model_id
stringlengths 7
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|---|---|---|
JCRios/ces-ViTModel
|
<!-- 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. -->
# ces-ViTModel
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: 0.1427
- Accuracy: 0.9624
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.06 | 3.85 | 500 | 0.1427 | 0.9624 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Kotiks/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia
|
<!-- 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-kornia
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: 0.5886
- Accuracy: 0.5909
## Model description
More information needed
## Intended uses & 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 3 | 0.6243 | 0.6818 |
| No log | 2.0 | 6 | 0.5460 | 0.7273 |
| No log | 3.0 | 9 | 0.5540 | 0.7273 |
| 0.6502 | 4.0 | 12 | 0.5747 | 0.6818 |
| 0.6502 | 5.0 | 15 | 0.5886 | 0.5909 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"clethrionomys",
"sylvaemus"
] |
Taekor/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. -->
# Taekor/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: 2.8017
- Validation Loss: 1.6806
- Train Accuracy: 0.797
- 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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8017 | 1.6806 | 0.797 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- 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"
] |
Pa-satith/cloud_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. -->
# Pa-satith/cloud_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: 2.2214
- Validation Loss: 2.0617
- Train Accuracy: 0.368
- 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': 999, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.2214 | 2.0617 | 0.368 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"altocumulus",
"altostratus",
"cirroculumulus",
"cirrostratus",
"cirrus",
"cumulonimbus",
"cumulus",
"nimbostratus",
"stratocumulus",
"stratus"
] |
Coelhomatias/vit-cxr4
|
<!-- 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-cxr4
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3774
- Precision: 0.8587
- Recall: 0.9317
- F1: 0.8937
- Accuracy: 0.8924
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 96
- eval_batch_size: 64
- seed: 17
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3151 | 0.31 | 100 | 0.3317 | 0.8152 | 0.9143 | 0.8619 | 0.8552 |
| 0.319 | 0.63 | 200 | 0.3048 | 0.8670 | 0.8514 | 0.8591 | 0.8620 |
| 0.2926 | 0.94 | 300 | 0.2867 | 0.8580 | 0.8662 | 0.8621 | 0.8631 |
| 0.1884 | 1.25 | 400 | 0.2635 | 0.8468 | 0.9381 | 0.8901 | 0.8856 |
| 0.234 | 1.57 | 500 | 0.2639 | 0.8232 | 0.9677 | 0.8896 | 0.8814 |
| 0.2349 | 1.88 | 600 | 0.2478 | 0.8530 | 0.9328 | 0.8911 | 0.8874 |
| 0.1476 | 2.19 | 700 | 0.2560 | 0.8584 | 0.9297 | 0.8926 | 0.8895 |
| 0.1289 | 2.51 | 800 | 0.2698 | 0.8809 | 0.8916 | 0.8862 | 0.8869 |
| 0.1579 | 2.82 | 900 | 0.2614 | 0.8879 | 0.8715 | 0.8796 | 0.8822 |
| 0.0745 | 3.13 | 1000 | 0.2783 | 0.8854 | 0.8905 | 0.8880 | 0.8889 |
| 0.0697 | 3.45 | 1100 | 0.2844 | 0.8893 | 0.8879 | 0.8886 | 0.8900 |
| 0.0602 | 3.76 | 1200 | 0.3213 | 0.8797 | 0.8932 | 0.8864 | 0.8869 |
| 0.0246 | 4.08 | 1300 | 0.3393 | 0.8753 | 0.9096 | 0.8921 | 0.8913 |
| 0.0301 | 4.39 | 1400 | 0.3593 | 0.8644 | 0.9307 | 0.8964 | 0.8937 |
| 0.0348 | 4.7 | 1500 | 0.3804 | 0.8653 | 0.9344 | 0.8986 | 0.8957 |
| 0.011 | 5.02 | 1600 | 0.3897 | 0.8622 | 0.9365 | 0.8978 | 0.8947 |
| 0.0077 | 5.33 | 1700 | 0.4088 | 0.8754 | 0.9180 | 0.8962 | 0.8950 |
| 0.0064 | 5.64 | 1800 | 0.4281 | 0.8780 | 0.9170 | 0.8971 | 0.8960 |
| 0.0031 | 5.96 | 1900 | 0.4289 | 0.8736 | 0.9207 | 0.8965 | 0.8950 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"negative",
"positive"
] |
tbass134/beans
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0558
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0685 | 1.54 | 100 | 0.0671 | 0.9850 |
| 0.0048 | 3.08 | 200 | 0.0558 | 0.9774 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
tbass134/notebooks
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# google/vit-base-patch16-224-in21k
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0405
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1266 | 1.54 | 100 | 0.1121 | 0.9624 |
| 0.0167 | 3.08 | 200 | 0.0405 | 0.9774 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Aruno/gemini-beauty
|
<!-- 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. -->
# gemini-beauty
This model is a fine-tuned version of [](https://huggingface.co/) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1226
- Accuracy: 0.5158
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3724 | 1.0 | 148 | 1.2028 | 0.4586 |
| 1.3217 | 2.0 | 296 | 1.1831 | 0.4812 |
| 1.2649 | 3.0 | 444 | 1.1674 | 0.4981 |
| 1.2456 | 4.0 | 592 | 1.1236 | 0.5146 |
| 1.2176 | 5.0 | 740 | 1.1384 | 0.5040 |
| 1.2069 | 6.0 | 888 | 1.1165 | 0.5207 |
| 1.2083 | 7.0 | 1036 | 1.1663 | 0.4985 |
| 1.1663 | 8.0 | 1184 | 1.1226 | 0.5158 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"attractive",
"normal",
"ugly",
"very_attractive",
"very_ugly"
] |
deniseb/vit-base-patch16-224-finetuned-flower
|
<!-- 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-flower
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.
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
pittawat/vit-base-uppercase-english-characters
|
<!-- 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-uppercase-english-characters
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 pittawat/uppercase-english-characters dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3160
- Accuracy: 0.9573
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5944 | 1.35 | 100 | 0.5538 | 0.9487 |
| 0.2241 | 2.7 | 200 | 0.3160 | 0.9573 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
[
"a",
"b",
"k",
"l",
"m",
"n",
"o",
"p",
"q",
"r",
"s",
"t",
"c",
"u",
"v",
"w",
"x",
"y",
"z",
"d",
"e",
"f",
"g",
"h",
"i",
"j"
] |
augrit/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. -->
# augrit/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: 2.3004
- Validation Loss: 1.3738
- Train Accuracy: 0.843
- 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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.3004 | 1.3738 | 0.843 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- 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"
] |
julianz1/swin-tiny-patch4-window7-224-finetuned-food101
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-food101
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8486
- Accuracy: 0.7760
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4777 | 1.0 | 503 | 1.1436 | 0.7062 |
| 1.2418 | 2.0 | 1006 | 0.9227 | 0.7571 |
| 1.0657 | 3.0 | 1509 | 0.8486 | 0.7760 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare",
"waffles"
] |
VolodymyrChapman/vit-base-patch16-224-finetuned-flower
|
<!-- 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-flower
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.
## Model description
More information needed
## Intended uses & 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: 80
- eval_batch_size: 80
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
alirzb/WS800_BEiT_42820085
|
<!-- 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. -->
# WS800_BEiT_42820085
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0451
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 6 | 0.6592 | 0.85 |
| No log | 1.92 | 12 | 0.4828 | 0.9875 |
| No log | 2.88 | 18 | 0.3227 | 0.9875 |
| No log | 4.0 | 25 | 0.2001 | 0.9875 |
| No log | 4.96 | 31 | 0.1378 | 0.9875 |
| No log | 5.92 | 37 | 0.0838 | 1.0 |
| No log | 6.88 | 43 | 0.0635 | 1.0 |
| 0.297 | 8.0 | 50 | 0.0510 | 1.0 |
| 0.297 | 8.96 | 56 | 0.0462 | 1.0 |
| 0.297 | 9.6 | 60 | 0.0451 | 1.0 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"none_seizures",
"seizures"
] |
alirzb/WS800_ViT_42820348
|
<!-- 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. -->
# WS800_ViT_42820348
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1485
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 6 | 0.6689 | 0.925 |
| No log | 1.92 | 12 | 0.5584 | 0.975 |
| No log | 2.88 | 18 | 0.4389 | 0.9875 |
| No log | 4.0 | 25 | 0.3280 | 0.9875 |
| No log | 4.96 | 31 | 0.2582 | 1.0 |
| No log | 5.92 | 37 | 0.2105 | 1.0 |
| No log | 6.88 | 43 | 0.1805 | 1.0 |
| 0.3864 | 8.0 | 50 | 0.1601 | 1.0 |
| 0.3864 | 8.96 | 56 | 0.1504 | 1.0 |
| 0.3864 | 9.6 | 60 | 0.1485 | 1.0 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"none_seizures",
"seizures"
] |
alirzb/WS800_DeiT_42820350
|
<!-- 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. -->
# WS800_DeiT_42820350
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 6 | 0.1229 | 0.9625 |
| No log | 1.92 | 12 | 0.1149 | 0.95 |
| No log | 2.88 | 18 | 0.0406 | 0.9875 |
| No log | 4.0 | 25 | 0.0004 | 1.0 |
| No log | 4.96 | 31 | 0.0001 | 1.0 |
| No log | 5.92 | 37 | 0.0001 | 1.0 |
| No log | 6.88 | 43 | 0.0000 | 1.0 |
| 0.0759 | 8.0 | 50 | 0.0000 | 1.0 |
| 0.0759 | 8.96 | 56 | 0.0000 | 1.0 |
| 0.0759 | 9.6 | 60 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"none_seizures",
"seizures"
] |
alirzb/WS800_SwinT_42820409
|
<!-- 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. -->
# WS800_SwinT_42820409
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0091
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 6 | 0.6706 | 0.9625 |
| No log | 1.92 | 12 | 0.4819 | 1.0 |
| No log | 2.88 | 18 | 0.2188 | 1.0 |
| No log | 4.0 | 25 | 0.0947 | 1.0 |
| No log | 4.96 | 31 | 0.0464 | 1.0 |
| No log | 5.92 | 37 | 0.0263 | 1.0 |
| No log | 6.88 | 43 | 0.0163 | 1.0 |
| 0.2515 | 8.0 | 50 | 0.0112 | 1.0 |
| 0.2515 | 8.96 | 56 | 0.0103 | 1.0 |
| 0.2515 | 9.6 | 60 | 0.0091 | 1.0 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"none_seizures",
"seizures"
] |
julianz1/axis-inference-v0
|
<!-- 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. -->
# axis-inference-v0
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7092
- Accuracy: 0.5243
## Model description
More information needed
## Intended uses & 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.6101 | 0.94 | 12 | 0.9202 | 0.4701 |
| 0.8441 | 1.96 | 25 | 0.7214 | 0.5410 |
| 0.7249 | 2.98 | 38 | 0.7014 | 0.5131 |
| 0.6997 | 3.76 | 48 | 0.7092 | 0.5243 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
mejdik/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: 0.0860
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2841 | 1.0 | 190 | 0.1861 | 0.9515 |
| 0.1951 | 2.0 | 380 | 0.1127 | 0.9652 |
| 0.1413 | 3.0 | 570 | 0.0860 | 0.9748 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
ahmed-ai/skin_lesions_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. -->
# ahmed-ai/skin_lesions_classifier
<h2 style="color:red; font-size: 3rem">Important Warning</h2>
<p style="font-weight: bold; font-size: 1.5rem;">This model is currently undergoing development; as such, it should not be used for clinical diagnosis or relied upon for medical decision-making at this stage.</p>
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: 0.8374
- Validation Loss: 0.7696
- Train Accuracy: 0.7102
- 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': 109580, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.3152 | 1.0475 | 0.6511 | 0 |
| 1.0540 | 0.8775 | 0.6918 | 1 |
| 0.9540 | 0.8533 | 0.6814 | 2 |
| 0.8859 | 0.7491 | 0.7201 | 3 |
| 0.8374 | 0.7696 | 0.7102 | 4 |
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"actinic keratoses",
"basal cell carcinoma",
"melanoma",
"monkeypox",
"squamous cell carcinoma",
"vascular lesions",
"dermatofibroma",
"benign keratosis-like lesions",
"chickenpox",
"cowpox",
"dermatofibroma",
"hfmd",
"healthy",
"measles",
"melanocytic nevi"
] |
Coelhomatias/vit-cxr4-384
|
<!-- 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-cxr4-384
This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2413
- Precision: 0.8525
- Recall: 0.9419
- F1: 0.8950
- Accuracy: 0.8926
## Model description
More information needed
## Intended uses & 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: 24
- eval_batch_size: 24
- seed: 17
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3694 | 0.08 | 100 | 0.3851 | 0.8076 | 0.8435 | 0.8251 | 0.8234 |
| 0.3084 | 0.16 | 200 | 0.4478 | 0.7556 | 0.9889 | 0.8566 | 0.8364 |
| 0.3177 | 0.24 | 300 | 0.3073 | 0.8062 | 0.9572 | 0.8752 | 0.8652 |
| 0.3413 | 0.31 | 400 | 0.2936 | 0.8172 | 0.9434 | 0.8758 | 0.8678 |
| 0.2612 | 0.39 | 500 | 0.2936 | 0.8402 | 0.9122 | 0.8747 | 0.8709 |
| 0.3607 | 0.47 | 600 | 0.2717 | 0.8210 | 0.9603 | 0.8852 | 0.8769 |
| 0.274 | 0.55 | 700 | 0.2875 | 0.8373 | 0.9196 | 0.8765 | 0.8720 |
| 0.3127 | 0.63 | 800 | 0.2664 | 0.8156 | 0.9683 | 0.8854 | 0.8761 |
| 0.2875 | 0.71 | 900 | 0.2643 | 0.8369 | 0.9334 | 0.8825 | 0.8772 |
| 0.2652 | 0.78 | 1000 | 0.2659 | 0.8134 | 0.9683 | 0.8841 | 0.8746 |
| 0.2661 | 0.86 | 1100 | 0.2591 | 0.8334 | 0.9445 | 0.8855 | 0.8793 |
| 0.3019 | 0.94 | 1200 | 0.2729 | 0.8851 | 0.8599 | 0.8723 | 0.8756 |
| 0.229 | 1.02 | 1300 | 0.2548 | 0.8357 | 0.9603 | 0.8937 | 0.8871 |
| 0.1841 | 1.1 | 1400 | 0.2438 | 0.8586 | 0.9217 | 0.8891 | 0.8863 |
| 0.2257 | 1.18 | 1500 | 0.2365 | 0.8629 | 0.9254 | 0.8931 | 0.8905 |
| 0.2217 | 1.25 | 1600 | 0.2509 | 0.8888 | 0.8662 | 0.8773 | 0.8803 |
| 0.2619 | 1.33 | 1700 | 0.2588 | 0.8373 | 0.9582 | 0.8937 | 0.8874 |
| 0.2222 | 1.41 | 1800 | 0.2521 | 0.8644 | 0.9238 | 0.8931 | 0.8908 |
| 0.2044 | 1.49 | 1900 | 0.2598 | 0.8409 | 0.9588 | 0.8960 | 0.8900 |
| 0.2238 | 1.57 | 2000 | 0.2641 | 0.9117 | 0.8302 | 0.8691 | 0.8764 |
| 0.249 | 1.65 | 2100 | 0.2368 | 0.8464 | 0.9561 | 0.8979 | 0.8926 |
| 0.1773 | 1.72 | 2200 | 0.2233 | 0.8682 | 0.9265 | 0.8964 | 0.8942 |
| 0.1447 | 1.8 | 2300 | 0.2269 | 0.8760 | 0.9191 | 0.8970 | 0.8957 |
| 0.245 | 1.88 | 2400 | 0.2355 | 0.8578 | 0.9445 | 0.8991 | 0.8952 |
| 0.1685 | 1.96 | 2500 | 0.2312 | 0.8615 | 0.9344 | 0.8965 | 0.8934 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"negative",
"positive"
] |
calledice666/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0944
- Accuracy: 0.9684
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4761 | 1.0 | 351 | 0.1498 | 0.9514 |
| 0.3594 | 2.0 | 703 | 0.1006 | 0.966 |
| 0.3228 | 2.99 | 1053 | 0.0944 | 0.9684 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
PeteA2Z/kelp_binary_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. -->
# kelp_binary_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: 0.6605
- Accuracy: 0.6256
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6681 | 0.28 | 10 | 0.6698 | 0.6264 |
| 0.6628 | 0.57 | 20 | 0.6595 | 0.6300 |
| 0.6634 | 0.85 | 30 | 0.6605 | 0.6256 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"ja_kelp",
"nee_kelp"
] |
jaydip-tss/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8266
- Accuracy: 0.922
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1714 | 0.99 | 62 | 1.1657 | 0.896 |
| 0.8623 | 2.0 | 125 | 0.9087 | 0.915 |
| 0.7595 | 2.98 | 186 | 0.8252 | 0.925 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- 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"
] |
ahmed-ai/mit-b1-skin-lesion-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. -->
# ahmed-ai/mit-b1-skin-classifier
<h2 style="color:red; font-size: 3rem">Important Warning</h2>
<p style="font-weight: bold; font-size: 1.5rem;">This model is currently undergoing development; as such, it should not be used for clinical diagnosis or relied upon for medical decision-making at this stage.</p>
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4567
- Validation Loss: 0.6271
- Train Accuracy: 0.7610
- Epoch: 9
## Model description
More information needed
## 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': 191770, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.1152 | 0.8715 | 0.6858 | 0 |
| 0.8784 | 0.7400 | 0.7216 | 1 |
| 0.7774 | 0.7492 | 0.7273 | 2 |
| 0.7114 | 0.7229 | 0.7213 | 3 |
| 0.6492 | 0.6735 | 0.7393 | 4 |
| 0.6005 | 0.6498 | 0.7507 | 5 |
| 0.5560 | 0.6181 | 0.7596 | 6 |
| 0.5179 | 0.6473 | 0.7582 | 7 |
| 0.4913 | 0.6100 | 0.7711 | 8 |
| 0.4567 | 0.6271 | 0.7610 | 9 |
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.13.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"actinic keratoses",
"basal cell carcinoma",
"melanoma",
"monkeypox",
"squamous cell carcinoma",
"vascular lesions",
"dermatofibroma",
"benign keratosis-like lesions",
"chickenpox",
"cowpox",
"dermatofibroma",
"hfmd",
"healthy",
"measles",
"melanocytic nevi"
] |
amber90-dev/vit-base-patch16-224-finetuned-flower
|
<!-- 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-flower
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.
