model_id
stringlengths 7
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|---|---|---|
ckckfk/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0374
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0507 | 1.5385 | 100 | 0.0732 | 0.9850 |
| 0.0345 | 3.0769 | 200 | 0.0374 | 0.9925 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
SunnyO4/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0810
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.087 | 1.5385 | 100 | 0.0823 | 0.9699 |
| 0.0721 | 3.0769 | 200 | 0.0810 | 0.9774 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Seongmoon/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0791
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.111 | 1.5385 | 100 | 0.1190 | 0.9549 |
| 0.0128 | 3.0769 | 200 | 0.0791 | 0.9774 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
tritera/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0129
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1423 | 1.5385 | 100 | 0.0712 | 0.9850 |
| 0.0127 | 3.0769 | 200 | 0.0129 | 1.0 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
hyeongsun/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0479
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0984 | 1.5385 | 100 | 0.0648 | 0.9699 |
| 0.0331 | 3.0769 | 200 | 0.0479 | 0.9925 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
sssasdfqwerqwer/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0607
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1042 | 1.5385 | 100 | 0.0547 | 0.9774 |
| 0.0188 | 3.0769 | 200 | 0.0607 | 0.9774 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Hwooo92/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0948
- Accuracy: 0.9699
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0917 | 1.5385 | 100 | 0.0718 | 0.9774 |
| 0.0335 | 3.0769 | 200 | 0.0948 | 0.9699 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
kingo555/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2462
- Accuracy: 0.9474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0579 | 1.5385 | 100 | 0.0632 | 0.9850 |
| 0.0161 | 3.0769 | 200 | 0.2462 | 0.9474 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
djbp/swin-base-patch4-window7-224-in22k-MM_Classification_base_V2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224-in22k-MM_Classification_base_V2
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3018
- Accuracy: 0.8822
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0086 | 1.0 | 19 | 0.4613 | 0.8105 |
| 0.4528 | 2.0 | 38 | 0.3454 | 0.8592 |
| 0.3556 | 3.0 | 57 | 0.3289 | 0.8604 |
| 0.3404 | 4.0 | 76 | 0.3197 | 0.8784 |
| 0.3175 | 5.0 | 95 | 0.3018 | 0.8822 |
| 0.3007 | 6.0 | 114 | 0.3007 | 0.8809 |
| 0.2968 | 7.0 | 133 | 0.2967 | 0.8758 |
### Framework versions
- Transformers 4.43.3
- Pytorch 1.13.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"invalid",
"mid market",
"non mid market"
] |
Maria831Chowdhury/image_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. -->
# Maria831Chowdhury/image_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: 0.0400
- Validation Loss: 0.0153
- Train Accuracy: 0.9952
- 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': 5e-05, 'decay_steps': 8400, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1680 | 0.0335 | 0.9952 | 0 |
| 0.0622 | 0.0160 | 1.0 | 1 |
| 0.0451 | 0.0192 | 0.9976 | 2 |
| 0.0522 | 0.0108 | 1.0 | 3 |
| 0.0400 | 0.0153 | 0.9952 | 4 |
### Framework versions
- Transformers 4.43.3
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"dog",
"food"
] |
Rashed-Mamdi/vit_checkpoint
|
<!-- This model card 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_checkpoint
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0018 | 22.2222 | 200 | 0.0017 | 1.0 |
| 0.001 | 44.4444 | 400 | 0.0010 | 1.0 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"bike in a city",
"bike in a desert",
"bike on a mountain",
"car in a city",
"car in a desert",
"car on a mountain"
] |
n1hal/Pets_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. -->
# Pets_Classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8088
- Accuracy: 0.8511
## Model description
More information needed
## Intended uses & 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.8889 | 6 | 0.9810 | 0.6596 |
| 1.0184 | 1.9259 | 13 | 0.8452 | 0.8085 |
| 1.0184 | 2.6667 | 18 | 0.8088 | 0.8511 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat_images",
"dog_images",
"fish_images"
] |
UMCai-hf/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 ethz/food101 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7879
- eval_accuracy: 0.7898
- eval_runtime: 136.1038
- eval_samples_per_second: 55.656
- eval_steps_per_second: 6.958
- epoch: 1.0008
- step: 3791
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare",
"waffles"
] |
n1hal/Weeds_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. -->
# Weeds_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: 4.2276
- Accuracy: 0.2755
## Model description
More information needed
## Intended uses & 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 5.0535 | 0.9998 | 1125 | 4.9571 | 0.2170 |
| 4.4188 | 1.9996 | 2250 | 4.4125 | 0.2583 |
| 4.1784 | 2.9993 | 3375 | 4.2276 | 0.2755 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"abelmoschus ficulneus l. wight arn.",
"abelmoschus moschatus medik.",
"ageratum conyzoides l.",
"corchorus aestuans l.",
"corchorus capsularis l.",
"corchorus fascicularis l.",
"corchorus olitorius l.",
"corchorus trilocularis auct.",
"coronopus didymus l. sm.",
"crotalaria medicaginea lam.",
"crotalaria prostrata rottl. ex willd.",
"crotalaria sagittalis l.",
"crotalaria verrucosa l.",
"ageratum houstonianum mill.",
"croton bonplandianum baill.",
"cyanotis axillaris roem. schult. f.",
"cyanotis cristata linn. d. don",
"cyathula prostrata l. blume.",
"cynodon dactylon l. pers.",
"cyperus brevifolius rottb.",
"cyperus compressus l.",
"cyperus difformis l.",
"cyperus eragrostis lam.hibiscus lobatus j.a. murray o. ktze.",
"cyperus haspan l.",
"alternanthera bettzickiana regol voss",
"cyperus iria l.",
"cyperus kyllingia l.",
"cyperus rotundus l.",
"cyperus tenuispica l.",
"cyperus triceps l.",
"dactyloctenium aegyptium l. willd",
"dactyloctenium scindicum boiss.",
"datura metel l.",
"desmodium gangeticum l. dc.",
"desmodium laxiflorum. dc.",
"alternanthera brasiliana l. kuntze.",
"desmodium triflorum l. dc.",
"desmostachya bipinnata stapf.",
"dichanthium annulatum forssk. stapf.",
"digera arvensis forssk.",
"digitaria sanguinalis l. scop.",
"dinebra retroflexa vahl panzer.",
"diplocyclos palmatus l. c. jeffrey",
"dyschoriste depressa l. nees.",
"echinochloa colona l. link.",
"echinochloa crusgalli l. beauv.",
"alternanthera paronychioides a. st.-hil",
"echinochloa glabrescens l.",
"echinops echinatus roxb.",
"eclipta alba l. hassk.",
"eichhornia crassipes mart. solms.-laub.",
"eleusine indica l. gaertner.",
"emilia sonchifolia l. dc. ex wight.",
"eragrostis unioloides retz. nees. ex steud",
"eriocaulon sieboldianum siebold zucc. ex steud.",
"euphorbia dracunculoides lamarck.",
"euphorbia geniculata orteg.",
"alternanthera philoxeroides mart. grieb.",
"euphorbia hirta l.",
"euphorbia hypersifolia l.",
"euphorbia indica lam.",
"euphorbia microphylla heyne ex. roth.",
"euphorbia thymifolia l.",
"evolvulus alsinoides l. l.",
"evolvulus nummularius l. l.",
"fimbristylis dichotoma l. vahl.",
"fimbristylis miliaceae l. vahl.",
"fumaria parviflora lam.",
"alternanthera pungens h. b k.",
"galium aparine l.",
"glinus lotoides l.",
"glinus oppositifolius l.",
"gnaphalium polycephalum l.",
"gomphrena decumbens jacq.",
"grangea maderaspatana l. poir",
"heliotropium indicum l.",
"heliotropium ovalifolium forsk.",
"hemidesmus indicus l. r. br.",
"heteropogon contortus l. p. beauv.",
"alternanthera sessilis l. dc.",
"hibiscus lobatus j.a. murray o. ktze.",
"hibiscus panduriformis burm. f.",
"hibiscus vitifolius linn.",
"hydrolea zeylanica l. vahl.",
"hyptis suaveolens l. poit.",
"imperata cylindrica l. raeusch.",
"indigofera cordifolia heyne. ex roth.",
"indigofera linifolia l.f. retz.",
"indigofera linnaei ali.",
"indoneesiella echioides l. sreem.",
"alysicarpus bupleurifolius linn. dc.",
"ionidium suffruticosum l. roem. sch.",
"ipomoea aquatica forssk.",
"ipomoea cairica l. sweet.",
"ipomoea carnea jace.",
"ipomoea coccinea l.",
"ipomoea hederacea l. jacq.",
"ipomoea obscura l. ker gawl.",
"ipomoea pes-caprae l. r. br",
"ipomoea pes-tigridis l.",
"ipomoea turbinata lag.",
"alysicarpus longifolius wight arn.",
"ischaemum indicum houtt. merr.",
"ischaemum rugosum salisb.",
"lagascea mollis cav.",
"lantana camara l.",
"lathyrus aphaca l.",
"lathyrus sativus l.",
"launaea nudicaulis linn. hook. f.",
"launaea sarmentosa willd. sch.-bip.",
"leonitis nepetaefolia l. r. br.",
"lepidium sativum l.",
"abutilon hirtum lam. sweet.",
"alysicarpus monilifer l. dc.",
"leptochloa chinensis l. nees.",
"leucas aspera link.",
"leucas cephalotes roth.",
"leucas martinicensis r. br.",
"leucas zeylanica l. r. br.",
"limnocharis flava l. buchenau.",
"lindernia antipoda l. alston.",
"lindernia ciliata colsm. panuell.",
"lindernia crustacea l. f. muell.",
"lindernia procumbens krock. philcox.",
"alysicarpus ovalifolius schumach. j. eonard.",
"ludwigia adscendens l. h. hara.",
"ludwigia octovalvis jacq. raven.",
"ludwigia parviflora roxb.",
"malachra capitata linn. linn.",
"malva parviflora l.",
"malvastrum coromandelianum l. garcke.",
"marsilea quadrifolia linn.",
"martynia annua l.",
"mecardonia procumbens mill. swall.",
"medicago denticulata willd.",
"alysicarpus vaginalis l dc.",
"melilotus alba medikus.",
"melilotus indica l. all.",
"melochia corchorifolia l.",
"merremia aegyptia linn. urban",
"merremia dissecta jacq. hallier f.",
"merremia emarginata burm. f. hall. f.",
"merremia tuberosa l. rendle",
"mikania micrantha h.b.k.",
"mimosa invisa c. mart.",
"mimosa pudica l.",
"amaranthus spinosus l.",
"mitracarpus villosus sw. dc.",
"mollugo nudicaulis lam.",
"mollugo pentaphylla l.",
"monochoria vaginalis burm f. kunth.",
"mukia maderaspatana l. roem.",
"murdannia nudiflora l. brenam.",
"oldenlandia corymbosa l.",
"oldenlandia diffusa willd. roxb.",
"oldenlandia herbacea l. roxb.",
"operculina turpethum l. silva manso.",
"amaranthus viridis hook. f.",
"oplismenus burmannii retz. p.beauv",
"oxalis corniculata l.",
"oxlis martiana zucc.",
"parthenium hysterophorus l.",
"paspalidium flavidum retz. a. camus.",
"paspalum dilatatum poir.",
"paspalum distichum auct. nm l.",
"passiflora foetida l.",
"pedalium murex linn.",
"pergularia daemia forssk. choiv.",
"ammannia baccifera l.",
"peristrophe paniculata forssk. brummit.",
"phalaris minor retz.",
"phaseolus trilobus l. aiton auct.",
"phyla nodiflora l. greene.",
"phyllanthus maderaspatensis l.",
"phyllanthus multiflorus willd.",
"phyllanthus niruri l.",
"phyllanthus urinaria l.",
"phyllanthus virgatus forst.",
"physalis minima l.",
"anagallis arvensis l.",
"physalis peruviana l.",
"pluchea lanceolata dc. oliv. hiern",
"plumbago zeylanica l.",
"polygonum plebeium r. br.",
"polypogon monspeliensis l. desf.",
"portulaca oleracea l.",
"portulaca quadrifida l.",
"pouzolzia zeylanica l. bennet r. br.",
"pseudognaphalium luteo-album l. hillard burtt.",
"psoralea corylifolia l.",
"andrographis paniculata wall.",
"rhynchosia minima dc.",
"rottboellia cochinchinensis lour. w.d. clayton.",
"ruellia prostrata poir.",
"ruellia tuberosa l.",
"rumex dentatus l.",
"rungia pectinata l. nees.",
"rungia repens nees.",
"saccharum spontaneum l.",
"sacciolepis indica l.",
"sagittaria guyanensis h . b . k.",
"anisomeles indica l. o. kuntze.",
"salvinia molesta mitchell.",
"scoparia dulcis l.",
"sebastiana chamaelea linn. muell.",
"setaria glauca l. p. beauv.",
"setaria viridis l. p. beauv.",
"sida acuta burm. f.",
"sida cordata burm.f. borssum.",
"sida cordifolia linn",
"sida rhombifolia l.",
"sida spinosa l.",
"anisomeles malabarica r.br.",
"sisymbrium irio l.",
"solanum elaeagnifolium cav.",
"solanum nigrum l.",
"solanum sisymbrifolium lam.",
"solanum torvum sw.",
"solanum viarum dunal..",
"solanum xanthocarpum schrad. wendl",
"sonchus asper l. hill.",
"sonchus oleraceus l.",
"sopubia delphinifolia l. don.",
"abutilon indicum l. sweet.",
"antigonon leptopus hook. arn.",
"sorghum halapense l. pers.",
"spergula arvensis l.",
"sphaeranthus indicus l.",
"sphenoclea zeylanica gaertn.",
"spigelia anthelmia l.",
"spilanthes acmella auct. non l.",
"spilanthes calva dc.",
"sporobolus diander retz. p. beauv.",
"stachytarpeta indica l. vahl.",
"stellaria media l. vill.",
"apluda mutica l.",
"stemodia viscosa roxb.",
"striga asiatica l. kuntze.",
"synedrella nodiflora l. gaertn.",
"tephrosia purpuria l. pers.",
"themeda triandra forssk.",
"trianthema portulacastrum l.",
"tribulus terrestris l.",
"trichodesma indicum l.",
"trichodesma zeylanicum burm f. r. br.",
"tridax procumbens l.",
"argemone mexicana l.",
"trifolium fragiferum l.",
"trigonella polycerata auct. non linn.",
"triumfetta rhomboidea jacq.",
"typha angustata bory chaubard.",
"urena lobata l.",
"urena sinuata l.",
"verbascum chinense l. santapau fl.",
"verbesina encelioides cav.benth.",
"vernonia cinerea l. less.",
"vicia hirsuta l. s.f. gray.",
"arundo donax l.",
"vicia sativa l.",
"vicoa auriculata cass.",
"volutarella divaricata benth hook.",
"waltheria indica l.",
"wedelia chinensis osbeck merr.",
"xanthium strumarium l.",
"zornia gibbosa spanoghe.",
"asphodelus tenuifolius cav.",
"asteracantha longifolia l. nees.",
"asystasia gangetica t. anders.",
"atylosia scarabaeoides l. benth.",
"avena ludoviciana l. nees.",
"axonopus compressus beauv.",
"acalypha indica l.",
"barleria cristata l.",
"barleria prionitis l.",
"bergia ammannioides hayne ex roth.",
"bergia capensis l.",
"bidens pilosa l.",
"biophytum sensitivum l. don.",
"blainvillea acmella l. philipson.",
"blepharis maderaspatensis l. roth.",
"blumea lacera burm.f dc.",
"blumea oxyodonta dc.",
"acanthospermum hispidum dc.",
"blumea wightiana dc.",
"boerhavia diffusa l.",
"boerhavia erecta l.",
"borreria hispida l. k. schum.",
"borreria pusilla wall. dc.",
"brachiaria deflexa schumach. robyns",
"brachiaria eruciformis j.e.smith griseb.",
"brachiaria ramosa l. stapf.",
"brachiaria reptans gard. hubb.",
"cabomba aquatica piotr kuczynski",
"achyranthes aspera l.",
"caesulia axillaries roxb.",
"calotropis gigantea l. aiton.",
"calotropis procera aiton dryand. ex.",
"cannabis sativa l.",
"cardamine hirsuta l.",
"cardiospermum halicacabum l.",
"cassia absus l.",
"cassia mimosoides l.",
"cassia occidentalis l. link.",
"cassia pumila lam.",
"aerva javanica burm.f. schult",
"cassia tora l. roxb.",
"catharanthus pusillus murr. g. don.",
"cayratia trifolia l. domin.",
"celosia argentea l.",
"centella asiatica l.",
"centrosema pubescens benth.",
"chenopodium album l.",
"chenopodium murale l.",
"chloris barbata sw.",
"chromolaena odorata l. r.m. king h. rob.",
"aerva lanata l. juss.ex schult.",
"chrozophora plicata vahl a. juss. ex spreng",
"chrozophora rottleri klotzsch.",
"cichorium intybus l.",
"cirsium arvense l. scop.",
"cleome chelidonii linn.",
"cleome gynandra l.",
"cleome monophylla l.",
"cleome viscosa l.",
"clerodendron infortunatum gaertn.",
"clitoria ternatea l.",
"aeschynomene indica l.",
"cocculus hirsutus l. diels.",
"coix barbata roxb. r. br.",
"coix lacryma-jobi l.",
"commelina benghalensis l.",
"commelina diffusa l.",
"commelina forskaolii vahl.",
"convolvulus arvensis l.",
"convolvulus pluricaulis choisy.",
"conyza canadensis l. cronq.",
"conyza bonariensis l. cronq."
