model_id
stringlengths 7
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cotysong113/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: 1.5974
- Accuracy: 0.899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7165 | 0.992 | 62 | 2.5197 | 0.82 |
| 1.8377 | 2.0 | 125 | 1.7734 | 0.868 |
| 1.5955 | 2.976 | 186 | 1.5974 | 0.899 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1
|
[
"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"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-004
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-004
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0895
- Accuracy: 0.9444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.8954 | 5.7143 | 10 | 0.3421 | 0.9444 |
| 0.2087 | 11.4286 | 20 | 0.1405 | 0.9444 |
| 0.062 | 17.1429 | 30 | 0.0895 | 0.9444 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-005
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-005
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0719
- Accuracy: 0.9722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.582 | 6.5714 | 10 | 0.1846 | 0.9722 |
| 0.0293 | 13.2857 | 20 | 0.0766 | 0.9722 |
| 0.0021 | 19.8571 | 30 | 0.0719 | 0.9722 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-006
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-006
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1481
- Accuracy: 0.9722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0005 | 6.5714 | 10 | 0.1356 | 0.9722 |
| 0.0 | 13.2857 | 20 | 0.1541 | 0.9722 |
| 0.0 | 19.8571 | 30 | 0.1481 | 0.9722 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
Zainajabroh/image_emotion_classification_project_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_emotion_classification_project_4
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9052
- Accuracy: 0.5188
## Model description
More information needed
## Intended uses & 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: reduce_lr_on_plateau
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6977 | 1.0 | 640 | 1.5713 | 0.325 |
| 1.7006 | 2.0 | 1280 | 1.4543 | 0.4562 |
| 1.6725 | 3.0 | 1920 | 1.6124 | 0.4625 |
| 1.2312 | 4.0 | 2560 | 1.6711 | 0.5 |
| 0.6097 | 5.0 | 3200 | 1.8838 | 0.5312 |
| 1.264 | 6.0 | 3840 | 2.0933 | 0.4875 |
| 2.4064 | 7.0 | 4480 | 2.0628 | 0.5188 |
| 2.0741 | 8.0 | 5120 | 2.6505 | 0.4625 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-007
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-007
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0674
- Accuracy: 0.9722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 31
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0 | 6.5714 | 10 | 0.0367 | 0.9722 |
| 0.0 | 13.2857 | 20 | 0.0632 | 0.9722 |
| 0.0 | 19.8571 | 30 | 0.0674 | 0.9722 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-008
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-008
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0935
- Accuracy: 0.9722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0018 | 6.5714 | 10 | 0.1107 | 0.9722 |
| 0.0002 | 13.2857 | 20 | 0.1064 | 0.9722 |
| 0.0001 | 19.8571 | 30 | 0.0935 | 0.9722 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-009
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-009
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0429
- Accuracy: 1.0
## Model description
Dungeon Maps - Geo Morphs - with 0 to 3 entrances
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.7303 | 6.5714 | 10 | 0.2536 | 0.9444 |
| 0.0464 | 13.2857 | 20 | 0.0737 | 0.9444 |
| 0.0017 | 19.8571 | 30 | 0.0429 | 1.0 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-010
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-010
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1230
- Accuracy: 0.9444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.9545 | 6.5714 | 10 | 0.3644 | 0.9444 |
| 0.2033 | 13.2857 | 20 | 0.1559 | 0.9444 |
| 0.0472 | 19.8571 | 30 | 0.1230 | 0.9444 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-011
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-011
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1657
- Accuracy: 0.9444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0008 | 6.5714 | 10 | 0.1917 | 0.9444 |
| 0.0 | 13.2857 | 20 | 0.1489 | 0.9444 |
| 0.0 | 19.8571 | 30 | 0.1657 | 0.9444 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-012
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-012
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1487
- Accuracy: 0.9722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0 | 6.5714 | 10 | 0.1662 | 0.9722 |
| 0.0044 | 13.2857 | 20 | 0.2218 | 0.9444 |
| 0.0 | 19.8571 | 30 | 0.1487 | 0.9722 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
ansaritk/vit-base-patch16-224-finetuned-flower-classify
|
<!-- This model card 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-classify
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
chun061205/vit-base-beans
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0645
- 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2821 | 1.0 | 130 | 0.2170 | 0.9624 |
| 0.1291 | 2.0 | 260 | 0.1299 | 0.9699 |
| 0.1379 | 3.0 | 390 | 0.0972 | 0.9774 |
| 0.0803 | 4.0 | 520 | 0.0645 | 0.9850 |
| 0.1123 | 5.0 | 650 | 0.0791 | 0.9774 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
AhmadIshaqai/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: 1.6109
- Accuracy: 0.901
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7014 | 0.992 | 62 | 2.5097 | 0.847 |
| 1.8804 | 2.0 | 125 | 1.7599 | 0.89 |
| 1.6054 | 2.976 | 186 | 1.6109 | 0.901 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
masafresh/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8358
- Accuracy: 0.6580
- F1: 0.6497
## Model description
More information needed
## Intended uses & 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: 96
- eval_batch_size: 96
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 384
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 1.0613 | 0.9997 | 1973 | 0.9913 | 0.5967 | 0.5794 |
| 1.0041 | 2.0 | 3947 | 0.8844 | 0.6358 | 0.6275 |
| 0.9508 | 2.9992 | 5919 | 0.8358 | 0.6580 | 0.6497 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
|
[
"hglo",
"mglo",
"lglo",
"hgso",
"mwl",
"uglo",
"mgso",
"lgso",
"mws"
] |
brigettesegovia/plant_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. -->
# plant_model
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: 0.1169
- Accuracy: 0.9469
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1893 | 1.0 | 13 | 0.1545 | 0.9420 |
| 0.1421 | 2.0 | 26 | 0.1169 | 0.9469 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"angular leaf spot",
"bean rust",
"healthy"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-009-dungeon-geo-morphs-001
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-009-dungeon-geo-morphs-001
This model is a fine-tuned version of [griffio/vit-large-patch16-224-dungeon-geo-morphs-009](https://huggingface.co/griffio/vit-large-patch16-224-dungeon-geo-morphs-009) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0213
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0288 | 5.7143 | 10 | 0.0213 | 1.0 |
| 0.0002 | 11.4286 | 20 | 0.0726 | 0.9722 |
| 0.0001 | 17.1429 | 30 | 0.0599 | 0.9722 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-009-dungeon-geo-morphs-002
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-009-dungeon-geo-morphs-002
This model is a fine-tuned version of [griffio/vit-large-patch16-224-dungeon-geo-morphs-009](https://huggingface.co/griffio/vit-large-patch16-224-dungeon-geo-morphs-009) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0846
- Accuracy: 0.9444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.1962 | 5.7143 | 10 | 0.0717 | 1.0 |
| 0.0443 | 11.4286 | 20 | 0.0866 | 0.9722 |
| 0.0128 | 17.1429 | 30 | 0.0846 | 0.9444 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-009-dungeon-geo-morphs-003
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-009-dungeon-geo-morphs-003
This model is a fine-tuned version of [griffio/vit-large-patch16-224-dungeon-geo-morphs-009](https://huggingface.co/griffio/vit-large-patch16-224-dungeon-geo-morphs-009) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0943
- Accuracy: 0.9444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2469 | 5.7143 | 10 | 0.0943 | 0.9444 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-1005
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-1005
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1175
- Accuracy: 0.9444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.7083 | 5.7143 | 10 | 0.3063 | 0.8611 |
| 0.1533 | 11.4286 | 20 | 0.1348 | 0.9444 |
| 0.0426 | 17.1429 | 30 | 0.1175 | 0.9444 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-1006
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-1006
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0782
- Accuracy: 0.9444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.7742 | 5.7143 | 10 | 0.2863 | 0.9444 |
| 0.162 | 11.4286 | 20 | 0.1305 | 0.9444 |
| 0.039 | 17.1429 | 30 | 0.0782 | 0.9444 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-dungeon-geo-morphs-1007
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-1007
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0094
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0076 | 5.7143 | 10 | 0.0398 | 1.0 |
| 0.0006 | 11.4286 | 20 | 0.0392 | 1.0 |
| 0.0003 | 17.1429 | 30 | 0.0094 | 1.0 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"three",
"two",
"zero"
] |
n1hal/swinv2-plantclef
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-plantclef
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0548
- Accuracy: 0.8199
- F1: 0.8190
## Model description
More information needed
## Intended uses & 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.1414 | 1.0 | 897 | 0.9819 | 0.7171 | 0.7046 |
| 0.654 | 2.0 | 1794 | 0.7608 | 0.7694 | 0.7688 |
| 0.394 | 3.0 | 2691 | 0.7461 | 0.7795 | 0.7767 |
| 0.2437 | 4.0 | 3588 | 0.7369 | 0.7917 | 0.7908 |
| 0.1428 | 5.0 | 4485 | 0.7939 | 0.7945 | 0.7929 |
| 0.0878 | 6.0 | 5382 | 0.8352 | 0.7958 | 0.7950 |
| 0.0621 | 7.0 | 6279 | 0.8802 | 0.7945 | 0.7928 |
| 0.0353 | 8.0 | 7176 | 0.9028 | 0.8011 | 0.8005 |
| 0.0241 | 9.0 | 8073 | 0.9592 | 0.8043 | 0.8045 |
| 0.0241 | 10.0 | 8970 | 1.0075 | 0.8068 | 0.8047 |
| 0.0129 | 11.0 | 9867 | 1.0254 | 0.8127 | 0.8120 |
| 0.0058 | 12.0 | 10764 | 1.0340 | 0.8162 | 0.8151 |
| 0.007 | 13.0 | 11661 | 1.0661 | 0.8165 | 0.8159 |
| 0.0052 | 14.0 | 12558 | 1.0533 | 0.8168 | 0.8166 |
| 0.0049 | 15.0 | 13455 | 1.0660 | 0.8174 | 0.8164 |
| 0.015 | 16.0 | 14352 | 1.0548 | 0.8199 | 0.8190 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0
- Datasets 3.1.0
- Tokenizers 0.20.1
|
[
"1355868",
"1355869",
"1355885",
"1355886",
"1355894",
"1355897",
"1355898",
"1355899",
"1355900",
"1355901",
"1355902",
"1355903",
"1355870",
"1355907",
"1355908",
"1355914",
"1355926",
"1355927",
"1355928",
"1355932",
"1355934",
"1355935",
"1355936",
"1355871",
"1355937",
"1355941",
"1355948",
"1355950",
"1355952",
"1355953",
"1355964",
"1355967",
"1355968",
"1355969",
"1355872",
"1355970",
"1355971",
"1355972",
"1355977",
"1355978",
"1355984",
"1355986",
"1355987",
"1355989",
"1355990",
"1355873",
"1355991",
"1355992",
"1355993",
"1355994",
"1355995",
"1355997",
"1355998",
"1356001",
"1356007",
"1356008",
"1355880",
"1356012",
"1356013",
"1356017",
"1356022",
"1356023",
"1356024",
"1356033",
"1356040",
"1356042",
"1356044",
"1355881",
"1356045",
"1356046",
"1356052",
"1356054",
"1356055",
"1356058",
"1356062",
"1356063",
"1356064",
"1356065",
"1355882",
"1356066",
"1356067",
"1356069",
"1356070",
"1356072",
"1356075",
"1356078",
"1356079",
"1356081",
"1356082",
"1355884",
"1356083",
"1356084",
"1356086",
"1356089",
"1356091",
"1356094",
"1356095",
"1356105",
"1356106",
"1356107"
] |
nemik/frost-vision-v2-google_vit-base-patch16-224-v2024-11-14
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# frost-vision-v2-google_vit-base-patch16-224-v2024-11-14
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1577
- Accuracy: 0.9389
- F1: 0.8436
- Precision: 0.8655
- Recall: 0.8228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3381 | 1.2346 | 100 | 0.3271 | 0.8660 | 0.5669 | 0.8045 | 0.4376 |
| 0.2067 | 2.4691 | 200 | 0.2080 | 0.9194 | 0.7827 | 0.8514 | 0.7242 |
| 0.1745 | 3.7037 | 300 | 0.1864 | 0.9228 | 0.8003 | 0.8308 | 0.7720 |
| 0.1724 | 4.9383 | 400 | 0.1792 | 0.9299 | 0.8188 | 0.8493 | 0.7904 |
| 0.128 | 6.1728 | 500 | 0.1736 | 0.9327 | 0.8292 | 0.8437 | 0.8151 |
| 0.1034 | 7.4074 | 600 | 0.1672 | 0.9355 | 0.8348 | 0.8571 | 0.8136 |
| 0.0944 | 8.6420 | 700 | 0.1579 | 0.9392 | 0.8452 | 0.8622 | 0.8290 |
| 0.0919 | 9.8765 | 800 | 0.1631 | 0.9364 | 0.8347 | 0.8710 | 0.8012 |
| 0.0791 | 11.1111 | 900 | 0.1592 | 0.9380 | 0.8383 | 0.8771 | 0.8028 |
| 0.0684 | 12.3457 | 1000 | 0.1577 | 0.9389 | 0.8436 | 0.8655 | 0.8228 |
| 0.0737 | 13.5802 | 1100 | 0.1678 | 0.9380 | 0.8416 | 0.8613 | 0.8228 |
| 0.0625 | 14.8148 | 1200 | 0.1646 | 0.9426 | 0.8542 | 0.8692 | 0.8398 |
| 0.0591 | 16.0494 | 1300 | 0.1625 | 0.9432 | 0.8549 | 0.8756 | 0.8351 |
| 0.0464 | 17.2840 | 1400 | 0.1722 | 0.9386 | 0.8422 | 0.8676 | 0.8182 |
