | 0.4784 | 0.8394 | 0.8382 | 0.8517 | 0.8394 |
| 0.1905 | 2.88 | 1800 | 0.4314 | 0.8653 | 0.8669 | 0.8724 | 0.8653 |
| 0.111 | 3.04 | 1900 | 0.5080 | 0.8290 | 0.8309 | 0.8348 | 0.8290 |
| 0.0872 | 3.19 | 2000 | 0.5320 | 0.8549 | 0.8520 | 0.8649 | 0.8549 |
| 0.1169 | 3.35 | 2100 | 0.5110 | 0.8342 | 0.8386 | 0.8477 | 0.8342 |
| 0.1181 | 3.51 | 2200 | 0.4916 | 0.8446 | 0.8482 | 0.8563 | 0.8446 |
| 0.0879 | 3.67 | 2300 | 0.5428 | 0.8601 | 0.8657 | 0.8829 | 0.8601 |
| 0.1896 | 3.83 | 2400 | 0.5534 | 0.8497 | 0.8484 | 0.8536 | 0.8497 |
| 0.0794 | 3.99 | 2500 | 0.6542 | 0.8342 | 0.8259 | 0.8270 | 0.8342 |
| 0.0398 | 4.15 | 2600 | 0.5962 | 0.8187 | 0.8243 | 0.8338 | 0.8187 |
| 0.0512 | 4.31 | 2700 | 0.6286 | 0.8497 | 0.8447 | 0.8457 | 0.8497 |
| 0.0106 | 4.47 | 2800 | 0.6446 | 0.8394 | 0.8372 | 0.8377 | 0.8394 |
| 0.0058 | 4.63 | 2900 | 0.5754 | 0.8653 | 0.8616 | 0.8618 | 0.8653 |
| 0.0268 | 4.79 | 3000 | 0.5966 | 0.8653 | 0.8651 | 0.8658 | 0.8653 |
| 0.0146 | 4.95 | 3100 | 0.6707 | 0.8601 | 0.8535 | 0.8577 | 0.8601 |
| 0.0325 | 5.11 | 3200 | 0.6543 | 0.8549 | 0.8518 | 0.8511 | 0.8549 |
| 0.0063 | 5.27 | 3300 | 0.6780 | 0.8497 | 0.8519 | 0.8583 | 0.8497 |
| 0.003 | 5.43 | 3400 | 0.6675 | 0.8601 | 0.8577 | 0.8562 | 0.8601 |
| 0.0143 | 5.59 | 3500 | 0.6967 | 0.8601 | 0.8554 | 0.8539 | 0.8601 |
| 0.004 | 5.75 | 3600 | 0.6992 | 0.8601 | 0.8573 | 0.8552 | 0.8601 |
| 0.003 | 5.91 | 3700 | 0.6917 | 0.8549 | 0.8552 | 0.8560 | 0.8549 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
[
"mel",
"nv",
"bcc",
"akiec",
"bkl",
"df",
"vasc"
] |
regis-funke/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0587
- Accuracy: 0.9804
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.235 | 1.0 | 190 | 0.1109 | 0.9611 |
| 0.1616 | 2.0 | 380 | 0.0706 | 0.9774 |
| 0.1309 | 3.0 | 570 | 0.0587 | 0.9804 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.2.0.dev20231018
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
yusuf802/Leaf-Disease-Predictor
|
<!-- 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. -->
# working
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 leaf-images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0857
- Accuracy: 0.9801
## Model description
Finetuned model on 66000+ images of different species of leaves along with their diseases
## Intended uses & 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: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9728 | 0.08 | 100 | 0.9026 | 0.8922 |
| 0.4538 | 0.17 | 200 | 0.4412 | 0.9270 |
| 0.2368 | 0.25 | 300 | 0.2870 | 0.9399 |
| 0.2388 | 0.34 | 400 | 0.2208 | 0.9504 |
| 0.1422 | 0.42 | 500 | 0.2046 | 0.9508 |
| 0.1663 | 0.51 | 600 | 0.1538 | 0.9625 |
| 0.1535 | 0.59 | 700 | 0.1427 | 0.9653 |
| 0.1233 | 0.68 | 800 | 0.1133 | 0.9724 |
| 0.1079 | 0.76 | 900 | 0.1005 | 0.9759 |
| 0.1154 | 0.84 | 1000 | 0.0989 | 0.9748 |
| 0.08 | 0.93 | 1100 | 0.0857 | 0.9801 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
[
"apple_black_rot",
"apple_cedar_apple_rust",
"corn_(maize)_healthy",
"cotton_leaf_diseased",
"cotton_leaf_fresh",
"grape_black_rot",
"grape___esca_(black_measles)",
"grape___leaf_blight_(isariopsis_leaf_spot)",
"grape___healthy",
"orange_haunglongbing_(citrus_greening)",
"orange__black_rot",
"orange__canker",
"apple_powdery_mildew",
"orange__healthy",
"peach_bacterial_spot",
"peach_healthy",
"pepper,_bell_bacterial_spot",
"pepper,_bell_healthy",
"potato_early_blight",
"potato_late_blight",
"potato_healthy",
"squash_powdery_mildew",
"strawberry_leaf_scorch",
"apple_healthy",
"strawberry_healthy",
"tomato_bacterial_spot",
"tomato_early_blight",
"tomato_late_blight",
"tomato_leaf_mold",
"tomato_septoria_leaf_spot",
"tomato_spider_mites_two_spotted_spider_mite",
"tomato_target_spot",
"tomato_tomato_yellow_leaf_curl_virus",
"tomato_tomato_mosaic_virus",
"apple_scab",
"tomato_healthy",
"wheat_healthy",
"wheat_leaf_rust",
"wheat_nitrogen_deficiency",
"cherry_(including_sour)_powdery_mildew",
"cherry_(including_sour)_healthy",
"corn_(maize)_cercospora_leaf_spot gray_leaf_spot",
"corn_(maize)_common_rust",
"corn_(maize)_northern_leaf_blight"
] |
platzi/platzi-vit-model-gio-testing
|
<!-- 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. -->
# platzi-vit-model-gio-testing
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0153
- 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1508 | 3.85 | 500 | 0.0153 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Abhiram4/SwinMark2
|
<!-- 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. -->
# SwinMark2
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Accuracy: 0.9666
## Model description
More information needed
## Intended uses & 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1407 | 1.0 | 231 | 0.1230 | 0.9586 |
| 0.1209 | 2.0 | 462 | 0.1066 | 0.9630 |
| 0.0987 | 3.0 | 693 | 0.0952 | 0.9666 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
[
"cnv",
"dme",
"drusen",
"normal"
] |
zkdeng/resnet-50-finetuned-dangerousSpiders
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-50-finetuned-dangerousSpiders
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.8733
- eval_accuracy: 0.5635
- eval_precision: 0.1112
- eval_recall: 0.0821
- eval_f1: 0.0750
- eval_runtime: 120.0747
- eval_samples_per_second: 224.177
- eval_steps_per_second: 14.016
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Framework versions
- Transformers 4.33.2
- Pytorch 2.2.0.dev20230921
- Datasets 2.14.5
- Tokenizers 0.13.3
|
[
"acantholycosa_lignaria",
"aculepeira_ceropegia",
"agalenatea_redii",
"agelena_labyrinthica",
"aglaoctenus_castaneus",
"aglaoctenus_lagotis",
"allocosa_funerea",
"allotrochosina_schauinslandi",
"alopecosa_albofasciata",
"alopecosa_barbipes",
"alopecosa_cuneata",
"alopecosa_inquilina",
"alopecosa_kochi",
"alopecosa_pulverulenta",
"anahita_punctulata",
"anasaitis_canosa",
"ancylometes_bogotensis",
"ancylometes_concolor",
"ancylometes_rufus",
"anoteropsis_hilaris",
"anoteropsis_litoralis",
"anyphaena_accentuata",
"aphonopelma_hentzi",
"araneus_diadematus",
"araneus_marmoreus",
"araneus_quadratus",
"araneus_trifolium",
"araniella_displicata",
"arctosa_cinerea",
"arctosa_leopardus",
"arctosa_littoralis",
"arctosa_perita",
"arctosa_personata",
"argiope_argentata",
"argiope_aurantia",
"argiope_bruennichi",
"argiope_keyserlingi",
"argiope_lobata",
"argiope_trifasciata",
"asthenoctenus_borellii",
"attulus_fasciger",
"aulonia_albimana",
"austracantha_minax",
"badumna_longinqua",
"carrhotus_xanthogramma",
"centroctenus_brevipes",
"cheiracanthium_erraticum",
"cheiracanthium_gracile",
"cheiracanthium_inclusum",
"cheiracanthium_mildei",
"cheiracanthium_punctorium",
"colonus_hesperus",
"colonus_sylvanus",
"ctenus_amphora",
"ctenus_hibernalis",
"ctenus_medius",
"ctenus_ornatus",
"cupiennius_coccineus",
"cupiennius_getazi",
"cupiennius_salei",
"cyclosa_conica",
"cyrtophora_citricola",
"diapontia_uruguayensis",
"dolomedes_albineus",
"dolomedes_minor",
"dolomedes_scriptus",
"dolomedes_tenebrosus",
"dolomedes_triton",
"dysdera_crocata",
"ebrechtella_tricuspidata",
"enoplognatha_ovata",
"eratigena_agrestis",
"eratigena_duellica",
"eriophora_ravilla",
"eris_militaris",
"evarcha_arcuata",
"gasteracantha_cancriformis",
"geolycosa_vultuosa",
"gladicosa_gulosa",
"gladicosa_pulchra",
"habronattus_pyrrithrix",
"hasarius_adansoni",
"helpis_minitabunda",
"hentzia_mitrata",
"hentzia_palmarum",
"herpyllus_ecclesiasticus",
"heteropoda_venatoria",
"hippasa_holmerae",
"hogna_antelucana",
"hogna_baltimoriana",
"hogna_bivittata",
"hogna_carolinensis",
"hogna_crispipes",
"hogna_frondicola",
"hogna_gumia",
"hogna_radiata",
"holocnemus_pluchei",
"kukulcania_hibernalis",
"lampona_cylindrata",
"larinioides_cornutus",
"larinioides_sclopetarius",
"latrodectus_bishopi",
"latrodectus_curacaviensis",
"latrodectus_geometricus",
"latrodectus_hasselti",
"latrodectus_hesperus",
"latrodectus_katipo",
"latrodectus_mactans",
"latrodectus_mirabilis",
"latrodectus_renivulvatus",
"latrodectus_tredecimguttatus",
"latrodectus_variolus",
"leucauge_argyra",
"leucauge_argyrobapta",
"leucauge_dromedaria",
"leucauge_venusta",
"loxosceles_amazonica",
"loxosceles_deserta",
"loxosceles_laeta",
"loxosceles_reclusa",
"loxosceles_rufescens",
"loxosceles_tenochtitlan",
"loxosceles_yucatana",
"lycosa_erythrognatha",
"lycosa_hispanica",
"lycosa_pampeana",
"lycosa_praegrandis",
"lycosa_singoriensis",
"lycosa_tarantula",
"lyssomanes_viridis",
"maevia_inclemens",
"mangora_acalypha",
"maratus_griseus",
"marpissa_muscosa",
"mecynogea_lemniscata",
"menemerus_bivittatus",
"menemerus_semilimbatus",
"micrathena_gracilis",
"micrathena_sagittata",
"micrommata_virescens",
"missulena_bradleyi",
"missulena_occatoria",
"misumena_vatia",
"misumenoides_formosipes",
"misumessus_oblongus",
"naphrys_pulex",
"neoscona_arabesca",
"neoscona_crucifera",
"neoscona_oaxacensis",
"nephila_pilipes",
"neriene_radiata",
"nesticodes_rufipes",
"nuctenea_umbratica",
"oxyopes_salticus",
"oxyopes_scalaris",
"paraphidippus_aurantius",
"parasteatoda_tepidariorum",
"paratrochosina_amica",
"pardosa_amentata",
"pardosa_lapidicina",
"pardosa_mercurialis",
"pardosa_moesta",
"pardosa_wagleri",
"peucetia_viridans",
"phidippus_audax",
"phidippus_clarus",
"phidippus_johnsoni",
"phidippus_putnami",
"philaeus_chrysops",
"philodromus_dispar",
"pholcus_phalangioides",
"phoneutria_boliviensis",
"phoneutria_depilata",
"phoneutria_fera",
"phoneutria_nigriventer",
"phoneutria_pertyi",
"phoneutria_reidyi",
"pirata_piraticus",
"pisaura_mirabilis",
"pisaurina_mira",
"platycryptus_californicus",
"platycryptus_undatus",
"plebs_eburnus",
"plexippus_paykulli",
"portacosa_cinerea",
"rabidosa_hentzi",
"rabidosa_punctulata",
"rabidosa_rabida",
"salticus_scenicus",
"sassacus_vitis",
"schizocosa_avida",
"schizocosa_malitiosa",
"schizocosa_mccooki",
"scytodes_thoracica",
"sicarius_thomisoides",
"socca_pustulosa",
"sosippus_californicus",
"steatoda_grossa",
"steatoda_nobilis",
"steatoda_triangulosa",
"synema_globosum",
"thomisus_onustus",
"tigrosa_annexa",
"tigrosa_aspersa",
"tigrosa_georgicola",
"tigrosa_helluo",
"trichonephila_clavata",
"trichonephila_clavipes",
"trichonephila_edulis",
"trichonephila_plumipes",
"trochosa_ruricola",
"trochosa_sepulchralis",
"trochosa_terricola",
"tropicosa_moesta",
"venator_immansuetus",
"venator_spenceri",
"venatrix_furcillata",
"verrucosa_arenata",
"wadicosa_fidelis",
"xerolycosa_miniata",
"xerolycosa_nemoralis",
"zoropsis_spinimana",
"zygiella_x-notata"
] |
diana9m/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: 4.5666
- Accuracy: 0.7778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 4.5666 | 0.7778 |
| 5.077 | 2.0 | 13 | 1.7078 | 0.7778 |
| 5.077 | 2.77 | 18 | 1.4156 | 0.7778 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
|
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bdpc/vit-base_rvl_cdip_aurc
|
<!-- 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_rvl_cdip_aurc
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2759
- Accuracy: 0.893
- Brier Loss: 0.1798
- Nll: 0.8614
- F1 Micro: 0.893
- F1 Macro: 0.8928
- Ece: 0.0750
- Aurc: 0.0215