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
ggergues/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia
|
<!-- 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-kornia
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.
## Model description
More information needed
## Intended uses & 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
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
wahidww/swin-tiny-patch4-window7-224-finetuned-mobile-eye-tracking-dataset-v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-mobile-eye-tracking-dataset-v2
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: 0.2851
- Accuracy: 0.9094
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.0421 | 0.9941 | 127 | 1.9090 | 0.3566 |
| 1.2943 | 1.9922 | 254 | 1.2209 | 0.5679 |
| 0.8757 | 2.9902 | 381 | 0.8373 | 0.7108 |
| 0.7362 | 3.9961 | 509 | 0.6260 | 0.7967 |
| 0.6011 | 4.9941 | 636 | 0.5550 | 0.8049 |
| 0.5267 | 5.9922 | 763 | 0.4797 | 0.8339 |
| 0.4969 | 6.9902 | 890 | 0.4508 | 0.8583 |
| 0.4418 | 7.9961 | 1018 | 0.4243 | 0.8571 |
| 0.4564 | 8.9941 | 1145 | 0.4035 | 0.8618 |
| 0.4199 | 9.9922 | 1272 | 0.3678 | 0.8804 |
| 0.3929 | 10.9902 | 1399 | 0.3479 | 0.8897 |
| 0.3578 | 11.9961 | 1527 | 0.3355 | 0.8931 |
| 0.3405 | 12.9941 | 1654 | 0.3173 | 0.8955 |
| 0.3399 | 13.9922 | 1781 | 0.3177 | 0.9048 |
| 0.3219 | 14.9902 | 1908 | 0.3354 | 0.8943 |
| 0.3296 | 15.9961 | 2036 | 0.3175 | 0.8978 |
| 0.3259 | 16.9941 | 2163 | 0.2908 | 0.9048 |
| 0.3201 | 17.9922 | 2290 | 0.2883 | 0.9082 |
| 0.2922 | 18.9902 | 2417 | 0.2945 | 0.9013 |
| 0.3204 | 19.9961 | 2545 | 0.2961 | 0.9013 |
| 0.2926 | 20.9941 | 2672 | 0.2934 | 0.9048 |
| 0.3083 | 21.9922 | 2799 | 0.2960 | 0.9024 |
| 0.3174 | 22.9902 | 2926 | 0.2876 | 0.9059 |
| 0.2693 | 23.9961 | 3054 | 0.2896 | 0.9036 |
| 0.2807 | 24.9941 | 3181 | 0.2852 | 0.8990 |
| 0.289 | 25.9922 | 3308 | 0.2847 | 0.9048 |
| 0.2829 | 26.9902 | 3435 | 0.2888 | 0.9048 |
| 0.2749 | 27.9961 | 3563 | 0.2898 | 0.9059 |
| 0.2726 | 28.9941 | 3690 | 0.2780 | 0.9094 |
| 0.2744 | 29.9374 | 3810 | 0.2851 | 0.9094 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"central serous chorioretinopathy - color fundus",
"disc edema",
"glaucoma",
"macular scar",
"pterygium",
"retinal detachment",
"retinitis pigmentosa",
"cataract",
"diabetic_retinopathy",
"normal"
] |
yuramoomin/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6263
- Accuracy: 0.898
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.684 | 0.99 | 62 | 2.5207 | 0.82 |
| 1.845 | 2.0 | 125 | 1.7768 | 0.897 |
| 1.6058 | 2.98 | 186 | 1.6263 | 0.898 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- 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"
] |
wahidww/vit-base-patch16-224-in21k-finetuned-mobile-eye-tracking-dataset-v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-finetuned-mobile-eye-tracking-dataset-v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2615
- Accuracy: 0.9231
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.6641 | 0.6154 |
| No log | 2.0 | 4 | 0.6343 | 0.6154 |
| No log | 3.0 | 6 | 0.5990 | 0.6154 |
| No log | 4.0 | 8 | 0.5438 | 0.8462 |
| No log | 5.0 | 10 | 0.5108 | 0.9231 |
| No log | 6.0 | 12 | 0.4413 | 0.8462 |
| No log | 7.0 | 14 | 0.3947 | 0.8462 |
| No log | 8.0 | 16 | 0.3568 | 0.9231 |
| No log | 9.0 | 18 | 0.3297 | 0.9231 |
| 0.4923 | 10.0 | 20 | 0.3110 | 0.9231 |
| 0.4923 | 11.0 | 22 | 0.2988 | 0.9231 |
| 0.4923 | 12.0 | 24 | 0.2836 | 0.9231 |
| 0.4923 | 13.0 | 26 | 0.2702 | 0.9231 |
| 0.4923 | 14.0 | 28 | 0.2636 | 0.9231 |
| 0.4923 | 15.0 | 30 | 0.2615 | 0.9231 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"central serous chorioretinopathy - color fundus",
"disc edema",
"glaucoma",
"macular scar",
"pterygium",
"retinal detachment",
"retinitis pigmentosa",
"cataract",
"diabetic_retinopathy",
"normal"
] |
adhisetiawan/ViT-flowers-species
|
<!-- 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. -->
# adhisetiawan/ViT-flowers-species
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: 0.0831
- Validation Loss: 0.1388
- Train Accuracy: 0.9605
- 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': 14680, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.7563 | 0.3186 | 0.9482 | 0 |
| 0.2194 | 0.2133 | 0.9496 | 1 |
| 0.1417 | 0.1802 | 0.9550 | 2 |
| 0.0973 | 0.1482 | 0.9605 | 3 |
| 0.0831 | 0.1388 | 0.9605 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
DazMashaly/swin_larger
|
<!-- 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_larger
This model was trained from scratch on the zindi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7009
- eval_accuracy: 0.7617
- eval_runtime: 222.198
- eval_samples_per_second: 17.43
- eval_steps_per_second: 0.549
- epoch: 1.0
- step: 173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
DazMashaly/swin_cont
|
<!-- 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_cont
This model was trained from scratch on the zindi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4766
- eval_accuracy: 0.7545
- eval_runtime: 236.8539
- eval_samples_per_second: 16.352
- eval_steps_per_second: 0.515
- epoch: 2.0
- step: 347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
varcoder/resnet-101-finetuned-CivilEng11k
|
<!-- 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-101-finetuned-CivilEng11k
This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5490
- Accuracy: 0.8542
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 320
- 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 | 0.81 | 3 | 1.0724 | 0.5729 |
| No log | 1.89 | 7 | 0.9717 | 0.6542 |
| 1.0293 | 2.97 | 11 | 0.8594 | 0.6678 |
| 1.0293 | 3.78 | 14 | 0.7830 | 0.7017 |
| 1.0293 | 4.86 | 18 | 0.6764 | 0.7593 |
| 0.78 | 5.95 | 22 | 0.6072 | 0.7831 |
| 0.78 | 6.76 | 25 | 0.5745 | 0.8339 |
| 0.78 | 7.84 | 29 | 0.5489 | 0.8508 |
| 0.6037 | 8.11 | 30 | 0.5490 | 0.8542 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
|
[
"crazing",
"live_knot",
"steel_defect"
] |
yuramoomin/my_awesome_cat_horse_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_cat_horse_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: 0.1749
- Accuracy: 0.956
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6028 | 0.95 | 15 | 0.3283 | 0.961 |
| 0.2124 | 1.97 | 31 | 0.1926 | 0.953 |
| 0.1665 | 2.86 | 45 | 0.1749 | 0.956 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"horse",
"cat"
] |
yuramoomin/my_awesome_cat_horse_model_updated
|
<!-- 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_cat_horse_model_updated
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: 0.1033
- Accuracy: 0.967
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1431 | 0.95 | 15 | 0.1245 | 0.959 |
| 0.0769 | 1.97 | 31 | 0.0962 | 0.971 |
| 0.0751 | 2.86 | 45 | 0.1033 | 0.967 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"horse",
"cat"
] |
ytechnology/platzi-vit_model-alexander-ferreras
|
<!-- 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-alexander-ferreras
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: 0.0488
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1398 | 3.85 | 500 | 0.0488 | 0.9850 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
alirzb/WS800_BEiT_42895082
|
<!-- 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. -->
# WS800_BEiT_42895082
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0845
- Accuracy: 0.975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 0.6815 | 0.9375 |
| No log | 2.0 | 10 | 0.6041 | 0.95 |
| No log | 3.0 | 15 | 0.4946 | 0.9125 |
| No log | 4.0 | 20 | 0.3233 | 0.975 |
| No log | 5.0 | 25 | 0.2158 | 0.9875 |
| No log | 6.0 | 30 | 0.1514 | 0.9875 |
| No log | 7.0 | 35 | 0.1109 | 0.9875 |
| No log | 8.0 | 40 | 0.0909 | 0.9875 |
| No log | 9.0 | 45 | 0.0886 | 0.975 |
| 0.3029 | 10.0 | 50 | 0.1020 | 0.975 |
| 0.3029 | 11.0 | 55 | 0.1155 | 0.975 |
| 0.3029 | 12.0 | 60 | 0.1197 | 0.975 |
| 0.3029 | 13.0 | 65 | 0.1247 | 0.975 |
| 0.3029 | 14.0 | 70 | 0.1021 | 0.975 |
| 0.3029 | 15.0 | 75 | 0.2528 | 0.95 |
| 0.3029 | 16.0 | 80 | 0.1866 | 0.9625 |
| 0.3029 | 17.0 | 85 | 0.2019 | 0.9625 |
| 0.3029 | 18.0 | 90 | 0.1456 | 0.9625 |
| 0.3029 | 19.0 | 95 | 0.1014 | 0.975 |
| 0.0143 | 20.0 | 100 | 0.0845 | 0.975 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"none_seizures",
"seizures"
] |
alirzb/WS800_DeiT_42895082
|
<!-- 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. -->
# WS800_DeiT_42895082
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2587
- Accuracy: 0.975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 0.6389 | 0.5125 |
| No log | 2.0 | 10 | 0.1840 | 0.9375 |
| No log | 3.0 | 15 | 0.1272 | 0.9625 |
| No log | 4.0 | 20 | 0.1958 | 0.9625 |
| No log | 5.0 | 25 | 0.1635 | 0.975 |
| No log | 6.0 | 30 | 0.2280 | 0.975 |
| No log | 7.0 | 35 | 0.2664 | 0.9625 |
| No log | 8.0 | 40 | 0.2636 | 0.9625 |
| No log | 9.0 | 45 | 0.2582 | 0.975 |
| 0.1252 | 10.0 | 50 | 0.2571 | 0.975 |
| 0.1252 | 11.0 | 55 | 0.2571 | 0.975 |
| 0.1252 | 12.0 | 60 | 0.2572 | 0.975 |
| 0.1252 | 13.0 | 65 | 0.2574 | 0.975 |
| 0.1252 | 14.0 | 70 | 0.2577 | 0.975 |
| 0.1252 | 15.0 | 75 | 0.2580 | 0.975 |
| 0.1252 | 16.0 | 80 | 0.2582 | 0.975 |
| 0.1252 | 17.0 | 85 | 0.2584 | 0.975 |
| 0.1252 | 18.0 | 90 | 0.2586 | 0.975 |
| 0.1252 | 19.0 | 95 | 0.2587 | 0.975 |
| 0.0 | 20.0 | 100 | 0.2587 | 0.975 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"none_seizures",
"seizures"
] |
alirzb/WS800_SwinT_42895082
|
<!-- 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. -->
# WS800_SwinT_42895082
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0762
- Accuracy: 0.9875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 0.6874 | 0.925 |
| No log | 2.0 | 10 | 0.6195 | 0.95 |
| No log | 3.0 | 15 | 0.4087 | 0.9625 |
| No log | 4.0 | 20 | 0.2299 | 0.9875 |
| No log | 5.0 | 25 | 0.1265 | 0.9875 |
| No log | 6.0 | 30 | 0.0764 | 0.9875 |
| No log | 7.0 | 35 | 0.0752 | 0.9875 |
| No log | 8.0 | 40 | 0.0656 | 0.9875 |
| No log | 9.0 | 45 | 0.0668 | 0.9875 |
| 0.2735 | 10.0 | 50 | 0.1085 | 0.975 |
| 0.2735 | 11.0 | 55 | 0.1147 | 0.9625 |
| 0.2735 | 12.0 | 60 | 0.0731 | 0.9875 |
| 0.2735 | 13.0 | 65 | 0.1228 | 0.9625 |
| 0.2735 | 14.0 | 70 | 0.0732 | 0.9875 |
| 0.2735 | 15.0 | 75 | 0.0663 | 0.9875 |
| 0.2735 | 16.0 | 80 | 0.0674 | 0.9875 |
| 0.2735 | 17.0 | 85 | 0.0728 | 0.9875 |
| 0.2735 | 18.0 | 90 | 0.0750 | 0.9875 |
| 0.2735 | 19.0 | 95 | 0.0759 | 0.9875 |
| 0.0111 | 20.0 | 100 | 0.0762 | 0.9875 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"none_seizures",
"seizures"
] |
alirzb/WS800_ViT_42895082
|
<!-- 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. -->
# WS800_ViT_42895082
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0776
- Accuracy: 0.9875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 0.6859 | 0.925 |
| No log | 2.0 | 10 | 0.6328 | 0.975 |
| No log | 3.0 | 15 | 0.5301 | 0.975 |
| No log | 4.0 | 20 | 0.4404 | 0.9625 |
| No log | 5.0 | 25 | 0.3480 | 0.975 |
| No log | 6.0 | 30 | 0.2758 | 0.975 |
| No log | 7.0 | 35 | 0.2179 | 0.9875 |
| No log | 8.0 | 40 | 0.1789 | 0.9875 |
| No log | 9.0 | 45 | 0.1505 | 0.9875 |
| 0.3788 | 10.0 | 50 | 0.1296 | 0.9875 |
| 0.3788 | 11.0 | 55 | 0.1145 | 0.9875 |
| 0.3788 | 12.0 | 60 | 0.1034 | 0.9875 |
| 0.3788 | 13.0 | 65 | 0.0954 | 0.9875 |
| 0.3788 | 14.0 | 70 | 0.0895 | 0.9875 |
| 0.3788 | 15.0 | 75 | 0.0853 | 0.9875 |
| 0.3788 | 16.0 | 80 | 0.0822 | 0.9875 |
| 0.3788 | 17.0 | 85 | 0.0801 | 0.9875 |
| 0.3788 | 18.0 | 90 | 0.0787 | 0.9875 |
| 0.3788 | 19.0 | 95 | 0.0779 | 0.9875 |
| 0.0547 | 20.0 | 100 | 0.0776 | 0.9875 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"none_seizures",
"seizures"
] |
ChrisGuarino/cat_ds
|
<!-- 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. -->
# cat_ds
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: 0.0137
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4194 | 1.0 | 13 | 0.0737 | 1.0 |
| 0.063 | 2.0 | 26 | 0.0266 | 1.0 |
| 0.051 | 3.0 | 39 | 0.0151 | 1.0 |
| 0.014 | 4.0 | 52 | 0.0137 | 1.0 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0.post103
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"prim",
"rupe"
] |
Ricardo-H/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1229
- Accuracy: 0.9618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5796 | 1.0 | 176 | 0.2204 | 0.9462 |
| 0.3995 | 2.0 | 352 | 0.1403 | 0.9582 |
| 0.3781 | 3.0 | 528 | 0.1229 | 0.9618 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
Ricardo-H/swin-tiny-patch4-window7-224-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. -->
# swin-tiny-patch4-window7-224-finetuned-cifar10
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0760
- Accuracy: 0.9758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.314 | 1.0 | 176 | 0.1211 | 0.9612 |
| 0.2992 | 2.0 | 352 | 0.1186 | 0.9622 |
| 0.3544 | 3.0 | 528 | 0.0989 | 0.968 |
| 0.3068 | 4.0 | 704 | 0.0872 | 0.9724 |
| 0.3421 | 5.0 | 880 | 0.0858 | 0.972 |
| 0.2915 | 6.0 | 1056 | 0.0824 | 0.9724 |
| 0.3051 | 7.0 | 1232 | 0.0822 | 0.974 |
| 0.2849 | 8.0 | 1408 | 0.0770 | 0.975 |
| 0.2661 | 9.0 | 1584 | 0.0773 | 0.9756 |
| 0.2504 | 10.0 | 1760 | 0.0760 | 0.9758 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
sruthis/alzheimer_model_aug_deit5
|
<!-- 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. -->
# alzheimer_model_aug_deit5
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0472
- Accuracy: 0.9939
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 1234
- gradient_accumulation_steps: 10
- total_train_batch_size: 160
- 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 | 0.97 | 12 | 0.5252 | 0.8947 |
| No log | 1.94 | 24 | 0.1506 | 0.9636 |
| No log | 2.98 | 37 | 0.0787 | 0.9858 |
| No log | 3.95 | 49 | 0.0587 | 0.9919 |
| No log | 4.84 | 60 | 0.0472 | 0.9939 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"2-4d_upper",
"bacterial_spot_upper",
"cold_injury_upper",
"early_blight_upper",
"healthy_upperside",
"little_leaf_upperside",
"nutritional_disorder_upper",
"spider_mite_upper",
"tsw_upper",
"tylc_upper"
] |
ggergues/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- 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. -->
# ggergues/swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5115
- Validation Loss: 0.1813
- Validation Accuracy: 0.9437
- Epoch: 2
## Model description
More information needed
## 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': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:---------------:|:-------------------:|:-----:|
| 0.7934 | 0.2019 | 0.9333 | 0 |
| 0.5631 | 0.2128 | 0.9307 | 1 |
| 0.5115 | 0.1813 | 0.9437 | 2 |
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
gianlab/swin-tiny-patch4-window7-224-finetuned-crop-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-crop-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: 0.6957
- Accuracy: 0.7234
## Model description
This model was created by importing images of crop damage.