| 0.048 | 18.5185 | 1500 | 0.1694 | 0.9401 | 0.8472 | 0.8663 | 0.8290 |
| 0.0353 | 19.7531 | 1600 | 0.1715 | 0.9392 | 0.8462 | 0.8576 | 0.8351 |
| 0.0434 | 20.9877 | 1700 | 0.1817 | 0.9370 | 0.8386 | 0.8618 | 0.8166 |
| 0.0332 | 22.2222 | 1800 | 0.1797 | 0.9383 | 0.8423 | 0.8627 | 0.8228 |
| 0.0283 | 23.4568 | 1900 | 0.1810 | 0.9401 | 0.8482 | 0.8617 | 0.8351 |
| 0.0474 | 24.6914 | 2000 | 0.1765 | 0.9398 | 0.8454 | 0.8709 | 0.8213 |
| 0.0365 | 25.9259 | 2100 | 0.1835 | 0.9414 | 0.8516 | 0.8637 | 0.8398 |
| 0.0244 | 27.1605 | 2200 | 0.1822 | 0.9404 | 0.8479 | 0.8677 | 0.8290 |
| 0.0242 | 28.3951 | 2300 | 0.1808 | 0.9407 | 0.8483 | 0.8703 | 0.8274 |
| 0.0296 | 29.6296 | 2400 | 0.1817 | 0.9401 | 0.8477 | 0.864 | 0.8320 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"snowing",
"raining",
"sunny",
"cloudy",
"night",
"snow_on_road",
"partial_snow_on_road",
"clear_pavement",
"wet_pavement",
"iced_lens"
] |
Dev176/21BAI1229
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 21BAI1229
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: 0.4078
- Accuracy: 0.8734
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.6034 | 0.9873 | 39 | 2.0544 | 0.4520 |
| 1.4429 | 2.0 | 79 | 0.7736 | 0.7849 |
| 0.8307 | 2.9873 | 118 | 0.5456 | 0.8413 |
| 0.6814 | 4.0 | 158 | 0.4881 | 0.8516 |
| 0.6199 | 4.9873 | 197 | 0.4614 | 0.8528 |
| 0.5578 | 6.0 | 237 | 0.4419 | 0.8615 |
| 0.5198 | 6.9873 | 276 | 0.4485 | 0.8603 |
| 0.4811 | 8.0 | 316 | 0.4355 | 0.8659 |
| 0.4568 | 8.9873 | 355 | 0.4182 | 0.8651 |
| 0.4268 | 10.0 | 395 | 0.4094 | 0.8702 |
| 0.4281 | 10.9873 | 434 | 0.4158 | 0.8706 |
| 0.4143 | 12.0 | 474 | 0.4078 | 0.8734 |
| 0.4009 | 12.9873 | 513 | 0.4066 | 0.8714 |
| 0.3642 | 14.0 | 553 | 0.4131 | 0.8683 |
| 0.3659 | 14.9873 | 592 | 0.4047 | 0.8726 |
| 0.3487 | 16.0 | 632 | 0.4054 | 0.8710 |
| 0.35 | 16.9873 | 671 | 0.4107 | 0.8722 |
| 0.3291 | 18.0 | 711 | 0.4099 | 0.8698 |
| 0.338 | 18.9873 | 750 | 0.4063 | 0.8718 |
| 0.3419 | 19.7468 | 780 | 0.4066 | 0.8702 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"calling",
"clapping",
"cycling",
"dancing",
"drinking",
"eating",
"fighting",
"hugging",
"laughing",
"listening_to_music",
"running",
"sitting",
"sleeping",
"texting",
"using_laptop"
] |
Docty/nose-mask-classification
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nose-mask-classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2744
- Validation Loss: 0.0564
- Train Accuracy: 1.0
- 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': 4500, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2744 | 0.0564 | 1.0 | 0 |
### Framework versions
- Transformers 4.46.2
- TensorFlow 2.17.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"withmask",
"withoutmask"
] |
masafresh/swin-transformer
|
<!-- This model card 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
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7366
- Accuracy: 0.39
- F1: 0.2753
## Model description
More information needed
## Intended uses & 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: 96
- eval_batch_size: 96
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 384
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log | 0.7273 | 2 | 2.0766 | 0.3 | 0.2161 |
| No log | 1.8182 | 5 | 1.7687 | 0.37 | 0.2461 |
| No log | 2.1818 | 6 | 1.7366 | 0.39 | 0.2753 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
|
[
"hglo",
"mglo",
"lglo",
"hgso",
"mwl",
"uglo",
"mgso",
"lgso",
"mws"
] |
CGscorpion/vit-base-patch32-384-finetuned-eurosat-albumentations
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch32-384-finetuned-eurosat-albumentations
This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co/google/vit-base-patch32-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1871
- Accuracy: 0.9726
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.7204 | 0.9412 | 12 | 0.5695 | 0.7397 |
| 0.4269 | 1.9804 | 25 | 0.2537 | 0.9178 |
| 0.1605 | 2.9412 | 37 | 0.3347 | 0.8767 |
| 0.0758 | 3.9804 | 50 | 0.2203 | 0.9041 |
| 0.0405 | 4.9412 | 62 | 0.3563 | 0.9178 |
| 0.0358 | 5.9804 | 75 | 0.2326 | 0.9315 |
| 0.0188 | 6.9412 | 87 | 0.2046 | 0.9315 |
| 0.026 | 7.9804 | 100 | 0.2195 | 0.8904 |
| 0.0582 | 8.9412 | 112 | 0.3378 | 0.9178 |
| 0.0113 | 9.9804 | 125 | 0.2685 | 0.9178 |
| 0.0081 | 10.9412 | 137 | 0.2443 | 0.9315 |
| 0.0091 | 11.9804 | 150 | 0.4675 | 0.9041 |
| 0.0065 | 12.9412 | 162 | 0.3252 | 0.9452 |
| 0.0026 | 13.9804 | 175 | 0.1871 | 0.9726 |
| 0.0043 | 14.9412 | 187 | 0.2256 | 0.9589 |
| 0.0094 | 15.9804 | 200 | 0.1980 | 0.9452 |
| 0.0028 | 16.9412 | 212 | 0.2928 | 0.9315 |
| 0.0003 | 17.9804 | 225 | 0.2241 | 0.9726 |
| 0.0006 | 18.9412 | 237 | 0.2396 | 0.9726 |
| 0.0012 | 19.9804 | 250 | 0.2663 | 0.9315 |
| 0.0001 | 20.9412 | 262 | 0.2266 | 0.9726 |
| 0.0002 | 21.9804 | 275 | 0.2637 | 0.9452 |
| 0.0001 | 22.9412 | 287 | 0.2873 | 0.9452 |
| 0.0003 | 23.9804 | 300 | 0.2068 | 0.9589 |
| 0.0001 | 24.9412 | 312 | 0.2485 | 0.9452 |
| 0.0047 | 25.9804 | 325 | 0.3375 | 0.9178 |
| 0.0015 | 26.9412 | 337 | 0.3132 | 0.9589 |
| 0.0001 | 27.9804 | 350 | 0.3148 | 0.9452 |
| 0.0025 | 28.9412 | 362 | 0.2533 | 0.9452 |
| 0.0038 | 29.9804 | 375 | 0.2860 | 0.9315 |
| 0.0025 | 30.9412 | 387 | 0.2785 | 0.9452 |
| 0.0031 | 31.9804 | 400 | 0.3246 | 0.9452 |
| 0.0 | 32.9412 | 412 | 0.3367 | 0.9452 |
| 0.0006 | 33.9804 | 425 | 0.2625 | 0.9726 |
| 0.0 | 34.9412 | 437 | 0.2689 | 0.9589 |
| 0.0007 | 35.9804 | 450 | 0.2891 | 0.9726 |
| 0.0003 | 36.9412 | 462 | 0.4523 | 0.9315 |
| 0.0003 | 37.9804 | 475 | 0.3426 | 0.9452 |
| 0.0001 | 38.9412 | 487 | 0.3167 | 0.9589 |
| 0.0 | 39.9804 | 500 | 0.3237 | 0.9589 |
| 0.0002 | 40.9412 | 512 | 0.3085 | 0.9589 |
| 0.0 | 41.9804 | 525 | 0.3095 | 0.9589 |
| 0.0 | 42.9412 | 537 | 0.3049 | 0.9589 |
| 0.0002 | 43.9804 | 550 | 0.3039 | 0.9589 |
| 0.0001 | 44.9412 | 562 | 0.3044 | 0.9589 |
| 0.0001 | 45.9804 | 575 | 0.3031 | 0.9726 |
| 0.0 | 46.9412 | 587 | 0.3028 | 0.9726 |
| 0.0 | 47.9804 | 600 | 0.3027 | 0.9726 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.3
|
[
"answer",
"delete_line"
] |
Twipsy/vit-base-oxford-iiit-pets
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1763
- Accuracy: 0.9499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3698 | 1.0 | 370 | 0.2753 | 0.9296 |
| 0.2212 | 2.0 | 740 | 0.2142 | 0.9378 |
| 0.1741 | 3.0 | 1110 | 0.1975 | 0.9432 |
| 0.1546 | 4.0 | 1480 | 0.1899 | 0.9432 |
| 0.1355 | 5.0 | 1850 | 0.1883 | 0.9472 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"sphynx",
"maine coon",
"keeshond",
"yorkshire terrier",
"havanese",
"leonberger",
"wheaten terrier",
"american bulldog",
"english setter",
"boxer",
"newfoundland",
"bengal",
"samoyed",
"british shorthair",
"great pyrenees",
"abyssinian",
"pug",
"saint bernard",
"russian blue",
"scottish terrier"
] |
wagodo/vit-base-oxford-iiit-pets
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2294
- Accuracy: 0.9364
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3645 | 1.0 | 370 | 0.2793 | 0.9296 |
| 0.2042 | 2.0 | 740 | 0.2111 | 0.9310 |
| 0.1733 | 3.0 | 1110 | 0.1835 | 0.9405 |
| 0.15 | 4.0 | 1480 | 0.1776 | 0.9432 |
| 0.1223 | 5.0 | 1850 | 0.1761 | 0.9459 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"sphynx",
"maine coon",
"keeshond",
"yorkshire terrier",
"havanese",
"leonberger",
"wheaten terrier",
"american bulldog",
"english setter",
"boxer",
"newfoundland",
"bengal",
"samoyed",
"british shorthair",
"great pyrenees",
"abyssinian",
"pug",
"saint bernard",
"russian blue",
"scottish terrier"
] |
sogueeti/vit-base-oxford-iiit-pets
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2022
- Accuracy: 0.9391
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3716 | 1.0 | 370 | 0.3101 | 0.9283 |
| 0.2157 | 2.0 | 740 | 0.2396 | 0.9323 |
| 0.1558 | 3.0 | 1110 | 0.2290 | 0.9350 |
| 0.1375 | 4.0 | 1480 | 0.2166 | 0.9364 |
| 0.1301 | 5.0 | 1850 | 0.2135 | 0.9418 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"sphynx",
"maine coon",
"keeshond",
"yorkshire terrier",
"havanese",
"leonberger",
"wheaten terrier",
"american bulldog",
"english setter",
"boxer",
"newfoundland",
"bengal",
"samoyed",
"british shorthair",
"great pyrenees",
"abyssinian",
"pug",
"saint bernard",
"russian blue",
"scottish terrier"
] |
nemethomas/vit-base-oxford-iiit-pets
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2038
- Accuracy: 0.9445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.373 | 1.0 | 370 | 0.2732 | 0.9337 |
| 0.2127 | 2.0 | 740 | 0.2148 | 0.9405 |
| 0.1801 | 3.0 | 1110 | 0.1918 | 0.9445 |
| 0.1448 | 4.0 | 1480 | 0.1857 | 0.9472 |
| 0.1308 | 5.0 | 1850 | 0.1814 | 0.9445 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"sphynx",
"maine coon",
"keeshond",
"yorkshire terrier",
"havanese",
"leonberger",
"wheaten terrier",
"american bulldog",
"english setter",
"boxer",
"newfoundland",
"bengal",
"samoyed",
"british shorthair",
"great pyrenees",
"abyssinian",
"pug",
"saint bernard",
"russian blue",
"scottish terrier"
] |
mahmuili/vit-base-oxford-iiit-pets
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1733
- Accuracy: 0.9553
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3654 | 1.0 | 370 | 0.3021 | 0.9378 |
| 0.2271 | 2.0 | 740 | 0.2237 | 0.9418 |
| 0.1618 | 3.0 | 1110 | 0.2024 | 0.9472 |
| 0.1535 | 4.0 | 1480 | 0.1923 | 0.9445 |
| 0.1349 | 5.0 | 1850 | 0.1886 | 0.9472 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"sphynx",
"maine coon",
"keeshond",
"yorkshire terrier",
"havanese",
"leonberger",
"wheaten terrier",
"american bulldog",
"english setter",
"boxer",
"newfoundland",
"bengal",
"samoyed",
"british shorthair",
"great pyrenees",
"abyssinian",
"pug",
"saint bernard",
"russian blue",
"scottish terrier"
] |
majorSeaweed/SWIN_BASE_PRETRAINED
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SWIN_BASE_PRETRAINED
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-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
- num_epochs: 10
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"0",
"1",
"2",
"3",
"4"
] |
masafresh/swin-transformer2
|
<!-- This model card 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-transformer2
This model is a fine-tuned version of [microsoft/swin-large-patch4-window12-384](https://huggingface.co/microsoft/swin-large-patch4-window12-384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2129
- Accuracy: 0.6386
- F1: 0.6328
## Model description
More information needed
## Intended uses & 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 1.6336 | 0.9840 | 46 | 1.6510 | 0.2530 | 0.1876 |
| 1.2894 | 1.9893 | 93 | 1.2218 | 0.4458 | 0.3780 |
| 1.0959 | 2.9947 | 140 | 1.1383 | 0.5060 | 0.3518 |
| 1.0467 | 4.0 | 187 | 0.9372 | 0.5542 | 0.4352 |
| 0.9879 | 4.9840 | 233 | 1.0139 | 0.5301 | 0.4718 |
| 0.9086 | 5.9893 | 280 | 0.8822 | 0.6627 | 0.6359 |
| 0.9776 | 6.9947 | 327 | 1.0269 | 0.5542 | 0.5139 |
| 0.9715 | 8.0 | 374 | 0.7964 | 0.5663 | 0.5588 |
| 0.9049 | 8.9840 | 420 | 0.7839 | 0.5904 | 0.5346 |
| 0.8697 | 9.9893 | 467 | 1.0379 | 0.5663 | 0.4921 |
| 0.882 | 10.9947 | 514 | 0.9132 | 0.5663 | 0.5379 |
| 0.832 | 12.0 | 561 | 0.8513 | 0.5783 | 0.5008 |
| 0.7475 | 12.9840 | 607 | 0.7612 | 0.6627 | 0.6427 |
| 0.9056 | 13.9893 | 654 | 0.8431 | 0.6145 | 0.5725 |
| 0.9978 | 14.9947 | 701 | 0.7221 | 0.7108 | 0.6983 |
| 0.6956 | 16.0 | 748 | 0.7545 | 0.6145 | 0.5888 |
| 0.7185 | 16.9840 | 794 | 0.6561 | 0.6627 | 0.6499 |
| 0.8139 | 17.9893 | 841 | 0.7512 | 0.6506 | 0.6386 |
| 0.6837 | 18.9947 | 888 | 0.6491 | 0.6988 | 0.6849 |
| 0.5191 | 20.0 | 935 | 0.7290 | 0.6386 | 0.6336 |
| 0.6538 | 20.9840 | 981 | 0.8000 | 0.6988 | 0.6621 |
| 0.7912 | 21.9893 | 1028 | 1.0183 | 0.6145 | 0.5824 |
| 0.6093 | 22.9947 | 1075 | 0.9124 | 0.6506 | 0.6396 |
| 0.5312 | 24.0 | 1122 | 0.9098 | 0.6024 | 0.5581 |
| 0.6654 | 24.9840 | 1168 | 1.0432 | 0.5422 | 0.5028 |
| 0.5798 | 25.9893 | 1215 | 0.7369 | 0.6627 | 0.6553 |
| 0.506 | 26.9947 | 1262 | 0.9057 | 0.6265 | 0.6236 |
| 0.4638 | 28.0 | 1309 | 0.7950 | 0.6867 | 0.6644 |
| 0.371 | 28.9840 | 1355 | 1.0368 | 0.6627 | 0.6473 |
| 0.4721 | 29.9893 | 1402 | 0.8129 | 0.6747 | 0.6673 |
| 0.54 | 30.9947 | 1449 | 1.0379 | 0.6627 | 0.6491 |
| 0.3978 | 32.0 | 1496 | 1.3857 | 0.5904 | 0.5481 |
| 0.3503 | 32.9840 | 1542 | 1.0920 | 0.6024 | 0.5847 |
| 0.4407 | 33.9893 | 1589 | 1.1912 | 0.5904 | 0.5505 |
| 0.3786 | 34.9947 | 1636 | 1.5071 | 0.6024 | 0.5915 |
| 0.3482 | 36.0 | 1683 | 1.1161 | 0.6386 | 0.6240 |
| 0.2695 | 36.9840 | 1729 | 1.2040 | 0.5904 | 0.5704 |
| 0.2296 | 37.9893 | 1776 | 1.5781 | 0.5181 | 0.4691 |
| 0.2922 | 38.9947 | 1823 | 1.3713 | 0.6024 | 0.5879 |
| 0.1511 | 40.0 | 1870 | 1.1638 | 0.6506 | 0.6553 |
| 0.2814 | 40.9840 | 1916 | 1.3384 | 0.6988 | 0.6939 |
| 0.2196 | 41.9893 | 1963 | 1.2872 | 0.6506 | 0.6330 |
| 0.2477 | 42.9947 | 2010 | 1.5322 | 0.6627 | 0.6375 |
| 0.3296 | 44.0 | 2057 | 1.3479 | 0.6506 | 0.6353 |
| 0.2015 | 44.9840 | 2103 | 1.2521 | 0.6145 | 0.6044 |
| 0.3476 | 45.9893 | 2150 | 1.2464 | 0.6747 | 0.6641 |
| 0.189 | 46.9947 | 2197 | 1.4480 | 0.6506 | 0.6235 |
| 0.1852 | 48.0 | 2244 | 1.3611 | 0.6747 | 0.6594 |
| 0.2798 | 48.9840 | 2290 | 1.4427 | 0.6988 | 0.6957 |
| 0.1523 | 49.9893 | 2337 | 1.3352 | 0.6506 | 0.6450 |