## Model description
More information needed
## Intended uses & 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0303 | 1.0 | 500 | 0.1865 | 0.8795 | 0.1840 | 1.2087 | 0.8795 | 0.8791 | 0.0495 | 0.0241 |
| 0.0262 | 2.0 | 1000 | 0.2146 | 0.8788 | 0.1909 | 1.1956 | 0.8788 | 0.8789 | 0.0603 | 0.0257 |
| 0.0121 | 3.0 | 1500 | 0.2117 | 0.886 | 0.1799 | 1.0878 | 0.886 | 0.8865 | 0.0611 | 0.0230 |
| 0.0057 | 4.0 | 2000 | 0.2279 | 0.8878 | 0.1803 | 1.0108 | 0.8878 | 0.8879 | 0.0678 | 0.0228 |
| 0.0038 | 5.0 | 2500 | 0.2491 | 0.8872 | 0.1827 | 0.9661 | 0.8872 | 0.8877 | 0.0725 | 0.0234 |
| 0.0028 | 6.0 | 3000 | 0.2398 | 0.89 | 0.1806 | 0.9378 | 0.89 | 0.8901 | 0.0725 | 0.0215 |
| 0.0016 | 7.0 | 3500 | 0.2736 | 0.891 | 0.1792 | 0.8975 | 0.891 | 0.8914 | 0.0744 | 0.0221 |
| 0.0014 | 8.0 | 4000 | 0.2357 | 0.8905 | 0.1811 | 0.8993 | 0.8905 | 0.8910 | 0.0764 | 0.0210 |
| 0.001 | 9.0 | 4500 | 0.2714 | 0.8898 | 0.1807 | 0.8650 | 0.8898 | 0.8897 | 0.0783 | 0.0213 |
| 0.0009 | 10.0 | 5000 | 0.2759 | 0.893 | 0.1798 | 0.8614 | 0.893 | 0.8928 | 0.0750 | 0.0215 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip_ce
|
<!-- 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_rvl_cdip_ce
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5626
- Accuracy: 0.8932
- Brier Loss: 0.1854
- Nll: 0.8898
- F1 Micro: 0.8932
- F1 Macro: 0.8934
- Ece: 0.0831
- Aurc: 0.0199
## Model description
More information needed
## Intended uses & 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1771 | 1.0 | 500 | 0.4123 | 0.887 | 0.1720 | 1.2003 | 0.887 | 0.8872 | 0.0534 | 0.0204 |
| 0.1349 | 2.0 | 1000 | 0.4344 | 0.8895 | 0.1754 | 1.1219 | 0.8895 | 0.8900 | 0.0614 | 0.0207 |
| 0.0656 | 3.0 | 1500 | 0.4602 | 0.8852 | 0.1836 | 1.0477 | 0.8852 | 0.8856 | 0.0734 | 0.0197 |
| 0.0314 | 4.0 | 2000 | 0.5044 | 0.889 | 0.1851 | 1.0124 | 0.889 | 0.8888 | 0.0729 | 0.0230 |
| 0.0134 | 5.0 | 2500 | 0.5193 | 0.8895 | 0.1861 | 0.9779 | 0.8895 | 0.8905 | 0.0803 | 0.0207 |
| 0.0075 | 6.0 | 3000 | 0.5300 | 0.8915 | 0.1848 | 0.9515 | 0.8915 | 0.8922 | 0.0793 | 0.0203 |
| 0.0057 | 7.0 | 3500 | 0.5552 | 0.89 | 0.1893 | 0.9200 | 0.89 | 0.8897 | 0.0852 | 0.0205 |
| 0.0047 | 8.0 | 4000 | 0.5589 | 0.892 | 0.1871 | 0.9245 | 0.892 | 0.8923 | 0.0826 | 0.0198 |
| 0.0046 | 9.0 | 4500 | 0.5620 | 0.8935 | 0.1854 | 0.8987 | 0.8935 | 0.8937 | 0.0828 | 0.0199 |
| 0.0042 | 10.0 | 5000 | 0.5626 | 0.8932 | 0.1854 | 0.8898 | 0.8932 | 0.8934 | 0.0831 | 0.0199 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_AURC_64
|
<!-- 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_rvl_cdip-N1K_AURC_64
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3118
- Accuracy: 0.8952
- Brier Loss: 0.1766
- Nll: 0.8835
- F1 Micro: 0.8952
- F1 Macro: 0.8951
- Ece: 0.0747
- Aurc: 0.0206
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 250 | 0.1770 | 0.8875 | 0.1709 | 1.2031 | 0.8875 | 0.8885 | 0.0519 | 0.0208 |
| 0.0228 | 2.0 | 500 | 0.2135 | 0.8852 | 0.1813 | 1.1542 | 0.8852 | 0.8853 | 0.0557 | 0.0228 |
| 0.0228 | 3.0 | 750 | 0.1750 | 0.8918 | 0.1729 | 1.0088 | 0.8918 | 0.8917 | 0.0628 | 0.0192 |
| 0.0066 | 4.0 | 1000 | 0.2117 | 0.8955 | 0.1697 | 0.9611 | 0.8955 | 0.8954 | 0.0655 | 0.0189 |
| 0.0066 | 5.0 | 1250 | 0.2578 | 0.8958 | 0.1714 | 0.9234 | 0.8958 | 0.8958 | 0.0690 | 0.0194 |
| 0.0021 | 6.0 | 1500 | 0.2752 | 0.8962 | 0.1730 | 0.9093 | 0.8962 | 0.8964 | 0.0709 | 0.0197 |
| 0.0021 | 7.0 | 1750 | 0.2949 | 0.8972 | 0.1748 | 0.8841 | 0.8972 | 0.8972 | 0.0708 | 0.0200 |
| 0.0014 | 8.0 | 2000 | 0.3037 | 0.8955 | 0.1755 | 0.8842 | 0.8955 | 0.8954 | 0.0739 | 0.0204 |
| 0.0014 | 9.0 | 2250 | 0.3045 | 0.8952 | 0.1764 | 0.8839 | 0.8952 | 0.8951 | 0.0741 | 0.0206 |
| 0.0013 | 10.0 | 2500 | 0.3118 | 0.8952 | 0.1766 | 0.8835 | 0.8952 | 0.8951 | 0.0747 | 0.0206 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_ce_64
|
<!-- 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_rvl_cdip-N1K_ce_64
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5145
- Accuracy: 0.8908
- Brier Loss: 0.1847
- Nll: 0.9466
- F1 Micro: 0.8907
- F1 Macro: 0.8910
- Ece: 0.0829
- Aurc: 0.0191
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 250 | 0.4009 | 0.8892 | 0.1695 | 1.1791 | 0.8892 | 0.8896 | 0.0538 | 0.0185 |
| 0.1472 | 2.0 | 500 | 0.4214 | 0.8938 | 0.1688 | 1.1365 | 0.8938 | 0.8948 | 0.0527 | 0.0199 |
| 0.1472 | 3.0 | 750 | 0.4245 | 0.8898 | 0.1722 | 1.0919 | 0.8898 | 0.8900 | 0.0633 | 0.0185 |
| 0.0462 | 4.0 | 1000 | 0.4571 | 0.891 | 0.1776 | 1.0386 | 0.891 | 0.8914 | 0.0699 | 0.0198 |
| 0.0462 | 5.0 | 1250 | 0.4775 | 0.8922 | 0.1797 | 1.0236 | 0.8922 | 0.8926 | 0.0745 | 0.0196 |
| 0.0118 | 6.0 | 1500 | 0.4953 | 0.8878 | 0.1845 | 0.9920 | 0.8878 | 0.8882 | 0.0823 | 0.0190 |
| 0.0118 | 7.0 | 1750 | 0.5052 | 0.89 | 0.1847 | 0.9631 | 0.89 | 0.8903 | 0.0820 | 0.0193 |
| 0.0065 | 8.0 | 2000 | 0.5068 | 0.8905 | 0.1832 | 0.9653 | 0.8905 | 0.8910 | 0.0816 | 0.0190 |
| 0.0065 | 9.0 | 2250 | 0.5143 | 0.8905 | 0.1850 | 0.9551 | 0.8905 | 0.8908 | 0.0833 | 0.0191 |
| 0.0053 | 10.0 | 2500 | 0.5145 | 0.8908 | 0.1847 | 0.9466 | 0.8907 | 0.8910 | 0.0829 | 0.0191 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_ce_32
|
<!-- 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_rvl_cdip-N1K_ce_32
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5671
- Accuracy: 0.8915
- Brier Loss: 0.1895
- Nll: 0.9175
- F1 Micro: 0.8915
- F1 Macro: 0.8919
- Ece: 0.0850
- Aurc: 0.0200
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1771 | 1.0 | 500 | 0.4121 | 0.8885 | 0.1719 | 1.2085 | 0.8885 | 0.8888 | 0.0509 | 0.0203 |
| 0.134 | 2.0 | 1000 | 0.4415 | 0.8882 | 0.1782 | 1.1210 | 0.8882 | 0.8886 | 0.0626 | 0.0212 |
| 0.0682 | 3.0 | 1500 | 0.4722 | 0.8855 | 0.1847 | 1.0778 | 0.8855 | 0.8858 | 0.0740 | 0.0213 |
| 0.0325 | 4.0 | 2000 | 0.4851 | 0.8905 | 0.1796 | 1.0195 | 0.8905 | 0.8911 | 0.0712 | 0.0213 |
| 0.0145 | 5.0 | 2500 | 0.5409 | 0.8842 | 0.1946 | 1.0096 | 0.8842 | 0.8850 | 0.0860 | 0.0217 |
| 0.0082 | 6.0 | 3000 | 0.5378 | 0.8872 | 0.1886 | 0.9573 | 0.8872 | 0.8879 | 0.0858 | 0.0206 |
| 0.0059 | 7.0 | 3500 | 0.5446 | 0.8895 | 0.1870 | 0.9288 | 0.8895 | 0.8897 | 0.0844 | 0.0206 |
| 0.0046 | 8.0 | 4000 | 0.5580 | 0.8885 | 0.1874 | 0.9153 | 0.8885 | 0.8889 | 0.0859 | 0.0203 |
| 0.0043 | 9.0 | 4500 | 0.5675 | 0.8905 | 0.1903 | 0.9313 | 0.8905 | 0.8910 | 0.0864 | 0.0201 |
| 0.004 | 10.0 | 5000 | 0.5671 | 0.8915 | 0.1895 | 0.9175 | 0.8915 | 0.8919 | 0.0850 | 0.0200 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_AURC_32
|
<!-- 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_rvl_cdip-N1K_AURC_32
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3439
- Accuracy: 0.8962
- Brier Loss: 0.1805
- Nll: 0.8184
- F1 Micro: 0.8962
- F1 Macro: 0.8963
- Ece: 0.0767
- Aurc: 0.0220
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0301 | 1.0 | 500 | 0.1897 | 0.8808 | 0.1804 | 1.1636 | 0.8808 | 0.8807 | 0.0528 | 0.0227 |
| 0.0229 | 2.0 | 1000 | 0.2504 | 0.883 | 0.1834 | 1.1357 | 0.883 | 0.8832 | 0.0573 | 0.0248 |
| 0.0081 | 3.0 | 1500 | 0.2251 | 0.8858 | 0.1787 | 1.0242 | 0.8858 | 0.8858 | 0.0653 | 0.0221 |
| 0.004 | 4.0 | 2000 | 0.3075 | 0.886 | 0.1831 | 0.9279 | 0.886 | 0.8850 | 0.0744 | 0.0227 |
| 0.0023 | 5.0 | 2500 | 0.2491 | 0.8908 | 0.1791 | 0.9302 | 0.8907 | 0.8916 | 0.0728 | 0.0212 |
| 0.0014 | 6.0 | 3000 | 0.3067 | 0.8925 | 0.1795 | 0.8631 | 0.8925 | 0.8929 | 0.0752 | 0.0215 |
| 0.0012 | 7.0 | 3500 | 0.3277 | 0.8925 | 0.1812 | 0.8729 | 0.8925 | 0.8922 | 0.0764 | 0.0218 |
| 0.0009 | 8.0 | 4000 | 0.3386 | 0.895 | 0.1797 | 0.8406 | 0.895 | 0.8951 | 0.0760 | 0.0219 |
| 0.0007 | 9.0 | 4500 | 0.3383 | 0.8968 | 0.1808 | 0.8293 | 0.8968 | 0.8969 | 0.0747 | 0.0220 |
| 0.0006 | 10.0 | 5000 | 0.3439 | 0.8962 | 0.1805 | 0.8184 | 0.8962 | 0.8963 | 0.0767 | 0.0220 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_AURC_16
|
<!-- 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_rvl_cdip-N1K_AURC_16
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2895
- Accuracy: 0.8925
- Brier Loss: 0.1833
- Nll: 0.8632
- F1 Micro: 0.8925
- F1 Macro: 0.8927
- Ece: 0.0768
- Aurc: 0.0218
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0448 | 1.0 | 1000 | 0.1956 | 0.8758 | 0.1900 | 1.1701 | 0.8758 | 0.8769 | 0.0566 | 0.0252 |
| 0.0381 | 2.0 | 2000 | 0.2463 | 0.8715 | 0.1989 | 1.1688 | 0.8715 | 0.8716 | 0.0715 | 0.0261 |
| 0.0136 | 3.0 | 3000 | 0.2947 | 0.87 | 0.2081 | 1.0890 | 0.87 | 0.8693 | 0.0752 | 0.0271 |
| 0.0092 | 4.0 | 4000 | 0.2718 | 0.881 | 0.1901 | 1.0230 | 0.881 | 0.8811 | 0.0759 | 0.0253 |
| 0.0048 | 5.0 | 5000 | 0.2823 | 0.8812 | 0.1934 | 0.9914 | 0.8812 | 0.8814 | 0.0777 | 0.0238 |
| 0.0045 | 6.0 | 6000 | 0.2555 | 0.8855 | 0.1889 | 0.9305 | 0.8855 | 0.8861 | 0.0768 | 0.0223 |
| 0.0022 | 7.0 | 7000 | 0.2754 | 0.886 | 0.1873 | 0.8958 | 0.886 | 0.8860 | 0.0804 | 0.0221 |
| 0.0019 | 8.0 | 8000 | 0.2784 | 0.8858 | 0.1914 | 0.9248 | 0.8858 | 0.8866 | 0.0796 | 0.0229 |
| 0.0008 | 9.0 | 9000 | 0.2855 | 0.8878 | 0.1885 | 0.8671 | 0.8878 | 0.8876 | 0.0809 | 0.0226 |
| 0.0005 | 10.0 | 10000 | 0.2895 | 0.8925 | 0.1833 | 0.8632 | 0.8925 | 0.8927 | 0.0768 | 0.0218 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_ce_16
|
<!-- 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_rvl_cdip-N1K_ce_16
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6681
- Accuracy: 0.89
- Brier Loss: 0.2001
- Nll: 0.9073
- F1 Micro: 0.89
- F1 Macro: 0.8905
- Ece: 0.0923
- Aurc: 0.0219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.209 | 1.0 | 1000 | 0.4595 | 0.8775 | 0.1885 | 1.1949 | 0.8775 | 0.8784 | 0.0616 | 0.0237 |
| 0.1707 | 2.0 | 2000 | 0.4835 | 0.881 | 0.1887 | 1.1366 | 0.881 | 0.8803 | 0.0720 | 0.0237 |
| 0.0893 | 3.0 | 3000 | 0.5434 | 0.8808 | 0.1991 | 1.0313 | 0.8808 | 0.8805 | 0.0830 | 0.0237 |
| 0.0442 | 4.0 | 4000 | 0.5746 | 0.8845 | 0.1964 | 0.9971 | 0.8845 | 0.8850 | 0.0858 | 0.0234 |
| 0.0176 | 5.0 | 5000 | 0.6168 | 0.8802 | 0.2062 | 1.0035 | 0.8802 | 0.8799 | 0.0935 | 0.0241 |
| 0.0098 | 6.0 | 6000 | 0.6533 | 0.882 | 0.2074 | 0.9667 | 0.882 | 0.8829 | 0.0953 | 0.0237 |
| 0.0066 | 7.0 | 7000 | 0.6557 | 0.8838 | 0.2041 | 0.9568 | 0.8838 | 0.8833 | 0.0942 | 0.0235 |
| 0.0049 | 8.0 | 8000 | 0.6557 | 0.8878 | 0.1995 | 0.9076 | 0.8878 | 0.8883 | 0.0934 | 0.0220 |
| 0.0027 | 9.0 | 9000 | 0.6693 | 0.8882 | 0.2024 | 0.9127 | 0.8882 | 0.8888 | 0.0939 | 0.0222 |
| 0.0031 | 10.0 | 10000 | 0.6681 | 0.89 | 0.2001 | 0.9073 | 0.89 | 0.8905 | 0.0923 | 0.0219 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_AURC_8
|
<!-- 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_rvl_cdip-N1K_AURC_8
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3096
- Accuracy: 0.883
- Brier Loss: 0.2014
- Nll: 0.9150
- F1 Micro: 0.883
- F1 Macro: 0.8832
- Ece: 0.0891
- Aurc: 0.0256
## Model description
More information needed
## Intended uses & 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0736 | 1.0 | 2000 | 0.2385 | 0.8592 | 0.2172 | 1.2230 | 0.8592 | 0.8608 | 0.0734 | 0.0313 |
| 0.0594 | 2.0 | 4000 | 0.2561 | 0.8712 | 0.2047 | 1.2297 | 0.8713 | 0.8716 | 0.0678 | 0.0283 |
| 0.0421 | 3.0 | 6000 | 0.2432 | 0.867 | 0.2104 | 1.1813 | 0.867 | 0.8679 | 0.0749 | 0.0303 |