] |
ChispiDEV/autotrain-1tqht-w0zz7
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.4714449942111969
f1: 0.6666666666666666
precision: 0.5
recall: 1.0
auc: 1.0
accuracy: 0.5
|
[
"maduras",
"normales"
] |
Robotkid2696/finetuned-indian-food
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-indian-food
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2086
- Accuracy: 0.9633
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8456 | 0.9346 | 100 | 0.7054 | 0.93 |
| 0.4284 | 1.8692 | 200 | 0.3311 | 0.95 |
| 0.2374 | 2.8037 | 300 | 0.2373 | 0.9567 |
| 0.155 | 3.7383 | 400 | 0.2086 | 0.9633 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"burger",
"butter_naan",
"kaathi_rolls",
"kadai_paneer",
"kulfi",
"masala_dosa",
"momos",
"paani_puri",
"pakode",
"pav_bhaji",
"pizza",
"samosa",
"chai",
"chapati",
"chole_bhature",
"dal_makhani",
"dhokla",
"fried_rice",
"idli",
"jalebi"
] |
Rashed-Mamdi/Rashed-vit-model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Rashed-vit-model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0047
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2279 | 1.9048 | 200 | 0.4485 | 0.9111 |
| 0.1335 | 3.8095 | 400 | 0.0680 | 0.9889 |
| 0.0061 | 5.7143 | 600 | 0.0047 | 1.0 |
| 0.0025 | 7.6190 | 800 | 0.0606 | 0.9778 |
| 0.0624 | 9.5238 | 1000 | 0.2500 | 0.9556 |
| 0.0013 | 11.4286 | 1200 | 0.0868 | 0.9889 |
| 0.001 | 13.3333 | 1400 | 0.0908 | 0.9889 |
| 0.0008 | 15.2381 | 1600 | 0.0935 | 0.9889 |
| 0.0006 | 17.1429 | 1800 | 0.0960 | 0.9889 |
| 0.0005 | 19.0476 | 2000 | 0.0979 | 0.9889 |
| 0.0004 | 20.9524 | 2200 | 0.0996 | 0.9889 |
| 0.0004 | 22.8571 | 2400 | 0.1013 | 0.9889 |
| 0.0003 | 24.7619 | 2600 | 0.1027 | 0.9889 |
| 0.0003 | 26.6667 | 2800 | 0.1040 | 0.9889 |
| 0.0003 | 28.5714 | 3000 | 0.1054 | 0.9889 |
| 0.0002 | 30.4762 | 3200 | 0.1065 | 0.9889 |
| 0.0002 | 32.3810 | 3400 | 0.1076 | 0.9889 |
| 0.0002 | 34.2857 | 3600 | 0.1085 | 0.9889 |
| 0.0002 | 36.1905 | 3800 | 0.1094 | 0.9889 |
| 0.0002 | 38.0952 | 4000 | 0.1102 | 0.9889 |
| 0.0002 | 40.0 | 4200 | 0.1109 | 0.9889 |
| 0.0001 | 41.9048 | 4400 | 0.1115 | 0.9889 |
| 0.0001 | 43.8095 | 4600 | 0.1120 | 0.9889 |
| 0.0001 | 45.7143 | 4800 | 0.1124 | 0.9889 |
| 0.0001 | 47.6190 | 5000 | 0.1126 | 0.9889 |
| 0.0001 | 49.5238 | 5200 | 0.1128 | 0.9889 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"bike in a city",
"bike in a desert",
"bike on a mountain",
"car in a city",
"car in a desert",
"car on a mountain"
] |
ivansuteja96/autotrain-48ci8-roib9
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.20756371319293976
f1: 0.0
precision: 0.0
recall: 0.0
auc: 0.8461538461538461
accuracy: 0.9285714285714286
|
[
"normal",
"nsfw"
] |
ChispiDEV/autotrain-pky99-ias73
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.42044156789779663
f1: 0.9361702127659575
precision: 0.88
recall: 1.0
auc: 0.8340909090909091
accuracy: 0.8888888888888888
|
[
"rotten banana",
"yellow banana"
] |
diwashrestha/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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/diwashrestha/huggingface/runs/hxu9knpm)
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9907
- Accuracy: 0.833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.996 | 0.992 | 62 | 2.9907 | 0.833 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
anrikus/lexical_classifier_bangla_assamese_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. -->
# lexical_classifier_bangla_assamese_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 [https://huggingface.co/datasets/anrikus/lexical_diff_bangla_assamese_v2].
It achieves the following results on the evaluation set:
- Loss: 1.1317
- Accuracy: 0.7033
- Precision: 0.7480
- Recall: 0.6133
- F1: 0.6740
## Model description
More information needed
## Intended uses & 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1501 | 1.0 | 35 | 1.0773 | 0.7286 | 0.7302 | 0.6866 | 0.7077 |
| 0.1789 | 2.0 | 70 | 0.8471 | 0.7714 | 0.7869 | 0.7164 | 0.7500 |
| 0.1463 | 3.0 | 105 | 1.3021 | 0.7071 | 0.7407 | 0.5970 | 0.6612 |
| 0.1664 | 4.0 | 140 | 1.1251 | 0.6857 | 0.6825 | 0.6418 | 0.6615 |
| 0.1746 | 5.0 | 175 | 0.8354 | 0.7214 | 0.7692 | 0.5970 | 0.6723 |
| 0.2282 | 6.0 | 210 | 1.2394 | 0.6929 | 0.6935 | 0.6418 | 0.6667 |
| 0.103 | 7.0 | 245 | 1.3935 | 0.6857 | 0.6769 | 0.6567 | 0.6667 |
| 0.186 | 8.0 | 280 | 1.1753 | 0.7 | 0.6812 | 0.7015 | 0.6912 |
| 0.2189 | 9.0 | 315 | 1.1819 | 0.6929 | 0.7069 | 0.6119 | 0.6560 |
| 0.1476 | 10.0 | 350 | 1.4722 | 0.65 | 0.65 | 0.5821 | 0.6142 |
| 0.2055 | 11.0 | 385 | 0.7656 | 0.7571 | 0.7895 | 0.6716 | 0.7258 |
| 0.1607 | 12.0 | 420 | 0.9756 | 0.7071 | 0.76 | 0.5672 | 0.6496 |
| 0.1532 | 13.0 | 455 | 0.9945 | 0.7071 | 0.7031 | 0.6716 | 0.6870 |
| 0.1023 | 14.0 | 490 | 1.1967 | 0.7071 | 0.7031 | 0.6716 | 0.6870 |
| 0.2389 | 15.0 | 525 | 0.7984 | 0.7643 | 0.7742 | 0.7164 | 0.7442 |
| 0.1925 | 16.0 | 560 | 0.9343 | 0.7143 | 0.7368 | 0.6269 | 0.6774 |
| 0.2038 | 17.0 | 595 | 1.1440 | 0.6857 | 0.6949 | 0.6119 | 0.6508 |
| 0.2193 | 18.0 | 630 | 0.9709 | 0.7071 | 0.7167 | 0.6418 | 0.6772 |
| 0.1719 | 19.0 | 665 | 0.9007 | 0.7429 | 0.7818 | 0.6418 | 0.7049 |
| 0.2334 | 20.0 | 700 | 0.8711 | 0.7429 | 0.7818 | 0.6418 | 0.7049 |
| 0.131 | 21.0 | 735 | 1.0785 | 0.7143 | 0.7288 | 0.6418 | 0.6825 |
| 0.2316 | 22.0 | 770 | 1.1080 | 0.6643 | 0.6786 | 0.5672 | 0.6179 |
| 0.1815 | 23.0 | 805 | 1.2657 | 0.6929 | 0.7308 | 0.5672 | 0.6387 |
| 0.1521 | 24.0 | 840 | 1.2584 | 0.7 | 0.6812 | 0.7015 | 0.6912 |
| 0.244 | 25.0 | 875 | 1.0375 | 0.7786 | 0.7812 | 0.7463 | 0.7634 |
| 0.3668 | 26.0 | 910 | 1.1253 | 0.7286 | 0.7458 | 0.6567 | 0.6984 |
| 0.1564 | 27.0 | 945 | 0.9891 | 0.7214 | 0.7414 | 0.6418 | 0.688 |
| 0.1782 | 28.0 | 980 | 0.9936 | 0.7357 | 0.75 | 0.6716 | 0.7087 |
| 0.1945 | 29.0 | 1015 | 0.9586 | 0.7357 | 0.7419 | 0.6866 | 0.7132 |
| 0.271 | 30.0 | 1050 | 0.8128 | 0.7357 | 0.7778 | 0.6269 | 0.6942 |
| 0.1889 | 31.0 | 1085 | 1.2141 | 0.6714 | 0.7059 | 0.5373 | 0.6102 |
| 0.1928 | 32.0 | 1120 | 1.0059 | 0.7143 | 0.7368 | 0.6269 | 0.6774 |
| 0.2035 | 33.0 | 1155 | 1.1185 | 0.6929 | 0.7069 | 0.6119 | 0.6560 |
| 0.226 | 34.0 | 1190 | 1.1719 | 0.6286 | 0.6271 | 0.5522 | 0.5873 |
| 0.1801 | 35.0 | 1225 | 1.1689 | 0.6786 | 0.6719 | 0.6418 | 0.6565 |
| 0.2353 | 36.0 | 1260 | 1.1392 | 0.7 | 0.6923 | 0.6716 | 0.6818 |
| 0.1686 | 37.0 | 1295 | 1.2064 | 0.6429 | 0.6667 | 0.5075 | 0.5763 |
| 0.2278 | 38.0 | 1330 | 0.8528 | 0.75 | 0.7759 | 0.6716 | 0.7200 |
| 0.1905 | 39.0 | 1365 | 1.2736 | 0.6643 | 0.6786 | 0.5672 | 0.6179 |
| 0.2136 | 40.0 | 1400 | 1.0255 | 0.7214 | 0.7333 | 0.6567 | 0.6929 |
| 0.1544 | 41.0 | 1435 | 0.9427 | 0.7214 | 0.7333 | 0.6567 | 0.6929 |
| 0.2691 | 42.0 | 1470 | 1.0433 | 0.7286 | 0.7544 | 0.6418 | 0.6935 |
| 0.2804 | 43.0 | 1505 | 1.2006 | 0.6929 | 0.7143 | 0.5970 | 0.6504 |
| 0.2345 | 44.0 | 1540 | 0.9487 | 0.75 | 0.7857 | 0.6567 | 0.7154 |
| 0.2541 | 45.0 | 1575 | 0.9468 | 0.7429 | 0.7246 | 0.7463 | 0.7353 |
| 0.2718 | 46.0 | 1610 | 1.3955 | 0.6714 | 0.6909 | 0.5672 | 0.6230 |
| 0.3179 | 47.0 | 1645 | 1.3356 | 0.6786 | 0.7037 | 0.5672 | 0.6281 |
| 0.4808 | 48.0 | 1680 | 0.9297 | 0.7429 | 0.7719 | 0.6567 | 0.7097 |
| 0.3231 | 49.0 | 1715 | 0.8732 | 0.7429 | 0.7818 | 0.6418 | 0.7049 |
| 0.3681 | 50.0 | 1750 | 1.0578 | 0.6857 | 0.7255 | 0.5522 | 0.6271 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"assamese",
"bangla"
] |
VietTung04/vit-l-16-food-classifier
|
# vit-l-16-food-classifier
This model is a fine-tuned version of [google/vit-large-patch16-384](https://huggingface.co/google/vit-large-patch16-384) on 30VNFOODS and Custom Vietnamese foods dataset crawled on Google and ShopeeFood.
It achieves the following results on the evaluation set:
- Loss: 0.3214
- Accuracy: 0.9155
## Model description
Fine-tuned ViT-L-16 in 3 epochs.
## Intended uses & limitations
This model is used for our project at UET-VNU Campathon 2024.
## Training and evaluation data
30VNFOODS Dataset and self-crawled dataset.
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 293 | 0.4138 | 0.8999 |
| 0.8179 | 2.0 | 586 | 0.3482 | 0.9151 |
| 0.8179 | 3.0 | 879 | 0.3214 | 0.9155 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"banh cuon",
"mi quang",
"banh trang nuong",
"goi cuon",
"banh gio",
"bun mam",
"canh chua",
"nem chua",
"bun rieu",
"banh duc",
"banh pia",
"banh canh",
"banh khot",
"banh bot loc",
"banh can",
"bun thit nuong",
"banh chung",
"bun dau mam tom",
"banh beo",
"banh tet",
"cao lau",
"chao long",
"ca kho to",
"bun bo hue",
"pho",
"xoi xeo",
"banh mi",
"banh xeo",
"com tam",
"hu tieu",
"bánh cu đơ",
"nem nướng",
"bánh mì cay",
"cơm cháy",
"bò bía",
"bánh đậu xanh",
"bánh đa cua",
"bún cá"
] |
JYL480/vit-base-images
|
<!-- This model card 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-images
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 marmal88/skin_cancer dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0918
- Accuracy: 0.981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8785 | 0.4 | 100 | 0.7795 | 0.711 |
| 0.7076 | 0.8 | 200 | 0.5421 | 0.818 |
| 0.4283 | 1.2 | 300 | 0.3951 | 0.876 |
| 0.4251 | 1.6 | 400 | 0.3818 | 0.864 |
| 0.335 | 2.0 | 500 | 0.2474 | 0.924 |
| 0.2286 | 2.4 | 600 | 0.1675 | 0.952 |
| 0.1523 | 2.8 | 700 | 0.1641 | 0.954 |
| 0.1346 | 3.2 | 800 | 0.1120 | 0.969 |
| 0.0638 | 3.6 | 900 | 0.1025 | 0.978 |
| 0.0574 | 4.0 | 1000 | 0.0918 | 0.981 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"benign_keratosis-like_lesions",
"basal_cell_carcinoma",
"actinic_keratoses",
"dermatofibroma",
"melanocytic_nevi"
] |
elvispresniy/vit-food101
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-food101
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4925
- 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.682 | 0.6369 | 100 | 2.5501 | 0.802 |
| 1.312 | 1.2739 | 200 | 1.3870 | 0.855 |
| 0.7605 | 1.9108 | 300 | 0.9167 | 0.862 |
| 0.3844 | 2.5478 | 400 | 0.6248 | 0.88 |
| 0.1957 | 3.1847 | 500 | 0.5220 | 0.896 |
| 0.1756 | 3.8217 | 600 | 0.4925 | 0.899 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"apple",
"aquarium_fish",
"bowl",
"boy",
"bridge",
"bus",
"butterfly",
"camel",
"can",
"castle",
"caterpillar",
"cattle",
"baby",
"chair",
"chimpanzee",
"clock",
"cloud",
"cockroach",
"couch",
"cra",
"crocodile",
"cup",
"dinosaur",
"bear",
"dolphin",
"elephant",
"flatfish",
"forest",
"fox",
"girl",
"hamster",
"house",
"kangaroo",
"keyboard",
"beaver",
"lamp",
"lawn_mower",
"leopard",
"lion",
"lizard",
"lobster",
"man",
"maple_tree",
"motorcycle",
"mountain",
"bed",
"mouse",
"mushroom",
"oak_tree",
"orange",
"orchid",
"otter",
"palm_tree",
"pear",
"pickup_truck",
"pine_tree",
"bee",
"plain",
"plate",
"poppy",
"porcupine",
"possum",
"rabbit",
"raccoon",
"ray",
"road",
"rocket",
"beetle",
"rose",
"sea",
"seal",
"shark",
"shrew",
"skunk",
"skyscraper",
"snail",
"snake",
"spider",
"bicycle",
"squirrel",
"streetcar",
"sunflower",
"sweet_pepper",
"table",
"tank",
"telephone",
"television",
"tiger",
"tractor",
"bottle",
"train",
"trout",
"tulip",
"turtle",
"wardrobe",
"whale",
"willow_tree",
"wolf",
"woman",
"worm"
] |
kate1130/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 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2484
- Accuracy: 0.9242
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3657 | 1.0 | 693 | 0.3477 | 0.9045 |
| 0.278 | 2.0 | 1386 | 0.2998 | 0.9099 |
| 0.1907 | 3.0 | 2079 | 0.2756 | 0.9175 |
| 0.1764 | 4.0 | 2772 | 0.2664 | 0.9217 |
| 0.1579 | 5.0 | 3465 | 0.2606 | 0.9171 |
| 0.1198 | 6.0 | 4158 | 0.2554 | 0.9184 |
| 0.1145 | 7.0 | 4851 | 0.2538 | 0.9213 |
| 0.0996 | 8.0 | 5544 | 0.2513 | 0.9209 |
| 0.107 | 9.0 | 6237 | 0.2532 | 0.9196 |
| 0.0928 | 10.0 | 6930 | 0.2507 | 0.9196 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"white-breasted kingfisher",
"indian roller",
"forest wagtail",
"white-breasted waterhen",
"common myna",
"rufous treepie",
"common rosefinch",
"red-wattled lapwing",
"gray wagtail",
"jungle babbler",
"hoopoe",
"house crow",
"sarus crane",
"ruddy shelduck",
"indian peacock",
"common tailorbird",
"asian green bee-eater",
"brown-headed barbet",
"northern lapwing",
"coppersmith barbet",
"cattle egret",
"common kingfisher",
"indian grey hornbill",
"white wagtail",
"indian pitta"
] |
LongLe3102000/herbal_identification_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]
- **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]
|
[
"10_tuc_doan",
"11_thien_mon",
"12_sai_ho",
"13_vien_chi",
"14_su_quan_tu",
"15_bach_mao_can",
"16_cau_ky_tu",
"17_do_trong",
"18_dang_sam",
"19_cau_tich",
"1_boi_mau",
"20_tho_ty_tu",
"21_hoang_ky",
"22_coi_xay",
"23_huyen_sam",
"24_tang_chi",
"25_diep_ha_chau",
"26_kim_anh",
"27_cat_can",
"28_co_ngot",
"29_cuc_hoa",
"2_hoe_hoa",
"30_to_moc",
"31_kim_tien_thao",
"32_dan_sam",
"33_chi_tu",
"34_ngai_cuu",
"35_sinh_dia",
"36_nguu_tat",
"37_bach_truat",
"38_nhan_tran",
"39_duong_quy",
"3_linh_chi",
"40_nho_noi",
"41_dao_nhan",
"42_cat_canh",
"43_ha_kho_thao",
"44_xa_tien_tu",
"45_che_day",
"46_xa_can",
"47_tang_diep",
"48_ngu_boi_tu",
"49_ngu_gia_bi",
"4_thong_thao",
"50_rau_ngo",
"51_nguu_bang_tu",
"52_cam_thao_dat",
"53_dai_hoang",
"54_hoai_son",
"55_dam_duong_hoac",
"56_moc_qua",
"57_bo_cong_anh",
"58_tho_phuc_linh",
"59_mach_mon",
"5_trach_ta",
"60_ke_dau_ngua",
"61_tang_bach_bi",
"62_cam_thao_bac",
"63_o_tac_cot",
"64_thao_quyet_minh",
"65_dai_tao",
"66_kim_ngan_hoa",
"67_tao_nhan",
"68_ban_ha",
"69_ca_gai_leo",
"6_y_di",
"70_kho_qua",
"71_xuyen_tam_lien",
"72_nhan_sam",
"73_bach_gioi_tu",
"74_tam_that",
"75_bach_chi",
"76_sa_sam",
"77_bach_thuoc",
"7_can_khuong",
"8_ty_giai",
"9_cot_toai_bo"
] |
legekka/AI-Anime-Image-Detector-ViT
|
# AI Anime Image Detector ViT
This is a proof of concept model for detecting anime style AI images. Using Vision Transformer, it was trained on 1M human-made real and 217K AI generated anime images. During training either type appeared in equal amount to avoid biases. The model was trained on a single RTX 3090 GPU for about 40 hours, ~35 epochs.