| 0.1224 | 50.9947 | 2384 | 1.8088 | 0.6386 | 0.6201 |
| 0.0926 | 52.0 | 2431 | 1.4695 | 0.6506 | 0.6296 |
| 0.2071 | 52.9840 | 2477 | 1.4673 | 0.6867 | 0.6806 |
| 0.1063 | 53.9893 | 2524 | 1.4862 | 0.7108 | 0.6975 |
| 0.1831 | 54.9947 | 2571 | 1.4666 | 0.6506 | 0.6161 |
| 0.158 | 56.0 | 2618 | 1.8832 | 0.6988 | 0.6673 |
| 0.26 | 56.9840 | 2664 | 1.5855 | 0.6386 | 0.5986 |
| 0.1697 | 57.9893 | 2711 | 1.2184 | 0.7470 | 0.7434 |
| 0.2024 | 58.9947 | 2758 | 1.3524 | 0.6867 | 0.6682 |
| 0.2495 | 60.0 | 2805 | 1.7523 | 0.6627 | 0.6427 |
| 0.1247 | 60.9840 | 2851 | 1.7007 | 0.6506 | 0.6372 |
| 0.1436 | 61.9893 | 2898 | 1.9171 | 0.6386 | 0.6120 |
| 0.1438 | 62.9947 | 2945 | 1.8998 | 0.6265 | 0.5897 |
| 0.1137 | 64.0 | 2992 | 2.4028 | 0.5904 | 0.5498 |
| 0.1619 | 64.9840 | 3038 | 1.7087 | 0.7470 | 0.7473 |
| 0.1105 | 65.9893 | 3085 | 1.6545 | 0.6988 | 0.6975 |
| 0.1597 | 66.9947 | 3132 | 1.8024 | 0.6747 | 0.6758 |
| 0.0338 | 68.0 | 3179 | 1.8962 | 0.6747 | 0.6706 |
| 0.1184 | 68.9840 | 3225 | 2.1642 | 0.7108 | 0.7102 |
| 0.0878 | 69.9893 | 3272 | 2.0974 | 0.6506 | 0.6610 |
| 0.0963 | 70.9947 | 3319 | 1.8719 | 0.7108 | 0.7162 |
| 0.0827 | 72.0 | 3366 | 1.7538 | 0.6988 | 0.7000 |
| 0.0933 | 72.9840 | 3412 | 1.9357 | 0.6988 | 0.6988 |
| 0.0593 | 73.9893 | 3459 | 1.9924 | 0.6506 | 0.6420 |
| 0.0423 | 74.9947 | 3506 | 2.2029 | 0.6627 | 0.6702 |
| 0.0311 | 76.0 | 3553 | 1.9236 | 0.7108 | 0.7155 |
| 0.1881 | 76.9840 | 3599 | 1.9606 | 0.6747 | 0.6787 |
| 0.0566 | 77.9893 | 3646 | 2.1122 | 0.6265 | 0.6206 |
| 0.0266 | 78.9947 | 3693 | 2.1469 | 0.6506 | 0.6536 |
| 0.1015 | 80.0 | 3740 | 2.0335 | 0.6506 | 0.6587 |
| 0.1083 | 80.9840 | 3786 | 2.2123 | 0.6506 | 0.6509 |
| 0.0161 | 81.9893 | 3833 | 2.3094 | 0.6988 | 0.7064 |
| 0.0194 | 82.9947 | 3880 | 2.3315 | 0.6145 | 0.6101 |
| 0.113 | 84.0 | 3927 | 2.5276 | 0.6867 | 0.6908 |
| 0.0653 | 84.9840 | 3973 | 2.0321 | 0.6265 | 0.6263 |
| 0.0684 | 85.9893 | 4020 | 2.0302 | 0.6627 | 0.6706 |
| 0.1724 | 86.9947 | 4067 | 2.5865 | 0.5904 | 0.5860 |
| 0.028 | 88.0 | 4114 | 2.3814 | 0.5904 | 0.5804 |
| 0.0528 | 88.9840 | 4160 | 2.2804 | 0.6386 | 0.6410 |
| 0.0341 | 89.9893 | 4207 | 2.0635 | 0.5783 | 0.5736 |
| 0.0074 | 90.9947 | 4254 | 2.3491 | 0.6024 | 0.5993 |
| 0.0165 | 92.0 | 4301 | 2.2152 | 0.6145 | 0.6036 |
| 0.0157 | 92.9840 | 4347 | 2.3380 | 0.6145 | 0.6036 |
| 0.0544 | 93.9893 | 4394 | 2.3319 | 0.6265 | 0.6221 |
| 0.0577 | 94.9947 | 4441 | 2.2671 | 0.6265 | 0.6221 |
| 0.1516 | 96.0 | 4488 | 2.2034 | 0.6265 | 0.6204 |
| 0.0318 | 96.9840 | 4534 | 2.1932 | 0.6265 | 0.6204 |
| 0.043 | 97.9893 | 4581 | 2.2178 | 0.6265 | 0.6204 |
| 0.0099 | 98.3957 | 4600 | 2.2129 | 0.6386 | 0.6328 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
|
[
"lglo",
"mglo",
"hglo",
"uglo",
"mgso",
"lgso",
"hgso",
"mws",
"mwl"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-002
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-002
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0232
- Accuracy: 0.9787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.0832 | 4.4444 | 10 | 0.4421 | 0.9149 |
| 0.1422 | 8.8889 | 20 | 0.0481 | 1.0 |
| 0.0055 | 13.3333 | 30 | 0.0213 | 1.0 |
| 0.0007 | 17.7778 | 40 | 0.0223 | 0.9787 |
| 0.0003 | 22.2222 | 50 | 0.0205 | 0.9787 |
| 0.0002 | 26.6667 | 60 | 0.0223 | 0.9787 |
| 0.0002 | 31.1111 | 70 | 0.0232 | 0.9787 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-004
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-004
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0309
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.2665 | 4.4444 | 10 | 0.5981 | 0.8936 |
| 0.4646 | 8.8889 | 20 | 0.1825 | 0.9787 |
| 0.135 | 13.3333 | 30 | 0.0772 | 0.9574 |
| 0.0486 | 17.7778 | 40 | 0.0660 | 0.9574 |
| 0.0254 | 22.2222 | 50 | 0.0434 | 0.9574 |
| 0.0108 | 26.6667 | 60 | 0.0309 | 1.0 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
masafresh/swin-transformer3
|
<!-- This model card 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-transformer3
This model is a fine-tuned version of [microsoft/swin-large-patch4-window12-384](https://huggingface.co/microsoft/swin-large-patch4-window12-384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1081
- Accuracy: 0.5667
- F1: 0.5667
## Model description
More information needed
## Intended uses & 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 1.6659 | 0.9925 | 33 | 1.0639 | 0.6333 | 0.6250 |
| 0.7561 | 1.9850 | 66 | 0.7258 | 0.5167 | 0.3520 |
| 0.7106 | 2.9774 | 99 | 0.7334 | 0.5 | 0.3755 |
| 0.6749 | 4.0 | 133 | 0.7088 | 0.4833 | 0.3661 |
| 0.751 | 4.9925 | 166 | 0.7356 | 0.4833 | 0.3661 |
| 0.7146 | 5.9850 | 199 | 0.7837 | 0.4833 | 0.3150 |
| 0.6699 | 6.9774 | 232 | 0.7569 | 0.4833 | 0.3424 |
| 0.6521 | 8.0 | 266 | 0.7255 | 0.5333 | 0.4674 |
| 0.6885 | 8.9925 | 299 | 0.7253 | 0.5167 | 0.4070 |
| 0.6407 | 9.9850 | 332 | 0.6506 | 0.6 | 0.5909 |
| 0.6436 | 10.9774 | 365 | 0.6720 | 0.55 | 0.4442 |
| 0.7865 | 12.0 | 399 | 0.6606 | 0.55 | 0.4792 |
| 0.7191 | 12.9925 | 432 | 0.6407 | 0.65 | 0.6466 |
| 0.5889 | 13.9850 | 465 | 0.8008 | 0.4833 | 0.3619 |
| 0.5489 | 14.9774 | 498 | 0.7298 | 0.5333 | 0.4674 |
| 0.596 | 16.0 | 532 | 0.7465 | 0.6667 | 0.6591 |
| 0.6136 | 16.9925 | 565 | 0.9118 | 0.5333 | 0.4692 |
| 0.5961 | 17.9850 | 598 | 0.6902 | 0.65 | 0.6298 |
| 0.6327 | 18.9774 | 631 | 0.8260 | 0.5667 | 0.5190 |
| 0.6518 | 20.0 | 665 | 0.6919 | 0.5833 | 0.5715 |
| 0.5551 | 20.9925 | 698 | 1.1780 | 0.55 | 0.516 |
| 0.511 | 21.9850 | 731 | 0.7414 | 0.6 | 0.6 |
| 0.4749 | 22.9774 | 764 | 0.7978 | 0.6167 | 0.6129 |
| 0.4607 | 24.0 | 798 | 0.8087 | 0.55 | 0.5420 |
| 0.5837 | 24.9925 | 831 | 0.8271 | 0.5667 | 0.5456 |
| 0.4608 | 25.9850 | 864 | 0.8539 | 0.6 | 0.5863 |
| 0.536 | 26.9774 | 897 | 0.9802 | 0.5333 | 0.5026 |
| 0.4225 | 28.0 | 931 | 0.9275 | 0.6 | 0.5910 |
| 0.4325 | 28.9925 | 964 | 0.8834 | 0.6167 | 0.6099 |
| 0.4874 | 29.9850 | 997 | 0.8721 | 0.6167 | 0.6168 |
| 0.4165 | 30.9774 | 1030 | 1.0360 | 0.6167 | 0.6163 |
| 0.4773 | 32.0 | 1064 | 1.2210 | 0.5833 | 0.5759 |
| 0.3756 | 32.9925 | 1097 | 1.1291 | 0.5833 | 0.5830 |
| 0.636 | 33.9850 | 1130 | 1.0178 | 0.5833 | 0.5830 |
| 0.5474 | 34.9774 | 1163 | 0.9479 | 0.5667 | 0.5608 |
| 0.3462 | 36.0 | 1197 | 0.9585 | 0.6167 | 0.6163 |
| 0.3057 | 36.9925 | 1230 | 1.2014 | 0.6167 | 0.6163 |
| 0.2304 | 37.9850 | 1263 | 1.1975 | 0.6333 | 0.6333 |
| 0.2628 | 38.9774 | 1296 | 1.5224 | 0.5833 | 0.5793 |
| 0.3774 | 40.0 | 1330 | 1.2903 | 0.5667 | 0.5516 |
| 0.2604 | 40.9925 | 1363 | 1.4082 | 0.5667 | 0.5608 |
| 0.2522 | 41.9850 | 1396 | 1.1783 | 0.6167 | 0.6163 |
| 0.1925 | 42.9774 | 1429 | 1.3613 | 0.6167 | 0.6163 |
| 0.3436 | 44.0 | 1463 | 1.6383 | 0.5333 | 0.5173 |
| 0.1955 | 44.9925 | 1496 | 1.8947 | 0.5 | 0.4829 |
| 0.2206 | 45.9850 | 1529 | 1.4390 | 0.6 | 0.6 |
| 0.1912 | 46.9774 | 1562 | 1.5288 | 0.65 | 0.6400 |
| 0.2794 | 48.0 | 1596 | 1.7393 | 0.55 | 0.5420 |
| 0.3166 | 48.9925 | 1629 | 2.0414 | 0.5667 | 0.5608 |
| 0.173 | 49.9850 | 1662 | 1.6377 | 0.6 | 0.5991 |
| 0.1375 | 50.9774 | 1695 | 1.6228 | 0.6 | 0.6 |
| 0.2659 | 52.0 | 1729 | 1.6452 | 0.6333 | 0.6333 |
| 0.2045 | 52.9925 | 1762 | 1.9706 | 0.5667 | 0.5608 |
| 0.1081 | 53.9850 | 1795 | 1.9546 | 0.6167 | 0.6009 |
| 0.1782 | 54.9774 | 1828 | 2.1268 | 0.5667 | 0.5608 |
| 0.244 | 56.0 | 1862 | 1.8301 | 0.6167 | 0.6098 |
| 0.1783 | 56.9925 | 1895 | 2.5808 | 0.5667 | 0.5071 |
| 0.2429 | 57.9850 | 1928 | 2.1214 | 0.6167 | 0.6059 |
| 0.2 | 58.9774 | 1961 | 2.2282 | 0.5667 | 0.5657 |
| 0.1646 | 60.0 | 1995 | 2.3272 | 0.5833 | 0.5662 |
| 0.1663 | 60.9925 | 2028 | 2.4723 | 0.5333 | 0.5323 |
| 0.1935 | 61.9850 | 2061 | 2.3384 | 0.6 | 0.5973 |
| 0.2079 | 62.9774 | 2094 | 1.9271 | 0.5833 | 0.5830 |
| 0.1797 | 64.0 | 2128 | 1.8707 | 0.6167 | 0.6151 |
| 0.173 | 64.9925 | 2161 | 2.6292 | 0.5167 | 0.5031 |
| 0.1815 | 65.9850 | 2194 | 2.6567 | 0.6 | 0.5973 |
| 0.0665 | 66.9774 | 2227 | 3.2104 | 0.5167 | 0.5031 |
| 0.1084 | 68.0 | 2261 | 3.6692 | 0.5333 | 0.5228 |
| 0.1298 | 68.9925 | 2294 | 3.4104 | 0.55 | 0.5373 |
| 0.1338 | 69.9850 | 2327 | 2.8215 | 0.6 | 0.5973 |
| 0.0795 | 70.9774 | 2360 | 2.9208 | 0.5833 | 0.5830 |
| 0.1138 | 72.0 | 2394 | 3.4277 | 0.5333 | 0.5302 |
| 0.1644 | 72.9925 | 2427 | 2.8141 | 0.5833 | 0.5830 |
| 0.1659 | 73.9850 | 2460 | 2.8723 | 0.6 | 0.6 |
| 0.0453 | 74.9774 | 2493 | 2.8769 | 0.6333 | 0.6309 |
| 0.0956 | 76.0 | 2527 | 3.2970 | 0.6167 | 0.6098 |
| 0.1581 | 76.9925 | 2560 | 3.6672 | 0.5833 | 0.5816 |
| 0.157 | 77.9850 | 2593 | 3.5317 | 0.55 | 0.5501 |
| 0.0662 | 78.9774 | 2626 | 3.9003 | 0.55 | 0.5456 |
| 0.1954 | 80.0 | 2660 | 3.3000 | 0.5833 | 0.5834 |
| 0.0527 | 80.9925 | 2693 | 3.9596 | 0.5667 | 0.5638 |
| 0.1578 | 81.9850 | 2726 | 3.6724 | 0.55 | 0.5481 |
| 0.0737 | 82.9774 | 2759 | 4.0222 | 0.5167 | 0.5119 |
| 0.0617 | 84.0 | 2793 | 3.5510 | 0.5833 | 0.5834 |
| 0.0531 | 84.9925 | 2826 | 3.5110 | 0.6 | 0.6 |
| 0.0993 | 85.9850 | 2859 | 4.0699 | 0.55 | 0.5481 |
| 0.1545 | 86.9774 | 2892 | 3.6860 | 0.5667 | 0.5667 |
| 0.0554 | 88.0 | 2926 | 3.4409 | 0.6 | 0.6 |
| 0.0641 | 88.9925 | 2959 | 3.8304 | 0.55 | 0.5496 |
| 0.0633 | 89.9850 | 2992 | 4.0899 | 0.55 | 0.5456 |
| 0.0991 | 90.9774 | 3025 | 3.7344 | 0.6 | 0.6 |
| 0.0772 | 92.0 | 3059 | 3.8448 | 0.6 | 0.5991 |
| 0.0646 | 92.9925 | 3092 | 3.7794 | 0.6 | 0.5991 |
| 0.0562 | 93.9850 | 3125 | 3.9340 | 0.5833 | 0.5830 |
| 0.0475 | 94.9774 | 3158 | 4.2388 | 0.55 | 0.5481 |
| 0.0715 | 96.0 | 3192 | 4.2732 | 0.5333 | 0.5302 |
| 0.0875 | 96.9925 | 3225 | 4.1521 | 0.5667 | 0.5657 |
| 0.0253 | 97.9850 | 3258 | 4.0813 | 0.5667 | 0.5667 |
| 0.1037 | 98.9774 | 3291 | 4.1074 | 0.5667 | 0.5667 |
| 0.1094 | 99.2481 | 3300 | 4.1081 | 0.5667 | 0.5667 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
|
[
"lglo",
"mglo",
"hglo",
"uglo",
"mgso",
"lgso",
"hgso",
"mws",
"mwl"
] |
theofilusdf/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7049
- Accuracy: 0.3875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.9292 | 0.2625 |
| No log | 2.0 | 80 | 1.7516 | 0.3187 |
| No log | 3.0 | 120 | 1.7049 | 0.3875 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-006
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-006
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1766
- Accuracy: 0.94
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1261 | 4.4444 | 10 | 0.5915 | 0.84 |
| 0.3737 | 8.8889 | 20 | 0.1990 | 0.94 |
| 0.1009 | 13.3333 | 30 | 0.1418 | 0.94 |
| 0.0351 | 17.7778 | 40 | 0.1632 | 0.94 |
| 0.02 | 22.2222 | 50 | 0.1713 | 0.94 |
| 0.0117 | 26.6667 | 60 | 0.1766 | 0.94 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
haywoodsloan/ai-image-detector-deploy
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.09541802108287811
f1: 0.9853826686447335
precision: 0.9808886765408504
recall: 0.9899180291938807
auc: 0.9957081876919603
accuracy: 0.9794339738473816
|
[
"artificial",
"real"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-009
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-009
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0997
- Accuracy: 0.96
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1702 | 4.4444 | 10 | 0.5499 | 0.94 |
| 0.1925 | 8.8889 | 20 | 0.1645 | 0.94 |
| 0.0112 | 13.3333 | 30 | 0.0997 | 0.96 |
| 0.0011 | 17.7778 | 40 | 0.1255 | 0.96 |
| 0.0005 | 22.2222 | 50 | 0.1313 | 0.96 |
| 0.0004 | 26.6667 | 60 | 0.1238 | 0.96 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
masafresh/vit-transformer3
|
<!-- This model card 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-transformer3
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8890
- Accuracy: 0.6833
- F1: 0.6840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 1.8752 | 0.9552 | 16 | 0.9886 | 0.6667 | 0.6484 |
| 0.7728 | 1.9701 | 33 | 0.6862 | 0.5667 | 0.4099 |
| 0.7065 | 2.9851 | 50 | 0.6627 | 0.6333 | 0.6132 |
| 0.6845 | 4.0 | 67 | 0.7065 | 0.55 | 0.4922 |
| 0.6513 | 4.9552 | 83 | 0.7202 | 0.4667 | 0.3905 |
| 0.6567 | 5.9701 | 100 | 0.7677 | 0.5333 | 0.4667 |
| 0.6539 | 6.9851 | 117 | 0.6269 | 0.6167 | 0.6047 |
| 0.7025 | 8.0 | 134 | 0.6838 | 0.65 | 0.6107 |
| 0.6698 | 8.9552 | 150 | 0.6313 | 0.6667 | 0.6337 |
| 0.6986 | 9.9701 | 167 | 0.6200 | 0.6667 | 0.6484 |
| 0.6811 | 10.9851 | 184 | 0.5869 | 0.6833 | 0.6840 |
| 0.6132 | 12.0 | 201 | 0.5881 | 0.6833 | 0.6687 |
| 0.7235 | 12.9552 | 217 | 0.5732 | 0.65 | 0.6274 |
| 0.5768 | 13.9701 | 234 | 0.5802 | 0.6833 | 0.6825 |
| 0.5307 | 14.9851 | 251 | 0.6610 | 0.7 | 0.7010 |
| 0.552 | 16.0 | 268 | 0.6229 | 0.7333 | 0.7296 |
| 0.5548 | 16.9552 | 284 | 0.6186 | 0.7167 | 0.7036 |
| 0.4863 | 17.9701 | 301 | 0.8409 | 0.5667 | 0.5366 |
| 0.5048 | 18.9851 | 318 | 1.0019 | 0.4833 | 0.4015 |
| 0.4919 | 20.0 | 335 | 0.6475 | 0.7333 | 0.7333 |
| 0.4788 | 20.9552 | 351 | 0.6931 | 0.6333 | 0.6282 |
| 0.5076 | 21.9701 | 368 | 0.6798 | 0.7 | 0.6983 |
| 0.5047 | 22.9851 | 385 | 0.6784 | 0.7 | 0.7 |
| 0.3477 | 24.0 | 402 | 0.8261 | 0.7 | 0.6983 |
| 0.4508 | 24.9552 | 418 | 0.6846 | 0.6833 | 0.6825 |
| 0.4948 | 25.9701 | 435 | 0.7509 | 0.6833 | 0.6804 |
| 0.3661 | 26.9851 | 452 | 0.7321 | 0.6667 | 0.6678 |
| 0.3072 | 28.0 | 469 | 0.8338 | 0.6833 | 0.6839 |
| 0.3573 | 28.9552 | 485 | 0.9031 | 0.65 | 0.6434 |
| 0.3828 | 29.9701 | 502 | 0.8582 | 0.6667 | 0.6667 |
| 0.2931 | 30.9851 | 519 | 0.7648 | 0.65 | 0.6515 |