| 0.0256 | 4.0 | 8000 | 0.2882 | 0.8632 | 0.2199 | 1.1103 | 0.8632 | 0.8635 | 0.0847 | 0.0310 |
| 0.0147 | 5.0 | 10000 | 0.4246 | 0.8515 | 0.2466 | 1.1118 | 0.8515 | 0.8489 | 0.1059 | 0.0360 |
| 0.0105 | 6.0 | 12000 | 0.2747 | 0.8668 | 0.2220 | 1.0335 | 0.8668 | 0.8691 | 0.0986 | 0.0278 |
| 0.004 | 7.0 | 14000 | 0.2954 | 0.878 | 0.2034 | 0.9467 | 0.878 | 0.8783 | 0.0865 | 0.0264 |
| 0.0034 | 8.0 | 16000 | 0.3339 | 0.8708 | 0.2185 | 0.9551 | 0.8708 | 0.8713 | 0.0969 | 0.0286 |
| 0.0017 | 9.0 | 18000 | 0.3125 | 0.8748 | 0.2099 | 0.9454 | 0.8748 | 0.8761 | 0.0953 | 0.0265 |
| 0.0009 | 10.0 | 20000 | 0.3096 | 0.883 | 0.2014 | 0.9150 | 0.883 | 0.8832 | 0.0891 | 0.0256 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_ce_8
|
<!-- 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_rvl_cdip-N1K_ce_8
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8308
- Accuracy: 0.8822
- Brier Loss: 0.2169
- Nll: 0.9246
- F1 Micro: 0.8822
- F1 Macro: 0.8823
- Ece: 0.1044
- Aurc: 0.0265
## Model description
More information needed
## Intended uses & 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.2906 | 1.0 | 2000 | 0.5302 | 0.867 | 0.2106 | 1.2043 | 0.867 | 0.8685 | 0.0746 | 0.0284 |
| 0.236 | 2.0 | 4000 | 0.5819 | 0.8695 | 0.2142 | 1.1215 | 0.8695 | 0.8688 | 0.0909 | 0.0267 |
| 0.1236 | 3.0 | 6000 | 0.7115 | 0.8605 | 0.2390 | 1.1453 | 0.8605 | 0.8604 | 0.1069 | 0.0295 |
| 0.0703 | 4.0 | 8000 | 0.6965 | 0.8715 | 0.2265 | 1.0124 | 0.8715 | 0.8720 | 0.1015 | 0.0290 |
| 0.0307 | 5.0 | 10000 | 0.7503 | 0.8742 | 0.2229 | 0.9824 | 0.8742 | 0.8746 | 0.1052 | 0.0257 |
| 0.0229 | 6.0 | 12000 | 0.8042 | 0.874 | 0.2304 | 1.0125 | 0.874 | 0.8742 | 0.1091 | 0.0269 |
| 0.0114 | 7.0 | 14000 | 0.8335 | 0.8715 | 0.2283 | 1.0146 | 0.8715 | 0.8709 | 0.1103 | 0.0267 |
| 0.0082 | 8.0 | 16000 | 0.8655 | 0.873 | 0.2297 | 1.0222 | 0.8730 | 0.8735 | 0.1112 | 0.0279 |
| 0.002 | 9.0 | 18000 | 0.8350 | 0.8808 | 0.2180 | 0.9519 | 0.8808 | 0.8812 | 0.1067 | 0.0266 |
| 0.0041 | 10.0 | 20000 | 0.8308 | 0.8822 | 0.2169 | 0.9246 | 0.8822 | 0.8823 | 0.1044 | 0.0265 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
galbitang/autotrain-bed_frame_1021-96393146649
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 96393146649
- CO2 Emissions (in grams): 0.3812
## Validation Metrics
- Loss: 0.224
- Accuracy: 0.926
- Macro F1: 0.925
- Micro F1: 0.926
- Weighted F1: 0.927
- Macro Precision: 0.917
- Micro Precision: 0.926
- Weighted Precision: 0.928
- Macro Recall: 0.934
- Micro Recall: 0.926
- Weighted Recall: 0.926
|
[
"casual",
"classic",
"modern",
"natural",
"romantic"
] |
galbitang/autotrain-lamp_1021-96396146650
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 96396146650
- CO2 Emissions (in grams): 2.4774
## Validation Metrics
- Loss: 0.402
- Accuracy: 0.881
- Macro F1: 0.805
- Micro F1: 0.881
- Weighted F1: 0.873
- Macro Precision: 0.884
- Micro Precision: 0.881
- Weighted Precision: 0.881
- Macro Recall: 0.764
- Micro Recall: 0.881
- Weighted Recall: 0.881
|
[
"casual",
"classic",
"modern",
"natural",
"romantic"
] |
galbitang/autotrain-chair_1021-96395146651
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 96395146651
- CO2 Emissions (in grams): 2.3369
## Validation Metrics
- Loss: 0.364
- Accuracy: 0.857
- Macro F1: 0.839
- Micro F1: 0.857
- Weighted F1: 0.855
- Macro Precision: 0.876
- Micro Precision: 0.857
- Weighted Precision: 0.860
- Macro Recall: 0.810
- Micro Recall: 0.857
- Weighted Recall: 0.857
|
[
"casual",
"classic",
"modern",
"natural",
"romantic"
] |
galbitang/autotrain-sofa_1021-96392146654
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 96392146654
- CO2 Emissions (in grams): 2.8405
## Validation Metrics
- Loss: 0.290
- Accuracy: 0.905
- Macro F1: 0.892
- Micro F1: 0.905
- Weighted F1: 0.905
- Macro Precision: 0.905
- Micro Precision: 0.905
- Weighted Precision: 0.906
- Macro Recall: 0.881
- Micro Recall: 0.905
- Weighted Recall: 0.905
|
[
"casual",
"classic",
"modern",
"natural",
"romantic"
] |
galbitang/autotrain-table_1021_2-96399146655
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 96399146655
- CO2 Emissions (in grams): 0.3221
## Validation Metrics
- Loss: 0.552
- Accuracy: 0.827
- Macro F1: 0.789
- Micro F1: 0.827
- Weighted F1: 0.823
- Macro Precision: 0.866
- Micro Precision: 0.827
- Weighted Precision: 0.833
- Macro Recall: 0.750
- Micro Recall: 0.827
- Weighted Recall: 0.827
|
[
"casual",
"classic",
"modern",
"natural",
"romantic"
] |
bdpc/vit-base_rvl_cdip-N1K_AURC_4
|
<!-- 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_rvl_cdip-N1K_AURC_4
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2768
- Accuracy: 0.8738
- Brier Loss: 0.2167
- Nll: 0.9821
- F1 Micro: 0.8738
- F1 Macro: 0.8749
- Ece: 0.0970
- Aurc: 0.0292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1764 | 1.0 | 4000 | 0.3808 | 0.8217 | 0.2750 | 1.2675 | 0.8217 | 0.8194 | 0.1016 | 0.0461 |
| 0.1131 | 2.0 | 8000 | 0.3321 | 0.8413 | 0.2583 | 1.3120 | 0.8413 | 0.8421 | 0.0949 | 0.0418 |
| 0.113 | 3.0 | 12000 | 0.3781 | 0.8207 | 0.2910 | 1.4889 | 0.8207 | 0.8213 | 0.1162 | 0.0496 |
| 0.0814 | 4.0 | 16000 | 0.4793 | 0.8157 | 0.3036 | 1.4208 | 0.8157 | 0.8151 | 0.1302 | 0.0552 |
| 0.0542 | 5.0 | 20000 | 0.2914 | 0.8658 | 0.2279 | 1.1541 | 0.8658 | 0.8657 | 0.0955 | 0.0320 |
| 0.0238 | 6.0 | 24000 | 0.3059 | 0.8568 | 0.2401 | 1.1686 | 0.8568 | 0.8581 | 0.1012 | 0.0354 |
| 0.0197 | 7.0 | 28000 | 0.3077 | 0.8545 | 0.2390 | 1.1659 | 0.8545 | 0.8553 | 0.1059 | 0.0354 |
| 0.0116 | 8.0 | 32000 | 0.3169 | 0.8705 | 0.2172 | 1.0323 | 0.8705 | 0.8704 | 0.0918 | 0.0314 |
| 0.0054 | 9.0 | 36000 | 0.2850 | 0.8738 | 0.2199 | 1.0171 | 0.8738 | 0.8747 | 0.0960 | 0.0302 |
| 0.0128 | 10.0 | 40000 | 0.2768 | 0.8738 | 0.2167 | 0.9821 | 0.8738 | 0.8749 | 0.0970 | 0.0292 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_ce_4
|
<!-- 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_rvl_cdip-N1K_ce_4
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9480
- Accuracy: 0.8792
- Brier Loss: 0.2240
- Nll: 1.0075
- F1 Micro: 0.8793
- F1 Macro: 0.8794
- Ece: 0.1101
- Aurc: 0.0274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.4172 | 1.0 | 4000 | 0.6321 | 0.8475 | 0.2427 | 1.1862 | 0.8475 | 0.8484 | 0.0957 | 0.0352 |
| 0.3421 | 2.0 | 8000 | 0.6729 | 0.8645 | 0.2301 | 1.1766 | 0.8645 | 0.8642 | 0.1020 | 0.0295 |
| 0.2091 | 3.0 | 12000 | 0.7988 | 0.854 | 0.2563 | 1.1608 | 0.854 | 0.8555 | 0.1183 | 0.0352 |
| 0.1319 | 4.0 | 16000 | 0.8683 | 0.861 | 0.2503 | 1.1575 | 0.861 | 0.8617 | 0.1188 | 0.0354 |
| 0.0673 | 5.0 | 20000 | 0.9057 | 0.8642 | 0.2479 | 1.1524 | 0.8643 | 0.8635 | 0.1195 | 0.0314 |
| 0.0333 | 6.0 | 24000 | 0.9553 | 0.8605 | 0.2524 | 1.1006 | 0.8605 | 0.8600 | 0.1226 | 0.0366 |
| 0.0223 | 7.0 | 28000 | 0.9393 | 0.8708 | 0.2350 | 1.1027 | 0.8708 | 0.8713 | 0.1159 | 0.0274 |
| 0.0194 | 8.0 | 32000 | 1.0108 | 0.8705 | 0.2407 | 1.0850 | 0.8705 | 0.8704 | 0.1169 | 0.0309 |
| 0.0015 | 9.0 | 36000 | 0.9412 | 0.876 | 0.2291 | 1.0136 | 0.8760 | 0.8763 | 0.1123 | 0.0270 |
| 0.004 | 10.0 | 40000 | 0.9480 | 0.8792 | 0.2240 | 1.0075 | 0.8793 | 0.8794 | 0.1101 | 0.0274 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
khleeloo/vit-focal-skin
|
<!-- 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-focal-skin
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: 0.5830
- Accuracy: 0.8497
- F1: 0.8472
- Precision: 0.8527
- Recall: 0.8497
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1586 | 1.0 | 626 | 0.3295 | 0.8808 | 0.8764 | 0.9007 | 0.8808 |
| 0.096 | 2.0 | 1252 | 0.4315 | 0.8601 | 0.8562 | 0.8600 | 0.8601 |
| 0.0181 | 3.0 | 1878 | 0.4395 | 0.8756 | 0.8685 | 0.8799 | 0.8756 |
| 0.0058 | 4.0 | 2504 | 0.5563 | 0.8549 | 0.8571 | 0.8653 | 0.8549 |
| 0.0004 | 5.0 | 3130 | 0.6044 | 0.8653 | 0.8619 | 0.8688 | 0.8653 |
| 0.0003 | 6.0 | 3756 | 0.5830 | 0.8497 | 0.8472 | 0.8527 | 0.8497 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
[
"mel",
"nv",
"bcc",
"akiec",
"bkl",
"df",
"vasc"
] |
SeyedAli/Remote-Sensing-UAV-image-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. -->
# Remote-Sensing-UAV-image-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 [jonathan-roberts1/RSSCN7](https://huggingface.co/datasets/jonathan-roberts1/RSSCN7) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0593
- Accuracy: 0.9907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3922 | 0.71 | 100 | 0.4227 | 0.8821 |
| 0.2986 | 1.43 | 200 | 0.3142 | 0.9089 |
| 0.1109 | 2.14 | 300 | 0.2056 | 0.9518 |
| 0.0864 | 2.86 | 400 | 0.2472 | 0.9375 |
| 0.0193 | 3.57 | 500 | 0.0593 | 0.9907 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"field",
"forest",
"grass",
"industry",
"parking",
"resident",
"river or lake"
] |
JLB-JLB/Model_folder
|
<!-- 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. -->
# Model_folder
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0171
- Matthews Correlation: 0.9888
## Model description
More information needed
## Intended uses & 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.0488 | 0.91 | 30 | 0.1366 | 0.9449 |
| 0.0077 | 1.82 | 60 | 0.0508 | 0.9775 |
| 0.0057 | 2.73 | 90 | 0.0366 | 0.9888 |
| 0.0042 | 3.64 | 120 | 0.0171 | 0.9888 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
arslanafzal/birds_transform_full
|
<!-- 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. -->
# birds_transform_full
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:
- Accuracy: 0.7303
- Loss: 1.4588
## Model description
More information needed
## Intended uses & 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:------:|:--------:|:---------------:|
| 5.6427 | 1.0 | 1984 | 0.4519 | 5.2504 |
| 4.6563 | 2.0 | 3968 | 0.5068 | 4.2749 |
| 3.6656 | 3.0 | 5952 | 0.5454 | 3.3311 |
| 2.7653 | 4.0 | 7936 | 0.5748 | 2.5181 |
| 2.0465 | 5.0 | 9920 | 0.6300 | 1.9205 |
| 1.5876 | 6.0 | 11904 | 0.6593 | 1.5696 |
| 1.3174 | 7.0 | 13888 | 0.6870 | 1.3831 |
| 1.1279 | 8.0 | 15872 | 0.7064 | 1.2516 |
| 1.0051 | 9.0 | 17856 | 0.7067 | 1.1999 |
| 0.9318 | 10.0 | 19840 | 0.7077 | 1.1631 |
| 0.8294 | 11.0 | 21824 | 0.7089 | 1.1444 |
| 0.7976 | 12.0 | 23808 | 0.7175 | 1.1156 |
| 0.7084 | 13.0 | 25792 | 0.7218 | 1.1209 |
| 0.6752 | 14.0 | 27776 | 0.7198 | 1.1032 |
| 0.6641 | 15.0 | 29760 | 0.7198 | 1.1192 |
| 0.6083 | 16.0 | 31744 | 0.7268 | 1.1044 |
| 0.5703 | 17.0 | 33728 | 0.7248 | 1.1287 |
| 0.5376 | 18.0 | 35712 | 0.7286 | 1.1115 |
| 0.5073 | 19.0 | 37696 | 0.7218 | 1.1429 |
| 0.5072 | 20.0 | 39680 | 0.7208 | 1.1519 |
| 0.4945 | 21.0 | 41664 | 0.7228 | 1.1636 |
| 0.4651 | 22.0 | 43648 | 0.7213 | 1.1771 |
| 0.4408 | 23.0 | 45632 | 0.7233 | 1.1650 |
| 0.4222 | 24.0 | 47616 | 0.7157 | 1.1841 |
| 0.409 | 25.0 | 49600 | 0.7145 | 1.2150 |
| 0.403 | 26.0 | 51584 | 0.7152 | 1.2203 |
| 0.3813 | 27.0 | 53568 | 0.7238 | 1.2064 |
| 0.3756 | 28.0 | 55552 | 0.7177 | 1.2526 |
| 0.365 | 29.0 | 57536 | 0.7208 | 1.2670 |
| 0.3729 | 30.0 | 59520 | 0.7180 | 1.2659 |
| 0.36 | 31.0 | 61504 | 0.7127 | 1.2545 |
| 0.3596 | 32.0 | 63488 | 0.7182 | 1.2728 |
| 0.3606 | 33.0 | 65472 | 0.7180 | 1.2886 |
| 0.325 | 34.0 | 67456 | 0.7157 | 1.2929 |