The training logs are available on my [wandb](https://wandb.ai/legekka/AI-Image-Detector).
## Evaluation
Each checkpoint was evaluated on 500-500 real and AI images.
Final result:
- Training Loss: 0.1009
- Eval Loss: 0.1386
It seems like using random crops helped the model to generalize better, however, the training dataset only contained 512x512 images, which meant that every cropped image had bilinear interpolation. Training the model on 1024x1024 images could probably further improve its performance. *(Maybe I'll do it later)*
## Performance comparison
We did a small eval test with ~5000 images on the current available AI image detectors. **Note that these models were not specificly trained on anime images.**
| Model | Accuracy |
|------------------------------------------------|------------|
| dima806/ai_vs_real_image_detection | 35,97% |
| Organika/sdxl-detector | 43,29% |
| Nahrawy/AIorNot | 64,74% |
| jacoballessio/ai-image-detect-distilled | 68,94% |
| umm-maybe/AI-image-detector | 75,45% |
| mmanikanta/VIT_AI_image_detector | 79,65% |
| *legekka/AI-Anime-Image-Detector-HD-ViT WIP* | *94,26%* |
| **legekka/AI-Anime-Image-Detector-ViT (Ours)** | **94,68%** |
## Usage
Example inference code:
```python
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
import torch
from PIL import Image
model = AutoModelForImageClassification.from_pretrained("legekka/AI-Anime-Image-Detector-ViT")
feature_extractor = AutoFeatureExtractor.from_pretrained("legekka/AI-Anime-Image-Detector-ViT")
model.eval()
image = Image.open("example.jpg")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
label = model.config.id2label[torch.argmax(logits).item()]
confidence = torch.nn.functional.softmax(logits, dim=1)[0][torch.argmax(logits)].item()
print(f"Prediction: {label} ({round(confidence * 100)}%)")
```
|
[
"real",
"ai"
] |
angusleung100/bad-anatomy-realism-classifier
|
# Model Card for Bad-Anatomy-Realism-Classifier
A finetuned Vision Transformer model for classifying AI-generated pictures for bad anatomy and realism.
This model is currently a support model for my Youtube series. Feel free to build on top of this.
## Model Detail
<!-- Provide a quick summary of what the model is/does. -->
**Detecting Bad Anatomy in Realistic AI-Generated Images** - Not all Image Generation models generate images with good anatomy. Some might generate the typical "bad hands" where the hand might have more than 5 fingers. This model's goaal is to detect such anatomy issues in AI-generated images.
**Determining True Realism Versus AI Realism** - AI-generated images tend to have an issue when attempting to achieve realism, which is the skin and generation style. Compared to a normal post on social media, a High-Definition upscaled AI-generated image can be easily identified by, characteristic such as shiny skin or very bright lighting. Below are some examples of such:
<img src="https://huggingface.co/angusleung100/bad-anatomy-realism-classifier/resolve/main/Unrealistic_Good_Anatomy_29.png" alt="Unrealistic Good Anatomy AI-generated image number 29" width="512" height="512"/>
<img src="https://huggingface.co/angusleung100/bad-anatomy-realism-classifier/resolve/main/Unrealistic_Good_Anatomy_31.png" alt="Unrealistic Good Anatomy AI-generated image number 31" width="512" height="512"/>
### Model Description
<!-- Provide a longer summary of what this model is. -->
This was fine-tuned on the [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) Vision Transformer (ViT).
## 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. -->
- Detecting whether an image is actually real or is a very well AI-generated image
- Detecting bad anatomy in AI-generated images to trigger a regeneration
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
- Racism
- Illegal activities where doing illegal things is a crime
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This initial model was trained on images generated on Stable Diffusion v1.5 on the [Beautiful Realistic Asians v6](https://civitai.com/models/25494?modelVersionId=113479) checkpoint by pleasebankai.
The dataset for this model was only 134 images, with only 6 being Unrealistic Bad Anatomy. (Additions of dataset details will be placed in the model card in later updates to documentation)
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Recommendation is to build on the dataset and continue training with more variety of characters to raise performance for images that do not conform to the characteristics of images used in training.
## How to Get Started with the Model
### Finetuning
Please refer to the initial finetune script for this model in the supporting Github Repository here: [https://github.com/angusleung100/barc-finetuning-gh](https://github.com/angusleung100/barc-finetuning-gh)
### Using The Model For Classification
Please refer to the Hugging Face documentation example here for Image Classification: [https://huggingface.co/docs/transformers/en/tasks/image_classification#inference](https://huggingface.co/docs/transformers/en/tasks/image_classification#inference)
## Training Details
### Training and Testing 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. -->
## Dataset Image Label Criteria
### Bad / Good Anatomy
- Any deformed body parts or extra limbs for the character
- Background does not overly matte (As it can always be removed or changed in post-processing with professional editing software)
### Realistic vs. Unrealistic
The criteria is more interesting for determining realism. Since a lot of people like to use filters now, it's actually quite hard to determine what is a good standard for realism. Here is what I narrowed it down to for this model:
- **First glance reaction** - Do I take a closer look and feel skeptical? Or do I know instantly it isn't real.
- **Lighting** - It is easier to sort amateur style images since I can move onto the next criteria first. Some professional images do look AI-generated but are actually heavily edited. But we can definitely base it also off of unnatural lighting
- **Skin and hair** - If the skin and hair are too shiny (Like the images at the start of the Model Card) or there is not enough detail on an upscaled image. Or there is TOO much detail on an upscaled image.
- **Photography style** - This could lead to false positives or false negatives, but if the shot looks like the focal point is weird or just very airbrushed, it could be unrealistic
Overall it is based on "gut feeling" for the sorting. The model also has a goal to be able to replicate "gut feeling" and just your underlying feel for the image.
### Compatible Images For Dataset
Since the default data collator is used and images are primarily from SD 1.5, I am not entirely certain whether images and sizes from different models will break the training, even if the testing pipeline didn't have any problems for the 3 images we used later on.
Here are a list of models where default image sizes should work:
- Stable Diffusion 1.5
- OpenDalle v1.1
- Flux 1
- Dall-E 3 on Copilot
## Dataset Stats
```
Number Images Per Label
=======================
Realistic Bad Anatomy: 6 (4.48%)
Realistic Good Anatomy: 15 (11.19%)
Unrealistic Bad Anatomy: 81 (60.45%)
Unrealistic Good Anatomy: 32 (23.88%)
Total Number of Images: 134
```
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Results
```
***** train metrics *****
epoch = 3.0
total_flos = 20135801GF
train_loss = 0.8453
train_runtime = 0:00:42.83
train_samples_per_second = 6.514
train_steps_per_second = 0.841
```
```
***** eval metrics *****
epoch = 3.0
eval_accuracy = 0.6341
eval_f1 = 0.513
eval_loss = 0.8219
eval_precision = 0.464
eval_recall = 0.6341
eval_runtime = 0:00:06.95
eval_samples_per_second = 5.893
eval_steps_per_second = 0.862
```
#### Summary
The initial dataset and finetune resulted in a 64.41% accuracy and 51.3% F1 score, which is low but expected for a small amateur dataset.
Hopefully I will have time to further build on the dataset and improve the model's performance in the future.
**The next steps would be:**
- Have more variety of characters and poses
- More variety of clothing styles and lighting
- Different camera styles
- Different model generations from different models -> Currently dominated by the SD1.5 BRAV6 and BRAV7 checkpoints
## Model Examination
<!-- Relevant interpretability work for the model goes here -->
You can view example pipeline inferences and their results on the [Initial Finetune notebook](https://nbviewer.org/github/angusleung100/barc-finetuning-gh/blob/main/Bad_Anatomy_and_Realism_Classification_Model_Initial_Fine_Tune.ipynb)
The examples are at the bottom of the notebook. You can do ```ctr+f``` and search for ```Test Model With Custom Inputs``` to reach it faster.
## Model Card Contact
Feel free to contact me if you have any questions or find me on Github
- [Twitter](https://twitter.com/angusleung100)
- [Github](https://github.com/angusleung100)
|
[
"realistic_bad_anatomy",
"realistic_good_anatomy",
"unrealistic_bad_anatomy",
"unrealistic_good_anatomy"
] |
Dangurangu/ViT_cancer_tumor
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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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).
- **Hardware Type:** [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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|
[
"benign",
"malignant",
"normal"
] |
djbp/swin-base-patch4-window7-224-in22k-MM_Classification_base_V10
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224-in22k-MM_Classification_base_V10
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3245
- Accuracy: 0.8721
- Auc: 0.9534
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
### Framework versions
- Transformers 4.44.2
- Pytorch 1.13.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"immovable",
"invalid",
"movable"
] |
dudosya/vit_fine_tuned_test
|
# Model Card for Model ID
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## Model Details
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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.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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<!-- 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).
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[More Information Needed]
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[More Information Needed]
|
[
"benign",
"malignant",
"normal"
] |
dhritic99/model9912
|
# Model Card for Model ID
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[More Information Needed]
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## Environmental Impact
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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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|
[
"label_0",
"label_1",
"label_2",
"label_3"
] |
Kaspar/clip-heritage-weaver-classifier
|
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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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.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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|
[
"specimen",
"mobile telephone",
"telephone",
"cable",
"telegraph",
"hand printing block (trademark)",
"printing block",
"model",
"model - representation",
"radio receiver",
"telegraph block instrument",
"electric meter",
"black-and-white print - photograph",
"telephones",
"sign",
"lamp",
"receiver",
"microphone",
"telegraph instrument",
"radio transmitter",
"hand printing block",
"mimic board tile",
"steam engine",
"mobile generator",
"steam turbine",
"lathes",
"telephone handset",
"radio component",
"triode valve",
"block instrument",
"ammeter",
"incandescent electric lamp with carbon filament",
"leather workers' tool",
"transmitter",
"photographs",
"coherer",
"incandescent electric lamp with tungsten filament",
"voltmeter",
"badge",
"control desk",
"amplifier",
"sanitary appliances",
"seal",
"games",
"terrestrial globe",
"lamp check",
"radar equipment",
"fuse",
"models",
"hammer - tool",
"teleprinter",
"samples",
"loudspeakers",
"hand tools",
"scientific equipment",
"collection of metal foils and archives",
"electric switch",
"lathe - machine",
"radio",
"miner's oil safety lamp",
"watch and clockmakers tools",
"transceiver",
"lamp - light bulb",
"coil",
"spinning machinery - attachment",
"fabric samples",
"thermometers",
"explosive substitute",
"tool",
"incandescent lamps (lighting device components)",
"furnaces",
"mercury thermometer",
"axe - tool",
"touchstone",
"vacuum cleaner",
"scrapbook",
"incandescent electric lamp",
"chisels",
"tube",
"diagram",
"oil lamp",
"telephone exchange",
"loudspeaker",
"electric lamp - lighting device component",
"galvanometer",
"transformer",
"carbon filament lamp",
"circuit board",
"water frame",
"diesel internal combustion engine",
"trade union badge",
"valve",
"saws",
"crusies",
"train staff",
"tools & equipment",
"specimens",
"lampholder",
"moulding plane",
"seal and stamp",
"box",
"copper",
"ropework",
"headphones",
"micrometer",
"pick - tool",
"cipher machine",
"pay check",
"battery",
"scientific toy",
"television receiver",
"lunchboxes",
"aerial",
"gas mantle box",
"electricity supply meter",
"switchboard",
"label",
"audio cassette",
"spanner",
"component - object",
"telegraph peripheral",
"electric heater",
"amplifiers",
"trowels",
"propeller",
"blades",
"box - container",
"refrigerator",
"filament lamp",
"medal",
"electric room heater",
"spelling-telegraph instrument",
"case - container",
"scissors",
"grinding wheels",
"telephone component",
"signalling instrument",
"geiger-müller tube",
"hot air engine",
"superstructure",
"light bulb - lamp, incandescent: gas-filled",
"signal relay box",
"telephone cable",
"solar cell",
"dish",
"cigarette card album",
"radio - radio receiver, valve",
"tools",
"miner's electric lamp",