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/1qEskI6O-Jjv7UCanfQmUmzz8qUyg7FS3?usp=sharing
The possible classified data are:
Damage types
| Damage | Definition |
|-----------------|---------------------|
| DR | Drought |
| G | Good (growth) |
| ND | Nutrient Deficient |
| WD | Weed |
| other | Disease, Pest, Wind |
Crop 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7819 | 1.0 | 183 | 0.7262 | 0.7016 |
| 0.7104 | 1.99 | 366 | 0.6957 | 0.7234 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
IanTseng/vis_items_with_hand_classfier
|
<!-- 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. -->
# vis_items_with_hand_classfier
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: 0.0886
- Validation Loss: 0.0126
- Train Accuracy: 0.9981
- 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': 32405, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3574 | 0.0613 | 0.9932 | 0 |
| 0.1445 | 0.0334 | 0.9932 | 1 |
| 0.1196 | 0.0282 | 0.9963 | 2 |
| 0.0986 | 0.0208 | 0.9963 | 3 |
| 0.0886 | 0.0126 | 0.9981 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6"
] |
nicolasdupuisroy/vit-letter-identification-v3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-letter-identification-v3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3440
- Accuracy: 0.7615
## Model description
More information needed
## Intended uses & 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: 80
- eval_batch_size: 80
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 250.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log | 1.0 | 7 | 0.0154 | 3.9449 |
| 3.9333 | 2.0 | 14 | 0.0231 | 3.9367 |
| 3.8939 | 3.0 | 21 | 0.0308 | 3.9280 |
| 3.8939 | 4.0 | 28 | 0.0462 | 3.9167 |
| 3.8562 | 5.0 | 35 | 0.0692 | 3.9033 |
| 3.8008 | 6.0 | 42 | 0.0769 | 3.8874 |
| 3.8008 | 7.0 | 49 | 0.1077 | 3.8670 |
| 3.7555 | 8.0 | 56 | 0.1 | 3.8495 |
| 3.6917 | 9.0 | 63 | 0.1154 | 3.8305 |
| 3.6372 | 10.0 | 70 | 0.1385 | 3.8138 |
| 3.6372 | 11.0 | 77 | 0.1231 | 3.7966 |
| 3.5846 | 12.0 | 84 | 0.1538 | 3.7767 |
| 3.5047 | 13.0 | 91 | 0.2308 | 3.7516 |
| 3.5047 | 14.0 | 98 | 0.2385 | 3.7279 |
| 3.4547 | 15.0 | 105 | 0.2385 | 3.7031 |
| 3.3796 | 16.0 | 112 | 0.2692 | 3.6725 |
| 3.3796 | 17.0 | 119 | 0.2769 | 3.6462 |
| 3.3283 | 18.0 | 126 | 0.2923 | 3.6226 |
| 3.2728 | 19.0 | 133 | 0.2846 | 3.6022 |
| 3.2229 | 20.0 | 140 | 0.2769 | 3.5930 |
| 3.2229 | 21.0 | 147 | 0.3308 | 3.5748 |
| 3.1514 | 22.0 | 154 | 0.3385 | 3.5404 |
| 3.1179 | 23.0 | 161 | 0.3385 | 3.5146 |
| 3.1179 | 24.0 | 168 | 0.3462 | 3.4916 |
| 3.0559 | 25.0 | 175 | 0.3385 | 3.4733 |
| 3.0051 | 26.0 | 182 | 0.3615 | 3.4540 |
| 3.0051 | 27.0 | 189 | 0.3692 | 3.4499 |
| 2.9775 | 28.0 | 196 | 0.3769 | 3.4355 |
| 2.9277 | 29.0 | 203 | 0.3846 | 3.4166 |
| 2.9066 | 30.0 | 210 | 0.4 | 3.4007 |
| 2.9066 | 31.0 | 217 | 0.3692 | 3.3826 |
| 2.8464 | 32.0 | 224 | 0.4077 | 3.3698 |
| 2.8044 | 33.0 | 231 | 0.4077 | 3.3509 |
| 2.8044 | 34.0 | 238 | 0.3769 | 3.3243 |
| 2.7699 | 35.0 | 245 | 0.3923 | 3.3201 |
| 2.7251 | 36.0 | 252 | 0.4 | 3.3013 |
| 2.7251 | 37.0 | 259 | 0.4231 | 3.2936 |
| 2.6915 | 38.0 | 266 | 0.4538 | 3.2827 |
| 2.6527 | 39.0 | 273 | 0.4615 | 3.2627 |
| 2.6541 | 40.0 | 280 | 0.4615 | 3.2581 |
| 2.6541 | 41.0 | 287 | 0.4231 | 3.2342 |
| 2.5968 | 42.0 | 294 | 0.4385 | 3.2211 |
| 2.573 | 43.0 | 301 | 0.4077 | 3.2122 |
| 2.573 | 44.0 | 308 | 0.4615 | 3.2259 |
| 2.554 | 45.0 | 315 | 0.4308 | 3.2271 |
| 2.5222 | 46.0 | 322 | 0.4462 | 3.2208 |
| 2.5222 | 47.0 | 329 | 0.4462 | 3.2139 |
| 2.5085 | 48.0 | 336 | 0.4538 | 3.2040 |
| 2.4593 | 49.0 | 343 | 0.4923 | 3.2053 |
| 2.4585 | 50.0 | 350 | 0.4769 | 3.1822 |
| 2.4585 | 51.0 | 357 | 0.4692 | 3.1697 |
| 2.4228 | 52.0 | 364 | 0.4692 | 3.1589 |
| 2.3954 | 53.0 | 371 | 0.4769 | 3.1375 |
| 2.3954 | 54.0 | 378 | 0.4538 | 3.1092 |
| 2.3641 | 55.0 | 385 | 0.4769 | 3.0999 |
| 2.3651 | 56.0 | 392 | 0.4615 | 3.0860 |
| 2.3651 | 57.0 | 399 | 0.4615 | 3.0813 |
| 2.3182 | 58.0 | 406 | 0.4923 | 3.0692 |
| 2.3029 | 59.0 | 413 | 0.4846 | 3.0610 |
| 2.2988 | 60.0 | 420 | 0.4615 | 3.0627 |
| 2.2988 | 61.0 | 427 | 0.4692 | 3.0520 |
| 2.2865 | 62.0 | 434 | 0.4538 | 3.0395 |
| 2.2623 | 63.0 | 441 | 0.4615 | 3.0357 |
| 2.2623 | 64.0 | 448 | 0.4615 | 3.0333 |
| 2.2252 | 65.0 | 455 | 0.4769 | 3.0229 |
| 2.2339 | 66.0 | 462 | 0.4769 | 3.0203 |
| 2.2339 | 67.0 | 469 | 0.4923 | 3.0076 |
| 2.2017 | 68.0 | 476 | 0.4846 | 2.9876 |
| 2.1972 | 69.0 | 483 | 0.4923 | 2.9716 |
| 2.1964 | 70.0 | 490 | 0.5 | 2.9632 |
| 2.1964 | 71.0 | 497 | 0.4923 | 2.9597 |
| 2.1775 | 72.0 | 504 | 0.5 | 2.9581 |
| 2.1619 | 73.0 | 511 | 0.5077 | 2.9516 |
| 2.1619 | 74.0 | 518 | 0.5154 | 2.9356 |
| 2.1633 | 75.0 | 525 | 0.5077 | 2.9286 |
| 2.1207 | 76.0 | 532 | 0.5154 | 2.9266 |
| 2.1207 | 77.0 | 539 | 0.5231 | 2.9205 |
| 2.1353 | 78.0 | 546 | 0.5154 | 2.9131 |
| 2.1075 | 79.0 | 553 | 0.5231 | 2.9075 |
| 2.1025 | 80.0 | 560 | 0.5231 | 2.9073 |
| 2.1025 | 81.0 | 567 | 0.5154 | 2.9174 |
| 2.1031 | 82.0 | 574 | 0.5308 | 2.9131 |
| 2.0932 | 83.0 | 581 | 0.5308 | 2.9092 |
| 2.0932 | 84.0 | 588 | 0.5308 | 2.8978 |
| 2.0861 | 85.0 | 595 | 0.5308 | 2.8871 |
| 2.0478 | 86.0 | 602 | 0.5385 | 2.8829 |
| 2.0478 | 87.0 | 609 | 0.5462 | 2.8804 |
| 2.0815 | 88.0 | 616 | 0.5462 | 2.8725 |
| 2.0756 | 89.0 | 623 | 0.5462 | 2.8694 |
| 2.065 | 90.0 | 630 | 0.5462 | 2.8665 |
| 2.065 | 91.0 | 637 | 0.5462 | 2.8615 |
| 2.0572 | 92.0 | 644 | 0.5462 | 2.8599 |
| 2.0358 | 93.0 | 651 | 0.5462 | 2.8620 |
| 2.0358 | 94.0 | 658 | 0.5462 | 2.8629 |
| 2.0663 | 95.0 | 665 | 0.5538 | 2.8625 |
| 2.0353 | 96.0 | 672 | 0.5538 | 2.8628 |
| 2.0353 | 97.0 | 679 | 0.5538 | 2.8629 |
| 2.0506 | 98.0 | 686 | 0.5538 | 2.8622 |
| 2.0494 | 99.0 | 693 | 0.5538 | 2.8622 |
| 2.0566 | 100.0 | 700 | 0.5538 | 2.8622 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.4.0
- Tokenizers 0.15.0
|
[
"a_",
"b_",
"k_",
"l_",
"m_",
"n_",
"o_",
"p_",
"q_",
"r_",
"s_",
"t_",
"c_",
"u_",
"v_",
"w_",
"x_",
"y_",
"z_",
"a",
"b",
"c",
"d",
"d_",
"e",
"f",
"g",
"h",
"i",
"j",
"k",
"l",
"m",
"n",
"e_",
"o",
"p",
"q",
"r",
"s",
"t",
"u",
"v",
"w",
"x",
"f_",
"y",
"z",
"g_",
"h_",
"i_",
"j_"
] |
Devarshi/Swin_transformer_dent_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. -->
# Swin_transformer_dent_detection
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6854
- Accuracy: 0.55
- F1: 0.55
- Recall: 0.55
- Precision: 0.55
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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 | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:------:|:---------:|
| No log | 0.8 | 1 | 0.6854 | 0.55 | 0.55 | 0.55 | 0.55 |
| No log | 1.8 | 2 | 0.9226 | 0.55 | 0.55 | 0.55 | 0.55 |
| No log | 2.8 | 3 | 0.8610 | 0.55 | 0.55 | 0.55 | 0.55 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
[
"dent",
"perfect"
] |
sanjeev77/vit-fire-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-fire-detection
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: 0.0211
- Precision: 0.9947
- Recall: 0.9947
## Model description
More information needed
## Intended uses & 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.106 | 1.0 | 190 | 0.0632 | 0.9836 | 0.9828 |
| 0.0279 | 2.0 | 380 | 0.0257 | 0.9947 | 0.9947 |
| 0.0303 | 3.0 | 570 | 0.0431 | 0.9832 | 0.9828 |
| 0.0155 | 4.0 | 760 | 0.0253 | 0.9934 | 0.9934 |
| 0.0131 | 5.0 | 950 | 0.0243 | 0.9934 | 0.9934 |
| 0.0104 | 6.0 | 1140 | 0.0216 | 0.9921 | 0.9921 |
| 0.0133 | 7.0 | 1330 | 0.0210 | 0.9934 | 0.9934 |
| 0.0071 | 8.0 | 1520 | 0.0286 | 0.9921 | 0.9921 |
| 0.001 | 9.0 | 1710 | 0.0285 | 0.9921 | 0.9921 |
| 0.0086 | 10.0 | 1900 | 0.0211 | 0.9947 | 0.9947 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"fire",
"normal",
"smoke"
] |
ksuyash/finetuned-indian-food
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-indian-food
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 indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0144
- Accuracy: 0.9981
## Model description
More information needed
## Intended uses & 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9849 | 0.26 | 100 | 0.8445 | 0.8721 |
| 0.4628 | 0.51 | 200 | 0.4435 | 0.9201 |
| 0.4738 | 0.77 | 300 | 0.3339 | 0.9336 |
| 0.3603 | 1.02 | 400 | 0.2924 | 0.9328 |
| 0.1792 | 1.28 | 500 | 0.1862 | 0.9560 |
| 0.2304 | 1.53 | 600 | 0.1352 | 0.9711 |
| 0.1512 | 1.79 | 700 | 0.1244 | 0.9689 |
| 0.1805 | 2.04 | 800 | 0.0843 | 0.9805 |
| 0.1672 | 2.3 | 900 | 0.0576 | 0.9879 |
| 0.0154 | 2.55 | 1000 | 0.0498 | 0.9900 |
| 0.0357 | 2.81 | 1100 | 0.0359 | 0.9933 |
| 0.0241 | 3.06 | 1200 | 0.0290 | 0.9951 |
| 0.0133 | 3.32 | 1300 | 0.0228 | 0.9967 |
| 0.0088 | 3.57 | 1400 | 0.0193 | 0.9970 |
| 0.0511 | 3.83 | 1500 | 0.0144 | 0.9981 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"burger",
"butter_naan",
"kaathi_rolls",
"kadai_paneer",
"kulfi",
"masala_dosa",
"momos",
"paani_puri",
"pakode",
"pav_bhaji",
"pizza",
"samosa",
"chai",
"chapati",
"chole_bhature",
"dal_makhani",
"dhokla",
"fried_rice",
"idli",
"jalebi"
] |
JPeace18/autotrain-caridentifier
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 6.425095081329346
f1_macro: 0.005258975403754648
f1_micro: 0.029495571813631112
f1_weighted: 0.010640418098909527
precision_macro: 0.006409892728092866
precision_micro: 0.029495571813631112
precision_weighted: 0.01244378584000894
recall_macro: 0.014090852423880612
recall_micro: 0.029495571813631112
recall_weighted: 0.029495571813631112
accuracy: 0.029495571813631112
|
[
"acura_ilx_2013",
"acura_ilx_2014",
"acura_ilx_2015",
"acura_ilx_2016",
"acura_ilx_2017",
"acura_ilx_2019",
"acura_ilx_2020",
"acura_mdx_2011",
"acura_mdx_2012",
"acura_mdx_2013",
"acura_mdx_2014",
"acura_mdx_2016",
"acura_mdx_2017",
"acura_mdx_2019",
"acura_mdx_2020",
"acura_nsx_2001",
"acura_nsx_2002",
"acura_nsx_2003",
"acura_nsx_2004",
"acura_nsx_2005",
"acura_nsx_2016",
"acura_nsx_2017",
"acura_nsx_2019",
"acura_nsx_2020",
"acura_rdx_2011",
"acura_rdx_2012",
"acura_rdx_2013",
"acura_rdx_2014",
"acura_rdx_2015",
"acura_rdx_2016",
"acura_rdx_2017",
"acura_rdx_2019",
"acura_rdx_2020",
"acura_rlx_2014",
"acura_rlx_2015",
"acura_rlx_2016",
"acura_rlx_2017",
"acura_rlx_2019",
"acura_rlx_2020",
"acura_tlx_2015",
"acura_tlx_2016",
"acura_tlx_2017",
"acura_tlx_2019",
"acura_tlx_2020",
"alfa romeo_4c spider_2020",
"alfa romeo_4c_2015",
"alfa romeo_4c_2016",
"alfa romeo_4c_2017",
"alfa romeo_4c_2019",
"alfa romeo_giulia_2011",
"alfa romeo_giulia_2017",
"alfa romeo_giulia_2020",
"alfa romeo_stelvio_2019",
"alfa romeo_stelvio_2020",
"aston martin_db11_2017",
"aston martin_db11_2019",
"aston martin_dbs_2008",
"aston martin_dbs_2009",
"aston martin_dbs_2010",
"aston martin_dbs_2011",
"aston martin_dbs_2012",
"aston martin_dbs_2019",
"aston martin_vanquish_2003",
"aston martin_vanquish_2004",
"aston martin_vanquish_2005",
"aston martin_vanquish_2006",
"aston martin_vanquish_2014",
"aston martin_vanquish_2015",