| 0.3193 | 32.0 | 536 | 0.9218 | 0.6333 | 0.6333 |
| 0.2783 | 32.9552 | 552 | 0.8452 | 0.7 | 0.7013 |
| 0.2816 | 33.9701 | 569 | 0.8310 | 0.6833 | 0.6735 |
| 0.3018 | 34.9851 | 586 | 0.8437 | 0.7 | 0.6960 |
| 0.2256 | 36.0 | 603 | 1.0684 | 0.65 | 0.6507 |
| 0.2609 | 36.9552 | 619 | 0.9117 | 0.65 | 0.6491 |
| 0.2198 | 37.9701 | 636 | 1.1688 | 0.5833 | 0.5652 |
| 0.306 | 38.9851 | 653 | 0.9001 | 0.6167 | 0.6130 |
| 0.2243 | 40.0 | 670 | 1.2253 | 0.6333 | 0.6313 |
| 0.3482 | 40.9552 | 686 | 1.0028 | 0.65 | 0.6491 |
| 0.196 | 41.9701 | 703 | 0.8747 | 0.6667 | 0.6682 |
| 0.2261 | 42.9851 | 720 | 1.3642 | 0.65 | 0.6468 |
| 0.2802 | 44.0 | 737 | 1.3271 | 0.5833 | 0.5704 |
| 0.1965 | 44.9552 | 753 | 1.3784 | 0.6 | 0.6018 |
| 0.2198 | 45.9701 | 770 | 1.3224 | 0.6667 | 0.6682 |
| 0.1852 | 46.9851 | 787 | 1.5364 | 0.6333 | 0.6243 |
| 0.197 | 48.0 | 804 | 1.5706 | 0.6167 | 0.6174 |
| 0.1932 | 48.9552 | 820 | 1.3610 | 0.6667 | 0.6648 |
| 0.1495 | 49.9701 | 837 | 1.4687 | 0.6167 | 0.6174 |
| 0.1404 | 50.9851 | 854 | 1.3438 | 0.7 | 0.6983 |
| 0.1275 | 52.0 | 871 | 1.4674 | 0.6 | 0.5978 |
| 0.1545 | 52.9552 | 887 | 1.3120 | 0.6167 | 0.6183 |
| 0.147 | 53.9701 | 904 | 1.5816 | 0.6167 | 0.6183 |
| 0.1541 | 54.9851 | 921 | 1.5117 | 0.6667 | 0.6678 |
| 0.1283 | 56.0 | 938 | 1.5965 | 0.6667 | 0.6678 |
| 0.1715 | 56.9552 | 954 | 1.6750 | 0.65 | 0.6491 |
| 0.1513 | 57.9701 | 971 | 1.9170 | 0.5333 | 0.5164 |
| 0.2349 | 58.9851 | 988 | 1.5358 | 0.6333 | 0.6346 |
| 0.1248 | 60.0 | 1005 | 1.6686 | 0.6833 | 0.6840 |
| 0.1076 | 60.9552 | 1021 | 1.7018 | 0.6333 | 0.6346 |
| 0.1431 | 61.9701 | 1038 | 1.9088 | 0.6333 | 0.6333 |
| 0.0838 | 62.9851 | 1055 | 1.8821 | 0.6333 | 0.6346 |
| 0.0989 | 64.0 | 1072 | 1.6053 | 0.65 | 0.6491 |
| 0.1323 | 64.9552 | 1088 | 1.7114 | 0.6333 | 0.6312 |
| 0.0908 | 65.9701 | 1105 | 1.7326 | 0.65 | 0.6491 |
| 0.2056 | 66.9851 | 1122 | 1.7166 | 0.6167 | 0.6130 |
| 0.0752 | 68.0 | 1139 | 1.8009 | 0.65 | 0.6467 |
| 0.1116 | 68.9552 | 1155 | 1.6964 | 0.6667 | 0.6678 |
| 0.0821 | 69.9701 | 1172 | 1.7557 | 0.6167 | 0.6094 |
| 0.1284 | 70.9851 | 1189 | 1.8039 | 0.65 | 0.6491 |
| 0.1905 | 72.0 | 1206 | 1.7951 | 0.6167 | 0.6094 |
| 0.1031 | 72.9552 | 1222 | 1.6888 | 0.6667 | 0.6648 |
| 0.0706 | 73.9701 | 1239 | 1.8992 | 0.65 | 0.6467 |
| 0.0944 | 74.9851 | 1256 | 1.6965 | 0.6833 | 0.6840 |
| 0.1042 | 76.0 | 1273 | 1.6756 | 0.6833 | 0.6825 |
| 0.1599 | 76.9552 | 1289 | 1.4360 | 0.7333 | 0.7342 |
| 0.0896 | 77.9701 | 1306 | 1.5759 | 0.65 | 0.6467 |
| 0.0674 | 78.9851 | 1323 | 1.7071 | 0.7 | 0.7010 |
| 0.1133 | 80.0 | 1340 | 1.6499 | 0.6833 | 0.6840 |
| 0.0506 | 80.9552 | 1356 | 1.6546 | 0.6833 | 0.6825 |
| 0.1015 | 81.9701 | 1373 | 1.6468 | 0.7 | 0.7013 |
| 0.0923 | 82.9851 | 1390 | 1.8567 | 0.6667 | 0.6622 |
| 0.0752 | 84.0 | 1407 | 1.8140 | 0.7 | 0.7010 |
| 0.0768 | 84.9552 | 1423 | 1.8225 | 0.6667 | 0.6678 |
| 0.0683 | 85.9701 | 1440 | 1.8094 | 0.6833 | 0.6840 |
| 0.0454 | 86.9851 | 1457 | 1.8892 | 0.65 | 0.6491 |
| 0.054 | 88.0 | 1474 | 1.8180 | 0.7 | 0.7010 |
| 0.0449 | 88.9552 | 1490 | 1.7891 | 0.7333 | 0.7345 |
| 0.0645 | 89.9701 | 1507 | 1.8262 | 0.7 | 0.7010 |
| 0.0632 | 90.9851 | 1524 | 1.8187 | 0.7167 | 0.7179 |
| 0.0795 | 92.0 | 1541 | 1.7941 | 0.7333 | 0.7345 |
| 0.0923 | 92.9552 | 1557 | 1.8340 | 0.6833 | 0.6840 |
| 0.0486 | 93.9701 | 1574 | 1.8843 | 0.6667 | 0.6667 |
| 0.0821 | 94.9851 | 1591 | 1.8907 | 0.6667 | 0.6667 |
| 0.0384 | 95.5224 | 1600 | 1.8890 | 0.6833 | 0.6840 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
|
[
"lglo",
"mglo",
"hglo",
"uglo",
"mgso",
"lgso",
"hgso",
"mws",
"mwl"
] |
SABR22/food_models
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# food_models
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: 0.7386
- Accuracy: 0.8545
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7386 | 0.9994 | 1183 | 1.5360 | 0.7945 |
| 1.0097 | 1.9998 | 2367 | 0.8811 | 0.8401 |
| 0.8608 | 2.9985 | 3549 | 0.7386 | 0.8545 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
Soponnnn/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. -->
# Soponnnn/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: 0.3916
- Validation Loss: 0.3630
- Train Accuracy: 0.916
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8264 | 1.7259 | 0.779 | 0 |
| 1.2602 | 0.8512 | 0.871 | 1 |
| 0.7141 | 0.5674 | 0.885 | 2 |
| 0.5119 | 0.4395 | 0.908 | 3 |
| 0.3916 | 0.3630 | 0.916 | 4 |
### Framework versions
- Transformers 4.46.2
- TensorFlow 2.17.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
SABR22/ViT-threat-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-threat-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 a threat classification dataset.
This model was created for a Carleton University computer vision hacking event and serves as a proof of concept rather than complete model. It is trained on an extremely small and limited dataset and the performance is limited.
It achieves the following results on the evaluation set:
- Loss: 0.4568
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.328 | 0.9756 | 10 | 0.4556 | 0.875 |
| 0.3226 | 1.9512 | 20 | 0.4736 | 0.75 |
| 0.3619 | 2.9268 | 30 | 0.4568 | 1.0 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"non-threat",
"threat"
] |
cvmil/vit-base-patch16-224_rice-disease-02
|
<!-- This model card 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_rice-disease-02_111724
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: 0.3312
- Accuracy: 0.9029
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9444 | 1.0 | 423 | 1.3919 | 0.6420 |
| 0.9896 | 2.0 | 846 | 0.7862 | 0.7838 |
| 0.6372 | 3.0 | 1269 | 0.6040 | 0.8164 |
| 0.5079 | 4.0 | 1692 | 0.5136 | 0.8450 |
| 0.4377 | 5.0 | 2115 | 0.4580 | 0.8623 |
| 0.3922 | 6.0 | 2538 | 0.4210 | 0.8769 |
| 0.3608 | 7.0 | 2961 | 0.3966 | 0.8809 |
| 0.3386 | 8.0 | 3384 | 0.3762 | 0.8882 |
| 0.3207 | 9.0 | 3807 | 0.3641 | 0.8916 |
| 0.3078 | 10.0 | 4230 | 0.3519 | 0.8935 |
| 0.2975 | 11.0 | 4653 | 0.3441 | 0.8969 |
| 0.2898 | 12.0 | 5076 | 0.3380 | 0.9009 |
| 0.2845 | 13.0 | 5499 | 0.3341 | 0.9029 |
| 0.2805 | 14.0 | 5922 | 0.3319 | 0.9035 |
| 0.2786 | 15.0 | 6345 | 0.3312 | 0.9029 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"bacterial_leaf_blight",
"brown_spot",
"healthy",
"leaf_blast",
"leaf_scald",
"narrow_brown_spot",
"neck_blast",
"rice_hispa",
"sheath_blight",
"tungro"
] |
cvmil/beit-base-patch16-224_rice-disease-02
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beit-base-patch16-224_rice-disease-02_111724
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: 0.1321
- Accuracy: 0.9574
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3364 | 1.0 | 845 | 0.5174 | 0.8430 |
| 0.344 | 2.0 | 1690 | 0.2503 | 0.9222 |
| 0.2076 | 3.0 | 2535 | 0.1983 | 0.9375 |
| 0.1649 | 4.0 | 3380 | 0.1730 | 0.9468 |
| 0.1443 | 5.0 | 4225 | 0.1581 | 0.9528 |
| 0.1261 | 6.0 | 5070 | 0.1544 | 0.9554 |
| 0.1166 | 7.0 | 5915 | 0.1498 | 0.9528 |
| 0.1097 | 8.0 | 6760 | 0.1479 | 0.9554 |
| 0.1017 | 9.0 | 7605 | 0.1477 | 0.9501 |
| 0.1016 | 10.0 | 8450 | 0.1382 | 0.9561 |
| 0.0946 | 11.0 | 9295 | 0.1362 | 0.9574 |
| 0.0934 | 12.0 | 10140 | 0.1330 | 0.9587 |
| 0.0903 | 13.0 | 10985 | 0.1330 | 0.9548 |
| 0.0863 | 14.0 | 11830 | 0.1323 | 0.9568 |
| 0.0877 | 15.0 | 12675 | 0.1321 | 0.9574 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"bacterial_leaf_blight",
"brown_spot",
"healthy",
"leaf_blast",
"leaf_scald",
"narrow_brown_spot",
"neck_blast",
"rice_hispa",
"sheath_blight",
"tungro"
] |
theofilusdf/emotion-classifier
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion-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:
- Loss: 1.9405
- Accuracy: 0.2938
## Model description
More information needed
## Intended uses & 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: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 2.0322 | 0.2 |
| No log | 2.0 | 80 | 1.9634 | 0.2562 |
| No log | 3.0 | 120 | 1.9405 | 0.2938 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Tokenizers 0.20.3
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
cvmil/resnet-50_rice-disease-02
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-50_rice-disease-02_111724
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6774
- Accuracy: 0.8044
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1567 | 1.0 | 212 | 1.9092 | 0.5476 |
| 1.6124 | 2.0 | 424 | 1.3708 | 0.6773 |
| 1.2221 | 3.0 | 636 | 1.1384 | 0.7186 |
| 1.0356 | 4.0 | 848 | 0.9888 | 0.7339 |
| 0.9297 | 5.0 | 1060 | 0.9108 | 0.7425 |
| 0.8599 | 6.0 | 1272 | 0.8448 | 0.7538 |
| 0.8082 | 7.0 | 1484 | 0.8129 | 0.7645 |
| 0.7648 | 8.0 | 1696 | 0.7604 | 0.7864 |
| 0.7368 | 9.0 | 1908 | 0.7597 | 0.7738 |
| 0.7092 | 10.0 | 2120 | 0.7230 | 0.7884 |
| 0.6928 | 11.0 | 2332 | 0.7014 | 0.7884 |
| 0.6797 | 12.0 | 2544 | 0.6970 | 0.7917 |
| 0.6686 | 13.0 | 2756 | 0.6933 | 0.8017 |
| 0.6642 | 14.0 | 2968 | 0.6813 | 0.8024 |
| 0.6601 | 15.0 | 3180 | 0.6774 | 0.8044 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"bacterial_leaf_blight",
"brown_spot",
"healthy",
"leaf_blast",
"leaf_scald",
"narrow_brown_spot",
"neck_blast",
"rice_hispa",
"sheath_blight",
"tungro"
] |
damelia/emotion_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. -->
# emotion_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3105
- Accuracy: 0.5188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0819 | 1.0 | 10 | 2.0549 | 0.2375 |
| 2.0249 | 2.0 | 20 | 1.9696 | 0.3625 |
| 1.8988 | 3.0 | 30 | 1.8123 | 0.3937 |
| 1.7331 | 4.0 | 40 | 1.6707 | 0.4375 |
| 1.5894 | 5.0 | 50 | 1.5504 | 0.4938 |
| 1.4997 | 6.0 | 60 | 1.4963 | 0.5188 |
| 1.424 | 7.0 | 70 | 1.4749 | 0.4688 |
| 1.3576 | 8.0 | 80 | 1.4223 | 0.5125 |
| 1.2986 | 9.0 | 90 | 1.3850 | 0.5312 |
| 1.2358 | 10.0 | 100 | 1.3588 | 0.5375 |
| 1.2052 | 11.0 | 110 | 1.3226 | 0.55 |
| 1.1699 | 12.0 | 120 | 1.3446 | 0.525 |
| 1.1334 | 13.0 | 130 | 1.3223 | 0.525 |
| 1.1178 | 14.0 | 140 | 1.3089 | 0.575 |
| 1.1062 | 15.0 | 150 | 1.2776 | 0.5625 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Tokenizers 0.20.3
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
tdhcuong/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: 0.0701
- Accuracy: 0.9737
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2713 | 1.0 | 190 | 0.1596 | 0.9470 |
| 0.1888 | 2.0 | 380 | 0.0995 | 0.9644 |
| 0.1643 | 3.0 | 570 | 0.0701 | 0.9737 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
ArjTheHacker/vit_detection_of_retinology
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4"
] |
Docty/Blood-Cell
|
<!-- 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. -->
# Blood-Cell
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9897
- Validation Loss: 1.9904
- Train Accuracy: 0.3905
- 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': 10, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.9904 | 1.9904 | 0.3905 | 0 |
| 1.9894 | 1.9904 | 0.3905 | 1 |
| 1.9898 | 1.9904 | 0.3905 | 2 |
| 1.9897 | 1.9904 | 0.3905 | 3 |
| 1.9897 | 1.9904 | 0.3905 | 4 |
| 1.9901 | 1.9904 | 0.3905 | 5 |
| 1.9897 | 1.9904 | 0.3905 | 6 |
| 1.9897 | 1.9904 | 0.3905 | 7 |
| 1.9902 | 1.9904 | 0.3905 | 8 |
| 1.9897 | 1.9904 | 0.3905 | 9 |
### Framework versions
- Transformers 4.46.2
- TensorFlow 2.17.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"monocyte",
"ig",
"neutrophil",
"basophil",
"lymphocyte",
"erythroblast",
"eosinophil",
"platelet"
] |
RenSurii/vit-base-patch16-224-in21k-finetuned-image-classification
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# RenSurii/vit-base-patch16-224-in21k-finetuned-image-classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the mnist dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3712
- Train Accuracy: 0.9621
- Validation Loss: 0.3312
- Validation Accuracy: 0.9621
- Epoch: 5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': 1.0, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 2.0107 | 0.8548 | 1.5288 | 0.8548 | 0 |
| 1.3538 | 0.9149 | 0.9913 | 0.9149 | 1 |
| 0.9517 | 0.934 | 0.7421 | 0.9340 | 2 |
| 0.6882 | 0.9467 | 0.5690 | 0.9467 | 3 |
| 0.4999 | 0.9554 | 0.4264 | 0.9554 | 4 |
| 0.3712 | 0.9621 | 0.3312 | 0.9621 | 5 |
### Framework versions
- Transformers 4.47.0.dev0
- TensorFlow 2.18.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
masafresh/swin-transformer-class
|
<!-- This model card 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-class
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2549
- Accuracy: 0.4953
- F1: 0.4547
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:--------:|:----:|:---------------:|:--------:|:------:|
| 2.1381 | 0.9748 | 29 | 2.1103 | 0.2594 | 0.1420 |
| 1.9462 | 1.9832 | 59 | 1.8963 | 0.2783 | 0.1481 |
| 1.7299 | 2.9916 | 89 | 1.6978 | 0.3066 | 0.2504 |
| 1.6406 | 4.0 | 119 | 1.5954 | 0.3585 | 0.3221 |
| 1.5067 | 4.9748 | 148 | 1.5339 | 0.3915 | 0.3527 |
| 1.4566 | 5.9832 | 178 | 1.4972 | 0.4151 | 0.3769 |
| 1.4487 | 6.9916 | 208 | 1.4635 | 0.4387 | 0.3369 |
| 1.4335 | 8.0 | 238 | 1.4377 | 0.4481 | 0.3958 |
| 1.3974 | 8.9748 | 267 | 1.4213 | 0.4623 | 0.4066 |
| 1.3542 | 9.9832 | 297 | 1.4004 | 0.4575 | 0.4090 |
| 1.2964 | 10.9916 | 327 | 1.3880 | 0.4434 | 0.3832 |
| 1.3073 | 12.0 | 357 | 1.3716 | 0.4906 | 0.4449 |