"nuclear fuel",
"slag",
"portrait bust",
"weights",
"heat exchanger",
"table telephone",
"plug",
"telephone; electric; phone",
"gas meter",
"direct current generator",
"plane - tool",
"sprinkler head",
"electricity supply cable",
"components",
"telephone exchange accessory",
"light fitting",
"pump",
"fabric sample",
"tongs",
"brick",
"block",
"plane",
"gauge",
"thermometer (mercury)",
"nails",
"component",
"flask",
"drill",
"electric kettle",
"chisel",
"mobile phone accessory",
"electric light switches",
"hand tool",
"collection of objects and archives",
"meter",
"ore",
"plaque",
"semi-conductor",
"drawings",
"solar panel",
"cleaning product",
"bobbin",
"military passes",
"sample",
"section indicator",
"gas detection equipment",
"slide contact box",
"shovel",
"mobile telephone component",
"model steam engine",
"name plate",
"safety lamps",
"mounting block",
"roller bearing",
"light bulb",
"lathe parts",
"telephone overhead line tool",
"cryptograph",
"lamp holders",
"staionary engine",
"container",
"thermometer",
"circular dividing engine",
"lathe bed",
"quilt",
"hydraulic packing",
"cloth balance",
"wattmeter",
"radiometer",
"telephone box",
"packaging",
"sewing machine",
"staff",
"statue",
"signal post telephone",
"part",
"gas lamp shade",
"lead fume",
"telephony cable",
"telephone cable terminal",
"ball bearing",
"wire",
"junction box",
"switch handle commutator",
"electric cooker",
"cup and saucer",
"telephone exchange relay",
"knife",
"wire rope",
"engines",
"razors",
"oil lamps",
"battery charger",
"shuttles (textile working equipment)",
"hand drill",
"keyboards (machine components)",
"necktie",
"cypher machines",
"calorimeter",
"electrophone table",
"telephone tension gauges",
"stencil",
"ashtray",
"shuttle",
"makers plate",
"stereograph",
"waterwheels",
"vessels",
"lens",
"leg irons",
"fuel cell system",
"model generator",
"candlestick",
"valve radio receiver",
"electromedical instrument",
"seals",
"telephone exchange call meter",
"galvanometers",
"valve, model",
"fuse wire holder",
"tobacco",
"steamboats",
"toy",
"coffee mill",
"exploder key",
"cover",
"relay",
"pebbles",
"gas cooker",
"intercom system",
"eutectic alloy",
"pit prop",
"commemorative medal",
"hairdryer",
"air sampler",
"prints",
"rope sample",
"brushes",
"calendar",
"automation mechanism",
"handle",
"plaster cast",
"prototype - object genre",
"horseshoes (animal equipment)",
"adze",
"incandescent lamp",
"axe",
"toy - recreational artefact",
"track relay",
"truncheon",
"joiner's tools",
"journal - periodical",
"spinning wheel",
"accelerator",
"coal",
"cigarette card",
"cathode ray tube",
"vinyl recording",
"control unit part",
"plate",
"lampshade",
"bottle",
"depthing tool",
"cop tubing apparatus",
"case",
"skewer",
"roller",
"joint",
"pump - machinery",
"drill bits",
"wire drawing die",
"rf modulator",
"mask",
"cutting tool",
"keyboard",
"wheelwright's tool - spanner",
"hand printing block (pattern)",
"pocket watch"
] |
sergiopaniego/fine_tuning_vit_custom_dataset_breastcancer-ultrasound-images
|
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[
"benign",
"malignant",
"normal"
] |
Abdelkareem/dino_paraistic-vit
|
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[
"ascaris lumbricoides",
"capillaria philippinensis",
"enterobius vermicularis",
"fasciolopsis buski",
"hookworm egg",
"hymenolepis diminuta",
"hymenolepis nana",
"opisthorchis viverrine",
"paragonimus spp",
"taenia spp. egg",
"trichuris trichiura"
] |
Elisa14/vit-base-patch16-224-in21k-finetuned-lora-food101
|
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|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare",
"waffles"
] |
kawchar85/image-classification
|
# Image Classification
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: 0.2031
- Accuracy: 0.9459
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3727 | 1.0 | 370 | 0.2756 | 0.9337 |
| 0.2145 | 2.0 | 740 | 0.2168 | 0.9378 |
| 0.1835 | 3.0 | 1110 | 0.1918 | 0.9459 |
| 0.147 | 4.0 | 1480 | 0.1857 | 0.9472 |
| 0.1315 | 5.0 | 1850 | 0.1818 | 0.9472 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"sphynx",
"maine coon",
"keeshond",
"yorkshire terrier",
"havanese",
"leonberger",
"wheaten terrier",
"american bulldog",
"english setter",
"boxer",
"newfoundland",
"bengal",
"samoyed",
"british shorthair",
"great pyrenees",
"abyssinian",
"pug",
"saint bernard",
"russian blue",
"scottish terrier"
] |
B-K/plant-disease-swin-384
|
# 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]
### 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
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
[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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[More Information Needed]
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## 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]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
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|
[
"black_rot",
"downy_mildew",
"healthy_leaf",
"powdery_mildew",
"rust_leaf",
"sooty_mold",
"tar_spot",
"bad"
] |
karim155/swin-tiny-patch4-window7-224-finetuned
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned
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: 0.9034
- Accuracy: 0.6660
- Precision: 0.6546
- Recall: 0.6660
- F1: 0.6519
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0809 | 0.9846 | 32 | 1.0485 | 0.5833 | 0.5506 | 0.5833 | 0.5627 |
| 1.0052 | 2.0 | 65 | 1.0600 | 0.5727 | 0.5941 | 0.5727 | 0.5170 |
| 0.9429 | 2.9846 | 97 | 0.9755 | 0.6160 | 0.5878 | 0.6160 | 0.5837 |
| 0.9497 | 4.0 | 130 | 0.9318 | 0.6497 | 0.6458 | 0.6497 | 0.6313 |
| 0.8807 | 4.9846 | 162 | 0.9541 | 0.6304 | 0.6321 | 0.6304 | 0.6200 |
| 0.8089 | 6.0 | 195 | 0.9556 | 0.6266 | 0.6270 | 0.6266 | 0.6150 |
| 0.801 | 6.9846 | 227 | 0.9050 | 0.6603 | 0.6512 | 0.6603 | 0.6472 |
| 0.7753 | 8.0 | 260 | 0.9134 | 0.6506 | 0.6440 | 0.6506 | 0.6440 |
| 0.6986 | 8.9846 | 292 | 0.9138 | 0.6554 | 0.6468 | 0.6554 | 0.6436 |
| 0.7107 | 9.8462 | 320 | 0.9034 | 0.6660 | 0.6546 | 0.6660 | 0.6519 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
[
"g",
"wd",
"dr",
"nd",
"other"
] |
karim155/convnext-tiny-224-finetuned
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext-tiny-224-finetuned
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: 0.9272
- Accuracy: 0.6275
- Precision: 0.6426
- Recall: 0.6275
- F1: 0.6068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.281 | 0.9846 | 32 | 1.2165 | 0.5428 | 0.5230 | 0.5428 | 0.4989 |
| 1.0964 | 2.0 | 65 | 1.0549 | 0.5823 | 0.5459 | 0.5823 | 0.5427 |
| 0.9929 | 2.9846 | 97 | 0.9905 | 0.6169 | 0.5755 | 0.6169 | 0.5848 |
| 0.9804 | 4.0 | 130 | 0.9691 | 0.6131 | 0.5734 | 0.6131 | 0.5867 |
| 0.9389 | 4.9846 | 162 | 0.9539 | 0.6246 | 0.5874 | 0.6246 | 0.6007 |
| 0.9078 | 6.0 | 195 | 0.9536 | 0.6189 | 0.5910 | 0.6189 | 0.5973 |
| 0.8741 | 6.9846 | 227 | 0.9333 | 0.6333 | 0.5947 | 0.6333 | 0.6098 |
| 0.8523 | 8.0 | 260 | 0.9322 | 0.6323 | 0.5952 | 0.6323 | 0.6122 |
| 0.8222 | 8.9846 | 292 | 0.9354 | 0.6198 | 0.6361 | 0.6198 | 0.5992 |
| 0.7975 | 9.8462 | 320 | 0.9272 | 0.6275 | 0.6426 | 0.6275 | 0.6068 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"g",
"wd",
"dr",
"nd",
"other"
] |
losdos/fintunissitmo
|
# 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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- **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]
|
[
"not-interest",
"not-affected",
"affected"
] |
interestAI/sashes_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. -->
# sashes_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3784
- Accuracy: 0.8760
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 112
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:--------:|
| No log | 0.9697 | 8 | 2.2973 | 0.1434 |
| 2.2994 | 1.9394 | 16 | 2.2717 | 0.1957 |
| 2.2791 | 2.9091 | 24 | 2.2377 | 0.2287 |
| 2.2378 | 4.0 | 33 | 2.1866 | 0.3178 |
| 2.1604 | 4.9697 | 41 | 2.1096 | 0.3934 |
| 2.1604 | 5.9394 | 49 | 2.0257 | 0.4322 |
| 2.0801 | 6.9091 | 57 | 1.9312 | 0.4264 |
| 1.9587 | 8.0 | 66 | 1.7939 | 0.4942 |
| 1.821 | 8.9697 | 74 | 1.6869 | 0.5465 |
| 1.6903 | 9.9394 | 82 | 1.6025 | 0.5736 |
| 1.5687 | 10.9091 | 90 | 1.4849 | 0.6202 |
| 1.5687 | 12.0 | 99 | 1.4674 | 0.5407 |
| 1.4183 | 12.9697 | 107 | 1.3539 | 0.6163 |
| 1.3907 | 13.9394 | 115 | 1.2365 | 0.6938 |
| 1.3058 | 14.9091 | 123 | 1.2258 | 0.6938 |
| 1.2181 | 16.0 | 132 | 1.1759 | 0.6822 |
| 1.1537 | 16.9697 | 140 | 1.1413 | 0.7074 |
| 1.1537 | 17.9394 | 148 | 1.0586 | 0.7248 |
| 1.0819 | 18.9091 | 156 | 1.0059 | 0.7558 |
| 0.9905 | 20.0 | 165 | 0.9575 | 0.7578 |
| 1.0055 | 20.9697 | 173 | 0.9807 | 0.7442 |
| 0.9484 | 21.9394 | 181 | 0.9553 | 0.7539 |
| 0.9484 | 22.9091 | 189 | 0.8213 | 0.8004 |
| 0.8974 | 24.0 | 198 | 0.8305 | 0.8043 |
| 0.8545 | 24.9697 | 206 | 0.8273 | 0.7849 |
| 0.8724 | 25.9394 | 214 | 0.8177 | 0.7519 |
| 0.8642 | 26.9091 | 222 | 0.7692 | 0.7926 |
| 0.7609 | 28.0 | 231 | 0.7293 | 0.8062 |
| 0.7609 | 28.9697 | 239 | 0.7001 | 0.8198 |
| 0.7418 | 29.9394 | 247 | 0.7899 | 0.7636 |
| 0.7552 | 30.9091 | 255 | 0.6595 | 0.8101 |
| 0.7291 | 32.0 | 264 | 0.6971 | 0.7907 |
| 0.693 | 32.9697 | 272 | 0.7215 | 0.7946 |
| 0.6891 | 33.9394 | 280 | 0.6980 | 0.8004 |
| 0.6891 | 34.9091 | 288 | 0.6200 | 0.8372 |
| 0.6936 | 36.0 | 297 | 0.7245 | 0.7733 |
| 0.6698 | 36.9697 | 305 | 0.6724 | 0.7984 |
| 0.6502 | 37.9394 | 313 | 0.6701 | 0.8023 |
| 0.6988 | 38.9091 | 321 | 0.6049 | 0.8236 |
| 0.6709 | 40.0 | 330 | 0.6397 | 0.7965 |
| 0.6709 | 40.9697 | 338 | 0.5654 | 0.8391 |
| 0.652 | 41.9394 | 346 | 0.6371 | 0.8101 |
| 0.64 | 42.9091 | 354 | 0.6341 | 0.8062 |
| 0.6368 | 44.0 | 363 | 0.5662 | 0.8527 |
| 0.595 | 44.9697 | 371 | 0.5744 | 0.8411 |
| 0.595 | 45.9394 | 379 | 0.5465 | 0.8430 |
| 0.5823 | 46.9091 | 387 | 0.6254 | 0.7984 |
| 0.5514 | 48.0 | 396 | 0.5368 | 0.8333 |
| 0.5693 | 48.9697 | 404 | 0.5705 | 0.8043 |
| 0.5244 | 49.9394 | 412 | 0.5685 | 0.8314 |
| 0.5495 | 50.9091 | 420 | 0.5811 | 0.8120 |
| 0.5495 | 52.0 | 429 | 0.5037 | 0.8469 |
| 0.5501 | 52.9697 | 437 | 0.5423 | 0.8372 |
| 0.5405 | 53.9394 | 445 | 0.5487 | 0.8178 |
| 0.534 | 54.9091 | 453 | 0.5607 | 0.8217 |
| 0.5502 | 56.0 | 462 | 0.5141 | 0.8198 |
| 0.4772 | 56.9697 | 470 | 0.4813 | 0.8605 |
| 0.4772 | 57.9394 | 478 | 0.5007 | 0.8566 |
| 0.4823 | 58.9091 | 486 | 0.4847 | 0.8624 |
| 0.5107 | 60.0 | 495 | 0.5273 | 0.8333 |
| 0.5205 | 60.9697 | 503 | 0.4981 | 0.8430 |
| 0.5171 | 61.9394 | 511 | 0.4819 | 0.8430 |
| 0.5171 | 62.9091 | 519 | 0.4415 | 0.8682 |
| 0.5498 | 64.0 | 528 | 0.4578 | 0.8566 |
| 0.4732 | 64.9697 | 536 | 0.4614 | 0.8450 |
| 0.4623 | 65.9394 | 544 | 0.4923 | 0.8488 |
| 0.4406 | 66.9091 | 552 | 0.4556 | 0.8547 |
| 0.4889 | 68.0 | 561 | 0.4727 | 0.8488 |
| 0.4889 | 68.9697 | 569 | 0.4746 | 0.8469 |
| 0.4532 | 69.9394 | 577 | 0.4496 | 0.8585 |
| 0.3988 | 70.9091 | 585 | 0.4260 | 0.8702 |
| 0.4608 | 72.0 | 594 | 0.4464 | 0.8547 |
| 0.4429 | 72.9697 | 602 | 0.3946 | 0.8818 |
| 0.4502 | 73.9394 | 610 | 0.4566 | 0.8527 |
| 0.4502 | 74.9091 | 618 | 0.4472 | 0.8663 |
| 0.4381 | 76.0 | 627 | 0.4701 | 0.8372 |
| 0.4437 | 76.9697 | 635 | 0.4351 | 0.8488 |
| 0.4223 | 77.9394 | 643 | 0.4011 | 0.8779 |
| 0.4121 | 78.9091 | 651 | 0.4328 | 0.8547 |
| 0.4164 | 80.0 | 660 | 0.3908 | 0.8857 |
| 0.4164 | 80.9697 | 668 | 0.3774 | 0.8876 |
| 0.418 | 81.9394 | 676 | 0.4397 | 0.8643 |
| 0.3961 | 82.9091 | 684 | 0.4500 | 0.8585 |
| 0.4035 | 84.0 | 693 | 0.3968 | 0.8624 |
| 0.4269 | 84.9697 | 701 | 0.4457 | 0.8566 |
| 0.4269 | 85.9394 | 709 | 0.3987 | 0.8740 |
| 0.3694 | 86.9091 | 717 | 0.4074 | 0.8760 |
| 0.3642 | 88.0 | 726 | 0.3781 | 0.9012 |
| 0.3985 | 88.9697 | 734 | 0.3575 | 0.8934 |
| 0.4237 | 89.9394 | 742 | 0.4313 | 0.8508 |
| 0.4156 | 90.9091 | 750 | 0.3504 | 0.8934 |
| 0.4156 | 92.0 | 759 | 0.4116 | 0.8566 |
| 0.389 | 92.9697 | 767 | 0.3739 | 0.8779 |
| 0.3934 | 93.9394 | 775 | 0.3990 | 0.8779 |
| 0.4231 | 94.9091 | 783 | 0.4164 | 0.8624 |
| 0.3792 | 96.0 | 792 | 0.3808 | 0.8721 |
| 0.3928 | 96.9697 | 800 | 0.3534 | 0.8915 |
| 0.3928 | 97.9394 | 808 | 0.3643 | 0.8798 |
| 0.4003 | 98.9091 | 816 | 0.4150 | 0.8624 |
| 0.3929 | 100.0 | 825 | 0.3477 | 0.9050 |
| 0.3992 | 100.9697 | 833 | 0.4037 | 0.8682 |
| 0.387 | 101.9394 | 841 | 0.3453 | 0.9050 |
| 0.387 | 102.9091 | 849 | 0.4012 | 0.8682 |
| 0.3942 | 104.0 | 858 | 0.3843 | 0.8915 |
| 0.3794 | 104.9697 | 866 | 0.3478 | 0.8798 |
| 0.3794 | 105.9394 | 874 | 0.3111 | 0.9167 |
| 0.396 | 106.9091 | 882 | 0.3588 | 0.8818 |
| 0.3767 | 108.0 | 891 | 0.3602 | 0.8837 |
| 0.3767 | 108.6061 | 896 | 0.3784 | 0.8760 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"2 sashes fix bottom one field",
"2 sashes fix bottom two fields",
"2 sashes fix bottom two fields monoblock",
"2 sashes fix_bottom one field monoblock",
"2 sashes monoblock",
"2_sashes",
"3 sashes monoblock",
"3_sashes",
"4 sashes monoblock",
"4_sashes"
] |
Elisa14/vit-base-patch16-224-in21k-finetuned-lora-food101-version-2
|
# 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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## Uses
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### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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## 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
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[More Information Needed]
## Training Details
### Training Data
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### 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]
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#### Training Hyperparameters
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#### 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. -->
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[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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare",
"waffles"
] |