"aston martin_vanquish_2016",
"aston martin_vanquish_2017",
"aston martin_vanquish_2019",
"aston martin_vantage_2011",
"aston martin_vantage_2012",
"aston martin_vantage_2013",
"aston martin_vantage_2014",
"aston martin_vantage_2015",
"aston martin_vantage_2016",
"aston martin_vantage_2017",
"aston martin_vantage_2019",
"audi_a3_2010",
"audi_a3_2011",
"audi_a3_2012",
"audi_a3_2013",
"audi_a3_2015",
"audi_a3_2016",
"audi_a3_2017",
"audi_a3_2019",
"audi_a3_2020",
"audi_a4_2011",
"audi_a4_2012",
"audi_a4_2013",
"audi_a4_2014",
"audi_a4_2015",
"audi_a4_2016",
"audi_a4_2017",
"audi_a4_2019",
"audi_a5_2010",
"audi_a5_2011",
"audi_a5_2012",
"audi_a5_2013",
"audi_a5_2014",
"audi_a5_2015",
"audi_a5_2016",
"audi_a5_2017",
"audi_a5_2019",
"audi_a6_2011",
"audi_a6_2012",
"audi_a6_2013",
"audi_a6_2014",
"audi_a6_2015",
"audi_a6_2016",
"audi_a6_2017",
"audi_a6_2019",
"audi_a6_2020",
"audi_a7_2012",
"audi_a7_2013",
"audi_a7_2014",
"audi_a7_2015",
"audi_a7_2016",
"audi_a7_2017",
"audi_a7_2019",
"audi_a7_2020",
"audi_a8_2011",
"audi_a8_2012",
"audi_a8_2013",
"audi_a8_2014",
"audi_a8_2015",
"audi_a8_2016",
"audi_a8_2017",
"audi_a8_2019",
"audi_a8_2020",
"audi_q3_2015",
"audi_q3_2016",
"audi_q3_2017",
"audi_q3_2019",
"audi_q3_2020",
"audi_q5_2011",
"audi_q5_2012",
"audi_q5_2013",
"audi_q5_2014",
"audi_q5_2015",
"audi_q5_2016",
"audi_q5_2017",
"audi_q5_2019",
"audi_q5_2020",
"audi_q7_2010",
"audi_q7_2011",
"audi_q7_2012",
"audi_q7_2013",
"audi_q7_2014",
"audi_q7_2015",
"audi_q7_2017",
"audi_q7_2019",
"audi_q7_2020",
"audi_q8_2019",
"audi_q8_2020",
"audi_r8_2010",
"audi_r8_2011",
"audi_r8_2012",
"audi_r8_2014",
"audi_r8_2015",
"audi_r8_2017",
"audi_r8_2020",
"audi_tt_2011",
"audi_tt_2012",
"audi_tt_2013",
"audi_tt_2014",
"audi_tt_2015",
"audi_tt_2016",
"audi_tt_2017",
"audi_tt_2019",
"audi_tt_2020",
"audi_e-tron_2019",
"bmw_2-series_2014",
"bmw_2-series_2015",
"bmw_2-series_2016",
"bmw_2-series_2017",
"bmw_2-series_2019",
"bmw_2-series_2020",
"bmw_3-series_2011",
"bmw_3-series_2012",
"bmw_3-series_2013",
"bmw_3-series_2014",
"bmw_3-series_2015",
"bmw_3-series_2016",
"bmw_3-series_2017",
"bmw_3-series_2019",
"bmw_3-series_2020",
"bmw_4-series_2014",
"bmw_4-series_2015",
"bmw_4-series_2016",
"bmw_4-series_2017",
"bmw_4-series_2019",
"bmw_4-series_2020",
"bmw_5-series_2011",
"bmw_5-series_2012",
"bmw_5-series_2013",
"bmw_5-series_2014",
"bmw_5-series_2015",
"bmw_5-series_2016",
"bmw_5-series_2017",
"bmw_5-series_2019",
"bmw_5-series_2020",
"bmw_6-series_2010",
"bmw_6-series_2011",
"bmw_6-series_2012",
"bmw_6-series_2013",
"bmw_6-series_2014",
"bmw_6-series_2015",
"bmw_6-series_2016",
"bmw_6-series_2017",
"bmw_6-series_2019",
"bmw_7-series_2011",
"bmw_7-series_2012",
"bmw_7-series_2013",
"bmw_7-series_2014",
"bmw_7-series_2015",
"bmw_7-series_2016",
"bmw_7-series_2017",
"bmw_7-series_2019",
"bmw_7-series_2020",
"bmw_8-series_1996",
"bmw_8-series_2019",
"bmw_8-series_2020",
"bmw_x1_2013",
"bmw_x1_2014",
"bmw_x1_2015",
"bmw_x1_2016",
"bmw_x1_2017",
"bmw_x1_2019",
"bmw_x1_2020",
"bmw_x2_2020",
"bmw_x3_2011",
"bmw_x3_2012",
"bmw_x3_2013",
"bmw_x3_2014",
"bmw_x3_2015",
"bmw_x3_2016",
"bmw_x3_2017",
"bmw_x3_2019",
"bmw_x3_2020",
"bmw_x4_2015",
"bmw_x4_2016",
"bmw_x4_2017",
"bmw_x4_2019",
"bmw_x4_2020",
"bmw_x5_2011",
"bmw_x5_2012",
"bmw_x5_2013",
"bmw_x5_2014",
"bmw_x5_2015",
"bmw_x5_2016",
"bmw_x5_2017",
"bmw_x5_2019",
"bmw_x5_2020",
"bmw_x6_2011",
"bmw_x6_2012",
"bmw_x6_2013",
"bmw_x6_2014",
"bmw_x6_2015",
"bmw_x6_2016",
"bmw_x6_2017",
"bmw_x6_2019",
"bmw_x6_2020",
"bmw_x7_2019",
"bmw_x7_2020",
"bmw_z4_2009",
"bmw_z4_2010",
"bmw_z4_2011",
"bmw_z4_2012",
"bmw_z4_2014",
"bmw_z4_2015",
"bmw_z4_2019",
"bmw_z4_2020",
"bmw_i3_2014",
"bmw_i3_2015",
"bmw_i3_2016",
"bmw_i3_2017",
"bmw_i3_2019",
"bmw_i8_2014",
"bmw_i8_2015",
"bmw_i8_2016",
"bmw_i8_2020",
"bentley_bentayga_2017",
"bentley_bentayga_2020",
"bentley_continental gt_2011",
"bentley_continental gt_2012",
"bentley_continental gt_2013",
"bentley_continental gt_2014",
"bentley_continental gt_2015",
"bentley_continental gt_2016",
"bentley_continental gt_2017",
"bentley_continental gt_2019",
"bentley_continental gt_2020",
"bentley_flying spur_2011",
"bentley_flying spur_2012",
"bentley_flying spur_2013",
"bentley_flying spur_2014",
"bentley_flying spur_2015",
"bentley_flying spur_2016",
"bentley_flying spur_2017",
"bentley_flying spur_2019",
"bentley_flying spur_2020",
"bentley_mulsanne_2011",
"bentley_mulsanne_2012",
"bentley_mulsanne_2014",
"bentley_mulsanne_2015",
"bentley_mulsanne_2016",
"bentley_mulsanne_2020",
"buick_cascada_2016",
"buick_cascada_2017",
"buick_cascada_2019",
"buick_enclave_2011",
"buick_enclave_2012",
"buick_enclave_2013",
"buick_enclave_2014",
"buick_enclave_2015",
"buick_enclave_2016",
"buick_enclave_2017",
"buick_enclave_2019",
"buick_enclave_2020",
"buick_encore_2013",
"buick_encore_2014",
"buick_encore_2015",
"buick_encore_2016",
"buick_encore_2017",
"buick_encore_2019",
"buick_encore_2020",
"buick_envision_2016",
"buick_envision_2017",
"buick_envision_2019",
"buick_envision_2020",
"buick_lacrosse_2010",
"buick_lacrosse_2011",
"buick_lacrosse_2012",
"buick_lacrosse_2013",
"buick_lacrosse_2014",
"buick_lacrosse_2015",
"buick_lacrosse_2016",
"buick_lacrosse_2017",
"buick_lacrosse_2019",
"buick_regal_2011",
"buick_regal_2012",
"buick_regal_2013",
"buick_regal_2014",
"buick_regal_2015",
"buick_regal_2016",
"buick_regal_2017",
"buick_regal_2019",
"buick_regal_2020",
"cadillac_ats_2013",
"cadillac_ats_2014",
"cadillac_ats_2015",
"cadillac_ats_2016",
"cadillac_ats_2017",
"cadillac_ats_2019",
"cadillac_ct4_2020",
"cadillac_ct5_2020",
"cadillac_ct6_2016",
"cadillac_ct6_2017",
"cadillac_ct6_2019",
"cadillac_ct6_2020",
"cadillac_cts_2010",
"cadillac_cts_2011",
"cadillac_cts_2012",
"cadillac_cts_2013",
"cadillac_cts_2014",
"cadillac_cts_2015",
"cadillac_cts_2016",
"cadillac_cts_2017",
"cadillac_cts_2019",
"cadillac_escalade_2011",
"cadillac_escalade_2012",
"cadillac_escalade_2013",
"cadillac_escalade_2014",
"cadillac_escalade_2015",
"cadillac_escalade_2016",
"cadillac_escalade_2017",
"cadillac_escalade_2019",
"cadillac_escalade_2020",
"cadillac_xt4_2019",
"cadillac_xt4_2020",
"cadillac_xt5_2017",
"cadillac_xt5_2019",
"cadillac_xt5_2020",
"cadillac_xt6_2020",
"cadillac_xts_2012",
"cadillac_xts_2013",
"cadillac_xts_2014",
"cadillac_xts_2015",
"cadillac_xts_2016",
"cadillac_xts_2017",
"cadillac_xts_2019",
"chevrolet_blazer_1998",
"chevrolet_blazer_1999",
"chevrolet_blazer_2000",
"chevrolet_blazer_2001",
"chevrolet_blazer_2002",
"chevrolet_blazer_2003",
"chevrolet_blazer_2004",
"chevrolet_blazer_2019",
"chevrolet_blazer_2020",
"chevrolet_bolt ev_2017",
"chevrolet_bolt ev_2019",
"chevrolet_camaro_2011",
"chevrolet_camaro_2012",
"chevrolet_camaro_2013",
"chevrolet_camaro_2014",
"chevrolet_camaro_2015",
"chevrolet_camaro_2016",
"chevrolet_camaro_2017",
"chevrolet_camaro_2019",
"chevrolet_camaro_2020",
"chevrolet_colorado_2009",
"chevrolet_colorado_2010",
"chevrolet_colorado_2011",
"chevrolet_colorado_2012",
"chevrolet_colorado_2015",
"chevrolet_colorado_2016",
"chevrolet_colorado_2017",
"chevrolet_colorado_2019",
"chevrolet_colorado_2020",
"chevrolet_corvette_2011",
"chevrolet_corvette_2012",
"chevrolet_corvette_2013",
"chevrolet_corvette_2014",
"chevrolet_corvette_2015",
"chevrolet_corvette_2016",
"chevrolet_corvette_2017",
"chevrolet_corvette_2019",
"chevrolet_corvette_2020",
"chevrolet_cruze_2011",
"chevrolet_cruze_2012",
"chevrolet_cruze_2013",
"chevrolet_cruze_2014",
"chevrolet_cruze_2015",
"chevrolet_cruze_2016",
"chevrolet_cruze_2017",
"chevrolet_cruze_2019",
"chevrolet_equinox_2011",
"chevrolet_equinox_2012",
"chevrolet_equinox_2013",
"chevrolet_equinox_2014",
"chevrolet_equinox_2015",
"chevrolet_equinox_2016",
"chevrolet_equinox_2017",
"chevrolet_equinox_2019",
"chevrolet_equinox_2020",
"chevrolet_impala_2011",
"chevrolet_impala_2012",
"chevrolet_impala_2013",
"chevrolet_impala_2014",
"chevrolet_impala_2015",
"chevrolet_impala_2016",
"chevrolet_impala_2017",
"chevrolet_impala_2019",
"chevrolet_impala_2020",
"chevrolet_malibu_2011",
"chevrolet_malibu_2012",
"chevrolet_malibu_2013",
"chevrolet_malibu_2014",
"chevrolet_malibu_2015",
"chevrolet_malibu_2016",
"chevrolet_malibu_2017",
"chevrolet_malibu_2019",
"chevrolet_malibu_2020",
"chevrolet_silverado 1500_2011",
"chevrolet_silverado 1500_2012",
"chevrolet_silverado 1500_2013",
"chevrolet_silverado 1500_2014",
"chevrolet_silverado 1500_2015",
"chevrolet_silverado 1500_2016",
"chevrolet_silverado 1500_2017",
"chevrolet_silverado 1500_2019",
"chevrolet_silverado 1500_2020",
"chevrolet_silverado 2500hd_2011",
"chevrolet_silverado 2500hd_2012",
"chevrolet_silverado 2500hd_2013",
"chevrolet_silverado 2500hd_2014",
"chevrolet_silverado 2500hd_2015",
"chevrolet_silverado 2500hd_2016",
"chevrolet_silverado 2500hd_2017",
"chevrolet_silverado 2500hd_2019",
"chevrolet_silverado 2500hd_2020",
"chevrolet_sonic_2012",
"chevrolet_sonic_2013",
"chevrolet_sonic_2014",
"chevrolet_sonic_2015",
"chevrolet_sonic_2016",
"chevrolet_sonic_2017",
"chevrolet_sonic_2019",
"chevrolet_sonic_2020",
"chevrolet_spark_2013",
"chevrolet_spark_2014",
"chevrolet_spark_2015",
"chevrolet_spark_2016",
"chevrolet_spark_2017",
"chevrolet_spark_2019",
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"toyota_prius c_2019",
"toyota_prius_2011",
"toyota_prius_2012",
"toyota_prius_2013",
"toyota_prius_2014",
"toyota_prius_2015",
"toyota_prius_2016",
"toyota_prius_2017",
"toyota_prius_2019",
"toyota_prius_2020",
"toyota_rav4_2011",
"toyota_rav4_2012",
"toyota_rav4_2013",
"toyota_rav4_2014",
"toyota_rav4_2015",
"toyota_rav4_2016",
"toyota_rav4_2017",
"toyota_rav4_2019",
"toyota_rav4_2020",
"toyota_sequoia_2011",
"toyota_sequoia_2012",
"toyota_sequoia_2013",
"toyota_sequoia_2014",
"toyota_sequoia_2015",
"toyota_sequoia_2016",
"toyota_sequoia_2017",
"toyota_sequoia_2019",
"toyota_sequoia_2020",
"toyota_sienna_2011",
"toyota_sienna_2012",
"toyota_sienna_2013",
"toyota_sienna_2014",
"toyota_sienna_2015",
"toyota_sienna_2016",
"toyota_sienna_2017",
"toyota_sienna_2019",
"toyota_sienna_2020",
"toyota_supra_1990",
"toyota_supra_1994",
"toyota_supra_1997",
"toyota_supra_2020",
"toyota_tacoma_2011",
"toyota_tacoma_2012",
"toyota_tacoma_2013",
"toyota_tacoma_2014",
"toyota_tacoma_2015",
"toyota_tacoma_2016",
"toyota_tacoma_2017",
"toyota_tacoma_2019",
"toyota_tacoma_2020",
"toyota_tundra_2011",
"toyota_tundra_2012",
"toyota_tundra_2013",
"toyota_tundra_2014",
"toyota_tundra_2015",
"toyota_tundra_2016",
"toyota_tundra_2017",
"toyota_tundra_2019",
"toyota_yaris hatchback_2020",
"toyota_yaris_2011",
"toyota_yaris_2012",
"toyota_yaris_2013",
"toyota_yaris_2014",
"toyota_yaris_2015",
"toyota_yaris_2016",
"toyota_yaris_2017",
"toyota_yaris_2019",
"toyota_yaris_2020",
"volkswagen_arteon_2019",
"volkswagen_arteon_2020",
"volkswagen_atlas_2019",