| 1.3256 | 12.9748 | 386 | 1.3664 | 0.4528 | 0.4175 |
| 1.2867 | 13.9832 | 416 | 1.3622 | 0.4434 | 0.4033 |
| 1.3096 | 14.9916 | 446 | 1.3418 | 0.4764 | 0.4281 |
| 1.3012 | 16.0 | 476 | 1.3321 | 0.4528 | 0.4161 |
| 1.3086 | 16.9748 | 505 | 1.3248 | 0.4481 | 0.3578 |
| 1.2646 | 17.9832 | 535 | 1.3164 | 0.4717 | 0.4269 |
| 1.2647 | 18.9916 | 565 | 1.3140 | 0.4811 | 0.4394 |
| 1.2673 | 20.0 | 595 | 1.3073 | 0.4670 | 0.4311 |
| 1.2649 | 20.9748 | 624 | 1.2999 | 0.4906 | 0.4319 |
| 1.2721 | 21.9832 | 654 | 1.3007 | 0.4764 | 0.4236 |
| 1.317 | 22.9916 | 684 | 1.2982 | 0.4670 | 0.4167 |
| 1.2397 | 24.0 | 714 | 1.3031 | 0.4623 | 0.4115 |
| 1.209 | 24.9748 | 743 | 1.3075 | 0.4811 | 0.4379 |
| 1.1994 | 25.9832 | 773 | 1.3091 | 0.4245 | 0.3765 |
| 1.2695 | 26.9916 | 803 | 1.3017 | 0.4717 | 0.4362 |
| 1.2167 | 28.0 | 833 | 1.2986 | 0.4575 | 0.4153 |
| 1.234 | 28.9748 | 862 | 1.3082 | 0.4292 | 0.3773 |
| 1.2726 | 29.9832 | 892 | 1.3003 | 0.4670 | 0.4238 |
| 1.207 | 30.9916 | 922 | 1.2964 | 0.4670 | 0.4260 |
| 1.1534 | 32.0 | 952 | 1.3059 | 0.4292 | 0.3727 |
| 1.2477 | 32.9748 | 981 | 1.2924 | 0.4858 | 0.4397 |
| 1.2202 | 33.9832 | 1011 | 1.2924 | 0.4623 | 0.3850 |
| 1.2248 | 34.9916 | 1041 | 1.2969 | 0.4434 | 0.3680 |
| 1.1775 | 36.0 | 1071 | 1.2848 | 0.4953 | 0.4485 |
| 1.2401 | 36.9748 | 1100 | 1.2887 | 0.4575 | 0.4214 |
| 1.2311 | 37.9832 | 1130 | 1.2838 | 0.4858 | 0.4420 |
| 1.2143 | 38.9916 | 1160 | 1.2846 | 0.4906 | 0.4354 |
| 1.1548 | 40.0 | 1190 | 1.2828 | 0.4481 | 0.4057 |
| 1.1405 | 40.9748 | 1219 | 1.2878 | 0.4717 | 0.4356 |
| 1.1957 | 41.9832 | 1249 | 1.2839 | 0.4528 | 0.4063 |
| 1.211 | 42.9916 | 1279 | 1.2853 | 0.4670 | 0.4097 |
| 1.1849 | 44.0 | 1309 | 1.2779 | 0.4811 | 0.4360 |
| 1.1466 | 44.9748 | 1338 | 1.2765 | 0.4764 | 0.4341 |
| 1.1386 | 45.9832 | 1368 | 1.2836 | 0.4623 | 0.4184 |
| 1.2258 | 46.9916 | 1398 | 1.2718 | 0.4717 | 0.4293 |
| 1.2139 | 48.0 | 1428 | 1.2695 | 0.4906 | 0.4409 |
| 1.1938 | 48.9748 | 1457 | 1.2737 | 0.4764 | 0.4385 |
| 1.2171 | 49.9832 | 1487 | 1.2709 | 0.4670 | 0.4189 |
| 1.1804 | 50.9916 | 1517 | 1.2657 | 0.4764 | 0.4327 |
| 1.143 | 52.0 | 1547 | 1.2701 | 0.4764 | 0.4345 |
| 1.1723 | 52.9748 | 1576 | 1.2783 | 0.4717 | 0.4152 |
| 1.1454 | 53.9832 | 1606 | 1.2670 | 0.5047 | 0.4496 |
| 1.1957 | 54.9916 | 1636 | 1.2709 | 0.4670 | 0.4211 |
| 1.2383 | 56.0 | 1666 | 1.2752 | 0.4670 | 0.4136 |
| 1.1935 | 56.9748 | 1695 | 1.2670 | 0.4623 | 0.4201 |
| 1.159 | 57.9832 | 1725 | 1.2696 | 0.4717 | 0.4199 |
| 1.2267 | 58.9916 | 1755 | 1.2676 | 0.4858 | 0.4404 |
| 1.2047 | 60.0 | 1785 | 1.2659 | 0.4764 | 0.4336 |
| 1.1168 | 60.9748 | 1814 | 1.2680 | 0.4953 | 0.4466 |
| 1.2396 | 61.9832 | 1844 | 1.2741 | 0.4481 | 0.4045 |
| 1.1193 | 62.9916 | 1874 | 1.2791 | 0.4623 | 0.4184 |
| 1.1587 | 64.0 | 1904 | 1.2657 | 0.4858 | 0.4369 |
| 1.1492 | 64.9748 | 1933 | 1.2736 | 0.4717 | 0.4367 |
| 1.1303 | 65.9832 | 1963 | 1.2683 | 0.4811 | 0.4300 |
| 1.1672 | 66.9916 | 1993 | 1.2683 | 0.4953 | 0.4494 |
| 1.2035 | 68.0 | 2023 | 1.2667 | 0.4811 | 0.4447 |
| 1.1494 | 68.9748 | 2052 | 1.2645 | 0.4858 | 0.4476 |
| 1.1537 | 69.9832 | 2082 | 1.2714 | 0.4811 | 0.4434 |
| 1.18 | 70.9916 | 2112 | 1.2701 | 0.4811 | 0.4344 |
| 1.1386 | 72.0 | 2142 | 1.2688 | 0.4858 | 0.4440 |
| 1.1757 | 72.9748 | 2171 | 1.2694 | 0.4906 | 0.4514 |
| 1.1335 | 73.9832 | 2201 | 1.2712 | 0.4858 | 0.4419 |
| 1.1669 | 74.9916 | 2231 | 1.2701 | 0.5094 | 0.4651 |
| 1.1862 | 76.0 | 2261 | 1.2684 | 0.4764 | 0.4316 |
| 1.1695 | 76.9748 | 2290 | 1.2642 | 0.4906 | 0.4509 |
| 1.1317 | 77.9832 | 2320 | 1.2687 | 0.4811 | 0.4391 |
| 1.2023 | 78.9916 | 2350 | 1.2647 | 0.5 | 0.4579 |
| 1.1603 | 80.0 | 2380 | 1.2650 | 0.5 | 0.4596 |
| 1.1461 | 80.9748 | 2409 | 1.2623 | 0.4811 | 0.4396 |
| 1.1356 | 81.9832 | 2439 | 1.2621 | 0.4953 | 0.4449 |
| 1.1646 | 82.9916 | 2469 | 1.2713 | 0.4953 | 0.4526 |
| 1.152 | 84.0 | 2499 | 1.2661 | 0.5047 | 0.4632 |
| 1.0999 | 84.9748 | 2528 | 1.2685 | 0.5047 | 0.4576 |
| 1.1749 | 85.9832 | 2558 | 1.2716 | 0.4858 | 0.4459 |
| 1.1823 | 86.9916 | 2588 | 1.2624 | 0.4906 | 0.4441 |
| 1.1736 | 88.0 | 2618 | 1.2650 | 0.4811 | 0.4377 |
| 1.1565 | 88.9748 | 2647 | 1.2667 | 0.4670 | 0.4226 |
| 1.1565 | 89.9832 | 2677 | 1.2667 | 0.4953 | 0.4453 |
| 1.192 | 90.9916 | 2707 | 1.2634 | 0.5047 | 0.4635 |
| 1.1271 | 92.0 | 2737 | 1.2639 | 0.4764 | 0.4303 |
| 1.19 | 92.9748 | 2766 | 1.2631 | 0.4858 | 0.4412 |
| 1.1866 | 93.9832 | 2796 | 1.2616 | 0.4953 | 0.4555 |
| 1.0829 | 94.9916 | 2826 | 1.2586 | 0.4953 | 0.4522 |
| 1.1692 | 96.0 | 2856 | 1.2608 | 0.4906 | 0.4497 |
| 1.1503 | 96.9748 | 2885 | 1.2607 | 0.4953 | 0.4551 |
| 1.1263 | 97.9832 | 2915 | 1.2577 | 0.4953 | 0.4543 |
| 1.2199 | 98.9916 | 2945 | 1.2570 | 0.5047 | 0.4601 |
| 1.1347 | 100.0 | 2975 | 1.2555 | 0.4953 | 0.4503 |
| 1.1583 | 100.9748 | 3004 | 1.2557 | 0.5 | 0.4592 |
| 1.1697 | 101.9832 | 3034 | 1.2578 | 0.4858 | 0.4467 |
| 1.1918 | 102.9916 | 3064 | 1.2572 | 0.5047 | 0.4598 |
| 1.1959 | 104.0 | 3094 | 1.2563 | 0.5094 | 0.4649 |
| 1.2032 | 104.9748 | 3123 | 1.2551 | 0.4906 | 0.4480 |
| 1.2031 | 105.9832 | 3153 | 1.2552 | 0.4906 | 0.4491 |
| 1.1565 | 106.9916 | 3183 | 1.2544 | 0.5142 | 0.4668 |
| 1.1703 | 108.0 | 3213 | 1.2570 | 0.5 | 0.4598 |
| 1.2085 | 108.9748 | 3242 | 1.2550 | 0.5094 | 0.4639 |
| 1.1641 | 109.9832 | 3272 | 1.2578 | 0.4953 | 0.4551 |
| 1.1846 | 110.9916 | 3302 | 1.2579 | 0.4906 | 0.4510 |
| 1.1989 | 112.0 | 3332 | 1.2560 | 0.5 | 0.4579 |
| 1.111 | 112.9748 | 3361 | 1.2561 | 0.4953 | 0.4545 |
| 1.1703 | 113.9832 | 3391 | 1.2561 | 0.5047 | 0.4567 |
| 1.165 | 114.9916 | 3421 | 1.2567 | 0.5 | 0.4480 |
| 1.1295 | 116.0 | 3451 | 1.2582 | 0.4953 | 0.4475 |
| 1.1084 | 116.9748 | 3480 | 1.2574 | 0.5 | 0.4571 |
| 1.1577 | 117.9832 | 3510 | 1.2573 | 0.5047 | 0.4617 |
| 1.156 | 118.9916 | 3540 | 1.2565 | 0.4953 | 0.4559 |
| 1.1491 | 120.0 | 3570 | 1.2564 | 0.5 | 0.4573 |
| 1.1396 | 120.9748 | 3599 | 1.2572 | 0.5 | 0.4534 |
| 1.1545 | 121.9832 | 3629 | 1.2565 | 0.5 | 0.4604 |
| 1.1796 | 122.9916 | 3659 | 1.2563 | 0.5 | 0.4593 |
| 1.2012 | 124.0 | 3689 | 1.2559 | 0.4858 | 0.4454 |
| 1.1396 | 124.9748 | 3718 | 1.2567 | 0.4953 | 0.4555 |
| 1.1999 | 125.9832 | 3748 | 1.2558 | 0.4858 | 0.4450 |
| 1.1524 | 126.9916 | 3778 | 1.2569 | 0.4953 | 0.4554 |
| 1.2299 | 128.0 | 3808 | 1.2560 | 0.4953 | 0.4525 |
| 1.1548 | 128.9748 | 3837 | 1.2553 | 0.4764 | 0.4375 |
| 1.1869 | 129.9832 | 3867 | 1.2554 | 0.4811 | 0.4426 |
| 1.1891 | 130.9916 | 3897 | 1.2555 | 0.4811 | 0.4423 |
| 1.1353 | 132.0 | 3927 | 1.2565 | 0.4953 | 0.4554 |
| 1.1717 | 132.9748 | 3956 | 1.2569 | 0.5047 | 0.4643 |
| 1.1536 | 133.9832 | 3986 | 1.2556 | 0.5 | 0.4574 |
| 1.1667 | 134.9916 | 4016 | 1.2555 | 0.5 | 0.4594 |
| 1.1633 | 136.0 | 4046 | 1.2550 | 0.4953 | 0.4551 |
| 1.1646 | 136.9748 | 4075 | 1.2539 | 0.4858 | 0.4457 |
| 1.1618 | 137.9832 | 4105 | 1.2540 | 0.5047 | 0.4594 |
| 1.1581 | 138.9916 | 4135 | 1.2545 | 0.4858 | 0.4460 |
| 1.117 | 140.0 | 4165 | 1.2549 | 0.4858 | 0.4457 |
| 1.184 | 140.9748 | 4194 | 1.2552 | 0.4906 | 0.4504 |
| 1.1323 | 141.9832 | 4224 | 1.2553 | 0.4906 | 0.4504 |
| 1.1219 | 142.9916 | 4254 | 1.2550 | 0.4953 | 0.4547 |
| 1.1478 | 144.0 | 4284 | 1.2550 | 0.4953 | 0.4547 |
| 1.1177 | 144.9748 | 4313 | 1.2550 | 0.4953 | 0.4547 |
| 1.1326 | 145.9832 | 4343 | 1.2549 | 0.4953 | 0.4547 |
| 1.1392 | 146.2185 | 4350 | 1.2549 | 0.4953 | 0.4547 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
|
[
"lgso",
"mwl",
"hgso",
"mws",
"hglo",
"lglo",
"uglo",
"mglo",
"mgso"
] |
SABR22/ViT-threat-classification-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-threat-classification-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 an unknown dataset.
This is model created as a prrof of concept for a Carleton University computer vision event. It is by no means meant to be used in deliverable systems in its current state, and should be used exclusively for research and development.
It achieves the following results on the evaluation set:
- Loss: 0.0381
- F1: 0.9657
- Precision: 0.9563
- Recall: 0.9752
## Model description
More information needed
## Intended uses & limitations
More information needed
## Collaborators
[Angus Bailey](https://huggingface.co/boshy)
[Thomas Nolasque](https://github.com/thomasnol)
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------:|
| 0.0744 | 0.9985 | 326 | 0.0576 | 0.9466 | 0.9738 | 0.9208 |
| 0.0449 | 2.0 | 653 | 0.0397 | 0.9641 | 0.9747 | 0.9538 |
| 0.0207 | 2.9985 | 979 | 0.0409 | 0.9647 | 0.9607 | 0.9686 |
| 0.0342 | 4.0 | 1306 | 0.0382 | 0.9650 | 0.9518 | 0.9785 |
| 0.0286 | 4.9923 | 1630 | 0.0381 | 0.9657 | 0.9563 | 0.9752 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"non-threat",
"threat"
] |
cvmil/dinov2-base_rice-disease-02
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dinov2-base_rice-disease-02_111824
This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1191
- Accuracy: 0.9654
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 15
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.4008 | 1.0 | 212 | 0.8303 | 0.5579 |
| 0.331 | 2.0 | 424 | 0.9128 | 0.2795 |
| 0.192 | 3.0 | 636 | 0.9368 | 0.2083 |
| 0.1462 | 4.0 | 848 | 0.9488 | 0.1819 |
| 0.1224 | 5.0 | 1060 | 0.9534 | 0.1633 |
| 0.1067 | 6.0 | 1272 | 0.9521 | 0.1567 |
| 0.0954 | 7.0 | 1484 | 0.9574 | 0.1431 |
| 0.0879 | 8.0 | 1696 | 0.9594 | 0.1348 |
| 0.0809 | 9.0 | 1908 | 0.9594 | 0.1325 |
| 0.0759 | 10.0 | 2120 | 0.9634 | 0.1273 |
| 0.0721 | 11.0 | 2332 | 0.1264 | 0.9607 |
| 0.0688 | 12.0 | 2544 | 0.1212 | 0.9621 |
| 0.0662 | 13.0 | 2756 | 0.1199 | 0.9654 |
| 0.0645 | 14.0 | 2968 | 0.1191 | 0.9601 |
| 0.0631 | 15.0 | 3180 | 0.1191 | 0.9654 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"bacterial_leaf_blight",
"brown_spot",
"healthy",
"leaf_blast",
"leaf_scald",
"narrow_brown_spot",
"neck_blast",
"rice_hispa",
"sheath_blight",
"tungro"
] |
nemik/frost-vision-v2-google_vit-base-patch16-224
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# frost-vision-v2-google_vit-base-patch16-224
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1562
- Accuracy: 0.9359
- F1: 0.8381
- Precision: 0.8896
- Recall: 0.7922
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3416 | 1.1494 | 100 | 0.3273 | 0.8771 | 0.6124 | 0.9005 | 0.4640 |
| 0.2215 | 2.2989 | 200 | 0.2187 | 0.9183 | 0.7902 | 0.8537 | 0.7355 |
| 0.1753 | 3.4483 | 300 | 0.1899 | 0.9238 | 0.8098 | 0.8472 | 0.7756 |
| 0.1656 | 4.5977 | 400 | 0.1732 | 0.9272 | 0.8175 | 0.8606 | 0.7784 |
| 0.1288 | 5.7471 | 500 | 0.1562 | 0.9359 | 0.8381 | 0.8896 | 0.7922 |
| 0.1323 | 6.8966 | 600 | 0.1597 | 0.9322 | 0.8326 | 0.8609 | 0.8061 |
| 0.1004 | 8.0460 | 700 | 0.1613 | 0.9316 | 0.8324 | 0.8542 | 0.8116 |
| 0.0956 | 9.1954 | 800 | 0.1612 | 0.9336 | 0.8368 | 0.8620 | 0.8130 |
| 0.0841 | 10.3448 | 900 | 0.1621 | 0.9345 | 0.8383 | 0.8669 | 0.8116 |
| 0.0764 | 11.4943 | 1000 | 0.1586 | 0.9359 | 0.8438 | 0.8615 | 0.8269 |
| 0.0726 | 12.6437 | 1100 | 0.1546 | 0.9420 | 0.8594 | 0.8729 | 0.8463 |
| 0.0732 | 13.7931 | 1200 | 0.1529 | 0.9409 | 0.8565 | 0.87 | 0.8435 |
| 0.0626 | 14.9425 | 1300 | 0.1589 | 0.9377 | 0.8485 | 0.8637 | 0.8338 |
| 0.0481 | 16.0920 | 1400 | 0.1612 | 0.9394 | 0.8510 | 0.8767 | 0.8269 |
| 0.0507 | 17.2414 | 1500 | 0.1679 | 0.9339 | 0.8394 | 0.8539 | 0.8255 |
| 0.0446 | 18.3908 | 1600 | 0.1623 | 0.9417 | 0.8597 | 0.8664 | 0.8532 |
| 0.0498 | 19.5402 | 1700 | 0.1625 | 0.9417 | 0.8601 | 0.8643 | 0.8560 |
| 0.0458 | 20.6897 | 1800 | 0.1601 | 0.9397 | 0.8533 | 0.8693 | 0.8380 |
| 0.0307 | 21.8391 | 1900 | 0.1626 | 0.9432 | 0.8637 | 0.8673 | 0.8601 |
| 0.0334 | 22.9885 | 2000 | 0.1621 | 0.9443 | 0.8642 | 0.8829 | 0.8463 |
| 0.0339 | 24.1379 | 2100 | 0.1680 | 0.9435 | 0.8645 | 0.8675 | 0.8615 |
| 0.0222 | 25.2874 | 2200 | 0.1656 | 0.9394 | 0.8537 | 0.8628 | 0.8449 |
| 0.026 | 26.4368 | 2300 | 0.1687 | 0.9386 | 0.8515 | 0.8612 | 0.8421 |
| 0.0353 | 27.5862 | 2400 | 0.1666 | 0.9403 | 0.8555 | 0.8665 | 0.8449 |
| 0.0294 | 28.7356 | 2500 | 0.1660 | 0.9429 | 0.8614 | 0.8755 | 0.8476 |
| 0.0243 | 29.8851 | 2600 | 0.1664 | 0.9423 | 0.8590 | 0.8795 | 0.8393 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"snowing",
"raining",
"sunny",
"cloudy",
"night",
"snow_on_road",
"partial_snow_on_road",
"clear_pavement",
"wet_pavement",
"iced_lens"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-012
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-012
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2851
- Accuracy: 0.94
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0001 | 4.4444 | 10 | 0.3101 | 0.94 |
| 0.0 | 8.8889 | 20 | 0.2851 | 0.94 |
| 0.0 | 13.3333 | 30 | 0.3880 | 0.96 |
| 0.0 | 17.7778 | 40 | 0.3946 | 0.96 |
| 0.0 | 22.2222 | 50 | 0.3829 | 0.96 |
| 0.0 | 26.6667 | 60 | 0.3797 | 0.96 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-015
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-015
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4824
- Accuracy: 0.96
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0 | 8.0 | 10 | 0.4951 | 0.96 |
| 0.0 | 16.0 | 20 | 0.4893 | 0.96 |
| 0.0 | 24.0 | 30 | 0.4824 | 0.96 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
dima806/car_models_image_detection
|
Returns car brand with about 84% accuracy given on an image.