| 0.329 | 35.0 | 69440 | 0.7205 | 1.3074 |
| 0.3431 | 36.0 | 71424 | 0.7185 | 1.3122 |
| 0.3206 | 37.0 | 73408 | 0.7233 | 1.2993 |
| 0.3137 | 38.0 | 75392 | 0.7220 | 1.3206 |
| 0.3265 | 39.0 | 77376 | 0.7180 | 1.3246 |
| 0.3332 | 40.0 | 79360 | 0.7240 | 1.3163 |
| 0.3193 | 41.0 | 81344 | 0.7288 | 1.3259 |
| 0.3242 | 42.0 | 83328 | 0.7215 | 1.3320 |
| 0.2976 | 43.0 | 85312 | 0.7213 | 1.3283 |
| 0.3191 | 44.0 | 87296 | 0.7195 | 1.3453 |
| 0.3067 | 45.0 | 89280 | 0.7243 | 1.3550 |
| 0.2994 | 46.0 | 91264 | 0.7240 | 1.3324 |
| 0.3072 | 47.0 | 93248 | 0.7263 | 1.3412 |
| 0.2932 | 48.0 | 95232 | 0.7245 | 1.3345 |
| 0.2919 | 49.0 | 97216 | 0.7266 | 1.3759 |
| 0.2922 | 50.0 | 99200 | 0.7225 | 1.3873 |
| 0.304 | 51.0 | 101184 | 0.7235 | 1.3631 |
| 0.2898 | 52.0 | 103168 | 0.7205 | 1.3819 |
| 0.2773 | 53.0 | 105152 | 0.7251 | 1.3827 |
| 0.2756 | 54.0 | 107136 | 0.7228 | 1.3770 |
| 0.2789 | 55.0 | 109120 | 0.7248 | 1.3822 |
| 0.261 | 56.0 | 111104 | 0.7263 | 1.3878 |
| 0.2593 | 57.0 | 113088 | 0.7240 | 1.3955 |
| 0.2801 | 58.0 | 115072 | 0.7256 | 1.3659 |
| 0.2632 | 59.0 | 117056 | 0.7301 | 1.3719 |
| 0.2811 | 60.0 | 119040 | 0.7321 | 1.3775 |
| 0.2267 | 61.0 | 121024 | 0.7256 | 1.3689 |
| 0.2676 | 62.0 | 123008 | 0.7245 | 1.4069 |
| 0.2523 | 63.0 | 124992 | 0.7230 | 1.4166 |
| 0.2622 | 64.0 | 126976 | 0.7296 | 1.4018 |
| 0.2467 | 65.0 | 128960 | 0.7256 | 1.4287 |
| 0.2504 | 66.0 | 130944 | 0.7314 | 1.4019 |
| 0.2468 | 67.0 | 132928 | 0.7303 | 1.4058 |
| 0.2098 | 68.0 | 134912 | 0.7308 | 1.4093 |
| 0.2382 | 69.0 | 136896 | 0.7293 | 1.4206 |
| 0.2304 | 70.0 | 138880 | 0.7301 | 1.4078 |
| 0.251 | 71.0 | 140864 | 0.7251 | 1.4275 |
| 0.237 | 72.0 | 142848 | 0.7288 | 1.4283 |
| 0.2485 | 73.0 | 144832 | 0.7281 | 1.4338 |
| 0.2229 | 74.0 | 146816 | 0.7253 | 1.4386 |
| 0.2472 | 75.0 | 148800 | 0.7210 | 1.4440 |
| 0.2149 | 76.0 | 150784 | 0.7230 | 1.4319 |
| 0.2337 | 77.0 | 152768 | 0.7261 | 1.4422 |
| 0.2063 | 78.0 | 154752 | 0.7268 | 1.4456 |
| 0.216 | 79.0 | 156736 | 0.7218 | 1.4426 |
| 0.2249 | 80.0 | 158720 | 0.7198 | 1.4533 |
| 0.2148 | 81.0 | 160704 | 0.7230 | 1.4480 |
| 0.2321 | 82.0 | 162688 | 0.7273 | 1.4416 |
| 0.2306 | 83.0 | 164672 | 0.7286 | 1.4392 |
| 0.213 | 84.0 | 166656 | 0.7263 | 1.4609 |
| 0.2202 | 85.0 | 168640 | 0.7266 | 1.4590 |
| 0.206 | 86.0 | 170624 | 0.7245 | 1.4638 |
| 0.1987 | 87.0 | 172608 | 0.7251 | 1.4626 |
| 0.2181 | 88.0 | 174592 | 0.7261 | 1.4615 |
| 0.2076 | 89.0 | 176576 | 0.7253 | 1.4665 |
| 0.1999 | 90.0 | 178560 | 0.7251 | 1.4569 |
| 0.2287 | 91.0 | 180544 | 0.7266 | 1.4591 |
| 0.1985 | 92.0 | 182528 | 0.7263 | 1.4508 |
| 0.2166 | 93.0 | 184512 | 0.7266 | 1.4621 |
| 0.1943 | 94.0 | 186496 | 0.7276 | 1.4649 |
| 0.2189 | 95.0 | 188480 | 0.7293 | 1.4555 |
| 0.1911 | 96.0 | 190464 | 0.7306 | 1.4565 |
| 0.1954 | 97.0 | 192448 | 0.7271 | 1.4624 |
| 0.2053 | 98.0 | 194432 | 0.7286 | 1.4603 |
| 0.2067 | 99.0 | 196416 | 0.7306 | 1.4589 |
| 0.1917 | 100.0 | 198400 | 0.7303 | 1.4588 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"acadian_flycatcher",
"acorn_woodpecker",
"alder_flycatcher",
"allens_hummingbird",
"altamira_oriole",
"american_avocet",
"american_bittern",
"american_black_duck",
"american_coot",
"american_crow",
"american_dipper",
"american_golden_plover",
"american_goldfinch",
"american_kestrel",
"american_oystercatcher",
"american_pipit",
"american_redstart",
"american_robin",
"american_three_toed_woodpecker",
"american_tree_sparrow",
"american_white_pelican",
"american_wigeon",
"american_woodcock",
"anhinga",
"annas_hummingbird",
"arctic_tern",
"ash_throated_flycatcher",
"audubons_oriole",
"bairds_sandpiper",
"bald_eagle",
"baltimore_oriole",
"band_tailed_pigeon",
"barn_swallow",
"barred_owl",
"barrows_goldeneye",
"bay_breasted_warbler",
"bells_vireo",
"belted_kingfisher",
"bewicks_wren",
"black_guillemot",
"black_oystercatcher",
"black_phoebe",
"black_rosy_finch",
"black_scoter",
"black_skimmer",
"black_tern",
"black_turnstone",
"black_vulture",
"black_and_white_warbler",
"black_backed_woodpecker",
"black_bellied_plover",
"black_billed_cuckoo",
"black_billed_magpie",
"black_capped_chickadee",
"black_chinned_hummingbird",
"black_chinned_sparrow",
"black_crested_titmouse",
"black_crowned_night_heron",
"black_headed_grosbeak",
"black_legged_kittiwake",
"black_necked_stilt",
"black_throated_blue_warbler",
"black_throated_gray_warbler",
"black_throated_green_warbler",
"black_throated_sparrow",
"blackburnian_warbler",
"blackpoll_warbler",
"blue_grosbeak",
"blue_jay",
"blue_gray_gnatcatcher",
"blue_headed_vireo",
"blue_winged_teal",
"blue_winged_warbler",
"boat_tailed_grackle",
"bobolink",
"bohemian_waxwing",
"bonapartes_gull",
"boreal_chickadee",
"brandts_cormorant",
"brant",
"brewers_blackbird",
"brewers_sparrow",
"bridled_titmouse",
"broad_billed_hummingbird",
"broad_tailed_hummingbird",
"broad_winged_hawk",
"bronzed_cowbird",
"brown_creeper",
"brown_pelican",
"brown_thrasher",
"brown_capped_rosy_finch",
"brown_crested_flycatcher",
"brown_headed_cowbird",
"brown_headed_nuthatch",
"bufflehead",
"bullocks_oriole",
"burrowing_owl",
"bushtit",
"cackling_goose",
"cactus_wren",
"california_gull",
"california_quail",
"california_thrasher",
"california_towhee",
"calliope_hummingbird",
"canada_goose",
"canada_warbler",
"canvasback",
"canyon_towhee",
"canyon_wren",
"cape_may_warbler",
"carolina_chickadee",
"carolina_wren",
"caspian_tern",
"cassins_finch",
"cassins_kingbird",
"cassins_sparrow",
"cassins_vireo",
"cattle_egret",
"cave_swallow",
"cedar_waxwing",
"cerulean_warbler",
"chestnut_backed_chickadee",
"chestnut_collared_longspur",
"chestnut_sided_warbler",
"chihuahuan_raven",
"chimney_swift",
"chipping_sparrow",
"cinnamon_teal",
"clapper_rail",
"clarks_grebe",
"clarks_nutcracker",
"clay_colored_sparrow",
"cliff_swallow",
"common_black_hawk",
"common_eider",
"common_gallinule",
"common_goldeneye",
"common_grackle",
"common_ground_dove",
"common_loon",
"common_merganser",
"common_murre",
"common_nighthawk",
"common_raven",
"common_redpoll",
"common_tern",
"common_yellowthroat",
"connecticut_warbler",
"coopers_hawk",
"cordilleran_flycatcher",
"costas_hummingbird",
"couchs_kingbird",
"crested_caracara",
"curve_billed_thrasher",
"dark_eyed_junco",
"dickcissel",
"double_crested_cormorant",
"downy_woodpecker",
"dunlin",
"dusky_flycatcher",
"dusky_grouse",
"eared_grebe",
"eastern_bluebird",
"eastern_kingbird",
"eastern_meadowlark",
"eastern_phoebe",
"eastern_screech_owl",
"eastern_towhee",
"eastern_wood_pewee",
"elegant_trogon",
"elf_owl",
"eurasian_collared_dove",
"eurasian_wigeon",
"european_starling",
"evening_grosbeak",
"ferruginous_hawk",
"ferruginous_pygmy_owl",
"field_sparrow",
"fish_crow",
"florida_scrub_jay",
"forsters_tern",
"fox_sparrow",
"franklins_gull",
"fulvous_whistling_duck",
"gadwall",
"gambels_quail",
"gila_woodpecker",
"glaucous_gull",
"glaucous_winged_gull",
"glossy_ibis",
"golden_eagle",
"golden_crowned_kinglet",
"golden_crowned_sparrow",
"golden_fronted_woodpecker",
"golden_winged_warbler",
"grasshopper_sparrow",
"gray_catbird",
"gray_flycatcher",
"gray_jay",
"gray_kingbird",
"gray_cheeked_thrush",
"gray_crowned_rosy_finch",
"great_black_backed_gull",
"great_blue_heron",
"great_cormorant",
"great_crested_flycatcher",
"great_egret",
"great_gray_owl",
"great_horned_owl",
"great_kiskadee",
"great_tailed_grackle",
"greater_prairie_chicken",
"greater_roadrunner",
"greater_sage_grouse",
"greater_scaup",
"greater_white_fronted_goose",
"greater_yellowlegs",
"green_jay",
"green_tailed_towhee",
"green_winged_teal",
"groove_billed_ani",
"gull_billed_tern",
"hairy_woodpecker",
"hammonds_flycatcher",
"harlequin_duck",
"harriss_hawk",
"harriss_sparrow",
"heermanns_gull",
"henslows_sparrow",
"hepatic_tanager",
"hermit_thrush",
"herring_gull",
"hoary_redpoll",
"hooded_merganser",
"hooded_oriole",
"hooded_warbler",
"horned_grebe",
"horned_lark",
"house_finch",
"house_sparrow",
"house_wren",
"huttons_vireo",
"iceland_gull",
"inca_dove",
"indigo_bunting",
"killdeer",
"king_rail",
"ladder_backed_woodpecker",
"lapland_longspur",
"lark_bunting",
"lark_sparrow",
"laughing_gull",
"lazuli_bunting",
"le_contes_sparrow",
"least_bittern",
"least_flycatcher",
"least_grebe",
"least_sandpiper",
"least_tern",
"lesser_goldfinch",
"lesser_nighthawk",
"lesser_scaup",
"lesser_yellowlegs",
"lewiss_woodpecker",
"limpkin",
"lincolns_sparrow",
"little_blue_heron",
"loggerhead_shrike",
"long_billed_curlew",
"long_billed_dowitcher",
"long_billed_thrasher",
"long_eared_owl",
"long_tailed_duck",
"louisiana_waterthrush",
"magnificent_frigatebird",
"magnolia_warbler",
"mallard",
"marbled_godwit",
"marsh_wren",
"merlin",
"mew_gull",
"mexican_jay",
"mississippi_kite",
"monk_parakeet",
"mottled_duck",
"mountain_bluebird",
"mountain_chickadee",
"mountain_plover",
"mourning_dove",
"mourning_warbler",
"muscovy_duck",
"mute_swan",
"nashville_warbler",
"nelsons_sparrow",
"neotropic_cormorant",
"northern_bobwhite",
"northern_cardinal",
"northern_flicker",
"northern_gannet",
"northern_goshawk",
"northern_harrier",
"northern_hawk_owl",
"northern_mockingbird",
"northern_parula",
"northern_pintail",
"northern_rough_winged_swallow",
"northern_saw_whet_owl",
"northern_shrike",
"northern_waterthrush",
"nuttalls_woodpecker",
"oak_titmouse",
"olive_sparrow",
"olive_sided_flycatcher",
"orange_crowned_warbler",
"orchard_oriole",
"osprey",
"ovenbird",
"pacific_golden_plover",
"pacific_loon",
"pacific_wren",
"pacific_slope_flycatcher",
"painted_bunting",
"painted_redstart",
"palm_warbler",
"pectoral_sandpiper",
"peregrine_falcon",
"phainopepla",
"philadelphia_vireo",
"pied_billed_grebe",
"pigeon_guillemot",
"pileated_woodpecker",
"pine_grosbeak",
"pine_siskin",
"pine_warbler",
"piping_plover",
"plumbeous_vireo",
"prairie_falcon",
"prairie_warbler",
"prothonotary_warbler",
"purple_finch",
"purple_gallinule",
"purple_martin",
"purple_sandpiper",
"pygmy_nuthatch",
"pyrrhuloxia",
"red_crossbill",
"red_knot",
"red_phalarope",
"red_bellied_woodpecker",
"red_breasted_merganser",
"red_breasted_nuthatch",
"red_breasted_sapsucker",
"red_cockaded_woodpecker",
"red_eyed_vireo",
"red_headed_woodpecker",
"red_naped_sapsucker",
"red_necked_grebe",
"red_necked_phalarope",
"red_shouldered_hawk",
"red_tailed_hawk",
"red_throated_loon",
"red_winged_blackbird",
"reddish_egret",
"redhead",
"ring_billed_gull",
"ring_necked_duck",
"ring_necked_pheasant",
"rock_pigeon",
"rock_ptarmigan",
"rock_sandpiper",
"rock_wren",
"rose_breasted_grosbeak",
"roseate_tern",
"rosss_goose",
"rough_legged_hawk",
"royal_tern",
"ruby_crowned_kinglet",
"ruby_throated_hummingbird",
"ruddy_duck",
"ruddy_turnstone",
"ruffed_grouse",
"rufous_hummingbird",
"rufous_crowned_sparrow",
"rusty_blackbird",
"sage_thrasher",
"saltmarsh_sparrow",
"sanderling",
"sandhill_crane",
"sandwich_tern",
"says_phoebe",
"scaled_quail",
"scarlet_tanager",
"scissor_tailed_flycatcher",
"scotts_oriole",
"seaside_sparrow",
"sedge_wren",
"semipalmated_plover",
"semipalmated_sandpiper",
"sharp_shinned_hawk",
"sharp_tailed_grouse",
"short_billed_dowitcher",
"short_eared_owl",
"snail_kite",
"snow_bunting",