"volkswagen_atlas_2020",
"volkswagen_beetle_2010",
"volkswagen_beetle_2011",
"volkswagen_beetle_2012",
"volkswagen_beetle_2013",
"volkswagen_beetle_2014",
"volkswagen_beetle_2015",
"volkswagen_beetle_2016",
"volkswagen_beetle_2017",
"volkswagen_beetle_2019",
"volkswagen_golf_2011",
"volkswagen_golf_2012",
"volkswagen_golf_2013",
"volkswagen_golf_2014",
"volkswagen_golf_2015",
"volkswagen_golf_2016",
"volkswagen_golf_2017",
"volkswagen_golf_2019",
"volkswagen_golf_2020",
"volkswagen_jetta_2011",
"volkswagen_jetta_2012",
"volkswagen_jetta_2013",
"volkswagen_jetta_2014",
"volkswagen_jetta_2015",
"volkswagen_jetta_2016",
"volkswagen_jetta_2017",
"volkswagen_jetta_2019",
"volkswagen_jetta_2020",
"volkswagen_passat_2010",
"volkswagen_passat_2012",
"volkswagen_passat_2013",
"volkswagen_passat_2014",
"volkswagen_passat_2015",
"volkswagen_passat_2016",
"volkswagen_passat_2017",
"volkswagen_passat_2019",
"volkswagen_passat_2020",
"volkswagen_tiguan_2011",
"volkswagen_tiguan_2012",
"volkswagen_tiguan_2013",
"volkswagen_tiguan_2014",
"volkswagen_tiguan_2015",
"volkswagen_tiguan_2016",
"volkswagen_tiguan_2017",
"volkswagen_tiguan_2019",
"volkswagen_tiguan_2020",
"volkswagen_e-golf_2015",
"volkswagen_e-golf_2016",
"volkswagen_e-golf_2017",
"volkswagen_e-golf_2019",
"volvo_s60_2011",
"volvo_s60_2012",
"volvo_s60_2013",
"volvo_s60_2014",
"volvo_s60_2015",
"volvo_s60_2016",
"volvo_s60_2017",
"volvo_s60_2019",
"volvo_s60_2020",
"volvo_s90_1998",
"volvo_s90_2017",
"volvo_s90_2019",
"volvo_s90_2020",
"volvo_v60_2015",
"volvo_v60_2016",
"volvo_v60_2017",
"volvo_v60_2019",
"volvo_v60_2020",
"volvo_v90_1998",
"volvo_v90_2017",
"volvo_v90_2019",
"volvo_v90_2020",
"volvo_xc40_2019",
"volvo_xc40_2020",
"volvo_xc60_2011",
"volvo_xc60_2012",
"volvo_xc60_2013",
"volvo_xc60_2014",
"volvo_xc60_2015",
"volvo_xc60_2016",
"volvo_xc60_2017",
"volvo_xc60_2019",
"volvo_xc60_2020",
"volvo_xc90_2010",
"volvo_xc90_2011",
"volvo_xc90_2012",
"volvo_xc90_2013",
"volvo_xc90_2014",
"volvo_xc90_2016",
"volvo_xc90_2017",
"volvo_xc90_2019",
"volvo_xc90_2020",
"smart_fortwo_2010",
"smart_fortwo_2011",
"smart_fortwo_2012",
"smart_fortwo_2013",
"smart_fortwo_2014",
"smart_fortwo_2015",
"smart_fortwo_2016",
"smart_fortwo_2017",
"smart_fortwo_2019"
] |
Kur-der130/salida_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# salida_1
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: 0.0595
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1332 | 3.85 | 500 | 0.0595 | 0.9850 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
TtT609/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. -->
# TtT609/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: 2.7456
- Validation Loss: 1.5988
- 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': 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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7456 | 1.5988 | 0.839 | 0 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.19.2
- Tokenizers 0.19.1
|
[
"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"
] |
mohdadeeb/DR-ViT
|
<!-- 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. -->
# DR-ViT
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7068
- Train Accuracy: 0.7214
- Train Top-3-accuracy: 0.9677
- Validation Loss: 0.6596
- Validation Accuracy: 0.7345
- Validation Top-3-accuracy: 0.9782
- Epoch: 1
## Model description
More information needed
## 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': 4400, '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: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.8883 | 0.6645 | 0.9255 | 0.7075 | 0.7200 | 0.9655 | 0 |
| 0.7068 | 0.7214 | 0.9677 | 0.6596 | 0.7345 | 0.9782 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"proliferate dr",
"healthy",
"mild dr",
"severe dr",
"moderate dr"
] |
JPeace18/CarIdentifier2
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 5.987339019775391
f1_macro: 0.014272821191130344
f1_micro: 0.049443212901385813
f1_weighted: 0.024064270485240995
precision_macro: 0.021826631000654732
precision_micro: 0.049443212901385813
precision_weighted: 0.03411589816873522
recall_macro: 0.02794951571811764
recall_micro: 0.049443212901385813
recall_weighted: 0.049443212901385813
accuracy: 0.049443212901385813
|
[
"acura_ilx_2013",
"acura_ilx_2014",
"acura_ilx_2015",
"acura_ilx_2016",
"acura_ilx_2017",
"acura_ilx_2019",
"acura_ilx_2020",
"acura_mdx_2011",
"acura_mdx_2012",
"acura_mdx_2013",
"acura_mdx_2014",
"acura_mdx_2016",
"acura_mdx_2017",
"acura_mdx_2019",
"acura_mdx_2020",
"acura_nsx_2001",
"acura_nsx_2002",
"acura_nsx_2003",
"acura_nsx_2004",
"acura_nsx_2005",
"acura_nsx_2016",
"acura_nsx_2017",
"acura_nsx_2019",
"acura_nsx_2020",
"acura_rdx_2011",
"acura_rdx_2012",
"acura_rdx_2013",
"acura_rdx_2014",
"acura_rdx_2015",
"acura_rdx_2016",
"acura_rdx_2017",
"acura_rdx_2019",
"acura_rdx_2020",
"acura_rlx_2014",
"acura_rlx_2015",
"acura_rlx_2016",
"acura_rlx_2017",
"acura_rlx_2019",
"acura_rlx_2020",
"acura_tlx_2015",
"acura_tlx_2016",
"acura_tlx_2017",
"acura_tlx_2019",
"acura_tlx_2020",
"alfa romeo_4c spider_2020",
"alfa romeo_4c_2015",
"alfa romeo_4c_2016",
"alfa romeo_4c_2017",
"alfa romeo_4c_2019",
"alfa romeo_giulia_2011",
"alfa romeo_giulia_2017",
"alfa romeo_giulia_2020",
"alfa romeo_stelvio_2019",
"alfa romeo_stelvio_2020",
"aston martin_db11_2017",
"aston martin_db11_2019",
"aston martin_dbs_2008",
"aston martin_dbs_2009",
"aston martin_dbs_2010",
"aston martin_dbs_2011",
"aston martin_dbs_2012",
"aston martin_dbs_2019",
"aston martin_vanquish_2003",
"aston martin_vanquish_2004",
"aston martin_vanquish_2005",
"aston martin_vanquish_2006",
"aston martin_vanquish_2014",
"aston martin_vanquish_2015",
"aston martin_vanquish_2016",
"aston martin_vanquish_2017",
"aston martin_vanquish_2019",
"aston martin_vantage_2011",
"aston martin_vantage_2012",
"aston martin_vantage_2013",
"aston martin_vantage_2014",
"aston martin_vantage_2015",
"aston martin_vantage_2016",
"aston martin_vantage_2017",
"aston martin_vantage_2019",
"audi_a3_2010",
"audi_a3_2011",
"audi_a3_2012",
"audi_a3_2013",
"audi_a3_2015",
"audi_a3_2016",
"audi_a3_2017",
"audi_a3_2019",
"audi_a3_2020",
"audi_a4_2011",
"audi_a4_2012",
"audi_a4_2013",
"audi_a4_2014",
"audi_a4_2015",
"audi_a4_2016",
"audi_a4_2017",
"audi_a4_2019",
"audi_a5_2010",
"audi_a5_2011",
"audi_a5_2012",
"audi_a5_2013",
"audi_a5_2014",
"audi_a5_2015",
"audi_a5_2016",
"audi_a5_2017",
"audi_a5_2019",
"audi_a6_2011",
"audi_a6_2012",
"audi_a6_2013",
"audi_a6_2014",
"audi_a6_2015",
"audi_a6_2016",
"audi_a6_2017",
"audi_a6_2019",
"audi_a6_2020",
"audi_a7_2012",
"audi_a7_2013",
"audi_a7_2014",
"audi_a7_2015",
"audi_a7_2016",
"audi_a7_2017",
"audi_a7_2019",
"audi_a7_2020",
"audi_a8_2011",
"audi_a8_2012",
"audi_a8_2013",
"audi_a8_2014",
"audi_a8_2015",
"audi_a8_2016",
"audi_a8_2017",
"audi_a8_2019",
"audi_a8_2020",
"audi_q3_2015",
"audi_q3_2016",
"audi_q3_2017",
"audi_q3_2019",
"audi_q3_2020",
"audi_q5_2011",
"audi_q5_2012",
"audi_q5_2013",
"audi_q5_2014",
"audi_q5_2015",
"audi_q5_2016",
"audi_q5_2017",
"audi_q5_2019",
"audi_q5_2020",
"audi_q7_2010",
"audi_q7_2011",
"audi_q7_2012",
"audi_q7_2013",
"audi_q7_2014",
"audi_q7_2015",
"audi_q7_2017",
"audi_q7_2019",
"audi_q7_2020",
"audi_q8_2019",
"audi_q8_2020",
"audi_r8_2010",
"audi_r8_2011",
"audi_r8_2012",
"audi_r8_2014",
"audi_r8_2015",
"audi_r8_2017",
"audi_r8_2020",
"audi_tt_2011",
"audi_tt_2012",
"audi_tt_2013",
"audi_tt_2014",
"audi_tt_2015",
"audi_tt_2016",
"audi_tt_2017",
"audi_tt_2019",
"audi_tt_2020",
"audi_e-tron_2019",
"bmw_2-series_2014",
"bmw_2-series_2015",
"bmw_2-series_2016",
"bmw_2-series_2017",
"bmw_2-series_2019",
"bmw_2-series_2020",
"bmw_3-series_2011",
"bmw_3-series_2012",
"bmw_3-series_2013",
"bmw_3-series_2014",
"bmw_3-series_2015",
"bmw_3-series_2016",
"bmw_3-series_2017",
"bmw_3-series_2019",
"bmw_3-series_2020",
"bmw_4-series_2014",
"bmw_4-series_2015",
"bmw_4-series_2016",
"bmw_4-series_2017",
"bmw_4-series_2019",
"bmw_4-series_2020",
"bmw_5-series_2011",
"bmw_5-series_2012",
"bmw_5-series_2013",
"bmw_5-series_2014",
"bmw_5-series_2015",
"bmw_5-series_2016",
"bmw_5-series_2017",
"bmw_5-series_2019",
"bmw_5-series_2020",
"bmw_6-series_2010",
"bmw_6-series_2011",
"bmw_6-series_2012",
"bmw_6-series_2013",
"bmw_6-series_2014",
"bmw_6-series_2015",
"bmw_6-series_2016",
"bmw_6-series_2017",
"bmw_6-series_2019",
"bmw_7-series_2011",
"bmw_7-series_2012",
"bmw_7-series_2013",
"bmw_7-series_2014",
"bmw_7-series_2015",
"bmw_7-series_2016",
"bmw_7-series_2017",
"bmw_7-series_2019",
"bmw_7-series_2020",
"bmw_8-series_1996",
"bmw_8-series_2019",
"bmw_8-series_2020",
"bmw_x1_2013",
"bmw_x1_2014",
"bmw_x1_2015",
"bmw_x1_2016",
"bmw_x1_2017",
"bmw_x1_2019",
"bmw_x1_2020",
"bmw_x2_2020",
"bmw_x3_2011",
"bmw_x3_2012",
"bmw_x3_2013",
"bmw_x3_2014",
"bmw_x3_2015",
"bmw_x3_2016",
"bmw_x3_2017",
"bmw_x3_2019",
"bmw_x3_2020",
"bmw_x4_2015",
"bmw_x4_2016",
"bmw_x4_2017",
"bmw_x4_2019",
"bmw_x4_2020",
"bmw_x5_2011",
"bmw_x5_2012",
"bmw_x5_2013",
"bmw_x5_2014",
"bmw_x5_2015",
"bmw_x5_2016",
"bmw_x5_2017",
"bmw_x5_2019",
"bmw_x5_2020",
"bmw_x6_2011",
"bmw_x6_2012",
"bmw_x6_2013",
"bmw_x6_2014",
"bmw_x6_2015",
"bmw_x6_2016",
"bmw_x6_2017",
"bmw_x6_2019",
"bmw_x6_2020",
"bmw_x7_2019",
"bmw_x7_2020",
"bmw_z4_2009",
"bmw_z4_2010",
"bmw_z4_2011",
"bmw_z4_2012",
"bmw_z4_2014",
"bmw_z4_2015",
"bmw_z4_2019",
"bmw_z4_2020",
"bmw_i3_2014",
"bmw_i3_2015",
"bmw_i3_2016",
"bmw_i3_2017",
"bmw_i3_2019",
"bmw_i8_2014",
"bmw_i8_2015",
"bmw_i8_2016",
"bmw_i8_2020",
"bentley_bentayga_2017",
"bentley_bentayga_2020",
"bentley_continental gt_2011",
"bentley_continental gt_2012",
"bentley_continental gt_2013",
"bentley_continental gt_2014",
"bentley_continental gt_2015",
"bentley_continental gt_2016",
"bentley_continental gt_2017",
"bentley_continental gt_2019",
"bentley_continental gt_2020",
"bentley_flying spur_2011",
"bentley_flying spur_2012",
"bentley_flying spur_2013",
"bentley_flying spur_2014",
"bentley_flying spur_2015",
"bentley_flying spur_2016",
"bentley_flying spur_2017",
"bentley_flying spur_2019",
"bentley_flying spur_2020",
"bentley_mulsanne_2011",
"bentley_mulsanne_2012",
"bentley_mulsanne_2014",
"bentley_mulsanne_2015",
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"porsche_panamera_2015",
"porsche_panamera_2016",
"porsche_panamera_2017",
"porsche_panamera_2019",
"porsche_panamera_2020",
"porsche_taycan_2020",
"ram_1500_2011",
"ram_1500_2012",
"ram_1500_2013",
"ram_1500_2014",
"ram_1500_2015",
"ram_1500_2016",
"ram_1500_2017",
"ram_1500_2019",
"ram_1500_2020",
"ram_2500_2011",
"ram_2500_2012",
"ram_2500_2013",
"ram_2500_2014",
"ram_2500_2015",
"ram_2500_2016",
"ram_2500_2017",
"ram_2500_2019",
"ram_2500_2020",
"rolls-royce_cullinan_2020",
"rolls-royce_dawn_2016",
"rolls-royce_dawn_2017",
"rolls-royce_dawn_2020",
"rolls-royce_ghost_2011",
"rolls-royce_ghost_2012",