al-css/faces_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. -->
# faces_clasification_alcss
This model is a fine-tuned version of [lokeshk/Face-Recognition-NM](https://huggingface.co/lokeshk/Face-Recognition-NM) on the private_faces_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2189
- Accuracy: 0.5481
## Model description
More information needed
## Intended uses & 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 4.6929 | 0.0962 | 10 | 4.5349 | 0.0913 |
| 4.4502 | 0.1923 | 20 | 4.3568 | 0.1010 |
| 4.3178 | 0.2885 | 30 | 4.2414 | 0.0865 |
| 4.2083 | 0.3846 | 40 | 4.1396 | 0.1731 |
| 4.1225 | 0.4808 | 50 | 4.0550 | 0.1538 |
| 3.908 | 0.5769 | 60 | 3.9737 | 0.1683 |
| 3.9502 | 0.6731 | 70 | 3.9053 | 0.1683 |
| 3.8725 | 0.7692 | 80 | 3.8432 | 0.1731 |
| 3.7944 | 0.8654 | 90 | 3.7840 | 0.2115 |
| 3.7648 | 0.9615 | 100 | 3.7644 | 0.1971 |
| 3.7211 | 1.0577 | 110 | 3.7172 | 0.1971 |
| 3.5759 | 1.1538 | 120 | 3.6622 | 0.25 |
| 3.6082 | 1.25 | 130 | 3.6080 | 0.2644 |
| 3.4272 | 1.3462 | 140 | 3.5738 | 0.2837 |
| 3.3349 | 1.4423 | 150 | 3.5526 | 0.2740 |
| 3.189 | 1.5385 | 160 | 3.5162 | 0.2837 |
| 3.2959 | 1.6346 | 170 | 3.4639 | 0.2933 |
| 3.1273 | 1.7308 | 180 | 3.4308 | 0.3365 |
| 3.1621 | 1.8269 | 190 | 3.3436 | 0.3221 |
| 3.1053 | 1.9231 | 200 | 3.2973 | 0.3702 |
| 3.0641 | 2.0192 | 210 | 3.2637 | 0.3462 |
| 2.6593 | 2.1154 | 220 | 3.2149 | 0.3798 |
| 2.5709 | 2.2115 | 230 | 3.1658 | 0.3846 |
| 2.5412 | 2.3077 | 240 | 3.1359 | 0.3702 |
| 2.5775 | 2.4038 | 250 | 3.1104 | 0.3654 |
| 2.6372 | 2.5 | 260 | 3.1027 | 0.3798 |
| 2.6732 | 2.5962 | 270 | 3.0252 | 0.4231 |
| 2.6384 | 2.6923 | 280 | 3.0202 | 0.3942 |
| 2.4607 | 2.7885 | 290 | 2.9785 | 0.4135 |
| 2.5519 | 2.8846 | 300 | 2.9470 | 0.4327 |
| 2.2381 | 2.9808 | 310 | 2.9402 | 0.4231 |
| 2.1999 | 3.0769 | 320 | 2.9074 | 0.4519 |
| 2.179 | 3.1731 | 330 | 2.8780 | 0.4567 |
| 2.1427 | 3.2692 | 340 | 2.8331 | 0.4423 |
| 2.1335 | 3.3654 | 350 | 2.8051 | 0.4760 |
| 1.7641 | 3.4615 | 360 | 2.7798 | 0.4712 |
| 1.9687 | 3.5577 | 370 | 2.7607 | 0.4808 |
| 1.8046 | 3.6538 | 380 | 2.7381 | 0.4760 |
| 1.944 | 3.75 | 390 | 2.7244 | 0.4856 |
| 1.7403 | 3.8462 | 400 | 2.6899 | 0.4567 |
| 1.7732 | 3.9423 | 410 | 2.6656 | 0.4808 |
| 1.4105 | 4.0385 | 420 | 2.6526 | 0.4760 |
| 1.377 | 4.1346 | 430 | 2.6448 | 0.4904 |
| 1.5767 | 4.2308 | 440 | 2.5933 | 0.4663 |
| 1.3826 | 4.3269 | 450 | 2.5832 | 0.5 |
| 1.6504 | 4.4231 | 460 | 2.5573 | 0.5192 |
| 1.5579 | 4.5192 | 470 | 2.5666 | 0.5048 |
| 1.2466 | 4.6154 | 480 | 2.5197 | 0.5144 |
| 1.32 | 4.7115 | 490 | 2.5145 | 0.5240 |
| 1.5286 | 4.8077 | 500 | 2.4909 | 0.5192 |
| 1.394 | 4.9038 | 510 | 2.4882 | 0.5192 |
| 1.3982 | 5.0 | 520 | 2.4616 | 0.5192 |
| 1.1167 | 5.0962 | 530 | 2.4569 | 0.5096 |
| 1.3562 | 5.1923 | 540 | 2.4559 | 0.5192 |
| 1.0018 | 5.2885 | 550 | 2.4438 | 0.5192 |
| 1.2367 | 5.3846 | 560 | 2.4204 | 0.5192 |
| 1.1748 | 5.4808 | 570 | 2.4112 | 0.5337 |
| 1.022 | 5.5769 | 580 | 2.4130 | 0.5385 |
| 1.0954 | 5.6731 | 590 | 2.3992 | 0.5192 |
| 0.9759 | 5.7692 | 600 | 2.3683 | 0.5240 |
| 1.0327 | 5.8654 | 610 | 2.3577 | 0.5144 |
| 1.1167 | 5.9615 | 620 | 2.3547 | 0.5144 |
| 0.8077 | 6.0577 | 630 | 2.3452 | 0.5385 |
| 0.95 | 6.1538 | 640 | 2.3486 | 0.5433 |
| 0.7993 | 6.25 | 650 | 2.3428 | 0.5529 |
| 0.923 | 6.3462 | 660 | 2.3279 | 0.5337 |
| 0.7566 | 6.4423 | 670 | 2.3176 | 0.5385 |
| 0.8834 | 6.5385 | 680 | 2.3201 | 0.5288 |
| 0.9337 | 6.6346 | 690 | 2.3064 | 0.5529 |
| 0.7596 | 6.7308 | 700 | 2.3063 | 0.5288 |
| 0.973 | 6.8269 | 710 | 2.2847 | 0.5337 |
| 1.0212 | 6.9231 | 720 | 2.3006 | 0.5433 |
| 0.8315 | 7.0192 | 730 | 2.2813 | 0.5385 |
| 0.814 | 7.1154 | 740 | 2.2751 | 0.5481 |
| 0.7658 | 7.2115 | 750 | 2.2754 | 0.5433 |
| 0.5956 | 7.3077 | 760 | 2.2781 | 0.5433 |
| 0.8864 | 7.4038 | 770 | 2.2602 | 0.5529 |
| 0.7181 | 7.5 | 780 | 2.2568 | 0.5721 |
| 0.643 | 7.5962 | 790 | 2.2586 | 0.5481 |
| 0.7107 | 7.6923 | 800 | 2.2520 | 0.5337 |
| 0.6078 | 7.7885 | 810 | 2.2423 | 0.5385 |
| 0.873 | 7.8846 | 820 | 2.2478 | 0.5337 |
| 0.7018 | 7.9808 | 830 | 2.2357 | 0.5577 |
| 0.7177 | 8.0769 | 840 | 2.2340 | 0.5481 |
| 0.6543 | 8.1731 | 850 | 2.2337 | 0.5481 |
| 0.7563 | 8.2692 | 860 | 2.2259 | 0.5529 |
| 0.549 | 8.3654 | 870 | 2.2310 | 0.5529 |
| 0.6981 | 8.4615 | 880 | 2.2381 | 0.5529 |
| 0.6172 | 8.5577 | 890 | 2.2285 | 0.5481 |
| 0.587 | 8.6538 | 900 | 2.2165 | 0.5481 |
| 0.613 | 8.75 | 910 | 2.2215 | 0.5481 |
| 0.5558 | 8.8462 | 920 | 2.2274 | 0.5529 |
| 0.6892 | 8.9423 | 930 | 2.2221 | 0.5433 |
| 0.5876 | 9.0385 | 940 | 2.2209 | 0.5481 |
| 0.5845 | 9.1346 | 950 | 2.2211 | 0.5481 |
| 0.645 | 9.2308 | 960 | 2.2208 | 0.5481 |
| 0.5619 | 9.3269 | 970 | 2.2204 | 0.5481 |
| 0.5371 | 9.4231 | 980 | 2.2219 | 0.5481 |
| 0.5048 | 9.5192 | 990 | 2.2203 | 0.5481 |
| 0.6007 | 9.6154 | 1000 | 2.2192 | 0.5481 |
| 0.5706 | 9.7115 | 1010 | 2.2187 | 0.5481 |
| 0.571 | 9.8077 | 1020 | 2.2189 | 0.5481 |
| 0.6692 | 9.9038 | 1030 | 2.2191 | 0.5481 |
| 0.5411 | 10.0 | 1040 | 2.2189 | 0.5481 |
### Framework versions
- Transformers 4.44.1
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"label_23",
"label_24",
"label_25",
"label_26",
"label_27",
"label_28",
"label_29",
"label_30",
"label_31",
"label_32",
"label_33",
"label_34",
"label_35",
"label_36",
"label_37",
"label_38",
"label_39",
"label_40",
"label_41",
"label_42",
"label_43",
"label_44",
"label_45",
"label_46",
"label_47",
"label_48",
"label_49",
"label_50",
"label_51",
"label_52",
"label_53",
"label_54",
"label_55",
"label_56",
"label_57",
"label_58",
"label_59",
"label_60",
"label_61",
"label_62",
"label_63",
"label_64",
"label_65",
"label_66",
"label_67",
"label_68",
"label_69",
"label_70",
"label_71",
"label_72",
"label_73",
"label_74",
"label_75",
"label_76",
"label_77",
"label_78",
"label_79",
"label_80",
"label_81",
"label_82",
"label_83",
"label_84",
"label_85",
"label_86",
"label_87",
"label_88",
"label_89",
"label_90",
"label_91",
"label_92",
"label_93",
"label_94",
"label_95",
"label_96",
"label_97",
"label_98",
"label_99",
"label_100",
"label_101",
"label_102",
"label_103",
"label_104",
"label_105",
"label_106",
"label_107",
"label_108",
"label_109",
"label_110",
"label_111",
"label_112",
"label_113",
"label_114",
"label_115"
] |
Ticmate/checkpoint-334-fine-tuned-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]
|
[
"bird",
"cat",
"dog"
] |
al-css/Screenshots_detection_to_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. -->
# Screenshots_detection_to_classification
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the private_images_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1192
- Accuracy: 0.9881
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.44.1
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"1"
] |
djbp/swin-base-patch4-window7-224-in22k-construction_type
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224-in22k-construction_type
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3095
- Accuracy: 0.8804
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.9839 | 0.9836 | 15 | 0.4599 | 0.8183 |
| 0.4167 | 1.9672 | 30 | 0.3605 | 0.8628 |
| 0.3853 | 2.9508 | 45 | 0.3272 | 0.8799 |
| 0.3302 | 4.0 | 61 | 0.3227 | 0.8763 |
| 0.3302 | 4.9836 | 76 | 0.3269 | 0.8753 |
| 0.3049 | 5.9672 | 91 | 0.3138 | 0.8799 |
| 0.2951 | 6.8852 | 105 | 0.3095 | 0.8804 |
### Framework versions
- Transformers 4.44.2
- Pytorch 1.13.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"immovable",
"invalid",
"movable",
"unknown"
] |
DouglasBraga/swin-tiny-patch4-window7-224-finetuned-leukemia-08-2024
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-leukemia-08-2024
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: 0.1282
- Accuracy: 0.9527
## Model description
This model was developed to aid in the diagnosis of Leukemia. Leukemia is the cancer that most affects children between 4 and 10 years of age.
## Intended uses & limitations
In this version, the images used for testing correspond to 10% of the images in the image dataset. Consider that the images in the dataset are originally:
- All: 7,272
- Hem: 3,389
Applying Data augmentation, we arrive at: 20,000 images for each segment. Totaling 36,000 images for training and 4,000 for testing.
## Important Note:
The images used in the test may be similar to those used in training, which may cause overfit..
## Training and evaluation data
DataSet: Dataset ISBI-2019 (ISIC 2019 Challenge)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.328 | 0.9991 | 281 | 0.7951 | 0.6827 |
| 0.2832 | 1.9982 | 562 | 0.4021 | 0.8433 |
| 0.1886 | 2.9973 | 843 | 0.3305 | 0.8718 |
| 0.1789 | 4.0 | 1125 | 0.2242 | 0.9123 |
| 0.1269 | 4.9991 | 1406 | 0.1856 | 0.9315 |
| 0.0904 | 5.9982 | 1687 | 0.1282 | 0.9527 |
| 0.0754 | 6.9973 | 1968 | 0.1824 | 0.9377 |
| 0.0549 | 8.0 | 2250 | 0.2908 | 0.9105 |
| 0.0616 | 8.9991 | 2531 | 0.2961 | 0.9215 |
| 0.0502 | 9.9911 | 2810 | 0.2343 | 0.9345 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.3.0+cpu
- Datasets 2.21.0
- Tokenizers 0.19.1
## Author
Douglas Braga https://www.linkedin.com/in/douglas-braga-891a701/
|
[
"all",
"hem"
] |
crocutacrocuto/dinov2-base-DIN0-3
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"aardvark",
"bird",
"black-and-white colobus",
"blue duiker",
"blue monkey",
"buffalo",
"bushbuck",
"bushpig",
"cattle",
"chimpanzee",
"civet_genet",
"dog",
"elephant",
"galago_potto",
"goat",
"golden cat",
"gorilla",
"grey duiker",
"grey-cheeked mangabey",
"guineafowl",
"honey badger",
"hyrax",
"leopard",
"lhoest monkey",
"mandrill",
"mongoose",
"monkey",
"olive baboon",
"otter",
"pangolin",
"porcupine",
"red colobus",
"red duiker",
"red-capped mangabey",
"rodent",
"serval",
"side-striped jackal",
"spotted hyena",
"squirel",
"vervet monkey",
"water chevrotain"
] |
andrei-teodor/vit-base-brain-mri
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-brain-mri
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the BrainMRI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0577
- Accuracy: 0.5990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 72 | 0.9986 | 0.6098 |
| 1.098 | 2.0 | 144 | 0.8445 | 0.7003 |
| 0.7895 | 3.0 | 216 | 0.7318 | 0.7526 |
| 0.7895 | 4.0 | 288 | 0.6842 | 0.7474 |
| 0.6629 | 5.0 | 360 | 0.6328 | 0.7857 |
| 0.5966 | 6.0 | 432 | 0.5957 | 0.8101 |
| 0.5546 | 7.0 | 504 | 0.5646 | 0.8118 |
| 0.5546 | 8.0 | 576 | 0.5647 | 0.8049 |
| 0.5113 | 9.0 | 648 | 0.5340 | 0.8275 |
| 0.4882 | 10.0 | 720 | 0.5190 | 0.8328 |
| 0.4882 | 11.0 | 792 | 0.5197 | 0.8328 |
| 0.4789 | 12.0 | 864 | 0.5002 | 0.8258 |
| 0.4582 | 13.0 | 936 | 0.4957 | 0.8310 |
| 0.4426 | 14.0 | 1008 | 0.4821 | 0.8310 |
| 0.4426 | 15.0 | 1080 | 0.4706 | 0.8467 |
| 0.4328 | 16.0 | 1152 | 0.4821 | 0.8153 |
| 0.432 | 17.0 | 1224 | 0.4992 | 0.8275 |
| 0.432 | 18.0 | 1296 | 0.4799 | 0.8345 |
| 0.4196 | 19.0 | 1368 | 0.4838 | 0.8310 |
| 0.4287 | 20.0 | 1440 | 0.4598 | 0.8659 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
|
[
"glioma_tumor",
"meningioma_tumor",
"no_tumor",
"pituitary_tumor"
] |
horward/myVit
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# myVit
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cpu
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"calico",
"persian",
"siamese",
"tortoiseshell",
"tuxedo"
] |
Laimaimai/herbal_identification
|
# 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]
|
[
"10_tuc_doan",
"11_thien_mon",
"12_sai_ho",
"13_vien_chi",
"14_su_quan_tu",
"15_bach_mao_can",
"16_cau_ky_tu",
"17_do_trong",
"18_dang_sam",
"19_cau_tich",
"1_boi_mau",
"20_tho_ty_tu",
"21_hoang_ky",
"22_coi_xay",
"23_huyen_sam",
"24_tang_chi",
"25_diep_ha_chau",
"26_kim_anh",
"27_cat_can",
"28_co_ngot",
"29_cuc_hoa",
"2_hoe_hoa",
"30_to_moc",
"31_kim_tien_thao",
"32_dan_sam",
"33_chi_tu",
"34_ngai_cuu",
"35_sinh_dia",
"36_nguu_tat",
"37_bach_truat",
"38_nhan_tran",
"39_duong_quy",
"3_linh_chi",
"40_nho_noi",
"41_dao_nhan",
"42_cat_canh",
"43_ha_kho_thao",
"44_xa_tien_tu",
"45_che_day",
"46_xa_can",
"47_tang_diep",
"48_ngu_boi_tu",
"49_ngu_gia_bi",
"4_thong_thao",
"50_rau_ngo",
"51_nguu_bang_tu",
"52_cam_thao_dat",
"53_dai_hoang",
"54_hoai_son",
"55_dam_duong_hoac",
"56_moc_qua",
"57_bo_cong_anh",
"58_tho_phuc_linh",
"59_mach_mon",
"5_trach_ta",
"60_ke_dau_ngua",
"61_tang_bach_bi",
"62_cam_thao_bac",
"63_o_tac_cot",
"64_thao_quyet_minh",
"65_dai_tao",
"66_kim_ngan_hoa",
"67_tao_nhan",
"68_ban_ha",
"69_ca_gai_leo",
"6_y_di",
"70_kho_qua",
"71_xuyen_tam_lien",
"72_nhan_sam",
"73_bach_gioi_tu",
"74_tam_that",
"75_bach_chi",
"76_sa_sam",
"77_bach_thuoc",
"78_cam_thao_day",
"7_can_khuong",
"8_ty_giai",
"9_cot_toai_bo"
] |
andrei-teodor/resnet-pretrained-brain-mri
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-pretrained-brain-mri
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the BrainMRI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1450
- Accuracy: 0.5228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log | 1.0 | 72 | 0.4704 | 1.2440 |
| 1.2771 | 2.0 | 144 | 0.5575 | 1.1610 |
| 1.1543 | 3.0 | 216 | 0.6446 | 1.0949 |
| 1.1543 | 4.0 | 288 | 0.6812 | 1.0361 |
| 1.0664 | 5.0 | 360 | 0.6742 | 1.0100 |
| 0.9998 | 6.0 | 432 | 0.7003 | 0.9687 |
| 0.9537 | 7.0 | 504 | 0.6986 | 0.9484 |
| 0.9537 | 8.0 | 576 | 0.6934 | 0.9285 |
| 0.9239 | 9.0 | 648 | 0.7108 | 0.8992 |
| 0.893 | 10.0 | 720 | 0.7369 | 0.8723 |
| 0.893 | 11.0 | 792 | 0.7334 | 0.8635 |
| 0.8726 | 12.0 | 864 | 0.7474 | 0.8589 |
| 0.8482 | 13.0 | 936 | 0.7160 | 0.8423 |
| 0.8461 | 14.0 | 1008 | 0.7300 | 0.8481 |
| 0.8461 | 15.0 | 1080 | 0.7352 | 0.8312 |
| 0.8267 | 16.0 | 1152 | 0.7247 | 0.8319 |
| 0.8163 | 17.0 | 1224 | 0.7456 | 0.8136 |
| 0.8163 | 18.0 | 1296 | 0.7474 | 0.8151 |
| 0.8126 | 19.0 | 1368 | 0.7596 | 0.8071 |
| 0.8022 | 20.0 | 1440 | 0.7491 | 0.8210 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
|
[
"glioma_tumor",
"meningioma_tumor",
"no_tumor",
"pituitary_tumor"
] |
itsLeen/realFake-img
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# realFake-img