"snow_goose",
"snowy_egret",
"snowy_owl",
"snowy_plover",
"solitary_sandpiper",
"song_sparrow",
"sooty_grouse",
"sora",
"spotted_owl",
"spotted_sandpiper",
"spotted_towhee",
"spruce_grouse",
"stellers_jay",
"stilt_sandpiper",
"summer_tanager",
"surf_scoter",
"surfbird",
"swainsons_hawk",
"swainsons_thrush",
"swallow_tailed_kite",
"swamp_sparrow",
"tennessee_warbler",
"thayers_gull",
"townsends_solitaire",
"townsends_warbler",
"tree_swallow",
"tricolored_heron",
"tropical_kingbird",
"trumpeter_swan",
"tufted_titmouse",
"tundra_swan",
"turkey_vulture",
"upland_sandpiper",
"varied_thrush",
"veery",
"verdin",
"vermilion_flycatcher",
"vesper_sparrow",
"violet_green_swallow",
"virginia_rail",
"wandering_tattler",
"warbling_vireo",
"western_bluebird",
"western_grebe",
"western_gull",
"western_kingbird",
"western_meadowlark",
"western_sandpiper",
"western_screech_owl",
"western_scrub_jay",
"western_tanager",
"western_wood_pewee",
"whimbrel",
"white_ibis",
"white_breasted_nuthatch",
"white_crowned_sparrow",
"white_eyed_vireo",
"white_faced_ibis",
"white_headed_woodpecker",
"white_rumped_sandpiper",
"white_tailed_hawk",
"white_tailed_kite",
"white_tailed_ptarmigan",
"white_throated_sparrow",
"white_throated_swift",
"white_winged_crossbill",
"white_winged_dove",
"white_winged_scoter",
"wild_turkey",
"willet",
"williamsons_sapsucker",
"willow_flycatcher",
"willow_ptarmigan",
"wilsons_phalarope",
"wilsons_plover",
"wilsons_snipe",
"wilsons_warbler",
"winter_wren",
"wood_stork",
"wood_thrush",
"worm_eating_warbler",
"wrentit",
"yellow_warbler",
"yellow_bellied_flycatcher",
"yellow_bellied_sapsucker",
"yellow_billed_cuckoo",
"yellow_billed_magpie",
"yellow_breasted_chat",
"yellow_crowned_night_heron",
"yellow_eyed_junco",
"yellow_headed_blackbird",
"yellow_rumped_warbler",
"yellow_throated_vireo",
"yellow_throated_warbler",
"zone_tailed_hawk"
] |
aryap2/UBC-resnet-50-3eph-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. -->
# UBC-resnet-50-3eph-224
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7999
- Recall: 0.6061
- Specificity: 0.8937
- Precision: 0.7089
- Npv: 0.9097
- Accuracy: 0.6860
- F1: 0.6373
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Specificity | Precision | Npv | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------:|:---------:|:------:|:--------:|:------:|
| 0.9759 | 1.0 | 6080 | 0.9368 | 0.5185 | 0.8740 | 0.6852 | 0.8972 | 0.6337 | 0.5423 |
| 0.8617 | 2.0 | 12160 | 0.8285 | 0.5921 | 0.8910 | 0.6964 | 0.9062 | 0.6757 | 0.6221 |
| 0.8362 | 3.0 | 18240 | 0.7999 | 0.6061 | 0.8937 | 0.7089 | 0.9097 | 0.6860 | 0.6373 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
[
"cc",
"ec",
"hgsc",
"lgsc",
"mc"
] |
Mahendra42/swin-tiny-patch4-window7-224-finetunedRCC_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. -->
# swin-tiny-patch4-window7-224-finetunedRCC_Classifier
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: 6.0707
- F1: 0.0140
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0016 | 1.0 | 155 | 5.7392 | 0.0080 |
| 0.0008 | 2.0 | 310 | 5.3965 | 0.0218 |
| 0.0 | 3.0 | 465 | 6.0707 | 0.0140 |
### Framework versions
- Transformers 4.34.1
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"clear cell rcc",
"non clear cell"
] |
barten/vit-base-patch16-224-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. -->
# vit-base-patch16-224-finetuned-eurosat
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: 0.5725
- Accuracy: 0.8394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1364 | 0.99 | 53 | 0.5924 | 0.8217 |
| 0.0876 | 2.0 | 107 | 0.5917 | 0.8252 |
| 0.0874 | 2.99 | 160 | 0.6156 | 0.8239 |
| 0.0779 | 4.0 | 214 | 0.5792 | 0.8363 |
| 0.0747 | 4.95 | 265 | 0.5725 | 0.8394 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"louis vuitton",
"burberry",
"chanel",
"diesel",
"dolce gabanna",
"fendi",
"gucci",
"guess",
"nike",
"prada"
] |
dima806/closed_eyes_image_detection
|
Returns whether there is an open or a closed eye given an image from surrounding area with about 99% accuracy.
See https://www.kaggle.com/code/dima806/closed-eye-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
closeEye 0.9921 0.9888 0.9904 4296
openEye 0.9889 0.9921 0.9905 4295
accuracy 0.9905 8591
macro avg 0.9905 0.9905 0.9905 8591
weighted avg 0.9905 0.9905 0.9905 8591
```
|
[
"closeeye",
"openeye"
] |
barten/vit-base-patch16-224-type
|
<!-- 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-type
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: 0.7249
- Accuracy: 0.7583
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4991 | 0.99 | 78 | 1.2167 | 0.6019 |
| 1.0157 | 1.99 | 157 | 0.8529 | 0.7083 |
| 0.8163 | 3.0 | 236 | 0.7725 | 0.7287 |
| 0.7916 | 4.0 | 315 | 0.7622 | 0.7343 |
| 0.6525 | 4.99 | 393 | 0.7374 | 0.7361 |
| 0.6159 | 5.99 | 472 | 0.7188 | 0.75 |
| 0.5413 | 7.0 | 551 | 0.7029 | 0.7463 |
| 0.4838 | 8.0 | 630 | 0.7254 | 0.7352 |
| 0.4587 | 8.99 | 708 | 0.7219 | 0.7565 |
| 0.4332 | 9.99 | 787 | 0.7077 | 0.7528 |
| 0.379 | 11.0 | 866 | 0.7106 | 0.7583 |
| 0.4181 | 12.0 | 945 | 0.7158 | 0.7556 |
| 0.3798 | 12.99 | 1023 | 0.7234 | 0.7537 |
| 0.3841 | 13.99 | 1102 | 0.7211 | 0.7556 |
| 0.3464 | 14.86 | 1170 | 0.7249 | 0.7583 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"брюки",
"джинсы",
"пиджак",
"платье",
"рубашка",
"свитер",
"футболка",
"шорты",
"юбка"
] |
JLB-JLB/ViT_Seizure_Detection
|
<!-- 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_Seizure_Detection
This model is a fine-tuned version of [/content/drive/MyDrive/Seizure_EEG_Research/ViT_Seizure_Detection](https://huggingface.co//content/drive/MyDrive/Seizure_EEG_Research/ViT_Seizure_Detection) on the JLB-JLB/seizure_eeg_greyscale_224x224_6secWindow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1622
- Matthews Correlation: 0.4110
## Model description
More information needed
## Intended uses & 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.0742 | 0.79 | 10000 | 0.2080 | 0.4431 |
| 0.0409 | 1.57 | 20000 | 0.2175 | 0.4470 |
| 0.0345 | 2.36 | 30000 | 0.2514 | 0.4717 |
| 0.0184 | 3.14 | 40000 | 0.3040 | 0.4261 |
| 0.0092 | 3.93 | 50000 | 0.3495 | 0.4389 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"seiz",
"bckg"
] |
Pollathorn/food_classifier
|
<!-- 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. -->
# Pollathorn/food_classifier
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: 1.9782
- Validation Loss: 1.2511
- Train Accuracy: 0.849
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.9782 | 1.2511 | 0.849 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"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"
] |
mimunto/food_classifier
|
<!-- 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. -->
# mimunto/food_classifier
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: 1.9400
- Validation Loss: 1.2381
- Train Accuracy: 0.86
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.9400 | 1.2381 | 0.86 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"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"
] |
gojonumbertwo/food_classifier
|
<!-- 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. -->
# gojonumbertwo/food_classifier
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: 2.2045
- Validation Loss: 1.3878
- Train Accuracy: 0.839
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.2045 | 1.3878 | 0.839 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"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"
] |
KeeApichai6103/food_classifier
|
<!-- 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. -->
# KeeApichai6103/food_classifier
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: 2.7449
- Validation Loss: 1.6355
- Train Accuracy: 0.81
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7449 | 1.6355 | 0.81 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"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"
] |
aikidoaikido115/food_classifier
|
<!-- 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. -->
# aikidoaikido115/food_classifier
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: 2.7880
- Validation Loss: 1.6485
- Train Accuracy: 0.826
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7880 | 1.6485 | 0.826 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"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"
] |
jovanlopez32/vit_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_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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0261
- 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1441 | 3.85 | 500 | 0.0261 | 0.9925 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
dima806/jellyfish_types_image_detection
|
Returns jellyfish type based on image.
See https://www.kaggle.com/code/dima806/jellyfish-types-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
blue_jellyfish 1.0000 1.0000 1.0000 30
barrel_jellyfish 1.0000 1.0000 1.0000 30
mauve_stinger_jellyfish 1.0000 1.0000 1.0000 30
Moon_jellyfish 1.0000 1.0000 1.0000 30
compass_jellyfish 1.0000 1.0000 1.0000 30
lions_mane_jellyfish 1.0000 1.0000 1.0000 30
accuracy 1.0000 180
macro avg 1.0000 1.0000 1.0000 180
weighted avg 1.0000 1.0000 1.0000 180
```
|
[
"blue_jellyfish",
"barrel_jellyfish",
"mauve_stinger_jellyfish",
"moon_jellyfish",
"compass_jellyfish",
"lions_mane_jellyfish"
] |
justinsiow/vit_101
|
<!-- 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_101
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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6267
- Accuracy: 0.88
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7266 | 0.99 | 62 | 2.5317 | 0.814 |
| 1.8315 | 2.0 | 125 | 1.7931 | 0.864 |
| 1.5845 | 2.98 | 186 | 1.6267 | 0.88 |
### Framework versions
- Transformers 4.27.2
- Pytorch 2.1.0.dev20230428
- Datasets 2.10.1
- Tokenizers 0.13.2
|
[
"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"
] |
aspends/coco_multiclass_classification
|
<!-- 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. -->
# aspends/assignment_part_3
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 COCO dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0932
- Validation Loss: 0.2218
- Train Accuracy: 0.9313
- 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': 8000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.8768 | 0.4404 | 0.9387 | 0 |
| 0.3198 | 0.2664 | 0.9475 | 1 |
| 0.1919 | 0.2303 | 0.9425 | 2 |
| 0.1357 | 0.1959 | 0.9463 | 3 |
| 0.0932 | 0.2218 | 0.9313 | 4 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"cat",
"horse",
"train",
"zebra"
] |
ahmadmooktaree/food_classifier
|
<!-- 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. -->
# ahmadmooktaree/food_classifier
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: 2.8192
- Validation Loss: 1.6728
- Train Accuracy: 0.825
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8192 | 1.6728 | 0.825 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"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"
] |
dima806/215_mushroom_types_image_detection
|
Returns mushroom type given an image.