"rolls-royce_ghost_2014",
"rolls-royce_ghost_2015",
"rolls-royce_ghost_2016",
"rolls-royce_ghost_2017",
"rolls-royce_ghost_2019",
"rolls-royce_phantom_2011",
"rolls-royce_phantom_2012",
"rolls-royce_phantom_2014",
"rolls-royce_phantom_2015",
"rolls-royce_phantom_2017",
"rolls-royce_phantom_2019",
"rolls-royce_phantom_2020",
"rolls-royce_wraith_2014",
"rolls-royce_wraith_2015",
"rolls-royce_wraith_2016",
"rolls-royce_wraith_2017",
"rolls-royce_wraith_2020",
"subaru_ascent_2019",
"subaru_ascent_2020",
"subaru_brz_2013",
"subaru_brz_2014",
"subaru_brz_2015",
"subaru_brz_2016",
"subaru_brz_2017",
"subaru_brz_2019",
"subaru_brz_2020",
"subaru_crosstrek_2013",
"subaru_crosstrek_2014",
"subaru_crosstrek_2015",
"subaru_crosstrek_2016",
"subaru_crosstrek_2017",
"subaru_crosstrek_2019",
"subaru_crosstrek_2020",
"subaru_forester_2011",
"subaru_forester_2013",
"subaru_forester_2014",
"subaru_forester_2015",
"subaru_forester_2016",
"subaru_forester_2017",
"subaru_forester_2019",
"subaru_forester_2020",
"subaru_impreza_2011",
"subaru_impreza_2012",
"subaru_impreza_2013",
"subaru_impreza_2014",
"subaru_impreza_2015",
"subaru_impreza_2016",
"subaru_impreza_2017",
"subaru_impreza_2019",
"subaru_impreza_2020",
"subaru_legacy_2011",
"subaru_legacy_2012",
"subaru_legacy_2013",
"subaru_legacy_2014",
"subaru_legacy_2015",
"subaru_legacy_2016",
"subaru_legacy_2017",
"subaru_legacy_2019",
"subaru_legacy_2020",
"subaru_outback_2011",
"subaru_outback_2012",
"subaru_outback_2013",
"subaru_outback_2014",
"subaru_outback_2015",
"subaru_outback_2016",
"subaru_outback_2017",
"subaru_outback_2019",
"subaru_outback_2020",
"subaru_wrx_2011",
"subaru_wrx_2012",
"subaru_wrx_2013",
"subaru_wrx_2014",
"subaru_wrx_2015",
"subaru_wrx_2016",
"subaru_wrx_2017",
"subaru_wrx_2019",
"subaru_wrx_2020",
"tesla_model 3_2017",
"tesla_model 3_2019",
"tesla_model s_2012",
"tesla_model s_2013",
"tesla_model s_2014",
"tesla_model s_2015",
"tesla_model s_2016",
"tesla_model s_2017",
"tesla_model s_2019",
"tesla_model x_2016",
"tesla_model x_2017",
"tesla_model x_2019",
"tesla_model y_2020",
"toyota_4runner_2011",
"toyota_4runner_2012",
"toyota_4runner_2013",
"toyota_4runner_2014",
"toyota_4runner_2015",
"toyota_4runner_2016",
"toyota_4runner_2017",
"toyota_4runner_2019",
"toyota_4runner_2020",
"toyota_86_2017",
"toyota_86_2019",
"toyota_avalon_2011",
"toyota_avalon_2012",
"toyota_avalon_2013",
"toyota_avalon_2014",
"toyota_avalon_2015",
"toyota_avalon_2016",
"toyota_avalon_2017",
"toyota_avalon_2019",
"toyota_c-hr_2019",
"toyota_c-hr_2020",
"toyota_camry_2011",
"toyota_camry_2012",
"toyota_camry_2013",
"toyota_camry_2014",
"toyota_camry_2015",
"toyota_camry_2016",
"toyota_camry_2017",
"toyota_camry_2019",
"toyota_camry_2020",
"toyota_corolla_2011",
"toyota_corolla_2012",
"toyota_corolla_2013",
"toyota_corolla_2014",
"toyota_corolla_2015",
"toyota_corolla_2016",
"toyota_corolla_2017",
"toyota_corolla_2019",
"toyota_corolla_2020",
"toyota_highlander_2011",
"toyota_highlander_2012",
"toyota_highlander_2013",
"toyota_highlander_2014",
"toyota_highlander_2015",
"toyota_highlander_2016",
"toyota_highlander_2017",
"toyota_highlander_2019",
"toyota_highlander_2020",
"toyota_land cruiser_2010",
"toyota_land cruiser_2011",
"toyota_land cruiser_2013",
"toyota_land cruiser_2014",
"toyota_land cruiser_2015",
"toyota_land cruiser_2016",
"toyota_land cruiser_2017",
"toyota_land cruiser_2019",
"toyota_land cruiser_2020",
"toyota_mirai_2016",
"toyota_mirai_2017",
"toyota_mirai_2019",
"toyota_prius c_2012",
"toyota_prius c_2013",
"toyota_prius c_2014",
"toyota_prius c_2015",
"toyota_prius c_2016",
"toyota_prius c_2017",
"toyota_prius c_2019",
"toyota_prius_2011",
"toyota_prius_2012",
"toyota_prius_2013",
"toyota_prius_2014",
"toyota_prius_2015",
"toyota_prius_2016",
"toyota_prius_2017",
"toyota_prius_2019",
"toyota_prius_2020",
"toyota_rav4_2011",
"toyota_rav4_2012",
"toyota_rav4_2013",
"toyota_rav4_2014",
"toyota_rav4_2015",
"toyota_rav4_2016",
"toyota_rav4_2017",
"toyota_rav4_2019",
"toyota_rav4_2020",
"toyota_sequoia_2011",
"toyota_sequoia_2012",
"toyota_sequoia_2013",
"toyota_sequoia_2014",
"toyota_sequoia_2015",
"toyota_sequoia_2016",
"toyota_sequoia_2017",
"toyota_sequoia_2019",
"toyota_sequoia_2020",
"toyota_sienna_2011",
"toyota_sienna_2012",
"toyota_sienna_2013",
"toyota_sienna_2014",
"toyota_sienna_2015",
"toyota_sienna_2016",
"toyota_sienna_2017",
"toyota_sienna_2019",
"toyota_sienna_2020",
"toyota_supra_1990",
"toyota_supra_1994",
"toyota_supra_1997",
"toyota_supra_2020",
"toyota_tacoma_2011",
"toyota_tacoma_2012",
"toyota_tacoma_2013",
"toyota_tacoma_2014",
"toyota_tacoma_2015",
"toyota_tacoma_2016",
"toyota_tacoma_2017",
"toyota_tacoma_2019",
"toyota_tacoma_2020",
"toyota_tundra_2011",
"toyota_tundra_2012",
"toyota_tundra_2013",
"toyota_tundra_2014",
"toyota_tundra_2015",
"toyota_tundra_2016",
"toyota_tundra_2017",
"toyota_tundra_2019",
"toyota_yaris hatchback_2020",
"toyota_yaris_2011",
"toyota_yaris_2012",
"toyota_yaris_2013",
"toyota_yaris_2014",
"toyota_yaris_2015",
"toyota_yaris_2016",
"toyota_yaris_2017",
"toyota_yaris_2019",
"toyota_yaris_2020",
"volkswagen_arteon_2019",
"volkswagen_arteon_2020",
"volkswagen_atlas_2019",
"volkswagen_atlas_2020",
"volkswagen_beetle_2010",
"volkswagen_beetle_2011",
"volkswagen_beetle_2012",
"volkswagen_beetle_2013",
"volkswagen_beetle_2014",
"volkswagen_beetle_2015",
"volkswagen_beetle_2016",
"volkswagen_beetle_2017",
"volkswagen_beetle_2019",
"volkswagen_golf_2011",
"volkswagen_golf_2012",
"volkswagen_golf_2013",
"volkswagen_golf_2014",
"volkswagen_golf_2015",
"volkswagen_golf_2016",
"volkswagen_golf_2017",
"volkswagen_golf_2019",
"volkswagen_golf_2020",
"volkswagen_jetta_2011",
"volkswagen_jetta_2012",
"volkswagen_jetta_2013",
"volkswagen_jetta_2014",
"volkswagen_jetta_2015",
"volkswagen_jetta_2016",
"volkswagen_jetta_2017",
"volkswagen_jetta_2019",
"volkswagen_jetta_2020",
"volkswagen_passat_2010",
"volkswagen_passat_2012",
"volkswagen_passat_2013",
"volkswagen_passat_2014",
"volkswagen_passat_2015",
"volkswagen_passat_2016",
"volkswagen_passat_2017",
"volkswagen_passat_2019",
"volkswagen_passat_2020",
"volkswagen_tiguan_2011",
"volkswagen_tiguan_2012",
"volkswagen_tiguan_2013",
"volkswagen_tiguan_2014",
"volkswagen_tiguan_2015",
"volkswagen_tiguan_2016",
"volkswagen_tiguan_2017",
"volkswagen_tiguan_2019",
"volkswagen_tiguan_2020",
"volkswagen_e-golf_2015",
"volkswagen_e-golf_2016",
"volkswagen_e-golf_2017",
"volkswagen_e-golf_2019",
"volvo_s60_2011",
"volvo_s60_2012",
"volvo_s60_2013",
"volvo_s60_2014",
"volvo_s60_2015",
"volvo_s60_2016",
"volvo_s60_2017",
"volvo_s60_2019",
"volvo_s60_2020",
"volvo_s90_1998",
"volvo_s90_2017",
"volvo_s90_2019",
"volvo_s90_2020",
"volvo_v60_2015",
"volvo_v60_2016",
"volvo_v60_2017",
"volvo_v60_2019",
"volvo_v60_2020",
"volvo_v90_1998",
"volvo_v90_2017",
"volvo_v90_2019",
"volvo_v90_2020",
"volvo_xc40_2019",
"volvo_xc40_2020",
"volvo_xc60_2011",
"volvo_xc60_2012",
"volvo_xc60_2013",
"volvo_xc60_2014",
"volvo_xc60_2015",
"volvo_xc60_2016",
"volvo_xc60_2017",
"volvo_xc60_2019",
"volvo_xc60_2020",
"volvo_xc90_2010",
"volvo_xc90_2011",
"volvo_xc90_2012",
"volvo_xc90_2013",
"volvo_xc90_2014",
"volvo_xc90_2016",
"volvo_xc90_2017",
"volvo_xc90_2019",
"volvo_xc90_2020",
"smart_fortwo_2010",
"smart_fortwo_2011",
"smart_fortwo_2012",
"smart_fortwo_2013",
"smart_fortwo_2014",
"smart_fortwo_2015",
"smart_fortwo_2016",
"smart_fortwo_2017",
"smart_fortwo_2019"
] |
Takekazuchi/Caracam
|
# Caracam (gen 1)
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9156
- Accuracy: 0.5852
## Model description
First generation of my AI that tells you what car you took a picture of. \
More versions coming soon with accuracy ratings of 85% and higher! Trained on 70+ brands with 2700+ cars going from 1945-2024. \
***App coming soon (also called Caracam) to Android and IOS*** \
(Late March - Early April 2024).
In the future I will take user opinion into account on what brands to add. The app will be updated semi-yearly with user-suggested car brands! \
if you wish to support project Caracam please visit my [Patreon](https://www.patreon.com/Caracam) or my [Cashapp](https://cash.app/$Clippayy)!!
## Intended uses & limitations
***NOT FOR COMMERCIAL USE OUTSIDE OF OFFICIAL CARACAM MOBILE APP***
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.0308 | 1.0 | 5362 | 3.6948 | 0.2491 |
| 2.694 | 2.0 | 10725 | 2.2586 | 0.5199 |
| 2.4475 | 3.0 | 16086 | 1.9156 | 0.5852 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0
|
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"volvo 440 1993",
"volvo 480 1986",
"volvo 66 1975",
"volvo 760 1982",
"volvo 780 1986",
"volvo 850 1992",
"volvo 940 1990",
"volvo 960 1994",
"volvo c30 2006",
"volvo c30 2009",
"volvo c40 recharge 2022",
"volvo c70 1996",
"volvo c70 2005",
"volvo ex90 2022",
"volvo p1800 1961",
"volvo s40 1996",
"volvo s40 2007",
"volvo s60 2000",
"volvo s60 2008",
"volvo s60 2018",
"volvo s70 1997",
"volvo s80 1998",
"volvo s80 2003",
"volvo s80 2009",
"volvo s90 1997",
"volvo s90 2016",
"volvo s90 2019",
"volvo v40 2000",
"volvo v40 2012",
"volvo v40 2016",
"volvo v50 2004",
"volvo v50 2007",
"volvo v60 2010",
"volvo v70 2000",
"volvo v70 2004",
"volvo v70 2007",
"volvo xc60 2008",
"volvo xc60 2013",
"volvo xc70 2000",
"volvo xc70 2004",
"volvo xc70 2007",
"volvo xc90 2002",
"volvo xc90 2007",
"volvo xc90 2014",
"volvo xc90 2019",
"zaz-965",
"zaz-966"
] |
lrzjason/noise-classifier
|
An vit classifier for handling noise image like this
It has limitation inbetween clear and noise
```
from datasets import load_dataset
from PIL import Image
from transformers import ViTImageProcessor, ViTForImageClassification, TrainingArguments, Trainer
import torch
import numpy as np
from datasets import load_metric
import os
import shutil
model_name_or_path = 'lrzjason/noise-classifier'
image_processor = ViTImageProcessor.from_pretrained(model_name_or_path)
model = ViTForImageClassification.from_pretrained(model_name_or_path)
input_dir = ''
file = 'b5b457f4-5b52-4d68-be1b-9a2f557465f6.jpg'
image = Image.open(os.path.join(input_dir, file))
inputs = image_processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
```
|
[
"clear",
"noise"
] |
Rodolfo1987/vit-base-patch16-224-finetuned-flower
|
<!-- 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-flower
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.
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
BttDenis/vit-base-patch16-224-finetuned-flower
|
<!-- 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-flower
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.
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
jzeep3/vit-base-patch16-224-finetuned-flower
|
<!-- 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-flower
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.