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the ai_real_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4633
- Accuracy: 0.8836
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1137 | 1.9231 | 100 | 0.4869 | 0.8288 |
| 0.1002 | 3.8462 | 200 | 0.4633 | 0.8836 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
salunev/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: 0.0638
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2816 | 1.0 | 130 | 0.2115 | 0.9699 |
| 0.1323 | 2.0 | 260 | 0.1257 | 0.9774 |
| 0.1416 | 3.0 | 390 | 0.0936 | 0.9774 |
| 0.0855 | 4.0 | 520 | 0.0638 | 0.9925 |
| 0.1177 | 5.0 | 650 | 0.0766 | 0.9850 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Giecom/google-vit-base-patch16-224-Waste-O-I-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. -->
# google-vit-base-patch16-224-Waste-O-I-classification
This model is a fine-tuned version performed by [Miguel Calderon](https://huggingface.co/MiguelCalderon) of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.956
- Loss: 0.3036
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:------:|:-----:|:--------:|:---------------:|
| 0.2168 | 0.1580 | 1000 | 0.9525 | 0.1303 |
| 0.196 | 0.3159 | 2000 | 0.941 | 0.1638 |
| 0.1993 | 0.4739 | 3000 | 0.9285 | 0.2206 |
| 0.1849 | 0.6318 | 4000 | 0.9225 | 0.2288 |
| 0.199 | 0.7898 | 5000 | 0.9105 | 0.3331 |
| 0.2171 | 0.9477 | 6000 | 0.944 | 0.1582 |
| 0.1209 | 1.1057 | 7000 | 0.9495 | 0.1887 |
| 0.114 | 1.2636 | 8000 | 0.932 | 0.1950 |
| 0.1268 | 1.4216 | 9000 | 0.9335 | 0.1965 |
| 0.1272 | 1.5795 | 10000 | 0.9165 | 0.3112 |
| 0.1003 | 1.7375 | 11000 | 0.9575 | 0.1353 |
| 0.0844 | 1.8954 | 12000 | 0.9345 | 0.2635 |
| 0.0757 | 2.0534 | 13000 | 0.952 | 0.1434 |
| 0.053 | 2.2113 | 14000 | 0.933 | 0.3203 |
| 0.0994 | 2.3693 | 15000 | 0.9405 | 0.2165 |
| 0.0248 | 2.5272 | 16000 | 0.951 | 0.2400 |
| 0.0842 | 2.6852 | 17000 | 0.906 | 0.4092 |
| 0.0733 | 2.8432 | 18000 | 0.9515 | 0.1937 |
| 0.0542 | 3.0011 | 19000 | 0.938 | 0.2911 |
| 0.0202 | 3.1591 | 20000 | 0.936 | 0.3648 |
| 0.0237 | 3.3170 | 21000 | 0.9355 | 0.3618 |
| 0.0294 | 3.4750 | 22000 | 0.9255 | 0.4209 |
| 0.0375 | 3.6329 | 23000 | 0.943 | 0.2840 |
| 0.0176 | 3.7909 | 24000 | 0.9525 | 0.2604 |
| 0.0252 | 3.9488 | 25000 | 0.9515 | 0.2500 |
| 0.0024 | 4.1068 | 26000 | 0.9545 | 0.2892 |
| 0.0119 | 4.2647 | 27000 | 0.956 | 0.3036 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"i",
"o"
] |
mujerry/swin-tiny-patch4-window7-224-finetuned-papsmear
|
<!-- This model card 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-papsmear
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: 0.2644
- Accuracy: 0.9779
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7081 | 0.9935 | 38 | 1.6642 | 0.2868 |
| 1.4025 | 1.9869 | 76 | 1.3761 | 0.4632 |
| 1.0918 | 2.9804 | 114 | 1.0276 | 0.5515 |
| 0.8051 | 4.0 | 153 | 0.7679 | 0.6691 |
| 0.635 | 4.9935 | 191 | 0.5928 | 0.7868 |
| 0.6051 | 5.9869 | 229 | 0.6957 | 0.75 |
| 0.5539 | 6.9804 | 267 | 0.5016 | 0.7941 |
| 0.4683 | 8.0 | 306 | 0.4733 | 0.8235 |
| 0.4153 | 8.9935 | 344 | 0.4835 | 0.8529 |
| 0.3954 | 9.9869 | 382 | 0.5431 | 0.8309 |
| 0.3524 | 10.9804 | 420 | 0.4061 | 0.8235 |
| 0.3546 | 12.0 | 459 | 0.4925 | 0.8382 |
| 0.2922 | 12.9935 | 497 | 0.3637 | 0.875 |
| 0.2342 | 13.9869 | 535 | 0.3286 | 0.8971 |
| 0.2083 | 14.9804 | 573 | 0.3271 | 0.8824 |
| 0.2704 | 16.0 | 612 | 0.3700 | 0.8824 |
| 0.1871 | 16.9935 | 650 | 0.3447 | 0.8971 |
| 0.226 | 17.9869 | 688 | 0.4280 | 0.8603 |
| 0.245 | 18.9804 | 726 | 0.6445 | 0.8088 |
| 0.1545 | 20.0 | 765 | 0.4180 | 0.8603 |
| 0.0981 | 20.9935 | 803 | 0.3208 | 0.9044 |
| 0.1455 | 21.9869 | 841 | 0.4256 | 0.8603 |
| 0.2405 | 22.9804 | 879 | 0.3474 | 0.8971 |
| 0.1549 | 24.0 | 918 | 0.3940 | 0.9044 |
| 0.1721 | 24.9935 | 956 | 0.4279 | 0.8824 |
| 0.1378 | 25.9869 | 994 | 0.3871 | 0.9044 |
| 0.0924 | 26.9804 | 1032 | 0.7301 | 0.8456 |
| 0.1325 | 28.0 | 1071 | 0.3712 | 0.9044 |
| 0.1426 | 28.9935 | 1109 | 0.4400 | 0.8603 |
| 0.0866 | 29.9869 | 1147 | 0.2779 | 0.9412 |
| 0.0659 | 30.9804 | 1185 | 0.3207 | 0.9412 |
| 0.1175 | 32.0 | 1224 | 0.4339 | 0.9044 |
| 0.0455 | 32.9935 | 1262 | 0.4537 | 0.9265 |
| 0.1006 | 33.9869 | 1300 | 0.6521 | 0.875 |
| 0.033 | 34.9804 | 1338 | 0.5616 | 0.9044 |
| 0.0979 | 36.0 | 1377 | 0.3718 | 0.9191 |
| 0.1045 | 36.9935 | 1415 | 0.2529 | 0.9632 |
| 0.0815 | 37.9869 | 1453 | 0.3511 | 0.9338 |
| 0.0761 | 38.9804 | 1491 | 0.3114 | 0.9338 |
| 0.0747 | 40.0 | 1530 | 0.2837 | 0.9338 |
| 0.0545 | 40.9935 | 1568 | 0.4269 | 0.9412 |
| 0.0796 | 41.9869 | 1606 | 0.2331 | 0.9412 |
| 0.055 | 42.9804 | 1644 | 0.2900 | 0.9485 |
| 0.0706 | 44.0 | 1683 | 0.3368 | 0.9632 |
| 0.0505 | 44.9935 | 1721 | 0.3780 | 0.9485 |
| 0.0698 | 45.9869 | 1759 | 0.4822 | 0.9191 |
| 0.0275 | 46.9804 | 1797 | 0.3434 | 0.9632 |
| 0.0641 | 48.0 | 1836 | 0.3387 | 0.9706 |
| 0.0484 | 48.9935 | 1874 | 0.5350 | 0.9191 |
| 0.0388 | 49.9869 | 1912 | 0.3826 | 0.9118 |
| 0.0347 | 50.9804 | 1950 | 0.3739 | 0.9559 |
| 0.1046 | 52.0 | 1989 | 0.3075 | 0.9118 |
| 0.0298 | 52.9935 | 2027 | 0.3558 | 0.9559 |
| 0.0478 | 53.9869 | 2065 | 0.3056 | 0.9706 |
| 0.0285 | 54.9804 | 2103 | 0.2851 | 0.9632 |
| 0.0407 | 56.0 | 2142 | 0.3223 | 0.9559 |
| 0.0459 | 56.9935 | 2180 | 0.4575 | 0.9485 |
| 0.0409 | 57.9869 | 2218 | 0.2930 | 0.9632 |
| 0.0743 | 58.9804 | 2256 | 0.4032 | 0.9485 |
| 0.0346 | 60.0 | 2295 | 0.3738 | 0.9412 |
| 0.0302 | 60.9935 | 2333 | 0.3597 | 0.9485 |
| 0.0488 | 61.9869 | 2371 | 0.2595 | 0.9559 |
| 0.0562 | 62.9804 | 2409 | 0.3764 | 0.9412 |
| 0.0216 | 64.0 | 2448 | 0.2644 | 0.9779 |
| 0.0219 | 64.9935 | 2486 | 0.3092 | 0.9632 |
| 0.0272 | 65.9869 | 2524 | 0.2898 | 0.9632 |
| 0.027 | 66.9804 | 2562 | 0.2693 | 0.9632 |
| 0.0397 | 68.0 | 2601 | 0.3843 | 0.9412 |
| 0.0154 | 68.9935 | 2639 | 0.3051 | 0.9485 |
| 0.0004 | 69.9869 | 2677 | 0.3909 | 0.9412 |
| 0.0651 | 70.9804 | 2715 | 0.2977 | 0.9485 |
| 0.016 | 72.0 | 2754 | 0.2695 | 0.9632 |
| 0.0351 | 72.9935 | 2792 | 0.2720 | 0.9706 |
| 0.0206 | 73.9869 | 2830 | 0.2549 | 0.9706 |
| 0.0109 | 74.9804 | 2868 | 0.2412 | 0.9706 |
| 0.0012 | 76.0 | 2907 | 0.3494 | 0.9779 |
| 0.0418 | 76.9935 | 2945 | 0.3729 | 0.9632 |
| 0.0165 | 77.9869 | 2983 | 0.3471 | 0.9632 |
| 0.0163 | 78.9804 | 3021 | 0.2973 | 0.9706 |
| 0.0202 | 80.0 | 3060 | 0.3730 | 0.9559 |
| 0.0368 | 80.9935 | 3098 | 0.2877 | 0.9706 |
| 0.0374 | 81.9869 | 3136 | 0.4143 | 0.9632 |
| 0.0296 | 82.9804 | 3174 | 0.2895 | 0.9779 |
| 0.0405 | 84.0 | 3213 | 0.2927 | 0.9559 |
| 0.0097 | 84.9935 | 3251 | 0.3179 | 0.9632 |
| 0.0182 | 85.9869 | 3289 | 0.3047 | 0.9706 |
| 0.0207 | 86.9804 | 3327 | 0.3018 | 0.9779 |
| 0.0207 | 88.0 | 3366 | 0.3321 | 0.9632 |
| 0.003 | 88.9935 | 3404 | 0.3086 | 0.9706 |
| 0.0157 | 89.9869 | 3442 | 0.2948 | 0.9706 |
| 0.0428 | 90.9804 | 3480 | 0.3175 | 0.9706 |
| 0.0189 | 92.0 | 3519 | 0.3240 | 0.9632 |
| 0.0046 | 92.9935 | 3557 | 0.3414 | 0.9632 |
| 0.0057 | 93.9869 | 3595 | 0.3329 | 0.9632 |
| 0.0165 | 94.9804 | 3633 | 0.3240 | 0.9632 |
| 0.006 | 96.0 | 3672 | 0.3180 | 0.9706 |
| 0.0172 | 96.9935 | 3710 | 0.3103 | 0.9779 |
| 0.0109 | 97.9869 | 3748 | 0.3035 | 0.9779 |
| 0.0172 | 98.9804 | 3786 | 0.3034 | 0.9779 |
| 0.0219 | 99.3464 | 3800 | 0.3036 | 0.9779 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"ascus",
"cancer",
"hsil",
"lsil",
"nilm",
"non-diagnostic"
] |
zqTensor/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: 0.0079
- 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: 1337
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.2859 | 1.0 | 130 | 0.9624 | 0.2189 |
| 0.1316 | 2.0 | 260 | 0.9699 | 0.1334 |
| 0.1438 | 3.0 | 390 | 0.9699 | 0.0981 |
| 0.0833 | 4.0 | 520 | 0.9925 | 0.0656 |
| 0.1107 | 5.0 | 650 | 0.9774 | 0.0817 |
| 0.098 | 11.0 | 715 | 0.9925 | 0.0570 |
| 0.0935 | 12.0 | 780 | 1.0 | 0.0418 |
| 0.0907 | 13.0 | 845 | 0.9699 | 0.1093 |
| 0.0947 | 14.0 | 910 | 1.0 | 0.0347 |
| 0.1259 | 15.0 | 975 | 0.9850 | 0.0710 |
| 0.0325 | 16.0 | 1040 | 0.9774 | 0.0587 |
| 0.1397 | 17.0 | 1105 | 0.9925 | 0.0495 |
| 0.0456 | 18.0 | 1170 | 0.9774 | 0.0519 |
| 0.0439 | 19.0 | 1235 | 1.0 | 0.0216 |
| 0.0484 | 20.0 | 1300 | 0.9925 | 0.0316 |
| 0.0276 | 21.0 | 1365 | 1.0 | 0.0192 |
| 0.0348 | 22.0 | 1430 | 1.0 | 0.0177 |
| 0.0326 | 23.0 | 1495 | 1.0 | 0.0175 |
| 0.1014 | 24.0 | 1560 | 0.9925 | 0.0235 |
| 0.0395 | 25.0 | 1625 | 0.9850 | 0.0451 |
| 0.0265 | 26.0 | 1690 | 0.9925 | 0.0297 |
| 0.0569 | 27.0 | 1755 | 0.9925 | 0.0263 |
| 0.0666 | 28.0 | 1820 | 0.9850 | 0.0245 |
| 0.0285 | 29.0 | 1885 | 0.9774 | 0.0418 |
| 0.0892 | 30.0 | 1950 | 0.9925 | 0.0204 |
| 0.0371 | 31.0 | 2015 | 0.9850 | 0.0339 |
| 0.0105 | 32.0 | 2080 | 1.0 | 0.0143 |
| 0.0563 | 33.0 | 2145 | 1.0 | 0.0140 |
| 0.0573 | 34.0 | 2210 | 1.0 | 0.0102 |
| 0.0409 | 35.0 | 2275 | 1.0 | 0.0096 |
| 0.0523 | 36.0 | 2340 | 0.9925 | 0.0149 |
| 0.0131 | 37.0 | 2405 | 0.9925 | 0.0197 |
| 0.0329 | 38.0 | 2470 | 1.0 | 0.0109 |
| 0.0577 | 39.0 | 2535 | 1.0 | 0.0096 |
| 0.0085 | 40.0 | 2600 | 0.9925 | 0.0147 |
| 0.0618 | 41.0 | 2665 | 1.0 | 0.0094 |
| 0.0847 | 42.0 | 2730 | 0.9925 | 0.0197 |
| 0.0291 | 43.0 | 2795 | 1.0 | 0.0089 |
| 0.0568 | 44.0 | 2860 | 1.0 | 0.0087 |
| 0.0077 | 45.0 | 2925 | 1.0 | 0.0104 |
| 0.008 | 46.0 | 2990 | 1.0 | 0.0138 |
| 0.0272 | 47.0 | 3055 | 1.0 | 0.0081 |
| 0.008 | 48.0 | 3120 | 1.0 | 0.0084 |
| 0.0112 | 49.0 | 3185 | 1.0 | 0.0082 |
| 0.013 | 50.0 | 3250 | 1.0 | 0.0079 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
hexasix/rose_recognition
|
# 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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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
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[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here --
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## 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]
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## Technical Specifications [optional]
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## Glossary [optional]
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|
[
"rosa gallica",
"rosa × damascena"
] |
mujerry/mobilenet_v2_1.0_224-finetuned-papsmear
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilenet_v2_1.0_224-finetuned-papsmear
This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4698
- Accuracy: 0.8676
## Model description
More information needed
## Intended uses & 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: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7932 | 0.9935 | 38 | 1.7607 | 0.25 |
| 1.6542 | 1.9869 | 76 | 1.5736 | 0.3971 |
| 1.4692 | 2.9804 | 114 | 1.4805 | 0.3676 |
| 1.2759 | 4.0 | 153 | 1.2177 | 0.5809 |
| 1.1521 | 4.9935 | 191 | 1.0727 | 0.6471 |
| 1.078 | 5.9869 | 229 | 0.9996 | 0.6176 |
| 1.0235 | 6.9804 | 267 | 0.8680 | 0.7059 |
| 0.9554 | 8.0 | 306 | 0.9273 | 0.6397 |
| 0.7437 | 8.9935 | 344 | 0.7389 | 0.7059 |
| 0.7876 | 9.9869 | 382 | 0.6774 | 0.7426 |
| 0.7698 | 10.9804 | 420 | 0.6569 | 0.7206 |
| 0.7597 | 12.0 | 459 | 0.6758 | 0.7574 |
| 0.6114 | 12.9935 | 497 | 0.8279 | 0.7132 |
| 0.6847 | 13.9869 | 535 | 0.7505 | 0.7132 |
| 0.5902 | 14.9804 | 573 | 0.7919 | 0.6691 |
| 0.629 | 16.0 | 612 | 0.6117 | 0.7868 |
| 0.5071 | 16.9935 | 650 | 0.6048 | 0.7353 |
| 0.5453 | 17.9869 | 688 | 0.8086 | 0.7279 |
| 0.5071 | 18.9804 | 726 | 0.7835 | 0.7059 |
| 0.5328 | 20.0 | 765 | 0.6139 | 0.75 |
| 0.5053 | 20.9935 | 803 | 0.5981 | 0.7868 |
| 0.4436 | 21.9869 | 841 | 0.5219 | 0.8015 |
| 0.5025 | 22.9804 | 879 | 0.4959 | 0.8088 |
| 0.4984 | 24.0 | 918 | 0.5701 | 0.7794 |
| 0.4655 | 24.9935 | 956 | 0.7179 | 0.7206 |
| 0.3848 | 25.9869 | 994 | 0.5075 | 0.8088 |
| 0.3824 | 26.9804 | 1032 | 0.6645 | 0.7426 |
| 0.4901 | 28.0 | 1071 | 0.7288 | 0.6985 |
| 0.397 | 28.9935 | 1109 | 0.7251 | 0.7279 |
| 0.3818 | 29.9869 | 1147 | 0.6250 | 0.7941 |
| 0.3412 | 30.9804 | 1185 | 0.7065 | 0.7279 |
| 0.3627 | 32.0 | 1224 | 0.6877 | 0.7426 |
| 0.3557 | 32.9935 | 1262 | 0.4245 | 0.8529 |
| 0.441 | 33.9869 | 1300 | 0.6974 | 0.75 |
| 0.3036 | 34.9804 | 1338 | 0.6458 | 0.7426 |
| 0.3213 | 36.0 | 1377 | 0.5579 | 0.7941 |
| 0.402 | 36.9935 | 1415 | 0.4578 | 0.8382 |
| 0.2897 | 37.9869 | 1453 | 0.5369 | 0.7868 |
| 0.348 | 38.9804 | 1491 | 0.6819 | 0.7941 |
| 0.3929 | 40.0 | 1530 | 0.5810 | 0.7868 |
| 0.3173 | 40.9935 | 1568 | 0.7875 | 0.7426 |
| 0.3499 | 41.9869 | 1606 | 0.5051 | 0.8015 |
| 0.3053 | 42.9804 | 1644 | 0.7510 | 0.7426 |
| 0.4109 | 44.0 | 1683 | 0.6529 | 0.75 |
| 0.3846 | 44.9935 | 1721 | 0.9615 | 0.7132 |
| 0.3222 | 45.9869 | 1759 | 0.8889 | 0.6691 |
| 0.3293 | 46.9804 | 1797 | 0.4698 | 0.8676 |
| 0.293 | 48.0 | 1836 | 0.5996 | 0.8015 |
| 0.2363 | 48.9935 | 1874 | 0.5007 | 0.8309 |
| 0.2811 | 49.9869 | 1912 | 0.6748 | 0.7941 |
| 0.2403 | 50.9804 | 1950 | 0.6595 | 0.7941 |
| 0.2553 | 52.0 | 1989 | 0.5987 | 0.7794 |
| 0.2959 | 52.9935 | 2027 | 0.5459 | 0.8235 |
| 0.3066 | 53.9869 | 2065 | 0.6198 | 0.7868 |
| 0.2981 | 54.9804 | 2103 | 0.4886 | 0.8309 |
| 0.2658 | 56.0 | 2142 | 0.6422 | 0.7794 |
| 0.2371 | 56.9935 | 2180 | 0.5000 | 0.8382 |
| 0.2331 | 57.9869 | 2218 | 0.8854 | 0.7132 |
| 0.2777 | 58.9804 | 2256 | 0.6190 | 0.8015 |
| 0.3047 | 59.6078 | 2280 | 0.6048 | 0.7647 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"ascus",
"cancer",
"hsil",
"lsil",
"nilm",
"non-diagnostic"
] |
Mitsua/swin-base-multi-fractal-1k
|
# Model Card for Swin Base Multi Fractal 1k
Swin Transformer model pre-trained on Color Multi Fractal DB 1k (1 million images, 1k classes) at resolution 224x224 for 300 epochs, developed by [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine.
This model is trained exclusively on 1 million fractal images which relies solely on mathematical formulas, so no real images or pretrained models are used for this training.
## Model Details
### Model Description
The Swin Transformer is a type of Vision Transformer and can be utilized for various downstream tasks.
It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer).