See https://www.kaggle.com/code/dima806/mushroom-types-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
mosaic_puffball 1.0000 1.0000 1.0000 7
scarlet_elfcup 1.0000 1.0000 1.0000 7
splendid_waxcap 1.0000 0.4286 0.6000 7
tawny_grisette 0.8750 1.0000 0.9333 7
jubilee_waxcap 1.0000 1.0000 1.0000 6
king_alfreds_cakes 1.0000 0.8333 0.9091 6
heath_waxcap 0.7500 1.0000 0.8571 6
silky_rosegill 1.0000 1.0000 1.0000 6
golden_waxcap 0.4286 1.0000 0.6000 6
macro_mushroom 1.0000 0.8571 0.9231 7
spectacular_rustgill 0.7500 0.8571 0.8000 7
pink_waxcap 1.0000 1.0000 1.0000 6
brown_birch_bolete 0.8333 0.8333 0.8333 6
scaly_wood_mushroom 1.0000 1.0000 1.0000 6
stinkhorn 0.8571 1.0000 0.9231 6
blackening_brittlegill 1.0000 0.7143 0.8333 7
penny_bun 0.8571 1.0000 0.9231 6
chicken_of_the_woods 1.0000 1.0000 1.0000 7
common_bonnet 1.0000 0.7143 0.8333 7
common_rustgill 1.0000 0.8333 0.9091 6
hedgehog_fungus 1.0000 0.8333 0.9091 6
shaggy_scalycap 1.0000 0.8333 0.9091 6
dyers_mazegill 0.8571 1.0000 0.9231 6
earthballs 1.0000 1.0000 1.0000 7
purple_brittlegill 1.0000 0.8333 0.9091 6
smoky_bracket 0.7143 0.7143 0.7143 7
elfin_saddle 1.0000 1.0000 1.0000 6
shaggy_bracket 0.7778 1.0000 0.8750 7
greencracked_brittlegill 1.0000 0.6667 0.8000 6
sulphur_tuft 1.0000 1.0000 1.0000 6
warted_amanita 1.0000 0.7143 0.8333 7
white_domecap 0.7778 1.0000 0.8750 7
winter_chanterelle 1.0000 1.0000 1.0000 7
grey_knight 1.0000 0.8571 0.9231 7
pale_oyster 1.0000 0.5714 0.7273 7
medusa_mushroom 0.6667 0.8571 0.7500 7
spotted_toughshank 1.0000 1.0000 1.0000 7
dog_stinkhorn 1.0000 0.8333 0.9091 6
stubble_rosegill 1.0000 0.6667 0.8000 6
truffles 1.0000 1.0000 1.0000 6
panthercap 0.8000 0.6667 0.7273 6
vermillion_waxcap 1.0000 1.0000 1.0000 7
ascot_hat 0.8571 1.0000 0.9231 6
birch_polypore 1.0000 0.5000 0.6667 6
common_morel 0.7778 1.0000 0.8750 7
shaggy_parasol 1.0000 0.6667 0.8000 6
turkey_tail 0.6667 1.0000 0.8000 6
the_blusher 0.6250 0.8333 0.7143 6
deathcap 0.3333 1.0000 0.5000 7
chestnut_bolete 1.0000 0.7143 0.8333 7
grey_spotted_amanita 1.0000 0.8571 0.9231 7
slender_parasol 1.0000 0.8571 0.9231 7
horn_of_plenty 1.0000 1.0000 1.0000 7
magpie_inkcap 1.0000 0.8333 0.9091 6
fools_funnel 0.8333 0.8333 0.8333 6
orange_birch_bolete 1.0000 1.0000 1.0000 6
scarlet_waxcap 0.5714 0.6667 0.6154 6
yellow_stainer 1.0000 0.6667 0.8000 6
field_mushroom 1.0000 0.8333 0.9091 6
fragrant_funnel 0.8333 0.8333 0.8333 6
spring_fieldcap 0.8333 0.7143 0.7692 7
bronze_bolete 1.0000 0.4286 0.6000 7
orange_grisette 1.0000 0.8571 0.9231 7
parasol 0.8333 0.7143 0.7692 7
trooping_funnel 1.0000 0.7143 0.8333 7
beechwood_sickener 1.0000 0.6667 0.8000 6
rosy_bonnet 0.8333 0.8333 0.8333 6
dusky_puffball 1.0000 1.0000 1.0000 7
the_miller 0.7000 1.0000 0.8235 7
white_saddle 1.0000 1.0000 1.0000 7
old_man_of_the_woods 1.0000 1.0000 1.0000 6
crimped_gill 1.0000 0.8333 0.9091 6
blushing_rosette 1.0000 1.0000 1.0000 6
pine_bolete 1.0000 1.0000 1.0000 6
brown_rollrim 1.0000 0.8333 0.9091 6
deadly_webcap 1.0000 1.0000 1.0000 7
devils_bolete 1.0000 1.0000 1.0000 6
scarlet_caterpillarclub 1.0000 1.0000 1.0000 7
red_cracking_bolete 1.0000 1.0000 1.0000 6
false_chanterelle 1.0000 0.8333 0.9091 6
woodland_inkcap 0.6667 0.8571 0.7500 7
cucumber_cap 1.0000 0.8571 0.9231 7
leccinum_albostipitatum 1.0000 1.0000 1.0000 6
fairy_ring_champignons 0.8333 0.8333 0.8333 6
rooting_bolete 0.7500 1.0000 0.8571 6
wood_blewit 0.7500 1.0000 0.8571 6
lilac_bonnet 0.8333 0.8333 0.8333 6
butter_cap 1.0000 1.0000 1.0000 7
black_bulgar 1.0000 1.0000 1.0000 7
giant_puffball 0.8571 1.0000 0.9231 6
false_deathcap 0.0000 0.0000 0.0000 6
white_fibrecap 1.0000 1.0000 1.0000 6
velvet_shank 1.0000 0.8571 0.9231 7
slippery_jack 0.5556 0.8333 0.6667 6
white_dapperling 0.6667 0.8571 0.7500 7
parrot_waxcap 1.0000 0.8333 0.9091 6
wrinkled_peach 0.8571 1.0000 0.9231 6
silverleaf_fungus 1.0000 1.0000 1.0000 7
amanita_gemmata 1.0000 1.0000 1.0000 6
stinking_dapperling 1.0000 0.8333 0.9091 6
plums_and_custard 1.0000 0.6667 0.8000 6
peppery_bolete 0.8000 0.6667 0.7273 6
terracotta_hedgehog 0.8333 0.8333 0.8333 6
egghead_mottlegill 1.0000 1.0000 1.0000 6
bearded_milkcap 1.0000 0.8333 0.9091 6
inky_mushroom 1.0000 0.5000 0.6667 6
larch_bolete 0.8571 0.8571 0.8571 7
porcelain_fungus 0.8571 1.0000 0.9231 6
jelly_tooth 1.0000 1.0000 1.0000 6
scarletina_bolete 0.5000 1.0000 0.6667 6
yellow_foot_waxcap 1.0000 1.0000 1.0000 6
the_prince 1.0000 0.5000 0.6667 6
aniseed_funnel 1.0000 0.8333 0.9091 6
white_false_death_cap 0.5000 0.8333 0.6250 6
false_saffron_milkcap 1.0000 0.8333 0.9091 6
yellow_swamp_brittlegill 1.0000 0.8333 0.9091 6
semifree_morel 1.0000 1.0000 1.0000 7
bitter_bolete 1.0000 0.7143 0.8333 7
almond_mushroom 1.0000 1.0000 1.0000 6
shaggy_inkcap 0.8750 1.0000 0.9333 7
blushing_wood_mushroom 1.0000 0.6667 0.8000 6
common_puffball 1.0000 1.0000 1.0000 6
funeral_bell 0.7500 1.0000 0.8571 6
bay_bolete 1.0000 0.8333 0.9091 6
blackening_waxcap 1.0000 0.5714 0.7273 7
liberty_cap 0.6000 1.0000 0.7500 6
snowy_waxcap 0.6667 1.0000 0.8000 6
the_goblet 1.0000 1.0000 1.0000 7
deer_shield 1.0000 1.0000 1.0000 7
freckled_dapperling 0.6667 1.0000 0.8000 6
slimy_waxcap 0.6667 1.0000 0.8000 6
common_inkcap 0.7778 1.0000 0.8750 7
amethyst_chanterelle 0.8750 1.0000 0.9333 7
cedarwood_waxcap 0.7143 0.8333 0.7692 6
honey_fungus 1.0000 0.8571 0.9231 7
bruising_webcap 1.0000 0.4286 0.6000 7
stump_puffball 0.8571 1.0000 0.9231 6
giant_funnel 0.8333 0.8333 0.8333 6
tuberous_polypore 1.0000 0.6667 0.8000 6
poison_pie 0.8571 0.8571 0.8571 7
curry_milkcap 1.0000 1.0000 1.0000 6
amethyst_deceiver 1.0000 1.0000 1.0000 7
golden_bootleg 1.0000 0.7143 0.8333 7
clustered_domecap 1.0000 0.6667 0.8000 6
ochre_brittlegill 0.7143 0.7143 0.7143 7
blackening_polypore 1.0000 0.8333 0.9091 6
suede_bolete 1.0000 1.0000 1.0000 7
horse_mushroom 0.5455 1.0000 0.7059 6
geranium_brittlegill 0.6667 1.0000 0.8000 6
st_georges_mushroom 1.0000 0.8333 0.9091 6
destroying_angel 0.0000 0.0000 0.0000 6
field_blewit 1.0000 0.5714 0.7273 7
cinnamon_bracket 1.0000 1.0000 1.0000 6
lions_mane 1.0000 0.8333 0.9091 6
orange_peel_fungus 1.0000 1.0000 1.0000 6
chanterelle 0.8750 1.0000 0.9333 7
the_sickener 0.8571 1.0000 0.9231 6
birch_woodwart 0.8571 1.0000 0.9231 6
pavement_mushroom 0.7500 1.0000 0.8571 6
false_morel 1.0000 1.0000 1.0000 7
oak_bolete 1.0000 0.8333 0.9091 6
poplar_fieldcap 1.0000 0.5000 0.6667 6
jelly_ears 1.0000 1.0000 1.0000 6
summer_bolete 0.6250 0.8333 0.7143 6
frosted_chanterelle 0.5714 0.6667 0.6154 6
morel 1.0000 0.8333 0.9091 6
the_deceiver 1.0000 0.8571 0.9231 7
splitgill 0.8571 1.0000 0.9231 6
ruby_bolete 0.8571 0.8571 0.8571 7
sepia_bolete 1.0000 0.5714 0.7273 7
bovine_bolete 0.8750 1.0000 0.9333 7
fly_agaric 1.0000 1.0000 1.0000 7
thimble_morel 0.8571 1.0000 0.9231 6
black_morel 0.8333 0.8333 0.8333 6
poplar_bell 1.0000 1.0000 1.0000 6
fleecy_milkcap 0.7778 1.0000 0.8750 7
golden_scalycap 0.7500 1.0000 0.8571 6
yellow_stagshorn 1.0000 1.0000 1.0000 6
oak_polypore 1.0000 0.8333 0.9091 6
weeping_widow 0.7500 0.8571 0.8000 7
meadow_waxcap 0.8750 1.0000 0.9333 7
clouded_agaric 0.7500 0.8571 0.8000 7
woolly_milkcap 0.8750 1.0000 0.9333 7
snakeskin_grisette 1.0000 0.8333 0.9091 6
hairy_curtain_crust 0.8750 1.0000 0.9333 7
lurid_bolete 1.0000 0.6667 0.8000 6
wood_mushroom 0.8571 0.8571 0.8571 7
dryads_saddle 0.8750 1.0000 0.9333 7
sheathed_woodtuft 1.0000 0.8571 0.9231 7
orange_bolete 0.6667 1.0000 0.8000 6
lilac_fibrecap 1.0000 0.8571 0.9231 7
cauliflower_fungus 1.0000 1.0000 1.0000 7
saffron_milkcap 0.7500 0.5000 0.6000 6
pestle_puffball 1.0000 0.8571 0.9231 7
red_belted_bracket 1.0000 1.0000 1.0000 6
beefsteak_fungus 1.0000 1.0000 1.0000 7
oak_mazegill 1.0000 0.4286 0.6000 7
glistening_inkcap 0.8571 0.8571 0.8571 7
tripe_fungus 1.0000 0.6667 0.8000 6
blushing_bracket 0.7143 0.7143 0.7143 7
deadly_fibrecap 0.8571 1.0000 0.9231 6
root_rot 0.5556 0.8333 0.6667 6
powdery_brittlegill 1.0000 1.0000 1.0000 6
grisettes 0.6667 0.6667 0.6667 6
charcoal_burner 0.8333 0.7143 0.7692 7
rooting_shank 1.0000 1.0000 1.0000 6
hen_of_the_woods 0.8571 1.0000 0.9231 6
crimson_waxcap 1.0000 1.0000 1.0000 6
fenugreek_milkcap 1.0000 1.0000 1.0000 7
oyster_mushroom 0.6667 1.0000 0.8000 6
blue_roundhead 0.8571 1.0000 0.9231 6
hoof_fungus 0.7500 1.0000 0.8571 6
bitter_beech_bolete 1.0000 0.5714 0.7273 7
tawny_funnel 1.0000 1.0000 1.0000 6
yellow_false_truffle 1.0000 1.0000 1.0000 6
accuracy 0.8699 1376
macro avg 0.8933 0.8701 0.8670 1376
weighted avg 0.8949 0.8699 0.8676 1376
```
|
[
"mosaic_puffball",
"scarlet_elfcup",
"splendid_waxcap",
"tawny_grisette",
"jubilee_waxcap",
"king_alfreds_cakes",
"heath_waxcap",
"silky_rosegill",
"golden_waxcap",
"macro_mushroom",
"spectacular_rustgill",
"pink_waxcap",
"brown_birch_bolete",
"scaly_wood_mushroom",
"stinkhorn",
"blackening_brittlegill",
"penny_bun",
"chicken_of_the_woods",
"common_bonnet",
"common_rustgill",
"hedgehog_fungus",
"shaggy_scalycap",
"dyers_mazegill",
"earthballs",
"purple_brittlegill",
"smoky_bracket",
"elfin_saddle",
"shaggy_bracket",
"greencracked_brittlegill",
"sulphur_tuft",
"warted_amanita",
"white_domecap",
"winter_chanterelle",
"grey_knight",
"pale_oyster",
"medusa_mushroom",
"spotted_toughshank",
"dog_stinkhorn",
"stubble_rosegill",
"truffles",
"panthercap",
"vermillion_waxcap",
"ascot_hat",
"birch_polypore",
"common_morel",
"shaggy_parasol",
"turkey_tail",
"the_blusher",
"deathcap",
"chestnut_bolete",
"grey_spotted_amanita",
"slender_parasol",
"horn_of_plenty",
"magpie_inkcap",
"fools_funnel",
"orange_birch_bolete",
"scarlet_waxcap",
"yellow_stainer",
"field_mushroom",
"fragrant_funnel",
"spring_fieldcap",
"bronze_bolete",
"orange_grisette",
"parasol",
"trooping_funnel",
"beechwood_sickener",
"rosy_bonnet",
"dusky_puffball",
"the_miller",
"white_saddle",
"old_man_of_the_woods",
"crimped_gill",
"blushing_rosette",
"pine_bolete",
"brown_rollrim",
"deadly_webcap",
"devils_bolete",
"scarlet_caterpillarclub",
"red_cracking_bolete",
"false_chanterelle",
"woodland_inkcap",
"cucumber_cap",
"leccinum_albostipitatum",
"fairy_ring_champignons",
"rooting_bolete",
"wood_blewit",
"lilac_bonnet",
"butter_cap",
"black_bulgar",
"giant_puffball",
"false_deathcap",
"white_fibrecap",
"velvet_shank",
"slippery_jack",
"white_dapperling",
"parrot_waxcap",
"wrinkled_peach",
"silverleaf_fungus",
"amanita_gemmata",
"stinking_dapperling",
"plums_and_custard",
"peppery_bolete",
"terracotta_hedgehog",
"egghead_mottlegill",
"bearded_milkcap",
"inky_mushroom",
"larch_bolete",
"porcelain_fungus",
"jelly_tooth",
"scarletina_bolete",
"yellow_foot_waxcap",
"the_prince",
"aniseed_funnel",
"white_false_death_cap",
"false_saffron_milkcap",
"yellow_swamp_brittlegill",
"semifree_morel",
"bitter_bolete",
"almond_mushroom",
"shaggy_inkcap",
"blushing_wood_mushroom",
"common_puffball",
"funeral_bell",
"bay_bolete",
"blackening_waxcap",
"liberty_cap",
"snowy_waxcap",
"the_goblet",
"deer_shield",
"freckled_dapperling",
"slimy_waxcap",
"common_inkcap",
"amethyst_chanterelle",
"cedarwood_waxcap",
"honey_fungus",
"bruising_webcap",
"stump_puffball",
"giant_funnel",
"tuberous_polypore",
"poison_pie",
"curry_milkcap",
"amethyst_deceiver",
"golden_bootleg",
"clustered_domecap",
"ochre_brittlegill",
"blackening_polypore",
"suede_bolete",
"horse_mushroom",
"geranium_brittlegill",
"st_georges_mushroom",
"destroying_angel",
"field_blewit",
"cinnamon_bracket",
"lions_mane",
"orange_peel_fungus",
"chanterelle",
"the_sickener",
"birch_woodwart",
"pavement_mushroom",
"false_morel",
"oak_bolete",
"poplar_fieldcap",
"jelly_ears",
"summer_bolete",
"frosted_chanterelle",
"morel",
"the_deceiver",
"splitgill",
"ruby_bolete",
"sepia_bolete",
"bovine_bolete",
"fly_agaric",
"thimble_morel",
"black_morel",
"poplar_bell",
"fleecy_milkcap",
"golden_scalycap",
"yellow_stagshorn",
"oak_polypore",
"weeping_widow",
"meadow_waxcap",
"clouded_agaric",
"woolly_milkcap",
"snakeskin_grisette",
"hairy_curtain_crust",
"lurid_bolete",
"wood_mushroom",
"dryads_saddle",
"sheathed_woodtuft",
"orange_bolete",
"lilac_fibrecap",
"cauliflower_fungus",
"saffron_milkcap",
"pestle_puffball",
"red_belted_bracket",
"beefsteak_fungus",
"oak_mazegill",
"glistening_inkcap",
"tripe_fungus",
"blushing_bracket",
"deadly_fibrecap",
"root_rot",
"powdery_brittlegill",
"grisettes",
"charcoal_burner",
"rooting_shank",
"hen_of_the_woods",
"crimson_waxcap",
"fenugreek_milkcap",
"oyster_mushroom",
"blue_roundhead",
"hoof_fungus",
"bitter_beech_bolete",
"tawny_funnel",
"yellow_false_truffle"
] |
sakethbngr/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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Accuracy: 0.9696
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4921 | 1.0 | 351 | 0.1464 | 0.955 |
| 0.4008 | 2.0 | 703 | 0.1049 | 0.9668 |
| 0.3386 | 2.99 | 1053 | 0.0952 | 0.9696 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
arieg/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 the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7792
- Accuracy: 0.99
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.918 | 0.96 | 12 | 0.8973 | 0.97 |
| 0.8361 | 2.0 | 25 | 0.7851 | 0.995 |
| 0.7704 | 2.88 | 36 | 0.7792 | 0.99 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"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"
] |
arieg/food_classifier
|
<!-- 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. -->
# arieg/food_classifier
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: 0.2049
- Validation Loss: 0.2772
- Train Accuracy: 0.917
- 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': 4000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3304 | 0.3024 | 0.93 | 0 |
| 0.3047 | 0.3004 | 0.928 | 1 |
| 0.2481 | 0.2744 | 0.935 | 2 |
| 0.2262 | 0.2737 | 0.919 | 3 |
| 0.2049 | 0.2772 | 0.917 | 4 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"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"
] |
arieg/food_classifier_noaug
|
<!-- 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. -->
# arieg/food_classifier_noaug
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: 0.1400
- Validation Loss: 0.1328
- Train Accuracy: 0.969
- 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': 4000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1614 | 0.1377 | 0.971 | 0 |
| 0.1519 | 0.1422 | 0.968 | 1 |
| 0.1429 | 0.1329 | 0.968 | 2 |
| 0.1340 | 0.1328 | 0.969 | 3 |
| 0.1400 | 0.1328 | 0.969 | 4 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"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"
] |
dima806/pneumonia_chest_xray_image_detection
|
See https://www.kaggle.com/code/dima806/pneumonia-chest-x-ray-image-detection-vit for more details.