See https://www.kaggle.com/code/dima806/car-models-image-detection-vit for details.
```
Accuracy: 0.8410
F1 Score: 0.8372
Classification report:
precision recall f1-score support
Acura ILX 0.7004 0.8657 0.7743 216
Acura MDX 0.8211 0.7222 0.7685 216
Acura NSX 0.8434 0.7731 0.8068 216
Acura RDX 0.6456 0.8519 0.7345 216
Acura RLX 0.7159 0.8750 0.7875 216
Acura TLX 0.8125 0.9028 0.8553 216
Alfa Romeo 4C 0.9596 0.8796 0.9179 216
Alfa Romeo 4C Spider 0.9114 1.0000 0.9536 216
Alfa Romeo Giulia 0.9289 0.9676 0.9478 216
Alfa Romeo Stelvio 0.9721 0.9676 0.9698 216
Aston Martin DB11 0.9933 0.6898 0.8142 216
Aston Martin DBS 1.0000 0.6991 0.8229 216
Aston Martin Vanquish 0.9256 0.9213 0.9234 216
Aston Martin Vantage 0.6407 0.8791 0.7412 215
Audi A3 0.6429 0.6698 0.6560 215
Audi A4 0.6598 0.7395 0.6974 215
Audi A5 0.7440 0.7163 0.7299 215
Audi A6 0.6383 0.5556 0.5941 216
Audi A7 0.6611 0.7349 0.6960 215
Audi A8 0.6760 0.7860 0.7269 215
Audi Q3 0.9459 0.9767 0.9611 215
Audi Q5 0.7934 0.7860 0.7897 215
Audi Q7 0.8259 0.8565 0.8409 216
Audi Q8 0.9346 0.9302 0.9324 215
Audi R8 0.7215 0.7315 0.7264 216
Audi TT 0.6949 0.8791 0.7762 215
Audi e-tron 0.9908 1.0000 0.9954 216
BMW 2-Series 0.6548 0.5116 0.5744 215
BMW 3-Series 0.6575 0.6667 0.6621 216
BMW 4-Series 0.6411 0.7361 0.6853 216
BMW 5-Series 0.6224 0.4120 0.4958 216
BMW 6-Series 0.7765 0.6140 0.6857 215
BMW 7-Series 0.7195 0.7361 0.7277 216
BMW 8-Series 1.0000 0.8935 0.9438 216
BMW X1 0.8442 0.9070 0.8744 215
BMW X2 0.9231 1.0000 0.9600 216
BMW X3 0.7445 0.7824 0.7630 216
BMW X4 0.8700 0.8093 0.8386 215
BMW X5 0.7816 0.6326 0.6992 215
BMW X6 0.7137 0.7500 0.7314 216
BMW X7 0.9774 1.0000 0.9886 216
BMW Z4 0.8400 0.6837 0.7538 215
BMW i3 0.8729 0.9581 0.9135 215
BMW i8 0.8629 0.9907 0.9224 216
Bentley Bentayga 0.9591 0.9769 0.9679 216
Bentley Continental GT 0.7621 0.7269 0.7441 216
Bentley Flying Spur 0.7908 0.8750 0.8308 216
Bentley Mulsanne 0.8242 0.9769 0.8941 216
Buick Cascada 0.9770 0.9860 0.9815 215
Buick Enclave 0.7756 0.9120 0.8383 216
Buick Encore 0.8798 0.9491 0.9131 216
Buick Envision 0.8950 0.9861 0.9383 216
Buick Lacrosse 0.7005 0.6419 0.6699 215
Buick Regal 0.7939 0.6065 0.6877 216
Cadillac ATS 0.6867 0.7953 0.7371 215
Cadillac CT4 0.9908 1.0000 0.9954 216
Cadillac CT5 0.9908 1.0000 0.9954 216
Cadillac CT6 0.8472 0.8981 0.8719 216
Cadillac CTS 0.7337 0.6791 0.7053 215
Cadillac Escalade 0.8155 0.7814 0.7981 215
Cadillac XT4 1.0000 1.0000 1.0000 216
Cadillac XT5 0.9231 1.0000 0.9600 216
Cadillac XT6 0.9729 1.0000 0.9862 215
Cadillac XTS 0.8333 0.8565 0.8447 216
Chevrolet Blazer 0.9450 0.9537 0.9493 216
Chevrolet Bolt EV 1.0000 0.9769 0.9883 216
Chevrolet Camaro 0.7423 0.6698 0.7042 215
Chevrolet Colorado 0.7043 0.6093 0.6534 215
Chevrolet Corvette 0.8247 0.7407 0.7805 216
Chevrolet Cruze 0.7000 0.5833 0.6364 216
Chevrolet Equinox 0.7814 0.7814 0.7814 215
Chevrolet Impala 0.6955 0.9306 0.7960 216
Chevrolet Malibu 0.7562 0.5602 0.6436 216
Chevrolet Silverado 1500 0.6000 0.4167 0.4918 216
Chevrolet Silverado 2500HD 0.6494 0.7546 0.6981 216
Chevrolet Sonic 0.8925 0.8843 0.8884 216
Chevrolet Spark 0.8761 0.9209 0.8980 215
Chevrolet Suburban 0.8922 0.8426 0.8667 216
Chevrolet Tahoe 0.8914 0.9163 0.9037 215
Chevrolet TrailBlazer 0.9417 0.9722 0.9567 216
Chevrolet Traverse 0.8462 0.9167 0.8800 216
Chevrolet Trax 0.9381 0.9860 0.9615 215
Chevrolet Volt 0.7650 0.7721 0.7685 215
Chrysler 300 0.7261 0.8140 0.7675 215
Chrysler Pacifica 0.8233 0.8843 0.8527 216
Dodge Challenger 0.6932 0.8056 0.7452 216
Dodge Charger 0.6435 0.6435 0.6435 216
Dodge Durango 0.8832 0.8750 0.8791 216
Dodge Grand Caravan 0.9676 0.9676 0.9676 216
Dodge Journey 0.8286 0.9442 0.8826 215
FIAT 124 Spider 0.9953 0.9767 0.9859 215
FIAT 500 0.7944 0.7870 0.7907 216
FIAT 500L 0.9725 0.9860 0.9792 215
FIAT 500X 0.9513 0.9954 0.9729 216
FIAT 500e 0.9512 0.9028 0.9264 216
Ferrari 488 GTB 0.9633 0.9722 0.9677 216
Ferrari GTC4Lusso 1.0000 1.0000 1.0000 216
Ferrari Portofino 1.0000 1.0000 1.0000 216
Ford Ecosport 0.9729 1.0000 0.9862 215
Ford Edge 0.8621 0.4630 0.6024 216
Ford Escape 0.8973 0.6065 0.7238 216
Ford Expedition 0.8646 0.7685 0.8137 216
Ford Explorer 0.8048 0.7860 0.7953 215
Ford F-150 0.6718 0.6093 0.6390 215
Ford Fiesta 0.7425 0.5741 0.6475 216
Ford Flex 0.8688 0.8889 0.8787 216
Ford Fusion 0.7571 0.7395 0.7482 215
Ford Mustang 0.6471 0.5093 0.5699 216
Ford Ranger 0.8861 0.8287 0.8565 216
Ford Super Duty F-250 0.7540 0.8698 0.8078 215
Ford Taurus 0.7108 0.8233 0.7629 215
Ford Transit Connect Wagon 0.9809 0.9535 0.9670 215
GMC Acadia 0.9272 0.8884 0.9074 215
GMC Canyon 0.7717 0.9074 0.8340 216
GMC Sierra 1500 0.5957 0.3889 0.4706 216
GMC Sierra 2500HD 0.7056 0.6435 0.6731 216
GMC Terrain 0.8878 0.8426 0.8646 216
GMC Yukon 0.9224 0.9395 0.9309 215
Genesis G70 0.9904 0.9628 0.9764 215
Genesis G80 0.9474 1.0000 0.9730 216
Genesis G90 0.8777 0.9349 0.9054 215
Honda Accord 0.8019 0.3935 0.5280 216
Honda CR-V 0.7714 0.7535 0.7624 215
Honda Civic 0.6837 0.3102 0.4268 216
Honda Clarity 0.7886 0.8981 0.8398 216
Honda Fit 0.7865 0.7023 0.7420 215
Honda HR-V 0.9244 0.9630 0.9433 216
Honda Insight 0.7238 0.8047 0.7621 215
Honda Odyssey 0.8643 0.8843 0.8741 216
Honda Passport 0.8898 0.9767 0.9313 215
Honda Pilot 0.8009 0.7860 0.7934 215
Honda Ridgeline 0.7760 0.8981 0.8326 216
Hyundai Accent 0.7577 0.7963 0.7765 216
Hyundai Elantra 0.6067 0.5023 0.5496 215
Hyundai Ioniq 0.8361 0.9256 0.8786 215
Hyundai Kona 0.9899 0.9120 0.9494 216
Hyundai Kona Electric 0.9188 1.0000 0.9577 215
Hyundai NEXO 1.0000 1.0000 1.0000 215
Hyundai Palisade 0.9515 1.0000 0.9752 216
Hyundai Santa Fe 0.8392 0.5581 0.6704 215
Hyundai Sonata 0.5817 0.5628 0.5721 215
Hyundai Tucson 0.9249 0.7442 0.8247 215
Hyundai Veloster 0.8249 0.8287 0.8268 216
Hyundai Venue 0.9774 1.0000 0.9886 216
INFINITI Q50 0.8725 0.8279 0.8496 215
INFINITI Q60 0.8565 0.9398 0.8962 216
INFINITI Q70 0.9450 0.9537 0.9493 216
INFINITI QX30 0.9908 1.0000 0.9954 216
INFINITI QX50 0.8445 0.9349 0.8874 215
INFINITI QX60 0.8919 0.9167 0.9041 216
INFINITI QX80 0.9159 0.9628 0.9388 215
Jaguar E-Pace 0.9818 1.0000 0.9908 216
Jaguar F-Pace 0.9798 0.8981 0.9372 216
Jaguar F-Type 0.8768 0.8279 0.8517 215
Jaguar I-Pace 0.8471 0.9535 0.8972 215
Jaguar XE 0.7984 0.9167 0.8534 216
Jaguar XF 0.7467 0.5209 0.6137 215
Jaguar XJ 0.7568 0.7778 0.7671 216
Jeep Cherokee 0.9122 0.8698 0.8905 215
Jeep Compass 0.8756 0.8837 0.8796 215
Jeep Gladiator 1.0000 1.0000 1.0000 216
Jeep Grand Cherokee 0.8950 0.8287 0.8606 216
Jeep Renegade 0.9816 0.9861 0.9838 216
Jeep Wrangler 0.9810 0.9583 0.9696 216
Kia Cadenza 0.8164 0.9721 0.8875 215
Kia Forte 0.5972 0.5860 0.5915 215
Kia K900 0.9149 1.0000 0.9556 215
Kia Niro 0.8077 0.9722 0.8824 216
Kia Optima 0.7009 0.7269 0.7136 216
Kia Rio 0.7089 0.6991 0.7040 216
Kia Sedona 0.8475 0.9259 0.8850 216
Kia Sorento 0.7299 0.7163 0.7230 215
Kia Soul 0.7432 0.8884 0.8093 215
Kia Soul EV 0.9498 0.9674 0.9585 215
Kia Sportage 0.9100 0.8889 0.8993 216
Kia Stinger 0.9862 1.0000 0.9931 215
Kia Telluride 0.9163 0.9674 0.9412 215
Lamborghini Aventador 1.0000 1.0000 1.0000 215
Lamborghini Huracan 0.9488 0.9488 0.9488 215
Lamborghini Urus 0.9954 1.0000 0.9977 215
Land Rover Defender 0.9954 1.0000 0.9977 215
Land Rover Discovery 0.8793 0.9488 0.9128 215
Land Rover Discovery Sport 0.8723 0.9535 0.9111 215
Land Rover Range Rover 0.6016 0.7130 0.6525 216
Land Rover Range Rover Evoque 0.8807 0.8930 0.8868 215
Land Rover Range Rover Sport 0.7353 0.6944 0.7143 216
Land Rover Range Rover Velar 0.9770 0.9815 0.9792 216
Lexus ES 0.7277 0.7917 0.7583 216
Lexus GS 0.8247 0.7407 0.7805 216
Lexus GX 0.9177 0.9860 0.9507 215
Lexus IS 0.8095 0.7907 0.8000 215
Lexus LC 0.9685 1.0000 0.9840 215
Lexus LS 0.8419 0.8419 0.8419 215
Lexus LX 0.8750 0.8102 0.8413 216
Lexus NX 0.8846 0.9628 0.9220 215
Lexus RC 0.8211 0.8287 0.8249 216
Lexus RX 0.7611 0.7963 0.7783 216
Lexus UX 0.9513 1.0000 0.9751 215
Lincoln Aviator 0.9183 0.8884 0.9031 215
Lincoln Continental 0.7711 0.8889 0.8258 216
Lincoln Corsair 0.9191 1.0000 0.9579 216
Lincoln MKC 0.9635 0.9814 0.9724 215
Lincoln MKT 0.8814 0.9630 0.9204 216
Lincoln MKZ 0.7788 0.7824 0.7806 216
Lincoln Nautilus 0.9452 0.9628 0.9539 215
Lincoln Navigator 0.8767 0.8889 0.8828 216
MINI Clubman 0.8733 0.8935 0.8833 216
MINI Cooper 0.8155 0.7778 0.7962 216
MINI Cooper Countryman 0.8386 0.8698 0.8539 215
Maserati Ghibli 0.9427 0.9907 0.9661 216
Maserati GranTurismo 0.8357 0.8241 0.8298 216
Maserati Levante 0.9773 1.0000 0.9885 215
Maserati Quattroporte 0.9019 0.8977 0.8998 215
Mazda CX-3 0.9378 0.9769 0.9569 216
Mazda CX-30 0.9600 1.0000 0.9796 216
Mazda CX-5 0.8778 0.7315 0.7980 216
Mazda CX-9 0.8718 0.9444 0.9067 216
Mazda MAZDA3 0.7041 0.6389 0.6699 216
Mazda MAZDA6 0.6951 0.7176 0.7062 216
Mazda MX-5 Miata 0.8889 0.7778 0.8296 216
Mazda Mazda3 Hatchback 0.9954 1.0000 0.9977 215
McLaren 570GT 1.0000 1.0000 1.0000 216
McLaren 570S 1.0000 1.0000 1.0000 215
McLaren 720S 0.9774 1.0000 0.9886 216
Mercedes-Benz A Class 0.9474 1.0000 0.9730 216
Mercedes-Benz AMG GT 0.9295 0.9769 0.9526 216
Mercedes-Benz C Class 0.6261 0.3333 0.4350 216
Mercedes-Benz CLA Class 0.7036 0.9120 0.7944 216
Mercedes-Benz CLS Class 0.6714 0.6620 0.6667 216
Mercedes-Benz E Class 0.7026 0.6343 0.6667 216
Mercedes-Benz EQC 0.9862 1.0000 0.9931 215
Mercedes-Benz G Class 0.8390 0.9209 0.8780 215
Mercedes-Benz GLA Class 0.7935 0.9116 0.8485 215
Mercedes-Benz GLB Class 0.9389 1.0000 0.9685 215
Mercedes-Benz GLC Class 0.7989 0.6465 0.7147 215
Mercedes-Benz GLE Class 0.9103 0.6605 0.7655 215
Mercedes-Benz GLS Class 0.8471 1.0000 0.9172 216
Mercedes-Benz Metris 0.9774 1.0000 0.9886 216
Mercedes-Benz S Class 0.6364 0.5509 0.5906 216
Mercedes-Benz SL Class 0.7160 0.8326 0.7699 215
Mercedes-Benz SLC Class 0.9381 0.9815 0.9593 216
Mitsubishi Eclipse Cross 0.9908 1.0000 0.9954 216
Mitsubishi Mirage 0.8481 0.9349 0.8894 215
Mitsubishi Outlander 0.8554 0.6574 0.7435 216
Mitsubishi Outlander Sport 0.7600 0.8796 0.8155 216
Nissan 370Z 0.9742 0.8750 0.9220 216
Nissan Altima 0.8353 0.6605 0.7377 215
Nissan Armada 0.9193 0.9491 0.9339 216
Nissan Frontier 0.8738 0.8698 0.8718 215
Nissan GT-R 0.6301 0.7176 0.6710 216
Nissan Kicks 0.9474 1.0000 0.9730 216
Nissan Leaf 0.7673 0.7176 0.7416 216
Nissan Maxima 0.8479 0.8558 0.8519 215
Nissan Murano 0.8726 0.8605 0.8665 215
Nissan NV200 1.0000 1.0000 1.0000 215
Nissan Pathfinder 0.8028 0.8102 0.8065 216
Nissan Rogue 0.7822 0.8148 0.7982 216
Nissan Rogue Sport 0.9773 1.0000 0.9885 215
Nissan Sentra 0.6009 0.6343 0.6171 216
Nissan Titan 0.8042 0.7037 0.7506 216
Nissan Versa 0.7770 0.5023 0.6102 215
Porsche 718 0.9106 0.9907 0.9490 216
Porsche 718 Spyder 1.0000 1.0000 1.0000 216
Porsche 911 0.7701 0.6667 0.7146 216
Porsche Cayenne 0.7701 0.6667 0.7146 216
Porsche Macan 0.8432 0.9256 0.8825 215
Porsche Panamera 0.7018 0.7407 0.7207 216
Porsche Taycan 0.9336 0.9769 0.9548 216
Ram 1500 0.7523 0.7767 0.7643 215
Ram 2500 0.8287 0.8287 0.8287 216
Rolls-Royce Cullinan 0.9903 0.9491 0.9693 216
Rolls-Royce Dawn 1.0000 1.0000 1.0000 216
Rolls-Royce Ghost 0.9279 0.9581 0.9428 215
Rolls-Royce Phantom 0.9641 0.9954 0.9795 216
Rolls-Royce Wraith 1.0000 1.0000 1.0000 216
Subaru Ascent 0.8458 0.9907 0.9126 216
Subaru BRZ 0.8272 0.9306 0.8758 216
Subaru Crosstrek 0.8599 0.8279 0.8436 215
Subaru Forester 0.7889 0.7269 0.7566 216
Subaru Impreza 0.6215 0.6186 0.6200 215
Subaru Legacy 0.5024 0.4791 0.4905 215
Subaru Outback 0.7438 0.8333 0.7860 216
Subaru STI S209 1.0000 1.0000 1.0000 215
Subaru WRX 0.6816 0.7767 0.7261 215
Tesla Model 3 0.9310 1.0000 0.9643 216
Tesla Model S 0.7881 0.8611 0.8230 216
Tesla Model X 0.9908 1.0000 0.9954 216
Tesla Model Y 1.0000 1.0000 1.0000 216
Toyota 4Runner 0.9167 0.9167 0.9167 216
Toyota 86 1.0000 1.0000 1.0000 216
Toyota Avalon 0.7880 0.6713 0.7250 216
Toyota C-HR 0.9515 1.0000 0.9752 216
Toyota Camry 0.6745 0.6620 0.6682 216
Toyota Corolla 0.7586 0.6140 0.6787 215
Toyota Highlander 0.8539 0.7037 0.7716 216
Toyota Land Cruiser 0.9147 0.8935 0.9040 216
Toyota Mirai 0.9127 0.9676 0.9393 216
Toyota Prius 0.6484 0.7721 0.7049 215
Toyota Prius C 0.7092 0.9302 0.8048 215
Toyota RAV4 0.7403 0.6233 0.6768 215
Toyota Sequoia 0.9217 0.9259 0.9238 216
Toyota Sienna 0.9703 0.9074 0.9378 216
Toyota Supra 0.9505 0.9769 0.9635 216