- **Developed by:** [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- **Model type:** Image Classification
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
## Training Details
### Training Data
- [Color Multi Fractal DB 1k](https://huggingface.co/datasets/Mitsua/color-multi-fractal-db-1k) / CC BY 4.0
- **Curated by:** [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- Paper: [Improving Fractal Pre-training](https://catalys1.github.io/fractal-pretraining/) by Connor Anderson and Ryan Farrell
- Code : [Multi-Fractal-Dataset](https://github.com/FYGitHub1009/Multi-Fractal-Dataset) by FYSignate1009
|
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] |
ayubkfupm/swin-tiny-patch4-window7-224-finetuned-st-wsdmhar-xyz-auc
|
<!-- This model card 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-st-wsdmhar-xyz-auc
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: 0.1060
- Accuracy: 0.9766
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.586 | 1.0 | 53 | 1.4366 | 0.4559 |
| 0.9024 | 2.0 | 106 | 0.7526 | 0.7018 |
| 0.6157 | 3.0 | 159 | 0.5375 | 0.7944 |
| 0.5165 | 4.0 | 212 | 0.4298 | 0.8306 |
| 0.4315 | 5.0 | 265 | 0.3646 | 0.8609 |
| 0.3687 | 6.0 | 318 | 0.3054 | 0.8877 |
| 0.3352 | 7.0 | 371 | 0.2822 | 0.9005 |
| 0.3186 | 8.0 | 424 | 0.2764 | 0.9043 |
| 0.3155 | 9.0 | 477 | 0.2409 | 0.9215 |
| 0.2824 | 10.0 | 530 | 0.2459 | 0.9236 |
| 0.2575 | 11.0 | 583 | 0.2346 | 0.9129 |
| 0.2384 | 12.0 | 636 | 0.2445 | 0.9012 |
| 0.2117 | 13.0 | 689 | 0.1838 | 0.9342 |
| 0.2172 | 14.0 | 742 | 0.1789 | 0.9384 |
| 0.1918 | 15.0 | 795 | 0.1615 | 0.9480 |
| 0.1909 | 16.0 | 848 | 0.1516 | 0.9473 |
| 0.1911 | 17.0 | 901 | 0.1513 | 0.9494 |
| 0.2124 | 18.0 | 954 | 0.1524 | 0.9494 |
| 0.1631 | 19.0 | 1007 | 0.1729 | 0.9339 |
| 0.1766 | 20.0 | 1060 | 0.1329 | 0.9539 |
| 0.168 | 21.0 | 1113 | 0.1235 | 0.9590 |
| 0.1227 | 22.0 | 1166 | 0.1390 | 0.9483 |
| 0.1705 | 23.0 | 1219 | 0.1290 | 0.9566 |
| 0.1296 | 24.0 | 1272 | 0.1119 | 0.9621 |
| 0.1201 | 25.0 | 1325 | 0.1452 | 0.9497 |
| 0.1233 | 26.0 | 1378 | 0.1440 | 0.9487 |
| 0.1412 | 27.0 | 1431 | 0.1206 | 0.9573 |
| 0.1031 | 28.0 | 1484 | 0.1235 | 0.9580 |
| 0.1131 | 29.0 | 1537 | 0.1377 | 0.9501 |
| 0.1157 | 30.0 | 1590 | 0.1308 | 0.9580 |
| 0.0925 | 31.0 | 1643 | 0.1172 | 0.9601 |
| 0.0864 | 32.0 | 1696 | 0.1135 | 0.9621 |
| 0.0748 | 33.0 | 1749 | 0.0987 | 0.9656 |
| 0.1004 | 34.0 | 1802 | 0.0924 | 0.9728 |
| 0.0858 | 35.0 | 1855 | 0.1058 | 0.9659 |
| 0.0976 | 36.0 | 1908 | 0.1180 | 0.9587 |
| 0.0797 | 37.0 | 1961 | 0.1035 | 0.9676 |
| 0.0884 | 38.0 | 2014 | 0.0909 | 0.9707 |
| 0.0841 | 39.0 | 2067 | 0.0979 | 0.9707 |
| 0.0633 | 40.0 | 2120 | 0.0943 | 0.9697 |
| 0.0601 | 41.0 | 2173 | 0.1017 | 0.9687 |
| 0.0693 | 42.0 | 2226 | 0.1160 | 0.9652 |
| 0.0715 | 43.0 | 2279 | 0.0980 | 0.9704 |
| 0.0807 | 44.0 | 2332 | 0.1030 | 0.9711 |
| 0.0614 | 45.0 | 2385 | 0.0999 | 0.9707 |
| 0.0639 | 46.0 | 2438 | 0.1265 | 0.9632 |
| 0.0623 | 47.0 | 2491 | 0.1195 | 0.9614 |
| 0.0444 | 48.0 | 2544 | 0.1338 | 0.9659 |
| 0.0551 | 49.0 | 2597 | 0.1042 | 0.9728 |
| 0.0588 | 50.0 | 2650 | 0.0987 | 0.9731 |
| 0.0421 | 51.0 | 2703 | 0.1306 | 0.9607 |
| 0.0446 | 52.0 | 2756 | 0.1035 | 0.9718 |
| 0.0489 | 53.0 | 2809 | 0.1084 | 0.9714 |
| 0.0529 | 54.0 | 2862 | 0.1225 | 0.9663 |
| 0.0403 | 55.0 | 2915 | 0.1053 | 0.9711 |
| 0.0455 | 56.0 | 2968 | 0.1436 | 0.9645 |
| 0.0436 | 57.0 | 3021 | 0.1052 | 0.9714 |
| 0.0416 | 58.0 | 3074 | 0.1132 | 0.9666 |
| 0.0378 | 59.0 | 3127 | 0.1055 | 0.9721 |
| 0.0545 | 60.0 | 3180 | 0.1166 | 0.9704 |
| 0.0315 | 61.0 | 3233 | 0.1073 | 0.9711 |
| 0.0433 | 62.0 | 3286 | 0.1012 | 0.9735 |
| 0.0577 | 63.0 | 3339 | 0.1117 | 0.9714 |
| 0.0369 | 64.0 | 3392 | 0.1150 | 0.9697 |
| 0.0459 | 65.0 | 3445 | 0.1054 | 0.9731 |
| 0.0458 | 66.0 | 3498 | 0.1045 | 0.9745 |
| 0.0374 | 67.0 | 3551 | 0.1105 | 0.9725 |
| 0.0318 | 68.0 | 3604 | 0.1138 | 0.9718 |
| 0.0337 | 69.0 | 3657 | 0.1053 | 0.9728 |
| 0.0337 | 70.0 | 3710 | 0.1011 | 0.9738 |
| 0.0329 | 71.0 | 3763 | 0.1067 | 0.9738 |
| 0.0313 | 72.0 | 3816 | 0.1003 | 0.9756 |
| 0.0446 | 73.0 | 3869 | 0.1125 | 0.9714 |
| 0.047 | 74.0 | 3922 | 0.1040 | 0.9707 |
| 0.0256 | 75.0 | 3975 | 0.1165 | 0.9700 |
| 0.0535 | 76.0 | 4028 | 0.1129 | 0.9697 |
| 0.029 | 77.0 | 4081 | 0.1040 | 0.9752 |
| 0.044 | 78.0 | 4134 | 0.1116 | 0.9718 |
| 0.0405 | 79.0 | 4187 | 0.1130 | 0.9725 |
| 0.0417 | 80.0 | 4240 | 0.1094 | 0.9735 |
| 0.0257 | 81.0 | 4293 | 0.1143 | 0.9697 |
| 0.0293 | 82.0 | 4346 | 0.1111 | 0.9735 |
| 0.0234 | 83.0 | 4399 | 0.1253 | 0.9704 |
| 0.0295 | 84.0 | 4452 | 0.1133 | 0.9749 |
| 0.0261 | 85.0 | 4505 | 0.1048 | 0.9738 |
| 0.0215 | 86.0 | 4558 | 0.1072 | 0.9728 |
| 0.0304 | 87.0 | 4611 | 0.1061 | 0.9731 |
| 0.02 | 88.0 | 4664 | 0.1072 | 0.9742 |
| 0.0353 | 89.0 | 4717 | 0.1096 | 0.9738 |
| 0.0317 | 90.0 | 4770 | 0.1097 | 0.9745 |
| 0.0441 | 91.0 | 4823 | 0.1080 | 0.9745 |
| 0.0262 | 92.0 | 4876 | 0.1051 | 0.9752 |
| 0.0312 | 93.0 | 4929 | 0.1089 | 0.9738 |
| 0.025 | 94.0 | 4982 | 0.1094 | 0.9738 |
| 0.0243 | 95.0 | 5035 | 0.1106 | 0.9745 |
| 0.0245 | 96.0 | 5088 | 0.1076 | 0.9752 |
| 0.0233 | 97.0 | 5141 | 0.1068 | 0.9762 |
| 0.0279 | 98.0 | 5194 | 0.1063 | 0.9759 |
| 0.0285 | 99.0 | 5247 | 0.1057 | 0.9766 |
| 0.022 | 100.0 | 5300 | 0.1060 | 0.9766 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0.dev20240829+cu118
- Datasets 2.19.2
- Tokenizers 0.19.1
|
[
"downstairs",
"jogging",
"sitting",
"standing",
"upstairs",
"walking"
] |
ayubkfupm/swin-tiny-patch4-window7-224-finetuned-st-mean-wsdmhar-auc
|
<!-- This model card 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-st-mean-wsdmhar-auc
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: 0.1076
- Accuracy: 0.9704
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.448 | 1.0 | 53 | 1.2656 | 0.5530 |
| 0.7932 | 2.0 | 106 | 0.6547 | 0.7018 |
| 0.5983 | 3.0 | 159 | 0.4837 | 0.8106 |
| 0.4861 | 4.0 | 212 | 0.3822 | 0.8564 |
| 0.4762 | 5.0 | 265 | 0.3994 | 0.8140 |
| 0.4117 | 6.0 | 318 | 0.3143 | 0.8822 |
| 0.3659 | 7.0 | 371 | 0.2991 | 0.8819 |
| 0.355 | 8.0 | 424 | 0.3318 | 0.8688 |
| 0.3146 | 9.0 | 477 | 0.2399 | 0.9101 |
| 0.3243 | 10.0 | 530 | 0.2115 | 0.9260 |
| 0.3233 | 11.0 | 583 | 0.2022 | 0.9322 |
| 0.2736 | 12.0 | 636 | 0.1983 | 0.9256 |
| 0.2765 | 13.0 | 689 | 0.1739 | 0.9411 |
| 0.2191 | 14.0 | 742 | 0.1701 | 0.9421 |
| 0.2416 | 15.0 | 795 | 0.2053 | 0.9208 |
| 0.2039 | 16.0 | 848 | 0.1674 | 0.9401 |
| 0.2248 | 17.0 | 901 | 0.1700 | 0.9394 |
| 0.2331 | 18.0 | 954 | 0.1722 | 0.9439 |
| 0.1889 | 19.0 | 1007 | 0.1425 | 0.9470 |
| 0.1633 | 20.0 | 1060 | 0.1438 | 0.9494 |
| 0.174 | 21.0 | 1113 | 0.1357 | 0.9501 |
| 0.1599 | 22.0 | 1166 | 0.1346 | 0.9508 |
| 0.155 | 23.0 | 1219 | 0.1318 | 0.9518 |
| 0.1665 | 24.0 | 1272 | 0.1557 | 0.9477 |
| 0.1519 | 25.0 | 1325 | 0.1231 | 0.9583 |
| 0.143 | 26.0 | 1378 | 0.1247 | 0.9549 |
| 0.1393 | 27.0 | 1431 | 0.1615 | 0.9425 |
| 0.1518 | 28.0 | 1484 | 0.1246 | 0.9580 |
| 0.1239 | 29.0 | 1537 | 0.1178 | 0.9614 |
| 0.1297 | 30.0 | 1590 | 0.1141 | 0.9580 |
| 0.165 | 31.0 | 1643 | 0.1353 | 0.9539 |
| 0.1217 | 32.0 | 1696 | 0.1161 | 0.9621 |
| 0.1129 | 33.0 | 1749 | 0.1152 | 0.9618 |
| 0.1125 | 34.0 | 1802 | 0.1185 | 0.9621 |
| 0.1083 | 35.0 | 1855 | 0.1114 | 0.9642 |
| 0.1077 | 36.0 | 1908 | 0.1414 | 0.9590 |
| 0.0875 | 37.0 | 1961 | 0.1360 | 0.9559 |
| 0.1162 | 38.0 | 2014 | 0.1172 | 0.9618 |
| 0.0925 | 39.0 | 2067 | 0.1304 | 0.9583 |
| 0.109 | 40.0 | 2120 | 0.1172 | 0.9614 |
| 0.1178 | 41.0 | 2173 | 0.1525 | 0.9535 |
| 0.0886 | 42.0 | 2226 | 0.1616 | 0.9487 |
| 0.0983 | 43.0 | 2279 | 0.1197 | 0.9590 |
| 0.1209 | 44.0 | 2332 | 0.1183 | 0.9649 |
| 0.0957 | 45.0 | 2385 | 0.1268 | 0.9597 |
| 0.0919 | 46.0 | 2438 | 0.1143 | 0.9635 |
| 0.0831 | 47.0 | 2491 | 0.1319 | 0.9601 |
| 0.0888 | 48.0 | 2544 | 0.1040 | 0.9707 |
| 0.0761 | 49.0 | 2597 | 0.1088 | 0.9656 |
| 0.0843 | 50.0 | 2650 | 0.1046 | 0.9694 |
| 0.0615 | 51.0 | 2703 | 0.0982 | 0.9652 |
| 0.0705 | 52.0 | 2756 | 0.1136 | 0.9687 |
| 0.0775 | 53.0 | 2809 | 0.1272 | 0.9618 |
| 0.0739 | 54.0 | 2862 | 0.1185 | 0.9676 |
| 0.0758 | 55.0 | 2915 | 0.1185 | 0.9649 |
| 0.053 | 56.0 | 2968 | 0.1137 | 0.9663 |
| 0.0675 | 57.0 | 3021 | 0.1150 | 0.9656 |
| 0.0738 | 58.0 | 3074 | 0.1116 | 0.9676 |
| 0.067 | 59.0 | 3127 | 0.1092 | 0.9687 |
| 0.0689 | 60.0 | 3180 | 0.1116 | 0.9669 |
| 0.0647 | 61.0 | 3233 | 0.1107 | 0.9656 |
| 0.0707 | 62.0 | 3286 | 0.1183 | 0.9673 |
| 0.0708 | 63.0 | 3339 | 0.1283 | 0.9645 |
| 0.0675 | 64.0 | 3392 | 0.1222 | 0.9656 |
| 0.0622 | 65.0 | 3445 | 0.1259 | 0.9669 |
| 0.0541 | 66.0 | 3498 | 0.1142 | 0.9676 |
| 0.0528 | 67.0 | 3551 | 0.1103 | 0.9666 |
| 0.0641 | 68.0 | 3604 | 0.1363 | 0.9652 |
| 0.0448 | 69.0 | 3657 | 0.1448 | 0.9652 |
| 0.067 | 70.0 | 3710 | 0.1062 | 0.9687 |
| 0.0674 | 71.0 | 3763 | 0.1065 | 0.9697 |
| 0.0578 | 72.0 | 3816 | 0.1213 | 0.9669 |
| 0.0707 | 73.0 | 3869 | 0.1115 | 0.9659 |
| 0.0666 | 74.0 | 3922 | 0.1115 | 0.9707 |
| 0.0361 | 75.0 | 3975 | 0.1178 | 0.9680 |
| 0.047 | 76.0 | 4028 | 0.1167 | 0.9718 |
| 0.0769 | 77.0 | 4081 | 0.1073 | 0.9697 |
| 0.0422 | 78.0 | 4134 | 0.1116 | 0.9721 |
| 0.0411 | 79.0 | 4187 | 0.1186 | 0.9676 |
| 0.0402 | 80.0 | 4240 | 0.1048 | 0.9711 |
| 0.0504 | 81.0 | 4293 | 0.1105 | 0.9707 |
| 0.0579 | 82.0 | 4346 | 0.1007 | 0.9704 |
| 0.0514 | 83.0 | 4399 | 0.1105 | 0.9711 |
| 0.0398 | 84.0 | 4452 | 0.1130 | 0.9707 |
| 0.0477 | 85.0 | 4505 | 0.1097 | 0.9718 |
| 0.0413 | 86.0 | 4558 | 0.1091 | 0.9704 |
| 0.0538 | 87.0 | 4611 | 0.1068 | 0.9718 |
| 0.043 | 88.0 | 4664 | 0.1104 | 0.9725 |
| 0.0434 | 89.0 | 4717 | 0.1124 | 0.9707 |
| 0.0499 | 90.0 | 4770 | 0.1153 | 0.9711 |
| 0.0418 | 91.0 | 4823 | 0.1121 | 0.9700 |
| 0.0365 | 92.0 | 4876 | 0.1169 | 0.9711 |
| 0.0493 | 93.0 | 4929 | 0.1106 | 0.9690 |
| 0.0426 | 94.0 | 4982 | 0.1089 | 0.9680 |
| 0.0338 | 95.0 | 5035 | 0.1096 | 0.9711 |
| 0.0388 | 96.0 | 5088 | 0.1113 | 0.9694 |
| 0.0404 | 97.0 | 5141 | 0.1102 | 0.9707 |
| 0.0427 | 98.0 | 5194 | 0.1090 | 0.9704 |
| 0.0302 | 99.0 | 5247 | 0.1076 | 0.9697 |
| 0.0404 | 100.0 | 5300 | 0.1076 | 0.9704 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0.dev20240829+cu118
- Datasets 2.19.2
- Tokenizers 0.19.1
|
[
"downstairs",
"jogging",
"sitting",
"standing",
"upstairs",
"walking"
] |
geshijoker/distill-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. -->
# distill-vit
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4751
- Accuracy: 0.7656
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8986 | 1.0 | 65 | 0.7788 | 0.4286 |
| 0.8556 | 2.0 | 130 | 0.9774 | 0.4812 |
| 0.7581 | 3.0 | 195 | 0.6150 | 0.6541 |
| 0.6434 | 4.0 | 260 | 0.6455 | 0.6090 |
| 0.609 | 5.0 | 325 | 0.5329 | 0.7143 |
| 0.5503 | 6.0 | 390 | 0.5829 | 0.6466 |
| 0.5492 | 7.0 | 455 | 0.6716 | 0.6917 |
| 0.504 | 8.0 | 520 | 0.5342 | 0.6917 |
| 0.4966 | 9.0 | 585 | 0.5668 | 0.6617 |
| 0.4978 | 10.0 | 650 | 0.5347 | 0.6767 |