|
[
"normal",
"pneumonia"
] |
100rab25/swin-tiny-patch4-window7-224-fraud_number_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-fraud_number_classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0107
- Accuracy: 0.9963
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0229 | 1.0 | 19 | 0.0516 | 0.9851 |
| 0.0193 | 2.0 | 38 | 0.0107 | 0.9963 |
| 0.0062 | 3.0 | 57 | 0.0275 | 0.9963 |
| 0.0172 | 4.0 | 76 | 0.0313 | 0.9963 |
| 0.028 | 5.0 | 95 | 0.0431 | 0.9926 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"fraud_number",
"fraud_number_not_found"
] |
02shanky/vit-finetuned-cifar10
|
<!-- 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-cifar-10
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:
- eval_loss: 0.0831
- eval_accuracy: 0.9802
- eval_runtime: 75.4306
- eval_samples_per_second: 66.286
- eval_steps_per_second: 16.572
- epoch: 1.0
- step: 4500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
Mahendra42/vit-base-patch16-224-in21k-finetunedRCC_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. -->
# vit-base-patch16-224-in21k-finetunedRCC_Classifier
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: 2.5623
- Accuracy: 0.6074
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0019 | 1.0 | 155 | 2.0291 | 0.6532 |
| 0.0013 | 2.0 | 310 | 2.4863 | 0.6074 |
| 0.001 | 3.0 | 465 | 2.5623 | 0.6074 |
### Framework versions
- Transformers 4.34.1
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1
|
[
"clear cell rcc",
"non clear cell"
] |
emaeon/vit-base-patch16-224-in21k-finetuned-gecko
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-finetuned-gecko
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: 0.1890
- Accuracy: 0.9885
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.97 | 21 | 3.2699 | 0.6210 |
| No log | 1.98 | 43 | 2.0011 | 0.8468 |
| 3.1155 | 2.99 | 65 | 1.2851 | 0.8641 |
| 3.1155 | 4.0 | 87 | 0.7751 | 0.9389 |
| 1.1003 | 4.97 | 108 | 0.6060 | 0.9274 |
| 1.1003 | 5.98 | 130 | 0.4584 | 0.9378 |
| 0.5229 | 6.99 | 152 | 0.3417 | 0.9585 |
| 0.5229 | 8.0 | 174 | 0.2415 | 0.9816 |
| 0.5229 | 8.97 | 195 | 0.2014 | 0.9873 |
| 0.3249 | 9.66 | 210 | 0.1890 | 0.9885 |
### Framework versions
- Transformers 4.34.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"10_sd22-0010",
"11_sd22-0011",
"12_sd22-0012",
"13_sd21-0013",
"14_sd21-0014",
"15_sd21-0015",
"16_sd21-0016",
"17_sd22-0017",
"18_sd22-0018",
"19_sd22-0019",
"1_sd18-0001",
"20_sd22-0020",
"21_sd22-0021",
"22_sd22-0022",
"23_sd22-0023",
"24_sd22-0024",
"25_sd22-0025",
"26_sd22-0026",
"27_sd22-0027",
"28_sd22-0028",
"29_sd22-0029",
"2_sd22-0002",
"30_sd22-0030",
"31_sd22-0031",
"32_sd22-0032",
"33_sd22-0033",
"34_sd21-0034",
"35_sd21-0035",
"36_sd22-0036",
"37_ax21-0037",
"38_ax22-0038",
"39_hax22-0039",
"3_sd22-0003",
"40_hlax22-0020",
"41_ax22-0041",
"42_ax22-0042",
"43_lax22-0043",
"44_hax22-0044",
"45_sc22-0045",
"46_cal22-0046",
"47_cal22-0047",
"48_sc22-0048",
"49_sc23-0049",
"4_sd22-0004",
"50_sc23-0050",
"51_sab22-0051",
"52_lw21-0052",
"53_nor22-0053",
"54_nor22-0054",
"55_nor22-0055",
"5_sd22-0005",
"6_sd22-0006",
"7_sd22-0007",
"80_lw",
"81_lw",
"82_nor",
"83_nor",
"84_nor",
"85_nor",
"86_nor",
"87_nor",
"88_nor",
"89_nor(hax)",
"8_sd22-0008",
"90_lw_b",
"91_lw_b",
"92_nor_b",
"93_nor_b",
"9_sd22-0009"
] |
KevinTao511/pets_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. -->
# pets_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: 0.9289
- Accuracy: 0.8621
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 1.0377 | 0.6897 |
| No log | 1.87 | 7 | 0.9472 | 0.8276 |
| No log | 2.4 | 9 | 0.9289 | 0.8621 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"abyssinian",
"basset",
"beagle"
] |
elucidator8918/VIT-MUSH
|
# Transfer Learning Vision Transformer (ViT) - Google 224 ViT Base Patch
## Description
This model is a Transfer Learning Vision Transformer (ViT) based on Google's 224 ViT Base Patch architecture. It has been fine-tuned on a dataset consisting of fungal images from Russia, with a specific focus on various fungi and lichen species.
## Model Information
- Model Name: Transfer Learning ViT - Google 224 ViT Base Patch
- Model Architecture: Vision Transformer (ViT)
- Base Architecture: Google's 224 ViT Base Patch
- Pre-trained on General ImageNet dataset
- Fine-tuned on: Fungal image dataset from Russia
## Performance
- Accuracy: 90.31%
- F1 Score: 86.33%
## Training Details
- Training Loss:
- Initial: 1.043200
- Final: 0.116200
- Validation Loss:
- Initial: 0.822428
- Final: 0.335994
- Training Epochs: 10
- Training Runtime: 18575.04 seconds
- Training Samples per Second: 33.327
- Training Steps per Second: 1.042
- Total FLOPs: 4.801 x 10^19
## Recommended Use Cases
- Species classification of various fungi and lichen in Russia.
- Fungal biodiversity studies.
- Image recognition tasks related to fungi and lichen species.
## Limitations
- The model's performance is optimized for fungal species and may not generalize well to other domains.
- The model may not perform well on images of fungi and lichen species from regions other than Russia.
## Model Author
Siddhant Dutta
|
[
"boletus reticulatus",
"coprinopsis atramentaria",
"pleurotus pulmonarius",
"gyromitra infula",
"lactarius turpis",
"nectria cinnabarina",
"laetiporus sulphureus",
"phellinus tremulae",
"pholiota aurivella",
"peltigera aphthosa",
"lactarius torminosus",
"armillaria borealis",
"pseudevernia furfuracea",
"vulpicida pinastri",
"hericium coralloides",
"hypogymnia physodes",
"fomitopsis betulina",
"amanita muscaria",
"pleurotus ostreatus",
"verpa bohemica",
"coprinellus micaceus",
"xanthoria parietina",
"suillus luteus",
"sarcosoma globosum",
"coprinellus disseminatus",
"rhytisma acerinum",
"fomes fomentarius",
"stropharia aeruginosa",
"lycoperdon perlatum",
"suillus grevillei",
"sarcoscypha austriaca",
"cerioporus squamosus",
"coltricia perennis",
"paxillus involutus",
"kuehneromyces mutabilis",
"chondrostereum purpureum",
"trichaptum biforme",
"daedaleopsis tricolor",
"gyromitra gigas",
"cantharellus cibarius",
"macrolepiota procera",
"hygrophoropsis aurantiaca",
"hypholoma lateritium",
"coprinus comatus",
"peltigera praetextata",
"lepista nuda",
"phellinus igniarius",
"tremella mesenterica",
"apioperdon pyriforme",
"cladonia stellaris",
"flammulina velutipes",
"parmelia sulcata",
"leccinum aurantiacum",
"merulius tremellosus",
"daedaleopsis confragosa",
"pholiota squarrosa",
"lobaria pulmonaria",
"phaeophyscia orbicularis",
"calycina citrina",
"sarcomyxa serotina",
"fomitopsis pinicola",
"urnula craterium",
"cladonia rangiferina",
"leccinum versipelle",
"leccinum albostipitatum",
"boletus edulis",
"phallus impudicus",
"imleria badia",
"cladonia fimbriata",
"chlorociboria aeruginascens",
"amanita pantherina",
"trametes ochracea",
"mutinus ravenelii",
"schizophyllum commune",
"artomyces pyxidatus",
"graphis scripta",
"amanita citrina",
"crucibulum laeve",
"clitocybe nebularis",
"stereum hirsutum",
"cetraria islandica",
"bjerkandera adusta",
"suillus granulatus",
"hypholoma fasciculare",
"physcia adscendens",
"trametes hirsuta",
"gyromitra esculenta",
"tricholomopsis rutilans",
"panellus stipticus",
"lactarius deliciosus",
"inonotus obliquus",
"evernia mesomorpha",
"ganoderma applanatum",
"phlebia radiata",
"trametes versicolor",
"calocera viscosa",
"evernia prunastri",
"platismatia glauca",
"leccinum scabrum",
"amanita rubescens"
] |
bdpc/vit-base_rvl_cdip-N1K_ce_256
|
<!-- 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_rvl_cdip-N1K_ce_256
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4495
- Accuracy: 0.8935
- Brier Loss: 0.1753
- Nll: 1.0235
- F1 Micro: 0.8935
- F1 Macro: 0.8937
- Ece: 0.0696
- Aurc: 0.0181
## Model description
More information needed
## Intended uses & 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 63 | 0.3678 | 0.8972 | 0.1554 | 1.1865 | 0.8972 | 0.8975 | 0.0427 | 0.0165 |
| No log | 2.0 | 126 | 0.3774 | 0.896 | 0.1584 | 1.1527 | 0.8960 | 0.8962 | 0.0470 | 0.0170 |
| No log | 3.0 | 189 | 0.4050 | 0.892 | 0.1688 | 1.1092 | 0.892 | 0.8924 | 0.0578 | 0.0177 |
| No log | 4.0 | 252 | 0.4089 | 0.8945 | 0.1675 | 1.0874 | 0.8945 | 0.8948 | 0.0582 | 0.0177 |
| No log | 5.0 | 315 | 0.4255 | 0.8935 | 0.1704 | 1.0678 | 0.8935 | 0.8936 | 0.0640 | 0.0179 |
| No log | 6.0 | 378 | 0.4324 | 0.8945 | 0.1715 | 1.0540 | 0.8945 | 0.8948 | 0.0648 | 0.0179 |
| No log | 7.0 | 441 | 0.4404 | 0.894 | 0.1728 | 1.0302 | 0.894 | 0.8941 | 0.0672 | 0.0181 |
| 0.0579 | 8.0 | 504 | 0.4452 | 0.8932 | 0.1747 | 1.0316 | 0.8932 | 0.8934 | 0.0685 | 0.0180 |
| 0.0579 | 9.0 | 567 | 0.4479 | 0.8935 | 0.1749 | 1.0256 | 0.8935 | 0.8937 | 0.0693 | 0.0181 |
| 0.0579 | 10.0 | 630 | 0.4495 | 0.8935 | 0.1753 | 1.0235 | 0.8935 | 0.8937 | 0.0696 | 0.0181 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_AURC_256
|
<!-- 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_rvl_cdip-N1K_AURC_256
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2459
- Accuracy: 0.8968
- Brier Loss: 0.1720
- Nll: 0.9246
- F1 Micro: 0.8968
- F1 Macro: 0.8967
- Ece: 0.0709
- Aurc: 0.0191
## Model description
More information needed
## Intended uses & 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 63 | 0.1138 | 0.8922 | 0.1604 | 1.1695 | 0.8922 | 0.8926 | 0.0478 | 0.0170 |
| No log | 2.0 | 126 | 0.1565 | 0.8952 | 0.1607 | 1.1000 | 0.8952 | 0.8952 | 0.0532 | 0.0176 |
| No log | 3.0 | 189 | 0.1722 | 0.8972 | 0.1620 | 1.0250 | 0.8972 | 0.8973 | 0.0584 | 0.0175 |
| No log | 4.0 | 252 | 0.2006 | 0.897 | 0.1642 | 0.9921 | 0.897 | 0.8969 | 0.0615 | 0.0181 |