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
porkchopEnjoyer/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. -->
# porkchopEnjoyer/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: 0.0822
- Validation Loss: 0.6913
- Train Accuracy: 0.5
- 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': 1515, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6312 | 0.7057 | 0.5 | 0 |
| 0.4490 | 0.7302 | 0.5 | 1 |
| 0.2669 | 0.8137 | 0.75 | 2 |
| 0.1450 | 0.6267 | 0.5 | 3 |
| 0.0822 | 0.6913 | 0.5 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"loss",
"notloss"
] |
joeZhuang/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4765
- Accuracy: 0.921
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5391 | 0.99 | 62 | 2.3444 | 0.855 |
| 1.6948 | 2.0 | 125 | 1.6518 | 0.906 |
| 1.4136 | 2.98 | 186 | 1.4765 | 0.921 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- 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"
] |
nicolasdupuisroy/vit-gabor-detection-v3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-gabor-detection-v3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4139
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 200
- eval_batch_size: 200
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 120.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.6629 | 0.5 |
| No log | 2.0 | 2 | 0.6564 | 0.5 |
| No log | 3.0 | 3 | 0.6496 | 0.5 |
| No log | 4.0 | 4 | 0.6428 | 0.5 |
| No log | 5.0 | 5 | 0.6362 | 0.5 |
| No log | 6.0 | 6 | 0.6296 | 0.5 |
| No log | 7.0 | 7 | 0.6232 | 0.5 |
| No log | 8.0 | 8 | 0.6172 | 0.5 |
| No log | 9.0 | 9 | 0.6113 | 1.0 |
| 0.509 | 10.0 | 10 | 0.6058 | 1.0 |
| 0.509 | 11.0 | 11 | 0.6005 | 1.0 |
| 0.509 | 12.0 | 12 | 0.5950 | 1.0 |
| 0.509 | 13.0 | 13 | 0.5892 | 1.0 |
| 0.509 | 14.0 | 14 | 0.5832 | 1.0 |
| 0.509 | 15.0 | 15 | 0.5765 | 1.0 |
| 0.509 | 16.0 | 16 | 0.5699 | 1.0 |
| 0.509 | 17.0 | 17 | 0.5630 | 1.0 |
| 0.509 | 18.0 | 18 | 0.5562 | 1.0 |
| 0.509 | 19.0 | 19 | 0.5494 | 1.0 |
| 0.248 | 20.0 | 20 | 0.5426 | 1.0 |
| 0.248 | 21.0 | 21 | 0.5360 | 1.0 |
| 0.248 | 22.0 | 22 | 0.5295 | 1.0 |
| 0.248 | 23.0 | 23 | 0.5231 | 1.0 |
| 0.248 | 24.0 | 24 | 0.5175 | 1.0 |
| 0.248 | 25.0 | 25 | 0.5126 | 1.0 |
| 0.248 | 26.0 | 26 | 0.5079 | 1.0 |
| 0.248 | 27.0 | 27 | 0.5034 | 1.0 |
| 0.248 | 28.0 | 28 | 0.4991 | 1.0 |
| 0.248 | 29.0 | 29 | 0.4949 | 1.0 |
| 0.119 | 30.0 | 30 | 0.4908 | 1.0 |
| 0.119 | 31.0 | 31 | 0.4868 | 1.0 |
| 0.119 | 32.0 | 32 | 0.4833 | 1.0 |
| 0.119 | 33.0 | 33 | 0.4803 | 1.0 |
| 0.119 | 34.0 | 34 | 0.4777 | 1.0 |
| 0.119 | 35.0 | 35 | 0.4751 | 1.0 |
| 0.119 | 36.0 | 36 | 0.4727 | 1.0 |
| 0.119 | 37.0 | 37 | 0.4704 | 1.0 |
| 0.119 | 38.0 | 38 | 0.4681 | 1.0 |
| 0.119 | 39.0 | 39 | 0.4658 | 1.0 |
| 0.0692 | 40.0 | 40 | 0.4635 | 1.0 |
| 0.0692 | 41.0 | 41 | 0.4612 | 1.0 |
| 0.0692 | 42.0 | 42 | 0.4588 | 1.0 |
| 0.0692 | 43.0 | 43 | 0.4564 | 1.0 |
| 0.0692 | 44.0 | 44 | 0.4542 | 1.0 |
| 0.0692 | 45.0 | 45 | 0.4522 | 1.0 |
| 0.0692 | 46.0 | 46 | 0.4504 | 1.0 |
| 0.0692 | 47.0 | 47 | 0.4488 | 1.0 |
| 0.0692 | 48.0 | 48 | 0.4474 | 1.0 |
| 0.0692 | 49.0 | 49 | 0.4463 | 1.0 |
| 0.0487 | 50.0 | 50 | 0.4453 | 1.0 |
| 0.0487 | 51.0 | 51 | 0.4444 | 1.0 |
| 0.0487 | 52.0 | 52 | 0.4435 | 1.0 |
| 0.0487 | 53.0 | 53 | 0.4427 | 1.0 |
| 0.0487 | 54.0 | 54 | 0.4419 | 1.0 |
| 0.0487 | 55.0 | 55 | 0.4410 | 1.0 |
| 0.0487 | 56.0 | 56 | 0.4402 | 1.0 |
| 0.0487 | 57.0 | 57 | 0.4394 | 1.0 |
| 0.0487 | 58.0 | 58 | 0.4385 | 1.0 |
| 0.0487 | 59.0 | 59 | 0.4375 | 1.0 |
| 0.0374 | 60.0 | 60 | 0.4366 | 1.0 |
| 0.0374 | 61.0 | 61 | 0.4356 | 1.0 |
| 0.0374 | 62.0 | 62 | 0.4347 | 1.0 |
| 0.0374 | 63.0 | 63 | 0.4338 | 1.0 |
| 0.0374 | 64.0 | 64 | 0.4328 | 1.0 |
| 0.0374 | 65.0 | 65 | 0.4319 | 1.0 |
| 0.0374 | 66.0 | 66 | 0.4311 | 1.0 |
| 0.0374 | 67.0 | 67 | 0.4302 | 1.0 |
| 0.0374 | 68.0 | 68 | 0.4294 | 1.0 |
| 0.0374 | 69.0 | 69 | 0.4286 | 1.0 |
| 0.0321 | 70.0 | 70 | 0.4278 | 1.0 |
| 0.0321 | 71.0 | 71 | 0.4271 | 1.0 |
| 0.0321 | 72.0 | 72 | 0.4264 | 1.0 |
| 0.0321 | 73.0 | 73 | 0.4257 | 1.0 |
| 0.0321 | 74.0 | 74 | 0.4251 | 1.0 |
| 0.0321 | 75.0 | 75 | 0.4245 | 1.0 |
| 0.0321 | 76.0 | 76 | 0.4239 | 1.0 |
| 0.0321 | 77.0 | 77 | 0.4233 | 1.0 |
| 0.0321 | 78.0 | 78 | 0.4228 | 1.0 |
| 0.0321 | 79.0 | 79 | 0.4223 | 1.0 |
| 0.0285 | 80.0 | 80 | 0.4219 | 1.0 |
| 0.0285 | 81.0 | 81 | 0.4215 | 1.0 |
| 0.0285 | 82.0 | 82 | 0.4211 | 1.0 |
| 0.0285 | 83.0 | 83 | 0.4206 | 1.0 |
| 0.0285 | 84.0 | 84 | 0.4201 | 1.0 |
| 0.0285 | 85.0 | 85 | 0.4197 | 1.0 |
| 0.0285 | 86.0 | 86 | 0.4192 | 1.0 |
| 0.0285 | 87.0 | 87 | 0.4189 | 1.0 |
| 0.0285 | 88.0 | 88 | 0.4185 | 1.0 |
| 0.0285 | 89.0 | 89 | 0.4182 | 1.0 |
| 0.0268 | 90.0 | 90 | 0.4179 | 1.0 |
| 0.0268 | 91.0 | 91 | 0.4176 | 1.0 |
| 0.0268 | 92.0 | 92 | 0.4173 | 1.0 |
| 0.0268 | 93.0 | 93 | 0.4170 | 1.0 |
| 0.0268 | 94.0 | 94 | 0.4168 | 1.0 |
| 0.0268 | 95.0 | 95 | 0.4165 | 1.0 |
| 0.0268 | 96.0 | 96 | 0.4163 | 1.0 |
| 0.0268 | 97.0 | 97 | 0.4161 | 1.0 |
| 0.0268 | 98.0 | 98 | 0.4159 | 1.0 |
| 0.0268 | 99.0 | 99 | 0.4157 | 1.0 |
| 0.0249 | 100.0 | 100 | 0.4155 | 1.0 |
| 0.0249 | 101.0 | 101 | 0.4154 | 1.0 |
| 0.0249 | 102.0 | 102 | 0.4152 | 1.0 |
| 0.0249 | 103.0 | 103 | 0.4151 | 1.0 |
| 0.0249 | 104.0 | 104 | 0.4150 | 1.0 |
| 0.0249 | 105.0 | 105 | 0.4148 | 1.0 |
| 0.0249 | 106.0 | 106 | 0.4147 | 1.0 |
| 0.0249 | 107.0 | 107 | 0.4146 | 1.0 |
| 0.0249 | 108.0 | 108 | 0.4145 | 1.0 |
| 0.0249 | 109.0 | 109 | 0.4144 | 1.0 |
| 0.0242 | 110.0 | 110 | 0.4144 | 1.0 |
| 0.0242 | 111.0 | 111 | 0.4143 | 1.0 |
| 0.0242 | 112.0 | 112 | 0.4142 | 1.0 |
| 0.0242 | 113.0 | 113 | 0.4141 | 1.0 |
| 0.0242 | 114.0 | 114 | 0.4141 | 1.0 |
| 0.0242 | 115.0 | 115 | 0.4140 | 1.0 |
| 0.0242 | 116.0 | 116 | 0.4140 | 1.0 |
| 0.0242 | 117.0 | 117 | 0.4139 | 1.0 |
| 0.0242 | 118.0 | 118 | 0.4139 | 1.0 |
| 0.0242 | 119.0 | 119 | 0.4139 | 1.0 |
| 0.0292 | 120.0 | 120 | 0.4139 | 1.0 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.4.0
- Tokenizers 0.15.0
|
[
"absent",
"present"
] |
kashiram/swin-tiny-patch4-window7-224-finetuned-crop-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-crop-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: 0.6271
- Accuracy: 0.7468
## Model description
More information needed
## Intended uses & 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_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7134 | 1.0 | 733 | 0.6852 | 0.7265 |
| 0.6432 | 2.0 | 1467 | 0.6506 | 0.7349 |
| 0.6945 | 3.0 | 2200 | 0.6434 | 0.7415 |
| 0.6312 | 4.0 | 2932 | 0.6271 | 0.7468 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
sowbaranika13/vit-base-patch16-224-finetuned-flower
|
<!-- 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-flower
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.
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
jayspring/vit-base-patch16-224-finetuned-flower
|
<!-- 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-flower
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.
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
Yuuki0/anime-cartoon-detect
|
# Train Result:
- epoch: 1.0
- loss: 4895933612886589.0,
- runtime: 6608.2118,
- samples_per_second: 1.211,
- steps_per_second: 0.03
---
# Eval Result:
- epoch: 1.0
- accuracy: 0.9292134831460674
- loss: 443748739710976.0
- runtime: 142.4015
- samples_per_second: 6.25
- steps_per_second: 0.625
---
|
[
"anime",
"cartoon"
] |
LukeJacob2023/nsfw-image-detector
|
<!-- 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. -->
# nsfw-image-detector
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: 0.8138
- Accuracy: 0.9316
- Accuracy K: 0.9887
You can access 384 version on:
https://huggingface.co/LukeJacob2023/nsfw-image-detector-384
## Model description
Labels: ['drawings', 'hentai', 'neutral', 'porn', 'sexy']
## Intended uses & limitations
## Training and evaluation data
A custom dataset about 28k images, if you need to improve your domain's accurate, you can contribute the dataset to me.
## 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_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy K |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|
| 0.7836 | 1.0 | 720 | 0.3188 | 0.9085 | 0.9891 |
| 0.2441 | 2.0 | 1440 | 0.2382 | 0.9257 | 0.9936 |
| 0.1412 | 3.0 | 2160 | 0.2334 | 0.9335 | 0.9932 |
| 0.0857 | 4.0 | 2880 | 0.2934 | 0.9347 | 0.9934 |
| 0.0569 | 5.0 | 3600 | 0.4500 | 0.9307 | 0.9927 |
| 0.0371 | 6.0 | 4320 | 0.5524 | 0.9357 | 0.9910 |
| 0.0232 | 7.0 | 5040 | 0.6691 | 0.9347 | 0.9913 |
| 0.02 | 8.0 | 5760 | 0.7408 | 0.9335 | 0.9917 |
| 0.0154 | 9.0 | 6480 | 0.8138 | 0.9316 | 0.9887 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"drawings",
"hentai",
"neutral",
"porn",
"sexy"
] |
ambarish004/vit-base-patch16-224-finetuned-polyterrasse
|
<!-- 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-polyterrasse
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: 0.2635
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.86 | 3 | 0.5713 | 0.6667 |
| No log | 2.0 | 7 | 0.2635 | 1.0 |
| 0.3363 | 2.86 | 10 | 0.1832 | 1.0 |
| 0.3363 | 4.0 | 14 | 0.1458 | 1.0 |
| 0.3363 | 4.29 | 15 | 0.1437 | 1.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"no",
"yes"
] |
atuo/vit-base-patch16-224-in21k-finetuned-crop-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. -->
# vit-base-patch16-224-in21k-finetuned-crop-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: 0.6450
- Accuracy: 0.7472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8031 | 1.0 | 183 | 0.7603 | 0.7050 |
| 0.7311 | 2.0 | 367 | 0.7047 | 0.7250 |
| 0.7144 | 3.0 | 550 | 0.6968 | 0.7211 |
| 0.6516 | 4.0 | 734 | 0.6569 | 0.7376 |
| 0.6371 | 5.0 | 917 | 0.6483 | 0.7376 |
| 0.6246 | 6.0 | 1101 | 0.6492 | 0.7365 |
| 0.5659 | 7.0 | 1284 | 0.6481 | 0.7411 |
| 0.533 | 8.0 | 1468 | 0.6450 | 0.7472 |
| 0.5416 | 9.0 | 1651 | 0.6382 | 0.7453 |
| 0.5062 | 9.97 | 1830 | 0.6395 | 0.7461 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
satyam2652/vit-base-patch16-224-in21k-euroSat
|
<!-- 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. -->
# satyam2652/vit-base-patch16-224-in21k-euroSat
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: 3.1431
- Train Accuracy: 1.0
- Train Top-3-accuracy: 1.0
- Validation Loss: 3.6270
- Validation Accuracy: 0.9566
- Validation Top-3-accuracy: 0.9948
- Epoch: 9
## Model description
More information needed
## 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': 1680, '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 | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 5.3716 | 0.1168 | 0.1875 | 5.2023 | 0.3802 | 0.5556 | 0 |
| 4.8783 | 0.7768 | 0.8929 | 4.7966 | 0.7257 | 0.9115 | 1 |
| 4.4274 | 0.9360 | 0.9933 | 4.4790 | 0.8212 | 0.9618 | 2 |
| 4.0789 | 0.9769 | 0.9985 | 4.2315 | 0.8837 | 0.9740 | 3 |
| 3.8025 | 0.9926 | 1.0 | 4.0327 | 0.9062 | 0.9878 | 4 |
| 3.5840 | 0.9970 | 1.0 | 3.8840 | 0.9253 | 0.9913 | 5 |
| 3.4123 | 1.0 | 1.0 | 3.7695 | 0.9392 | 0.9931 | 6 |
| 3.2826 | 1.0 | 1.0 | 3.6876 | 0.9462 | 0.9948 | 7 |
| 3.1937 | 1.0 | 1.0 | 3.6414 | 0.9583 | 0.9948 | 8 |
| 3.1431 | 1.0 | 1.0 | 3.6270 | 0.9566 | 0.9948 | 9 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"u119",
"u115",
"u122",
"u210",
"u211",
"u215",
"u207",
"u212",
"u206",
"u222",
"u223",
"u220",
"u217",
"u126",
"u218",
"u225",
"u226",
"u224",
"u219",
"u221",
"u227",
"u232",
"u233",
"u228",
"u123",
"u229",
"u231",
"u234",
"u230",
"u235",
"u238",
"u237",
"u239",
"u236",
"u001",
"u120",
"u000",
"u002",
"u003",
"u009",
"u014",
"u016",
"u011",
"u017",
"u013",
"u005",
"u130",
"u010",
"u018",
"u008",
"u007",
"u004",
"u019",
"u006",
"u012",
"u015",
"u020",
"u128",
"u027",
"u031",
"u021",
"u024",
"u030",
"u029",
"u026",
"u028",
"u023",
"u032",
"u121",
"u025",
"u022",
"u033",
"u042",
"u037",
"u034",
"u043",
"u041",
"u040",
"u035",
"u129",
"u039",
"u044",
"u038",
"u036",
"u048",
"u053",
"u047",
"u052",
"u050",
"u046",
"u127",
"u045",
"u049",
"u051",
"u058",
"u057",
"u054",
"u062",
"u056",
"u063",
"u055",
"u124",
"u061",
"u059",
"u060",
"u068",
"u069",
"u070",
"u067",
"u072",
"u064",
"u065",
"u113",
"u131",
"u071",
"u066",
"u078",
"u076",
"u077",
"u073",
"u075",
"u074",
"u080",
"u081",
"u135",
"u079",
"u085",
"u087",
"u084",
"u088",
"u082",
"u086",
"u083",
"u089",
"u099",
"u132",
"u090",
"u095",
"u092",
"u093",
"u098",
"u100",
"u097",
"u094",
"u096",
"u091",
"u133",
"u101",
"u109",
"u104",
"u103",
"u108",
"u110",
"u102",
"u105",
"u107",
"u106",
"u134",
"u136",
"u140",
"u138",
"u139",
"u141",
"u112",
"u137",
"u149",
"u148",
"u143",
"u142",
"u145",
"u147",
"u146",
"u144",
"u150",
"u118",
"u151",
"u158",
"u154",
"u153",
"u152",
"u159",
"u160",
"u162",
"u161",
"u155",
"u111",
"u157",
"u156",
"u163",
"u168",
"u165",
"u167",
"u170",
"u166",
"u172",
"u173",
"u116",
"u169",
"u164",
"u171",
"u174",
"u176",
"u178",
"u181",
"u184",
"u179",
"u183",
"u117",
"u175",
"u182",
"u177",
"u180",
"u188",
"u192",
"u190",
"u195",
"u186",
"u185",
"u114",
"u189",
"u194",
"u191",
"u193",
"u196",
"u197",
"u187",
"u202",
"u204",
"u200",
"u125",
"u205",
"u199",
"u198",
"u201",
"u203",
"u213",
"u214",
"u216",
"u209",
"u208"
] |
amaye15/ViT-Standford-Dogs
|
# CanineNet: Dog Breed Classification for 'Le Refuge'
**Github:** [Here](https://github.com/amaye15/CanineNet/tree/main)
**Huggingface Model:** [Here](https://huggingface.co/amaye15/ViT-Standford-Dogs)
**Streamlit App:** [Here](https://caninenet.streamlit.app/)
**DagsHub & Mlflow Experiement Tracking:** [Here](https://dagshub.com/andrewmayes14/mlflow/experiments/#/)
## Project Overview
As a volunteer at 'Le Refuge,' a local animal protection association, I embarked on a mission to develop a machine learning tool that could aid in the classification of dog breeds from their vast image database. The project was inspired by my own experience finding my beloved pet, Snooky, through this association. To give back, I aimed to streamline their data management process by implementing an advanced breed classification algorithm.