Toyota Tacoma 0.6969 0.8233 0.7548 215
Toyota Tundra 0.7376 0.6930 0.7146 215
Toyota Yaris 0.6806 0.4537 0.5444 216
Toyota Yaris Hatchback 1.0000 1.0000 1.0000 216
Volkswagen Arteon 0.9471 1.0000 0.9729 215
Volkswagen Atlas 0.8921 1.0000 0.9430 215
Volkswagen Beetle 0.7839 0.8565 0.8186 216
Volkswagen Golf 0.7040 0.7269 0.7153 216
Volkswagen Jetta 0.5907 0.7083 0.6442 216
Volkswagen Passat 0.6947 0.4233 0.5260 215
Volkswagen Tiguan 0.7926 0.8000 0.7963 215
Volkswagen e-Golf 0.8584 0.9259 0.8909 216
Volvo S60 0.6640 0.3843 0.4868 216
Volvo S90 0.7878 0.8935 0.8373 216
Volvo V60 0.6966 0.7546 0.7244 216
Volvo V90 0.8833 0.9860 0.9319 215
Volvo XC40 0.9729 1.0000 0.9862 215
Volvo XC60 0.7841 0.8241 0.8036 216
Volvo XC90 0.8528 0.7778 0.8136 216
smart fortwo 0.8418 0.7639 0.8010 216
accuracy 0.8410 69639
macro avg 0.8406 0.8410 0.8372 69639
weighted avg 0.8406 0.8410 0.8372 69639
```
|
[
"acura ilx",
"acura mdx",
"acura nsx",
"acura rdx",
"acura rlx",
"acura tlx",
"alfa romeo 4c",
"alfa romeo 4c spider",
"alfa romeo giulia",
"alfa romeo stelvio",
"aston martin db11",
"aston martin dbs",
"aston martin vanquish",
"aston martin vantage",
"audi a3",
"audi a4",
"audi a5",
"audi a6",
"audi a7",
"audi a8",
"audi q3",
"audi q5",
"audi q7",
"audi q8",
"audi r8",
"audi tt",
"audi e-tron",
"bmw 2-series",
"bmw 3-series",
"bmw 4-series",
"bmw 5-series",
"bmw 6-series",
"bmw 7-series",
"bmw 8-series",
"bmw x1",
"bmw x2",
"bmw x3",
"bmw x4",
"bmw x5",
"bmw x6",
"bmw x7",
"bmw z4",
"bmw i3",
"bmw i8",
"bentley bentayga",
"bentley continental gt",
"bentley flying spur",
"bentley mulsanne",
"buick cascada",
"buick enclave",
"buick encore",
"buick envision",
"buick lacrosse",
"buick regal",
"cadillac ats",
"cadillac ct4",
"cadillac ct5",
"cadillac ct6",
"cadillac cts",
"cadillac escalade",
"cadillac xt4",
"cadillac xt5",
"cadillac xt6",
"cadillac xts",
"chevrolet blazer",
"chevrolet bolt ev",
"chevrolet camaro",
"chevrolet colorado",
"chevrolet corvette",
"chevrolet cruze",
"chevrolet equinox",
"chevrolet impala",
"chevrolet malibu",
"chevrolet silverado 1500",
"chevrolet silverado 2500hd",
"chevrolet sonic",
"chevrolet spark",
"chevrolet suburban",
"chevrolet tahoe",
"chevrolet trailblazer",
"chevrolet traverse",
"chevrolet trax",
"chevrolet volt",
"chrysler 300",
"chrysler pacifica",
"dodge challenger",
"dodge charger",
"dodge durango",
"dodge grand caravan",
"dodge journey",
"fiat 124 spider",
"fiat 500",
"fiat 500l",
"fiat 500x",
"fiat 500e",
"ferrari 488 gtb",
"ferrari gtc4lusso",
"ferrari portofino",
"ford ecosport",
"ford edge",
"ford escape",
"ford expedition",
"ford explorer",
"ford f-150",
"ford fiesta",
"ford flex",
"ford fusion",
"ford mustang",
"ford ranger",
"ford super duty f-250",
"ford taurus",
"ford transit connect wagon",
"gmc acadia",
"gmc canyon",
"gmc sierra 1500",
"gmc sierra 2500hd",
"gmc terrain",
"gmc yukon",
"genesis g70",
"genesis g80",
"genesis g90",
"honda accord",
"honda cr-v",
"honda civic",
"honda clarity",
"honda fit",
"honda hr-v",
"honda insight",
"honda odyssey",
"honda passport",
"honda pilot",
"honda ridgeline",
"hyundai accent",
"hyundai elantra",
"hyundai ioniq",
"hyundai kona",
"hyundai kona electric",
"hyundai nexo",
"hyundai palisade",
"hyundai santa fe",
"hyundai sonata",
"hyundai tucson",
"hyundai veloster",
"hyundai venue",
"infiniti q50",
"infiniti q60",
"infiniti q70",
"infiniti qx30",
"infiniti qx50",
"infiniti qx60",
"infiniti qx80",
"jaguar e-pace",
"jaguar f-pace",
"jaguar f-type",
"jaguar i-pace",
"jaguar xe",
"jaguar xf",
"jaguar xj",
"jeep cherokee",
"jeep compass",
"jeep gladiator",
"jeep grand cherokee",
"jeep renegade",
"jeep wrangler",
"kia cadenza",
"kia forte",
"kia k900",
"kia niro",
"kia optima",
"kia rio",
"kia sedona",
"kia sorento",
"kia soul",
"kia soul ev",
"kia sportage",
"kia stinger",
"kia telluride",
"lamborghini aventador",
"lamborghini huracan",
"lamborghini urus",
"land rover defender",
"land rover discovery",
"land rover discovery sport",
"land rover range rover",
"land rover range rover evoque",
"land rover range rover sport",
"land rover range rover velar",
"lexus es",
"lexus gs",
"lexus gx",
"lexus is",
"lexus lc",
"lexus ls",
"lexus lx",
"lexus nx",
"lexus rc",
"lexus rx",
"lexus ux",
"lincoln aviator",
"lincoln continental",
"lincoln corsair",
"lincoln mkc",
"lincoln mkt",
"lincoln mkz",
"lincoln nautilus",
"lincoln navigator",
"mini clubman",
"mini cooper",
"mini cooper countryman",
"maserati ghibli",
"maserati granturismo",
"maserati levante",
"maserati quattroporte",
"mazda cx-3",
"mazda cx-30",
"mazda cx-5",
"mazda cx-9",
"mazda mazda3",
"mazda mazda6",
"mazda mx-5 miata",
"mazda mazda3 hatchback",
"mclaren 570gt",
"mclaren 570s",
"mclaren 720s",
"mercedes-benz a class",
"mercedes-benz amg gt",
"mercedes-benz c class",
"mercedes-benz cla class",
"mercedes-benz cls class",
"mercedes-benz e class",
"mercedes-benz eqc",
"mercedes-benz g class",
"mercedes-benz gla class",
"mercedes-benz glb class",
"mercedes-benz glc class",
"mercedes-benz gle class",
"mercedes-benz gls class",
"mercedes-benz metris",
"mercedes-benz s class",
"mercedes-benz sl class",
"mercedes-benz slc class",
"mitsubishi eclipse cross",
"mitsubishi mirage",
"mitsubishi outlander",
"mitsubishi outlander sport",
"nissan 370z",
"nissan altima",
"nissan armada",
"nissan frontier",
"nissan gt-r",
"nissan kicks",
"nissan leaf",
"nissan maxima",
"nissan murano",
"nissan nv200",
"nissan pathfinder",
"nissan rogue",
"nissan rogue sport",
"nissan sentra",
"nissan titan",
"nissan versa",
"porsche 718",
"porsche 718 spyder",
"porsche 911",
"porsche cayenne",
"porsche macan",
"porsche panamera",
"porsche taycan",
"ram 1500",
"ram 2500",
"rolls-royce cullinan",
"rolls-royce dawn",
"rolls-royce ghost",
"rolls-royce phantom",
"rolls-royce wraith",
"subaru ascent",
"subaru brz",
"subaru crosstrek",
"subaru forester",
"subaru impreza",
"subaru legacy",
"subaru outback",
"subaru sti s209",
"subaru wrx",
"tesla model 3",
"tesla model s",
"tesla model x",
"tesla model y",
"toyota 4runner",
"toyota 86",
"toyota avalon",
"toyota c-hr",
"toyota camry",
"toyota corolla",
"toyota highlander",
"toyota land cruiser",
"toyota mirai",
"toyota prius",
"toyota prius c",
"toyota rav4",
"toyota sequoia",
"toyota sienna",
"toyota supra",
"toyota tacoma",
"toyota tundra",
"toyota yaris",
"toyota yaris hatchback",
"volkswagen arteon",
"volkswagen atlas",
"volkswagen beetle",
"volkswagen golf",
"volkswagen jetta",
"volkswagen passat",
"volkswagen tiguan",
"volkswagen e-golf",
"volvo s60",
"volvo s90",
"volvo v60",
"volvo v90",
"volvo xc40",
"volvo xc60",
"volvo xc90",
"smart fortwo"
] |
TuyenTrungLe/finetuned-vietnamese-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-vietnamese-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_vietnam_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3760
- Accuracy: 0.8958
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.1058 | 0.0910 | 100 | 1.9974 | 0.5694 |
| 1.4012 | 0.1820 | 200 | 1.4076 | 0.6855 |
| 1.3551 | 0.2730 | 300 | 1.1650 | 0.7264 |
| 1.1111 | 0.3640 | 400 | 1.0998 | 0.7062 |
| 1.0038 | 0.4550 | 500 | 0.9087 | 0.7483 |
| 0.9599 | 0.5460 | 600 | 0.8278 | 0.7682 |
| 1.0932 | 0.6369 | 700 | 0.9115 | 0.7360 |
| 0.7807 | 0.7279 | 800 | 0.8011 | 0.7730 |
| 0.8237 | 0.8189 | 900 | 0.8345 | 0.7726 |
| 0.7288 | 0.9099 | 1000 | 0.6427 | 0.8258 |
| 0.7982 | 1.0009 | 1100 | 0.6427 | 0.8215 |
| 0.7331 | 1.0919 | 1200 | 0.6423 | 0.8183 |
| 0.6849 | 1.1829 | 1300 | 0.6820 | 0.8151 |
| 0.671 | 1.2739 | 1400 | 0.6325 | 0.8191 |
| 0.7307 | 1.3649 | 1500 | 0.6079 | 0.8286 |
| 0.7499 | 1.4559 | 1600 | 0.5832 | 0.8346 |
| 0.7004 | 1.5469 | 1700 | 0.6048 | 0.8342 |
| 0.7543 | 1.6379 | 1800 | 0.5612 | 0.8394 |
| 0.5557 | 1.7288 | 1900 | 0.5740 | 0.8318 |
| 0.5019 | 1.8198 | 2000 | 0.5064 | 0.8561 |
| 0.7043 | 1.9108 | 2100 | 0.5513 | 0.8441 |
| 0.519 | 2.0018 | 2200 | 0.5862 | 0.8350 |
| 0.3366 | 2.0928 | 2300 | 0.5159 | 0.8517 |
| 0.4167 | 2.1838 | 2400 | 0.5386 | 0.8469 |
| 0.402 | 2.2748 | 2500 | 0.5614 | 0.8374 |
| 0.4133 | 2.3658 | 2600 | 0.4756 | 0.8652 |
| 0.4751 | 2.4568 | 2700 | 0.4882 | 0.8612 |
| 0.3108 | 2.5478 | 2800 | 0.4946 | 0.8648 |
| 0.3218 | 2.6388 | 2900 | 0.4707 | 0.8680 |
| 0.282 | 2.7298 | 3000 | 0.4407 | 0.8712 |
| 0.2823 | 2.8207 | 3100 | 0.4843 | 0.8712 |
| 0.3498 | 2.9117 | 3200 | 0.4609 | 0.8744 |
| 0.3196 | 3.0027 | 3300 | 0.4369 | 0.8763 |
| 0.2822 | 3.0937 | 3400 | 0.4662 | 0.8748 |
| 0.4166 | 3.1847 | 3500 | 0.4539 | 0.8779 |
| 0.1904 | 3.2757 | 3600 | 0.4205 | 0.8887 |
| 0.388 | 3.3667 | 3700 | 0.4163 | 0.8863 |
| 0.2851 | 3.4577 | 3800 | 0.4168 | 0.8891 |
| 0.2455 | 3.5487 | 3900 | 0.4004 | 0.8930 |
| 0.2804 | 3.6397 | 4000 | 0.4044 | 0.8938 |
| 0.2008 | 3.7307 | 4100 | 0.3833 | 0.8950 |
| 0.2487 | 3.8217 | 4200 | 0.3812 | 0.8958 |
| 0.2077 | 3.9126 | 4300 | 0.3760 | 0.8958 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"banh beo",
"banh bot loc",
"banh pia",
"banh tet",
"banh trang nuong",
"banh xeo",
"bun bo hue",
"bun dau mam tom",
"bun mam",
"bun rieu",
"bun thit nuong",
"ca kho to",
"banh can",
"canh chua",
"cao lau",
"chao long",
"com tam",
"goi cuon",
"hu tieu",
"mi quang",
"nem chua",
"pho",
"xoi xeo",
"banh canh",
"banh chung",
"banh cuon",
"banh duc",
"banh gio",
"banh khot",
"banh mi"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-016
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-016
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1175
- Accuracy: 0.96
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0003 | 8.0 | 10 | 0.1202 | 0.96 |
| 0.0002 | 16.0 | 20 | 0.1240 | 0.96 |
| 0.0001 | 24.0 | 30 | 0.1175 | 0.96 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
kdrianm/emotion_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. -->
# emotion_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: 1.5599
- Accuracy: 0.475
## Model description
More information needed
## Intended uses & 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 2.0884 | 0.1125 |
| 2.08 | 2.0 | 10 | 2.0750 | 0.1437 |
| 2.08 | 3.0 | 15 | 2.0519 | 0.2125 |
| 2.0091 | 4.0 | 20 | 2.0177 | 0.225 |
| 2.0091 | 5.0 | 25 | 1.9777 | 0.2625 |
| 1.8779 | 6.0 | 30 | 1.9381 | 0.3125 |
| 1.8779 | 7.0 | 35 | 1.8990 | 0.3438 |
| 1.7355 | 8.0 | 40 | 1.8592 | 0.3688 |
| 1.7355 | 9.0 | 45 | 1.8217 | 0.3812 |
| 1.598 | 10.0 | 50 | 1.7844 | 0.4 |
| 1.598 | 11.0 | 55 | 1.7536 | 0.4062 |
| 1.4689 | 12.0 | 60 | 1.7217 | 0.4188 |
| 1.4689 | 13.0 | 65 | 1.7019 | 0.4188 |
| 1.3534 | 14.0 | 70 | 1.6773 | 0.4188 |
| 1.3534 | 15.0 | 75 | 1.6614 | 0.425 |
| 1.2526 | 16.0 | 80 | 1.6448 | 0.4562 |
| 1.2526 | 17.0 | 85 | 1.6306 | 0.45 |
| 1.1657 | 18.0 | 90 | 1.6201 | 0.4562 |
| 1.1657 | 19.0 | 95 | 1.6067 | 0.4562 |
| 1.0918 | 20.0 | 100 | 1.5992 | 0.45 |
| 1.0918 | 21.0 | 105 | 1.5889 | 0.4562 |
| 1.0311 | 22.0 | 110 | 1.5852 | 0.4562 |
| 1.0311 | 23.0 | 115 | 1.5767 | 0.4625 |
| 0.9814 | 24.0 | 120 | 1.5733 | 0.45 |
| 0.9814 | 25.0 | 125 | 1.5688 | 0.4625 |
| 0.9439 | 26.0 | 130 | 1.5643 | 0.4562 |
| 0.9439 | 27.0 | 135 | 1.5620 | 0.4625 |
| 0.918 | 28.0 | 140 | 1.5599 | 0.475 |
| 0.918 | 29.0 | 145 | 1.5586 | 0.4625 |
| 0.9044 | 30.0 | 150 | 1.5582 | 0.4562 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
FA24-CS462-Group-26/convnext_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. -->
# convnext_model
This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0945 | 1.0 | 100 | 0.1009 |
| 0.0233 | 2.0 | 200 | 0.0851 |
| 0.0041 | 3.0 | 300 | 0.0755 |
| 0.0026 | 4.0 | 400 | 0.0715 |
| 0.0024 | 5.0 | 500 | 0.0712 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"cyclone",
"earthquake",
"flood",
"wildfire"
] |
kiranshivaraju/convnext-large-classify-diode
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"negative",
"positive"
] |
deyakovleva/vit-base-oxford-iiit-pets
|
# vit-base-oxford-iiit-pets
This model was trained to classify cats and dogs and define it's breed using transfer learning method. It is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2068
- Accuracy: 0.9350
## Model description
Since [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) was used as the base model, the final classification layer was modified to predict 37 classes of cats and dogs from the dataset.