| No log | 5.0 | 315 | 0.2142 | 0.8988 | 0.1668 | 0.9670 | 0.8988 | 0.8986 | 0.0640 | 0.0183 |
| No log | 6.0 | 378 | 0.2207 | 0.8975 | 0.1688 | 0.9482 | 0.8975 | 0.8975 | 0.0674 | 0.0186 |
| No log | 7.0 | 441 | 0.2310 | 0.897 | 0.1700 | 0.9397 | 0.897 | 0.8969 | 0.0697 | 0.0188 |
| 0.008 | 8.0 | 504 | 0.2401 | 0.8968 | 0.1714 | 0.9268 | 0.8968 | 0.8966 | 0.0705 | 0.0190 |
| 0.008 | 9.0 | 567 | 0.2441 | 0.8975 | 0.1719 | 0.9262 | 0.8975 | 0.8974 | 0.0709 | 0.0191 |
| 0.008 | 10.0 | 630 | 0.2459 | 0.8968 | 0.1720 | 0.9246 | 0.8968 | 0.8967 | 0.0709 | 0.0191 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_ce_128
|
<!-- 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_rvl_cdip-N1K_ce_128
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4776
- Accuracy: 0.8912
- Brier Loss: 0.1798
- Nll: 0.9844
- F1 Micro: 0.8912
- F1 Macro: 0.8915
- Ece: 0.0768
- Aurc: 0.0189
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 125 | 0.3896 | 0.893 | 0.1649 | 1.1887 | 0.893 | 0.8933 | 0.0484 | 0.0175 |
| No log | 2.0 | 250 | 0.3908 | 0.8948 | 0.1606 | 1.1433 | 0.8948 | 0.8950 | 0.0499 | 0.0179 |
| No log | 3.0 | 375 | 0.4188 | 0.892 | 0.1708 | 1.0860 | 0.892 | 0.8923 | 0.0607 | 0.0184 |
| 0.0953 | 4.0 | 500 | 0.4268 | 0.892 | 0.1707 | 1.0788 | 0.892 | 0.8924 | 0.0654 | 0.0184 |
| 0.0953 | 5.0 | 625 | 0.4414 | 0.8938 | 0.1719 | 1.0502 | 0.8938 | 0.8941 | 0.0664 | 0.0187 |
| 0.0953 | 6.0 | 750 | 0.4570 | 0.8932 | 0.1754 | 1.0253 | 0.8932 | 0.8936 | 0.0714 | 0.0187 |
| 0.0953 | 7.0 | 875 | 0.4681 | 0.891 | 0.1779 | 1.0018 | 0.891 | 0.8912 | 0.0752 | 0.0191 |
| 0.0128 | 8.0 | 1000 | 0.4720 | 0.8902 | 0.1792 | 0.9789 | 0.8902 | 0.8905 | 0.0771 | 0.0188 |
| 0.0128 | 9.0 | 1125 | 0.4757 | 0.8918 | 0.1794 | 0.9865 | 0.8918 | 0.8920 | 0.0760 | 0.0188 |
| 0.0128 | 10.0 | 1250 | 0.4776 | 0.8912 | 0.1798 | 0.9844 | 0.8912 | 0.8915 | 0.0768 | 0.0189 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_AURC_128
|
<!-- 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_rvl_cdip-N1K_AURC_128
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2754
- Accuracy: 0.8962
- Brier Loss: 0.1742
- Nll: 0.8794
- F1 Micro: 0.8962
- F1 Macro: 0.8963
- Ece: 0.0736
- Aurc: 0.0200
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 125 | 0.1357 | 0.8898 | 0.1657 | 1.2064 | 0.8898 | 0.8907 | 0.0492 | 0.0181 |
| No log | 2.0 | 250 | 0.1615 | 0.898 | 0.1602 | 1.0955 | 0.898 | 0.8986 | 0.0473 | 0.0181 |
| No log | 3.0 | 375 | 0.1795 | 0.896 | 0.1630 | 1.0031 | 0.8960 | 0.8959 | 0.0599 | 0.0180 |
| 0.0132 | 4.0 | 500 | 0.2094 | 0.8978 | 0.1662 | 0.9561 | 0.8978 | 0.8977 | 0.0633 | 0.0187 |
| 0.0132 | 5.0 | 625 | 0.2290 | 0.898 | 0.1692 | 0.9249 | 0.898 | 0.8979 | 0.0665 | 0.0190 |
| 0.0132 | 6.0 | 750 | 0.2430 | 0.898 | 0.1714 | 0.9150 | 0.898 | 0.8981 | 0.0690 | 0.0194 |
| 0.0132 | 7.0 | 875 | 0.2567 | 0.898 | 0.1718 | 0.8888 | 0.898 | 0.8979 | 0.0702 | 0.0196 |
| 0.0022 | 8.0 | 1000 | 0.2740 | 0.8975 | 0.1734 | 0.8800 | 0.8975 | 0.8975 | 0.0718 | 0.0199 |
| 0.0022 | 9.0 | 1125 | 0.2715 | 0.896 | 0.1743 | 0.8824 | 0.8960 | 0.8960 | 0.0737 | 0.0199 |
| 0.0022 | 10.0 | 1250 | 0.2754 | 0.8962 | 0.1742 | 0.8794 | 0.8962 | 0.8963 | 0.0736 | 0.0200 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
everycoffee/autotrain-coffee-bean-quality-97496146930
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 97496146930
- CO2 Emissions (in grams): 2.6219
## Validation Metrics
- Loss: 0.097
- Accuracy: 0.990
- Precision: 0.980
- Recall: 1.000
- AUC: 0.998
- F1: 0.990
|
[
"defect",
"good"
] |
02shanky/vit-finetuned-vanilla-cifar10-0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-finetuned-vanilla-cifar10-0
This model is a fine-tuned version of [02shanky/vit-finetuned-cifar10](https://huggingface.co/02shanky/vit-finetuned-cifar10) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0306
- Accuracy: 0.992
## Model description
More information needed
## Intended uses & 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 316 | 0.0619 | 0.9836 |
| 0.2651 | 2.0 | 633 | 0.0460 | 0.9867 |
| 0.2651 | 3.0 | 949 | 0.0415 | 0.9878 |
| 0.1967 | 4.0 | 1266 | 0.0326 | 0.9916 |
| 0.1552 | 4.99 | 1580 | 0.0306 | 0.992 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
arieg/food_classifier_noaug_streaming
|
<!-- 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. -->
# arieg/food_classifier_noaug_streaming
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: 0.4578
- Validation Loss: 1.3138
- Train Accuracy: 0.801
- 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': 20000, '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 | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 3.1605 | 2.7599 | 0.602 | 0 |
| 1.6013 | 1.9823 | 0.67 | 1 |
| 0.9193 | 1.5901 | 0.699 | 2 |
| 0.6189 | 1.3822 | 0.712 | 3 |
| 0.4578 | 1.3138 | 0.801 | 4 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"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"
] |
JLB-JLB/seizure_vit_jlb_231027
|
<!-- 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. -->
# seizure_vit_jlb_231027
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 JLB-JLB/seizure_eeg_greyscale_224x224_6secWindow_adjusted dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4759
- Roc Auc: 0.7822
## Model description
More information needed
## Intended uses & 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.4787 | 0.17 | 1000 | 0.5094 | 0.7706 |
| 0.3695 | 0.34 | 2000 | 0.5111 | 0.7359 |
| 0.337 | 0.51 | 3000 | 0.4734 | 0.7829 |
| 0.3604 | 0.68 | 4000 | 0.5508 | 0.7457 |
| 0.3222 | 0.85 | 5000 | 0.5817 | 0.7687 |
| 0.2315 | 1.02 | 6000 | 0.6515 | 0.7679 |
| 0.2388 | 1.19 | 7000 | 0.5681 | 0.7543 |
| 0.2691 | 1.36 | 8000 | 0.5307 | 0.7691 |
| 0.268 | 1.53 | 9000 | 0.5643 | 0.7610 |
| 0.131 | 1.7 | 10000 | 0.7293 | 0.7451 |
| 0.2303 | 1.87 | 11000 | 0.6291 | 0.7704 |
| 0.1442 | 2.04 | 12000 | 0.6372 | 0.7871 |
| 0.1325 | 2.21 | 13000 | 0.8672 | 0.7319 |
| 0.1986 | 2.38 | 14000 | 0.7352 | 0.7532 |
| 0.1669 | 2.55 | 15000 | 0.8195 | 0.7562 |
| 0.1228 | 2.72 | 16000 | 1.0106 | 0.7239 |
| 0.1071 | 2.89 | 17000 | 0.8957 | 0.7463 |
| 0.1322 | 3.06 | 18000 | 1.0871 | 0.7408 |
| 0.1676 | 3.24 | 19000 | 0.9173 | 0.7683 |
| 0.1105 | 3.41 | 20000 | 1.0175 | 0.7700 |
| 0.1451 | 3.58 | 21000 | 0.9357 | 0.7404 |
| 0.082 | 3.75 | 22000 | 1.1246 | 0.7404 |
| 0.1457 | 3.92 | 23000 | 1.0082 | 0.7502 |
| 0.0336 | 4.09 | 24000 | 1.3685 | 0.7443 |
| 0.0742 | 4.26 | 25000 | 1.5080 | 0.7227 |
| 0.0353 | 4.43 | 26000 | 1.3573 | 0.7421 |
| 0.0557 | 4.6 | 27000 | 1.2484 | 0.7472 |
| 0.075 | 4.77 | 28000 | 1.2750 | 0.7462 |
| 0.0569 | 4.94 | 29000 | 1.3954 | 0.7355 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"seiz",
"bckg"
] |
bdpc/vit-base_rvl_cdip-N1K_aAURC_128
|
<!-- 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_rvl_cdip-N1K_aAURC_128
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4634
- Accuracy: 0.8915
- Brier Loss: 0.1791
- Nll: 0.9824
- F1 Micro: 0.8915
- F1 Macro: 0.8918
- Ece: 0.0767
- Aurc: 0.0184
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 125 | 0.3790 | 0.8935 | 0.1649 | 1.1886 | 0.8935 | 0.8937 | 0.0488 | 0.0175 |
| No log | 2.0 | 250 | 0.3783 | 0.8958 | 0.1605 | 1.1495 | 0.8958 | 0.8959 | 0.0497 | 0.0178 |
| No log | 3.0 | 375 | 0.4065 | 0.8915 | 0.1700 | 1.0956 | 0.8915 | 0.8918 | 0.0617 | 0.0183 |
| 0.0928 | 4.0 | 500 | 0.4158 | 0.8932 | 0.1705 | 1.0843 | 0.8932 | 0.8936 | 0.0635 | 0.0183 |
| 0.0928 | 5.0 | 625 | 0.4328 | 0.8932 | 0.1721 | 1.0369 | 0.8932 | 0.8935 | 0.0673 | 0.0186 |
| 0.0928 | 6.0 | 750 | 0.4442 | 0.891 | 0.1764 | 1.0214 | 0.891 | 0.8913 | 0.0737 | 0.0183 |
| 0.0928 | 7.0 | 875 | 0.4542 | 0.8935 | 0.1770 | 1.0053 | 0.8935 | 0.8938 | 0.0722 | 0.0187 |
| 0.0125 | 8.0 | 1000 | 0.4587 | 0.891 | 0.1790 | 0.9941 | 0.891 | 0.8913 | 0.0767 | 0.0183 |
| 0.0125 | 9.0 | 1125 | 0.4616 | 0.891 | 0.1786 | 0.9847 | 0.891 | 0.8912 | 0.0767 | 0.0185 |
| 0.0125 | 10.0 | 1250 | 0.4634 | 0.8915 | 0.1791 | 0.9824 | 0.8915 | 0.8918 | 0.0767 | 0.0184 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
bdpc/vit-base_rvl_cdip-N1K_aAURC_64
|
<!-- 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_rvl_cdip-N1K_aAURC_64
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4857
- Accuracy: 0.8892
- Brier Loss: 0.1843
- Nll: 0.9506
- F1 Micro: 0.8892
- F1 Macro: 0.8895
- Ece: 0.0837
- Aurc: 0.0193
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 250 | 0.3824 | 0.888 | 0.1700 | 1.1756 | 0.888 | 0.8884 | 0.0548 | 0.0185 |
| 0.1403 | 2.0 | 500 | 0.3988 | 0.8925 | 0.1681 | 1.1230 | 0.8925 | 0.8936 | 0.0549 | 0.0199 |
| 0.1403 | 3.0 | 750 | 0.4099 | 0.8865 | 0.1756 | 1.0948 | 0.8865 | 0.8868 | 0.0672 | 0.0187 |
| 0.0442 | 4.0 | 1000 | 0.4297 | 0.8925 | 0.1747 | 1.0568 | 0.8925 | 0.8931 | 0.0685 | 0.0191 |
| 0.0442 | 5.0 | 1250 | 0.4467 | 0.8925 | 0.1775 | 1.0202 | 0.8925 | 0.8928 | 0.0734 | 0.0194 |
| 0.0119 | 6.0 | 1500 | 0.4612 | 0.8908 | 0.1808 | 0.9834 | 0.8907 | 0.8914 | 0.0772 | 0.0191 |
| 0.0119 | 7.0 | 1750 | 0.4762 | 0.8882 | 0.1845 | 0.9761 | 0.8882 | 0.8885 | 0.0827 | 0.0197 |
| 0.0062 | 8.0 | 2000 | 0.4763 | 0.892 | 0.1824 | 0.9652 | 0.892 | 0.8923 | 0.0789 | 0.0192 |
| 0.0062 | 9.0 | 2250 | 0.4854 | 0.8892 | 0.1844 | 0.9509 | 0.8892 | 0.8895 | 0.0834 | 0.0193 |
| 0.0051 | 10.0 | 2500 | 0.4857 | 0.8892 | 0.1843 | 0.9506 | 0.8892 | 0.8895 | 0.0837 | 0.0193 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"letter",
"form",
"email",
"handwritten",
"advertisement",
"scientific_report",
"scientific_publication",
"specification",
"file_folder",
"news_article",
"budget",
"invoice",
"presentation",
"questionnaire",
"resume",
"memo"
] |
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