```python
from transformers import pipeline
# Specify the model you want to use
model_name = "amaye15/ViT-Standford-Dogs" # Example: Vision Transformer
# Load the image classification pipeline with the specified model
image_classifier = pipeline("image-classification", model=model_name)
# Path or URL to your image
image_path_or_url = "path_or_url_to_your_image"
# Get predictions
predictions = image_classifier(image_path_or_url)
# Define the number of top predictions you want to see (top k)
top_k = 5
# Print the top k predictions
for prediction in predictions[:top_k]:
print(f"Class: {prediction['label']}, Confidence: {prediction['score']:.2f}")
```
|
[
"bull mastiff",
"chesapeake bay retriever",
"tibetan mastiff",
"bouvier des flandres",
"cardigan",
"miniature pinscher",
"toy poodle",
"kerry blue terrier",
"miniature schnauzer",
"bernese mountain dog",
"gordon setter",
"borzoi",
"bloodhound",
"irish setter",
"scottish deerhound",
"norwegian elkhound",
"keeshond",
"otterhound",
"dhole",
"norwich terrier",
"basenji",
"italian greyhound",
"pembroke",
"malinois",
"standard schnauzer",
"kelpie",
"old english sheepdog",
"yorkshire terrier",
"greater swiss mountain dog",
"miniature poodle",
"ibizan hound",
"english springer",
"german shepherd",
"schipperke",
"airedale",
"irish wolfhound",
"komondor",
"cairn",
"silky terrier",
"eskimo dog",
"english setter",
"tibetan terrier",
"saint bernard",
"redbone",
"labrador retriever",
"american staffordshire terrier",
"shetland sheepdog",
"toy terrier",
"west highland white terrier",
"pomeranian",
"pekinese",
"african hunting dog",
"brabancon griffon",
"dingo",
"collie",
"cocker spaniel",
"great pyrenees",
"japanese spaniel",
"standard poodle",
"papillon",
"english foxhound",
"french bulldog",
"chihuahua",
"australian terrier",
"black and tan coonhound",
"walker hound",
"border collie",
"samoyed",
"rhodesian ridgeback",
"malamute",
"bluetick",
"staffordshire bullterrier",
"scotch terrier",
"norfolk terrier",
"great dane",
"clumber",
"groenendael",
"saluki",
"leonberg",
"briard",
"basset",
"vizsla",
"irish terrier",
"newfoundland",
"entlebucher",
"mexican hairless",
"brittany spaniel",
"beagle",
"bedlington terrier",
"soft coated wheaten terrier",
"siberian husky",
"giant schnauzer",
"lakeland terrier",
"border terrier",
"flat coated retriever",
"irish water spaniel",
"curly coated retriever",
"blenheim spaniel",
"affenpinscher",
"lhasa",
"whippet",
"german short haired pointer",
"pug",
"boxer",
"wire haired fox terrier",
"sealyham terrier",
"welsh springer spaniel",
"golden retriever",
"maltese dog",
"afghan hound",
"appenzeller",
"rottweiler",
"dandie dinmont",
"weimaraner",
"boston bull",
"kuvasz",
"doberman",
"sussex spaniel",
"shih tzu",
"chow"
] |
Warins/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. -->
# Warins/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: 2.8240
- Validation Loss: 1.6827
- Train Accuracy: 0.818
- 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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8240 | 1.6827 | 0.818 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 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"
] |
kevintran/autotrain-image-classififier-3
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.15451037883758545
f1_macro: 0.9467270357903562
f1_micro: 0.946
f1_weighted: 0.9456145469707284
precision_macro: 0.9494787542337152
precision_micro: 0.946
precision_weighted: 0.9483015719746657
recall_macro: 0.946937588084916
recall_micro: 0.946
recall_weighted: 0.946
accuracy: 0.946
|
[
"buildings",
"forest",
"glacier",
"mountain",
"sea",
"street"
] |
nomis15/checkpoint-100
|
<!-- 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. -->
# checkpoint-100
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5022
- Accuracy: 0.8050
## Model description
More information needed
## Intended uses & 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: 20
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 80
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2967 | 1.24 | 50 | 0.9589 | 0.5975 |
| 0.721 | 2.48 | 100 | 0.6094 | 0.7528 |
| 0.5443 | 3.73 | 150 | 0.5702 | 0.7702 |
| 0.4597 | 4.97 | 200 | 0.5168 | 0.7938 |
| 0.3985 | 6.21 | 250 | 0.5091 | 0.7925 |
| 0.3407 | 7.45 | 300 | 0.5011 | 0.8 |
| 0.2989 | 8.7 | 350 | 0.5031 | 0.7963 |
| 0.271 | 9.94 | 400 | 0.4951 | 0.8012 |
| 0.2355 | 11.18 | 450 | 0.4973 | 0.8050 |
| 0.2047 | 12.42 | 500 | 0.5011 | 0.8025 |
| 0.1934 | 13.66 | 550 | 0.5018 | 0.8037 |
| 0.1861 | 14.91 | 600 | 0.5022 | 0.8050 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"adenocarcinoma",
"adenoma",
"normal_tissue",
"serrated_lesion"
] |
MattyB95/VIT-VoxCelebSpoof-MFCC-Synthetic-Voice-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-VoxCelebSpoof-MFCC-Synthetic-Voice-Detection
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0000
- F1: 1.0000
- Precision: 1.0
- Recall: 1.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0 | 1.0 | 29527 | 0.0006 | 0.9999 | 0.9999 | 0.9999 | 0.9999 |
| 0.0 | 2.0 | 59054 | 0.0002 | 0.9999 | 1.0000 | 1.0000 | 1.0000 |
| 0.0 | 3.0 | 88581 | 0.0001 | 1.0000 | 1.0000 | 1.0 | 1.0000 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"bonafide",
"spoof"
] |
MattyB95/VIT-VoxCelebSpoof-ConstantQ-Synthetic-Voice-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-VoxCelebSpoof-ConstantQ-Synthetic-Voice-Detection
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 1.0
- F1: 1.0
- Loss: 0.0000
- Precision: 1.0
- Recall: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0 | 1.0 | 29527 | 0.9999 | 0.9999 | 0.0006 | 0.9999 | 1.0 |
| 0.0 | 2.0 | 59054 | 1.0000 | 1.0000 | 0.0002 | 1.0 | 0.9999 |
| 0.0 | 3.0 | 88581 | 1.0 | 1.0 | 0.0000 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"bonafide",
"spoof"
] |
YoelCanaza/base-beans-classification-vit-model-yoel
|
<!-- 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. -->
# prueba-vit-model-yoel
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: 0.0081
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0212 | 3.85 | 500 | 0.0081 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
eskayML/swin-tiny-patch4-window7-224-finetuned-crop-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-crop-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: 0.6979
- Accuracy: 0.7208
## Model description
More information needed
## Intended uses & 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7918 | 1.0 | 183 | 0.7514 | 0.6989 |
| 0.7053 | 1.99 | 366 | 0.6979 | 0.7208 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
MattyB95/VIT-VoxCelebSpoof-Mel_Spectrogram-Synthetic-Voice-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-VoxCelebSpoof-Mel_Spectrogram-Synthetic-Voice-Detection
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 1.0000
- F1: 1.0000
- Precision: 1.0000
- Recall: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0048 | 1.0 | 29527 | 0.9998 | 0.9999 | 0.0010 | 0.9998 | 1.0 |
| 0.0 | 2.0 | 59054 | 0.0006 | 0.9999 | 0.9999 | 0.9999 | 0.9999 |
| 0.0 | 3.0 | 88581 | 0.0002 | 1.0000 | 1.0000 | 1.0000 | 1.0 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"bonafide",
"spoof"
] |
hlillemark/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8640
- Accuracy: 0.573
## Model description
More information needed
## Intended uses & 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: 512
- eval_batch_size: 512
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 2048
- 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 | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log | 1.0 | 2 | 0.036 | 4.5210 |
| No log | 2.0 | 4 | 0.278 | 4.4151 |
| No log | 3.0 | 6 | 0.437 | 4.3629 |
| No log | 4.0 | 8 | 4.2960 | 0.547 |
| 4.3122 | 5.0 | 10 | 4.1697 | 0.589 |
| 4.3122 | 6.0 | 12 | 4.0601 | 0.568 |
| 4.3122 | 7.0 | 14 | 3.9770 | 0.521 |
| 4.3122 | 8.0 | 16 | 3.9177 | 0.539 |
| 4.3122 | 9.0 | 18 | 3.8843 | 0.545 |
| 3.9792 | 10.0 | 20 | 3.8640 | 0.573 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"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"
] |
mmervecerit/vit-base-beans-tutorial
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-tutorial
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: 0.0442
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 1787
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0664 | 1.54 | 100 | 0.0698 | 0.9774 |
| 0.0115 | 3.08 | 200 | 0.0600 | 0.9850 |
| 0.008 | 4.62 | 300 | 0.0442 | 0.9850 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
judith0/classification_INE_v1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 0.2135
- Accuracy: 0.9767
## Model description
More information needed
## Intended uses & 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 | 0.96 | 6 | 0.4536 | 0.9186 |
| 0.691 | 1.92 | 12 | 0.2135 | 0.9767 |
| 0.691 | 2.88 | 18 | 0.1602 | 0.9767 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"other",
"anverso",
"reverso"
] |
judith0/classification_INE_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# classification_INE_v1-finetuned-eurosat
This model is a fine-tuned version of [judith0/classification_INE_v1](https://huggingface.co/judith0/classification_INE_v1) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0261
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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 | 0.96 | 6 | 0.0528 | 0.9884 |
| 0.1411 | 1.92 | 12 | 0.0261 | 1.0 |
| 0.1411 | 2.88 | 18 | 0.0182 | 1.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"other",
"anverso",
"reverso"
] |
ansilmbabl/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: 0.0690
- Accuracy: 0.9756
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2345 | 1.0 | 190 | 0.1822 | 0.9348 |
| 0.1618 | 2.0 | 380 | 0.0947 | 0.9670 |
| 0.1439 | 3.0 | 570 | 0.0690 | 0.9756 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
jalaneunos/vit-base-patch16-224-in21k-finetuned-FER2013
|
<!-- 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-FER2013
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: 0.3264
- Accuracy: 0.8732
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4811 | 1.0 | 202 | 0.4315 | 0.8004 |
| 0.4287 | 2.0 | 404 | 0.3579 | 0.8433 |
| 0.4184 | 3.0 | 606 | 0.3517 | 0.8467 |
| 0.3931 | 4.0 | 808 | 0.3308 | 0.8555 |
| 0.3667 | 5.0 | 1010 | 0.3204 | 0.8610 |
| 0.3545 | 6.0 | 1212 | 0.3144 | 0.8659 |
| 0.3137 | 7.0 | 1414 | 0.3308 | 0.8642 |
| 0.3178 | 8.0 | 1616 | 0.3230 | 0.8645 |
| 0.2998 | 9.0 | 1818 | 0.3206 | 0.8708 |
| 0.2773 | 10.0 | 2020 | 0.3264 | 0.8732 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"negative",
"positive"
] |
ansilmbabl/cards-swin-tiny-patch4-window7-224-finetuned-v1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cards-swin-tiny-patch4-window7-224-finetuned-v1
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: 1.3763
- Accuracy: 0.4107
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6417 | 1.0 | 734 | 1.6075 | 0.3000 |
| 1.577 | 2.0 | 1468 | 1.5511 | 0.3355 |
| 1.5699 | 3.0 | 2202 | 1.4887 | 0.3567 |
| 1.5361 | 4.0 | 2936 | 1.4659 | 0.3686 |
| 1.525 | 5.0 | 3670 | 1.4169 | 0.3920 |
| 1.4744 | 6.0 | 4404 | 1.4029 | 0.3957 |
| 1.4846 | 7.0 | 5138 | 1.3962 | 0.4029 |
| 1.4729 | 8.0 | 5872 | 1.3932 | 0.4026 |
| 1.4416 | 9.0 | 6606 | 1.3821 | 0.4088 |
| 1.4255 | 10.0 | 7340 | 1.3763 | 0.4107 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
|
[
"grade_01",
"grade_02",
"grade_03",
"grade_04",
"grade_05",
"grade_06",
"grade_07",
"grade_08",
"grade_09"
] |
enverkulahli/cat-sounds
|
<!-- 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. -->
# cat-sounds
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: 0.2256
- Accuracy: 0.9462
- F1: 0.9464
- Precision: 0.9477
- Recall: 0.9462
## Model description
More information needed
## Intended uses & 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2716 | 1.0 | 297 | 0.3630 | 0.8957 | 0.8961 | 0.9047 | 0.8957 |
| 0.098 | 2.0 | 594 | 0.2674 | 0.9344 | 0.9350 | 0.9372 | 0.9344 |
| 0.0487 | 3.0 | 891 | 0.2256 | 0.9462 | 0.9464 | 0.9477 | 0.9462 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"angry",
"defence",
"fighting",
"happy",
"huntingmind",
"mating",
"mothercall",
"paining",
"resting",
"warning"
] |
dvs/autotrain-kisd2-y8ibj
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.4466552734375
f1: 1.0
precision: 1.0
recall: 1.0
auc: 1.0
accuracy: 1.0
|
[
"mulder",
"scully"
] |
riyasankhe/vit-base-beans-tutorial
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-tutorial
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: 0.0180
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 1787
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0469 | 1.54 | 100 | 0.1320 | 0.9624 |
| 0.0186 | 3.08 | 200 | 0.0315 | 0.9925 |
| 0.0084 | 4.62 | 300 | 0.0180 | 0.9925 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
samink/vit-base-beans-tutorial-samink
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-tutorial-samink
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: 0.0472
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 1787
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0515 | 1.54 | 100 | 0.1152 | 0.9624 |
| 0.0279 | 3.08 | 200 | 0.0472 | 0.9925 |
| 0.0083 | 4.62 | 300 | 0.0680 | 0.9850 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
moreover18/vit-part1-friends-YB
|
<!-- 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-part1-friends-YB
This model is a fine-tuned version of [moreover18/vit-base-patch16-224-in21k-YB](https://huggingface.co/moreover18/vit-base-patch16-224-in21k-YB) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2325
- Accuracy: 0.9218
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5297 | 2.6 | 100 | 0.2325 | 0.9218 |
### Framework versions
- Transformers 4.37.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.1
|
[
"friends",
"other"
] |
moreover18/vit-part1-friends
|
<!-- 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-part1-friends
This model is a fine-tuned version of [moreover18/vit-base-patch16-224-in21k-YB](https://huggingface.co/moreover18/vit-base-patch16-224-in21k-YB) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2036
- Accuracy: 0.9381
## Model description
More information needed
## Intended uses & 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1627 | 1.3 | 50 | 0.2258 | 0.9202 |
| 0.1183 | 2.6 | 100 | 0.2148 | 0.9235 |
| 0.1223 | 3.9 | 150 | 0.2055 | 0.9267 |
| 0.0992 | 5.19 | 200 | 0.1976 | 0.9332 |
| 0.0824 | 6.49 | 250 | 0.2036 | 0.9381 |
### Framework versions
- Transformers 4.37.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.1
|
[
"friends",
"other"
] |
sahild23d23e23re23r/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:
- eval_loss: 0.0077
- eval_accuracy: 0.9990
- eval_runtime: 172.7146
- eval_samples_per_second: 50.372
- eval_steps_per_second: 1.575
- epoch: 1.0
- step: 611
## Model description
More information needed
## Intended uses & 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
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.14.1
|
[
"a",
"b",
"c",
"d",
"e",
"f",
"g",
"h",
"i",
"j",
"k",
"l",
"m",
"n",
"o",
"p",
"q",
"r",
"s",
"t",
"u",
"v",
"w",
"x",
"y",
"z",
"del",
"nothing",
"space"
] |
ares1123/photo_age_detection
|
**Photo Age Detection**
This is designed to analyze facial features and predict age of a person in an image.
|
[
"01",
"02",
"03",
"04",
"05",
"06-07",
"08-09",
"10-12",
"13-15",
"16-20",
"21-25",
"26-30",
"31-35",
"36-40",
"41-45",
"46-50",
"51-55",
"56-60",
"61-65",
"66-70",
"71-80",
"81-90",
"90+"
] |
sahild23d23e23re23r/sign-lan-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. -->
# sign-lan-model
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: 0.0087
- Accuracy: 0.9993
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2769 | 1.0 | 611 | 0.0087 | 0.9993 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.14.1
|
[
"a",
"b",
"c",
"d",
"e",
"f",
"g",
"h",
"i",
"j",
"k",
"l",
"m",
"n",
"o",
"p",
"q",
"r",
"s",
"t",
"u",
"v",
"w",
"x",
"y",
"z",
"del",
"nothing",
"space"
] |
Hector001/emotion-vit-model-hector
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion-vit-model-hector
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: 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
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
|
[
"happy",
"neutral",
"sad"
] |
moreover18/vit-part1-friends2
|
<!-- 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-part1-friends2
This model is a fine-tuned version of [moreover18/vit-part1-friends](https://huggingface.co/moreover18/vit-part1-friends) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7298
- Accuracy: 0.7395
## Model description
More information needed
## Intended uses & 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1497 | 7.41 | 50 | 0.7298 | 0.7395 |
### Framework versions
- Transformers 4.37.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.1
|
[
"friends",
"other"
] |
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