## Intended uses & limitations
This model is designed for educational purposes, enabling the classification of cats and dogs and the identification of their breeds. It currently supports 37 distinct breeds, offering a starting point for various learning and experimentation scenarios. Beyond its educational use, the model can serve as a foundation for further development, such as expanding its classification capabilities to include additional breeds, other animal species, or even entirely different tasks. With fine-tuning, this model could be adapted to broader applications in animal recognition, wildlife monitoring, and pet identification systems.
## Training and evaluation data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3625 | 1.0 | 370 | 0.2933 | 0.9269 |
| 0.2002 | 2.0 | 740 | 0.2221 | 0.9432 |
| 0.1511 | 3.0 | 1110 | 0.2057 | 0.9418 |
| 0.1253 | 4.0 | 1480 | 0.1876 | 0.9418 |
| 0.1236 | 5.0 | 1850 | 0.1825 | 0.9432 |
| 0.1078 | 6.0 | 2220 | 0.1785 | 0.9418 |
| 0.078 | 7.0 | 2590 | 0.1809 | 0.9364 |
| 0.0798 | 8.0 | 2960 | 0.1785 | 0.9378 |
| 0.0811 | 9.0 | 3330 | 0.1774 | 0.9364 |
| 0.0736 | 10.0 | 3700 | 0.1769 | 0.9391 |
### Evaluation results
| Metric | Value |
|--------------------------|----------------------|
| Evaluation Loss | 0.2202 |
| Evaluation Accuracy | 92.56% |
| Evaluation Runtime (s) | 7.39 |
| Samples Per Second | 100.04 |
| Steps Per Second | 12.59 |
| Epoch | 10 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.0.1+cu117
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"sphynx",
"maine coon",
"keeshond",
"yorkshire terrier",
"havanese",
"leonberger",
"wheaten terrier",
"american bulldog",
"english setter",
"boxer",
"newfoundland",
"bengal",
"samoyed",
"british shorthair",
"great pyrenees",
"abyssinian",
"pug",
"saint bernard",
"russian blue",
"scottish terrier"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-018
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-018
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1702
- Accuracy: 0.9623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use 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: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.9831 | 6.6667 | 10 | 0.4326 | 0.8868 |
| 0.112 | 13.3333 | 20 | 0.1814 | 0.9434 |
| 0.0087 | 20.0 | 30 | 0.1702 | 0.9623 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-019
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-019
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1377
- Accuracy: 0.9623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use 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: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.9688 | 6.6667 | 10 | 0.4105 | 0.8868 |
| 0.114 | 13.3333 | 20 | 0.1491 | 0.9623 |
| 0.0082 | 20.0 | 30 | 0.1377 | 0.9623 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-020
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-020
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1267
- Accuracy: 0.9811
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use 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: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1059 | 6.6667 | 10 | 0.5386 | 0.8868 |
| 0.3385 | 13.3333 | 20 | 0.1848 | 0.9434 |
| 0.1115 | 20.0 | 30 | 0.1267 | 0.9811 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-025
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-025
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5643
- Accuracy: 0.9434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0 | 3.6364 | 10 | 0.5643 | 0.9434 |
| 0.0 | 7.2727 | 20 | 0.6440 | 0.9434 |
| 0.0 | 10.9091 | 30 | 0.6484 | 0.9434 |
| 0.0 | 14.5455 | 40 | 0.6491 | 0.9434 |
| 0.0 | 18.1818 | 50 | 0.6515 | 0.9434 |
| 0.0 | 21.8182 | 60 | 0.6539 | 0.9434 |
| 0.0 | 25.4545 | 70 | 0.6547 | 0.9434 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
qubvel-hf/my_awesome_model
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9"
] |
kdrianm/vit-emotion_classifier
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-emotion_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4782
- Accuracy: 0.525
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0776 | 1.0 | 10 | 2.0731 | 0.1437 |
| 2.0526 | 2.0 | 20 | 2.0567 | 0.1688 |
| 1.9975 | 3.0 | 30 | 2.0160 | 0.2 |
| 1.8977 | 4.0 | 40 | 1.9550 | 0.3 |
| 1.778 | 5.0 | 50 | 1.8805 | 0.3625 |
| 1.6549 | 6.0 | 60 | 1.8073 | 0.375 |
| 1.5379 | 7.0 | 70 | 1.7428 | 0.4125 |
| 1.4241 | 8.0 | 80 | 1.6957 | 0.4062 |
| 1.3212 | 9.0 | 90 | 1.6550 | 0.45 |
| 1.2245 | 10.0 | 100 | 1.6271 | 0.4437 |
| 1.1336 | 11.0 | 110 | 1.5928 | 0.4562 |
| 1.0483 | 12.0 | 120 | 1.5695 | 0.4688 |
| 0.9669 | 13.0 | 130 | 1.5452 | 0.4875 |
| 0.8889 | 14.0 | 140 | 1.5248 | 0.4875 |
| 0.815 | 15.0 | 150 | 1.5063 | 0.5062 |
| 0.7466 | 16.0 | 160 | 1.4909 | 0.4938 |
| 0.6852 | 17.0 | 170 | 1.4782 | 0.525 |
| 0.6308 | 18.0 | 180 | 1.4615 | 0.5 |
| 0.5819 | 19.0 | 190 | 1.4541 | 0.5 |
| 0.5392 | 20.0 | 200 | 1.4458 | 0.5125 |
| 0.503 | 21.0 | 210 | 1.4393 | 0.5 |
| 0.4718 | 22.0 | 220 | 1.4289 | 0.5188 |
| 0.4458 | 23.0 | 230 | 1.4238 | 0.5188 |
| 0.4234 | 24.0 | 240 | 1.4211 | 0.5125 |
| 0.405 | 25.0 | 250 | 1.4182 | 0.5 |
| 0.3905 | 26.0 | 260 | 1.4157 | 0.5062 |
| 0.379 | 27.0 | 270 | 1.4125 | 0.5062 |
| 0.3706 | 28.0 | 280 | 1.4119 | 0.5062 |
| 0.3649 | 29.0 | 290 | 1.4115 | 0.5062 |
| 0.3618 | 30.0 | 300 | 1.4111 | 0.5062 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
quangtuyennguyen/food_classify_viT
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# food_classify_viT
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8683
- Accuracy: 0.8948
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.9970 | 83 | 1.6473 | 0.8236 |
| No log | 1.9940 | 166 | 1.1061 | 0.8863 |
| No log | 2.9910 | 249 | 0.9208 | 0.8820 |
| No log | 3.9880 | 332 | 0.8683 | 0.8948 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"burger",
"butter_naan",
"chai",
"chapati",
"chole_bhature",
"dal_makhani",
"dhokla",
"fried_rice",
"idli",
"jalebi",
"kaathi_rolls",
"kadai_paneer",
"kulfi",
"masala_dosa",
"momos",
"paani_puri",
"pakode",
"pav_bhaji",
"pizza",
"samosa"
] |
quangtuyennguyen/mri_classification_alzheimer_disease
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mri_classification_alzheimer_disease
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: 0.7795
- Accuracy: 0.6453
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 80 | 0.8764 | 0.5859 |
| No log | 2.0 | 160 | 0.8594 | 0.5703 |
| No log | 3.0 | 240 | 0.8095 | 0.6391 |
| No log | 4.0 | 320 | 0.7795 | 0.6453 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"mild_demented",
"moderate_demented",
"non_demented",
"very_mild_demented"
] |
cvmil/deit-base-patch16-224_rice-disease-02
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deit-base-patch16-224_rice-disease-02_112024
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3063
- Accuracy: 0.9148
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 15
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.8862 | 1.0 | 212 | 0.7092 | 1.2580 |
| 0.8631 | 2.0 | 424 | 0.8190 | 0.6676 |
| 0.5449 | 3.0 | 636 | 0.8523 | 0.5124 |
| 0.4396 | 4.0 | 848 | 0.8736 | 0.4459 |
| 0.3852 | 5.0 | 1060 | 0.8816 | 0.4026 |
| 0.3488 | 6.0 | 1272 | 0.8902 | 0.3763 |
| 0.324 | 7.0 | 1484 | 0.8942 | 0.3588 |
| 0.3072 | 8.0 | 1696 | 0.9062 | 0.3420 |
| 0.2928 | 9.0 | 1908 | 0.9055 | 0.3330 |
| 0.2826 | 10.0 | 2120 | 0.9082 | 0.3231 |
| 0.2732 | 11.0 | 2332 | 0.9115 | 0.3172 |
| 0.2669 | 12.0 | 2544 | 0.3119 | 0.9128 |
| 0.2619 | 13.0 | 2756 | 0.3086 | 0.9155 |
| 0.258 | 14.0 | 2968 | 0.3068 | 0.9155 |
| 0.2566 | 15.0 | 3180 | 0.3063 | 0.9148 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"bacterial_leaf_blight",
"brown_spot",
"healthy",
"leaf_blast",
"leaf_scald",
"narrow_brown_spot",
"neck_blast",
"rice_hispa",
"sheath_blight",
"tungro"
] |
cvmil/swin-base-patch4-window7-224_rice-disease-02
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224_rice-disease-02_112024
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: 0.2147
- Accuracy: 0.9281
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 15
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.7761 | 1.0 | 212 | 0.7405 | 0.9638 |
| 0.6771 | 2.0 | 424 | 0.8476 | 0.4818 |
| 0.4223 | 3.0 | 636 | 0.8756 | 0.3695 |
| 0.3403 | 4.0 | 848 | 0.8922 | 0.3168 |
| 0.2958 | 5.0 | 1060 | 0.9082 | 0.2835 |
| 0.2709 | 6.0 | 1272 | 0.2664 | 0.9075 |
| 0.2494 | 7.0 | 1484 | 0.2498 | 0.9168 |
| 0.2395 | 8.0 | 1696 | 0.2420 | 0.9182 |
| 0.2286 | 9.0 | 1908 | 0.2365 | 0.9215 |
| 0.22 | 10.0 | 2120 | 0.2296 | 0.9202 |
| 0.2137 | 11.0 | 2332 | 0.2230 | 0.9242 |
| 0.2093 | 12.0 | 2544 | 0.2178 | 0.9281 |
| 0.202 | 13.0 | 2756 | 0.2162 | 0.9295 |
| 0.2017 | 14.0 | 2968 | 0.2151 | 0.9275 |
| 0.1986 | 15.0 | 3180 | 0.2147 | 0.9281 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"bacterial_leaf_blight",
"brown_spot",
"healthy",
"leaf_blast",
"leaf_scald",
"narrow_brown_spot",
"neck_blast",
"rice_hispa",
"sheath_blight",
"tungro"
] |
kiranshivaraju/convnext-large-v1
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"bad",
"good"
] |
AmadFR/Emotion_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. -->
# Emotion_Classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3727
- Accuracy: 0.55
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.083 | 1.0 | 10 | 2.0798 | 0.1625 |
| 2.0591 | 2.0 | 20 | 2.0464 | 0.2812 |
| 2.0043 | 3.0 | 30 | 1.9889 | 0.325 |
| 1.9174 | 4.0 | 40 | 1.9087 | 0.3375 |
| 1.819 | 5.0 | 50 | 1.8037 | 0.3875 |
| 1.7161 | 6.0 | 60 | 1.6875 | 0.4125 |
| 1.6253 | 7.0 | 70 | 1.6207 | 0.4437 |
| 1.549 | 8.0 | 80 | 1.5978 | 0.4437 |
| 1.4946 | 9.0 | 90 | 1.5430 | 0.4688 |
| 1.4426 | 10.0 | 100 | 1.4995 | 0.5125 |
| 1.4061 | 11.0 | 110 | 1.4919 | 0.4938 |
| 1.3648 | 12.0 | 120 | 1.4628 | 0.525 |
| 1.3306 | 13.0 | 130 | 1.4207 | 0.5437 |
| 1.3071 | 14.0 | 140 | 1.4340 | 0.5188 |
| 1.2791 | 15.0 | 150 | 1.4126 | 0.5188 |
| 1.2589 | 16.0 | 160 | 1.4119 | 0.5375 |
| 1.2199 | 17.0 | 170 | 1.4168 | 0.4938 |
| 1.2189 | 18.0 | 180 | 1.3957 | 0.525 |
| 1.2096 | 19.0 | 190 | 1.4015 | 0.5625 |
| 1.2114 | 20.0 | 200 | 1.3932 | 0.5188 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Tokenizers 0.20.3
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
kiranshivaraju/convnext-large-v2
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"bad",
"good"
] |
nergizinal/vit-base-nationality
|
<!-- This model card 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-nationality
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2289
- Precision: 0.5992
- Recall: 0.6005
- Accuracy: 0.6005
- F1: 0.5861
- Score: 0.6005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|:------:|
| 1.2527 | 1.0 | 105 | 1.2744 | 0.5925 | 0.5820 | 0.5820 | 0.5631 | 0.5820 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"italian",
"russian",
"mexican",
"french",
"belgian",
"spanish",
"dutch",
"austrian",
"flemish",
"spanish,greek",
"german",
"french,british",
"french,jewish,belarusian",
"british",
"norwegian",
"german,swiss",
"american"
] |
kiranshivaraju/convnext-large-v3
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
## Glossary [optional]
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|
[
"bad",
"good"
] |
kiranshivaraju/convnext-large-v4
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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|
[
"bad",
"good"
] |
MBARI-org/mbari-uav-vit-b-16
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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|
[
"batray",
"bird",
"boat",
"buoy",
"egregia",
"foam",
"jelly",
"kelp",
"mola",
"mooring",
"otter",
"person",
"pinniped",
"poop",
"rib",
"reflectance",
"secci_disc",
"shark",
"surfboard",
"wave",
"whale",
"wood"
] |
tdhcuong/swin-tiny-patch4-window7-224-finetuned-azure-poc-img-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-azure-poc-img-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: 0.2119
- Accuracy: 0.9122
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5888 | 1.0 | 41 | 0.4436 | 0.8348 |
| 0.3118 | 2.0 | 82 | 0.3028 | 0.8692 |
| 0.2284 | 3.0 | 123 | 0.2879 | 0.8795 |
| 0.203 | 4.0 | 164 | 0.2368 | 0.8950 |
| 0.2254 | 5.0 | 205 | 0.2276 | 0.8985 |
| 0.1976 | 6.0 | 246 | 0.2339 | 0.8967 |
| 0.1603 | 7.0 | 287 | 0.2191 | 0.9036 |
| 0.1556 | 8.0 | 328 | 0.2249 | 0.9036 |
| 0.1488 | 9.0 | 369 | 0.2018 | 0.9071 |
| 0.158 | 10.0 | 410 | 0.2119 | 0.9122 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"concrete_anchors",
"steel_connectors",
"steel_fasteners",
"wood_connectors",
"wood_fasteners"
] |
kiranshivaraju/convnext-large-224-v5
|
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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|
[
"bad",
"good"
] |
kiranshivaraju/convnext-large-v6
|
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[More Information Needed]
## Training Details
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"bad",
"good"
] |
krisnadwipayanap/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [nateraw/vit-age-classifier](https://huggingface.co/nateraw/vit-age-classifier) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"0-2",
"3-9",
"10-19",
"20-29",
"30-39",
"40-49",
"50-59",
"60-69",
"more than 70"
] |
kiranshivaraju/convnext-xlarge-v7
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"bad",
"good"
] |
initial01/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: 1.6465
- Accuracy: 0.899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7306 | 0.992 | 62 | 2.5309 | 0.848 |
| 1.8719 | 2.0 | 125 | 1.7966 | 0.896 |
| 1.609 | 2.976 | 186 | 1.6465 | 0.899 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
kiranshivaraju/convnext-large-v8
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"bad",
"good"
] |
kiranshivaraju/convnext-xlarge-v9
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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|
[
"bad",
"good"
] |
ljttw/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0169
- F1: 0.9932
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0337 | 0.9994 | 830 | 0.0303 | 0.9776 |
| 0.0272 | 1.9991 | 1660 | 0.0286 | 0.9819 |
| 0.0071 | 2.9988 | 2490 | 0.0169 | 0.9932 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"type0",
"type1",
"type2",
"type3"
] |
ljttw/convnext-base-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. -->
# convnext-base-224-finetuned-eurosat
This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0081
- F1: 0.9949
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0242 | 0.9994 | 830 | 0.0168 | 0.9903 |
| 0.0127 | 1.9991 | 1660 | 0.0091 | 0.9941 |
| 0.0075 | 2.9988 | 2490 | 0.0081 | 0.9949 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"type0",
"type1",
"type2",
"type3"
] |
ljttw/convnext-tiny-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. -->
# convnext-tiny-224-finetuned-eurosat
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0116
- F1: 0.9917
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0369 | 0.9994 | 830 | 0.0378 | 0.9770 |
| 0.0152 | 1.9991 | 1660 | 0.0202 | 0.9903 |
| 0.0003 | 2.9988 | 2490 | 0.0116 | 0.9917 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"type0",
"type1",
"type2",
"type3"
] |
kiranshivaraju/convnext-large-v10
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"bad",
"good"
] |
ljttw/resnet-50-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-50-finetuned-eurosat
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0498
- F1: 0.9645
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.1303 | 0.9994 | 830 | 0.1197 | 0.7228 |
| 0.0878 | 1.9991 | 1660 | 0.0625 | 0.9522 |
| 0.0542 | 2.9988 | 2490 | 0.0498 | 0.9645 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"type0",
"type1",
"type2",
"type3"
] |
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-21Nov24-003
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-21Nov24-003
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1531
- Accuracy: 0.9583
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.0456 | 6.6667 | 10 | 0.5274 | 0.875 |
| 0.1182 | 13.3333 | 20 | 0.2556 | 0.9167 |
| 0.0095 | 20.0 | 30 | 0.1531 | 0.9583 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"four",
"three",
"two",
"zero"
] |
ljttw/swin-base-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-base-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0099
- F1: 0.9952
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0376 | 0.9994 | 830 | 0.0223 | 0.9759 |
| 0.0088 | 1.9991 | 1660 | 0.0148 | 0.9920 |
| 0.0042 | 2.9988 | 2490 | 0.0099 | 0.9952 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
[
"type0",
"type1",
"type2",
"type3"
] |
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