| 0.4535 | 11.0 | 715 | 0.5580 | 0.6090 |
| 0.4415 | 12.0 | 780 | 0.5085 | 0.7444 |
| 0.4308 | 13.0 | 845 | 0.5131 | 0.7068 |
| 0.4247 | 14.0 | 910 | 0.4808 | 0.7068 |
| 0.4307 | 15.0 | 975 | 0.5542 | 0.6917 |
| 0.4165 | 16.0 | 1040 | 0.5410 | 0.6992 |
| 0.3975 | 17.0 | 1105 | 0.5944 | 0.6015 |
| 0.3942 | 18.0 | 1170 | 0.4730 | 0.6917 |
| 0.3932 | 19.0 | 1235 | 0.4806 | 0.6917 |
| 0.3437 | 20.0 | 1300 | 0.5341 | 0.6842 |
| 0.3628 | 21.0 | 1365 | 0.5836 | 0.6692 |
| 0.3483 | 22.0 | 1430 | 0.6234 | 0.6316 |
| 0.3318 | 23.0 | 1495 | 0.4950 | 0.7143 |
| 0.3189 | 24.0 | 1560 | 0.4590 | 0.7068 |
| 0.3243 | 25.0 | 1625 | 0.5789 | 0.6692 |
| 0.3169 | 26.0 | 1690 | 0.5702 | 0.7218 |
| 0.3031 | 27.0 | 1755 | 0.4415 | 0.7519 |
| 0.2928 | 28.0 | 1820 | 0.4680 | 0.7368 |
| 0.3117 | 29.0 | 1885 | 0.5384 | 0.6842 |
| 0.3127 | 30.0 | 1950 | 0.5148 | 0.6767 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.2.1+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"label_0",
"label_1",
"label_2"
] |
DouglasBraga/swin-tiny-patch4-window7-224-finetuned-leukemia-08-2024.v1.1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-leukemia-08-2024.v1.1
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3423
- 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3578 | 0.9984 | 312 | 1.7841 | 0.4263 |
| 0.2403 | 2.0 | 625 | 0.9414 | 0.6808 |
| 0.1815 | 2.9984 | 937 | 0.6044 | 0.7784 |
| 0.2062 | 4.0 | 1250 | 0.5284 | 0.7643 |
| 0.1212 | 4.9984 | 1562 | 0.4432 | 0.8488 |
| 0.0723 | 6.0 | 1875 | 0.8276 | 0.7925 |
| 0.0656 | 6.9984 | 2187 | 0.3423 | 0.8958 |
| 0.0419 | 8.0 | 2500 | 0.5879 | 0.8770 |
| 0.0469 | 8.9984 | 2812 | 0.5126 | 0.8854 |
| 0.0302 | 9.984 | 3120 | 0.6068 | 0.8808 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"all",
"hem"
] |
rafimumtaz/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3640
- Accuracy: 0.55
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1309 | 1.0 | 20 | 1.3481 | 0.4938 |
| 1.0746 | 2.0 | 40 | 1.3706 | 0.475 |
| 1.0367 | 3.0 | 60 | 1.3161 | 0.5375 |
| 0.9814 | 4.0 | 80 | 1.3837 | 0.45 |
| 0.886 | 5.0 | 100 | 1.3633 | 0.4875 |
| 0.8096 | 6.0 | 120 | 1.3045 | 0.5125 |
| 0.7669 | 7.0 | 140 | 1.3903 | 0.4938 |
| 0.708 | 8.0 | 160 | 1.2867 | 0.5125 |
| 0.6265 | 9.0 | 180 | 1.2244 | 0.5625 |
| 0.6191 | 10.0 | 200 | 1.3461 | 0.525 |
| 0.5598 | 11.0 | 220 | 1.3266 | 0.5625 |
| 0.4667 | 12.0 | 240 | 1.3050 | 0.5563 |
| 0.4613 | 13.0 | 260 | 1.3329 | 0.5375 |
| 0.4268 | 14.0 | 280 | 1.4020 | 0.5312 |
| 0.4256 | 15.0 | 300 | 1.3770 | 0.5188 |
| 0.3727 | 16.0 | 320 | 1.3655 | 0.5188 |
| 0.316 | 17.0 | 340 | 1.3642 | 0.5188 |
| 0.3223 | 18.0 | 360 | 1.2535 | 0.5938 |
| 0.3064 | 19.0 | 380 | 1.4173 | 0.4875 |
| 0.2866 | 20.0 | 400 | 1.3343 | 0.5625 |
| 0.2781 | 21.0 | 420 | 1.5072 | 0.4813 |
| 0.3027 | 22.0 | 440 | 1.5067 | 0.5125 |
| 0.26 | 23.0 | 460 | 1.4456 | 0.5687 |
| 0.2156 | 24.0 | 480 | 1.4825 | 0.525 |
| 0.1908 | 25.0 | 500 | 1.5369 | 0.5375 |
| 0.213 | 26.0 | 520 | 1.5397 | 0.5188 |
| 0.241 | 27.0 | 540 | 1.4804 | 0.5125 |
| 0.1974 | 28.0 | 560 | 1.5786 | 0.5062 |
| 0.225 | 29.0 | 580 | 1.4677 | 0.5375 |
| 0.2459 | 30.0 | 600 | 1.5392 | 0.5312 |
| 0.2146 | 31.0 | 620 | 1.6734 | 0.4625 |
| 0.1891 | 32.0 | 640 | 1.5012 | 0.55 |
| 0.2231 | 33.0 | 660 | 1.6265 | 0.5 |
| 0.1903 | 34.0 | 680 | 1.5405 | 0.5312 |
| 0.1852 | 35.0 | 700 | 1.6295 | 0.5 |
| 0.1768 | 36.0 | 720 | 1.5758 | 0.5375 |
| 0.1486 | 37.0 | 740 | 1.6176 | 0.5188 |
| 0.1814 | 38.0 | 760 | 1.5107 | 0.5375 |
| 0.1642 | 39.0 | 780 | 1.5315 | 0.55 |
| 0.1822 | 40.0 | 800 | 1.6309 | 0.525 |
| 0.1819 | 41.0 | 820 | 1.7033 | 0.4938 |
| 0.1326 | 42.0 | 840 | 1.6107 | 0.5437 |
| 0.1452 | 43.0 | 860 | 1.6219 | 0.55 |
| 0.128 | 44.0 | 880 | 1.4348 | 0.5813 |
| 0.1103 | 45.0 | 900 | 1.6185 | 0.5687 |
| 0.1386 | 46.0 | 920 | 1.5848 | 0.5312 |
| 0.1021 | 47.0 | 940 | 1.6036 | 0.5563 |
| 0.1414 | 48.0 | 960 | 1.5455 | 0.575 |
| 0.1989 | 49.0 | 980 | 1.5955 | 0.525 |
| 0.1458 | 50.0 | 1000 | 1.5511 | 0.55 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
zazazaChiang/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 arrow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0667
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.2797 | 1.0 | 130 | 0.9624 | 0.2229 |
| 0.1283 | 2.0 | 260 | 0.9774 | 0.1240 |
| 0.1325 | 3.0 | 390 | 0.9774 | 0.0953 |
| 0.0809 | 4.0 | 520 | 0.9925 | 0.0667 |
| 0.1164 | 5.0 | 650 | 0.9774 | 0.0842 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
alder018/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: 0.5000
- Accuracy: 0.8255
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2399 | 1.0 | 15 | 0.7318 | 0.75 |
| 0.6435 | 2.0 | 30 | 0.5359 | 0.7972 |
| 0.5655 | 3.0 | 45 | 0.5000 | 0.8255 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"light damage",
"moderate damage",
"no damage",
"severe damage"
] |
chandra10/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2826
- Accuracy: 0.625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.071 | 1.0 | 10 | 2.0532 | 0.2125 |
| 1.9763 | 2.0 | 20 | 1.9614 | 0.3312 |
| 1.8031 | 3.0 | 30 | 1.8326 | 0.4562 |
| 1.6168 | 4.0 | 40 | 1.7015 | 0.5125 |
| 1.4508 | 5.0 | 50 | 1.6065 | 0.5188 |
| 1.3037 | 6.0 | 60 | 1.5397 | 0.5375 |
| 1.1709 | 7.0 | 70 | 1.4836 | 0.55 |
| 1.0481 | 8.0 | 80 | 1.4248 | 0.5813 |
| 0.9441 | 9.0 | 90 | 1.3915 | 0.5625 |
| 0.8551 | 10.0 | 100 | 1.3586 | 0.6 |
| 0.7772 | 11.0 | 110 | 1.3315 | 0.6 |
| 0.7174 | 12.0 | 120 | 1.3057 | 0.6062 |
| 0.6721 | 13.0 | 130 | 1.2936 | 0.6188 |
| 0.642 | 14.0 | 140 | 1.2933 | 0.6 |
| 0.6252 | 15.0 | 150 | 1.2826 | 0.625 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
amaye15/Beit-Base-Image-Orientation-Fixer
|
<!-- This model card 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-Base-Image-Orientation-Fixer
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: 0.1295
- F1: 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3053 | 0.001 | 10 | 1.1901 | 0.2711 |
| 1.1711 | 0.002 | 20 | 1.1128 | 0.2697 |
| 1.1158 | 0.003 | 30 | 1.0354 | 0.3505 |
| 1.0164 | 0.004 | 40 | 0.9894 | 0.3356 |
| 1.0899 | 0.005 | 50 | 0.9472 | 0.3613 |
| 0.9108 | 0.006 | 60 | 0.9176 | 0.4340 |
| 0.9161 | 0.007 | 70 | 0.9004 | 0.3447 |
| 0.8827 | 0.008 | 80 | 0.8459 | 0.5296 |
| 0.9068 | 0.009 | 90 | 0.7963 | 0.5104 |
| 0.8606 | 0.01 | 100 | 0.7538 | 0.5552 |
| 0.815 | 0.011 | 110 | 0.7165 | 0.5863 |
| 0.758 | 0.012 | 120 | 0.6884 | 0.5611 |
| 0.7998 | 0.013 | 130 | 0.6822 | 0.6414 |
| 0.8375 | 0.014 | 140 | 0.6660 | 0.5511 |
| 0.7375 | 0.015 | 150 | 0.6145 | 0.6981 |
| 0.6883 | 0.016 | 160 | 0.5882 | 0.6456 |
| 0.6961 | 0.017 | 170 | 0.5733 | 0.6803 |
| 0.575 | 0.018 | 180 | 0.5161 | 0.7218 |
| 0.4773 | 0.019 | 190 | 0.6457 | 0.6837 |
| 0.6862 | 0.02 | 200 | 0.5336 | 0.7211 |
| 0.5798 | 0.021 | 210 | 0.4840 | 0.7517 |
| 0.5621 | 0.022 | 220 | 0.4435 | 0.7674 |
| 0.475 | 0.023 | 230 | 0.4663 | 0.7113 |
| 0.6144 | 0.024 | 240 | 0.5058 | 0.6841 |
| 0.5164 | 0.025 | 250 | 0.4790 | 0.7295 |
| 0.524 | 0.026 | 260 | 0.4770 | 0.6971 |
| 0.5406 | 0.027 | 270 | 0.4121 | 0.7509 |
| 0.4378 | 0.028 | 280 | 0.4170 | 0.7875 |
| 0.5211 | 0.029 | 290 | 0.4221 | 0.7851 |
| 0.4258 | 0.03 | 300 | 0.3769 | 0.7990 |
| 0.4177 | 0.031 | 310 | 0.3696 | 0.8143 |
| 0.4183 | 0.032 | 320 | 0.3798 | 0.8072 |
| 0.487 | 0.033 | 330 | 0.4113 | 0.8098 |
| 0.3602 | 0.034 | 340 | 0.3808 | 0.8059 |
| 0.4247 | 0.035 | 350 | 0.3768 | 0.8271 |
| 0.4483 | 0.036 | 360 | 0.3503 | 0.7964 |
| 0.3606 | 0.037 | 370 | 0.3605 | 0.8314 |
| 0.3878 | 0.038 | 380 | 0.3359 | 0.8390 |
| 0.4085 | 0.039 | 390 | 0.2981 | 0.8606 |
| 0.3672 | 0.04 | 400 | 0.3072 | 0.8511 |
| 0.3539 | 0.041 | 410 | 0.3090 | 0.8481 |
| 0.4045 | 0.042 | 420 | 0.3052 | 0.8555 |
| 0.403 | 0.043 | 430 | 0.3610 | 0.8221 |
| 0.3892 | 0.044 | 440 | 0.3189 | 0.8396 |
| 0.4989 | 0.045 | 450 | 0.3337 | 0.8287 |
| 0.3922 | 0.046 | 460 | 0.3019 | 0.8540 |
| 0.3794 | 0.047 | 470 | 0.3157 | 0.8476 |
| 0.4158 | 0.048 | 480 | 0.3050 | 0.8553 |
| 0.3367 | 0.049 | 490 | 0.2884 | 0.8615 |
| 0.3991 | 0.05 | 500 | 0.3451 | 0.8407 |
| 0.411 | 0.051 | 510 | 0.2762 | 0.8623 |
| 0.2855 | 0.052 | 520 | 0.2766 | 0.8701 |
| 0.4612 | 0.053 | 530 | 0.2799 | 0.8712 |
| 0.4118 | 0.054 | 540 | 0.3085 | 0.8511 |
| 0.2906 | 0.055 | 550 | 0.2841 | 0.8759 |
| 0.3242 | 0.056 | 560 | 0.2719 | 0.8823 |
| 0.3575 | 0.057 | 570 | 0.3384 | 0.8561 |
| 0.3562 | 0.058 | 580 | 0.2722 | 0.8791 |
| 0.3944 | 0.059 | 590 | 0.3507 | 0.8465 |
| 0.3175 | 0.06 | 600 | 0.2901 | 0.8668 |
| 0.3608 | 0.061 | 610 | 0.2902 | 0.8660 |
| 0.2619 | 0.062 | 620 | 0.3373 | 0.8543 |
| 0.3243 | 0.063 | 630 | 0.2703 | 0.8884 |
| 0.361 | 0.064 | 640 | 0.2891 | 0.8662 |
| 0.3267 | 0.065 | 650 | 0.2739 | 0.8784 |
| 0.261 | 0.066 | 660 | 0.2602 | 0.8747 |
| 0.2521 | 0.067 | 670 | 0.2641 | 0.8883 |
| 0.391 | 0.068 | 680 | 0.2589 | 0.8870 |
| 0.3604 | 0.069 | 690 | 0.2622 | 0.8903 |
| 0.2983 | 0.07 | 700 | 0.2528 | 0.8846 |
| 0.2521 | 0.071 | 710 | 0.2571 | 0.8916 |
| 0.4368 | 0.072 | 720 | 0.2839 | 0.8877 |
| 0.3208 | 0.073 | 730 | 0.2898 | 0.8742 |
| 0.2887 | 0.074 | 740 | 0.2700 | 0.8839 |
| 0.3075 | 0.075 | 750 | 0.2707 | 0.8770 |
| 0.3465 | 0.076 | 760 | 0.2828 | 0.8695 |
| 0.2863 | 0.077 | 770 | 0.2874 | 0.8823 |
| 0.3402 | 0.078 | 780 | 0.2782 | 0.8781 |
| 0.3495 | 0.079 | 790 | 0.2538 | 0.8929 |
| 0.3177 | 0.08 | 800 | 0.2437 | 0.8779 |
| 0.3012 | 0.081 | 810 | 0.2865 | 0.8837 |
| 0.4079 | 0.082 | 820 | 0.2573 | 0.8830 |
| 0.2915 | 0.083 | 830 | 0.3135 | 0.8707 |
| 0.2407 | 0.084 | 840 | 0.2804 | 0.8844 |
| 0.2574 | 0.085 | 850 | 0.2810 | 0.8713 |
| 0.3141 | 0.086 | 860 | 0.2827 | 0.8802 |
| 0.2601 | 0.087 | 870 | 0.3076 | 0.8693 |
| 0.3462 | 0.088 | 880 | 0.2588 | 0.8714 |
| 0.3356 | 0.089 | 890 | 0.2677 | 0.8761 |
| 0.3135 | 0.09 | 900 | 0.2715 | 0.8740 |
| 0.369 | 0.091 | 910 | 0.2674 | 0.8705 |
| 0.2866 | 0.092 | 920 | 0.2617 | 0.8827 |
| 0.251 | 0.093 | 930 | 0.2483 | 0.8894 |
| 0.1822 | 0.094 | 940 | 0.2679 | 0.8817 |
| 0.2569 | 0.095 | 950 | 0.2810 | 0.8847 |
| 0.3046 | 0.096 | 960 | 0.2774 | 0.8773 |
| 0.2099 | 0.097 | 970 | 0.2738 | 0.8715 |
| 0.2961 | 0.098 | 980 | 0.2603 | 0.8860 |
| 0.2724 | 0.099 | 990 | 0.2661 | 0.8813 |
| 0.3179 | 0.1 | 1000 | 0.2414 | 0.8837 |
| 0.3635 | 0.101 | 1010 | 0.2433 | 0.8916 |
| 0.2815 | 0.102 | 1020 | 0.2562 | 0.8784 |
| 0.2758 | 0.103 | 1030 | 0.2358 | 0.8941 |
| 0.2664 | 0.104 | 1040 | 0.2571 | 0.8919 |
| 0.2584 | 0.105 | 1050 | 0.2617 | 0.8758 |
| 0.3165 | 0.106 | 1060 | 0.2690 | 0.8834 |
| 0.2877 | 0.107 | 1070 | 0.2362 | 0.8926 |
| 0.2713 | 0.108 | 1080 | 0.2416 | 0.8905 |
| 0.2598 | 0.109 | 1090 | 0.2525 | 0.8806 |
| 0.2796 | 0.11 | 1100 | 0.2433 | 0.8950 |
| 0.2558 | 0.111 | 1110 | 0.2562 | 0.8930 |
| 0.2443 | 0.112 | 1120 | 0.2714 | 0.8838 |
| 0.3383 | 0.113 | 1130 | 0.2387 | 0.8971 |
| 0.2636 | 0.114 | 1140 | 0.2721 | 0.8673 |
| 0.2851 | 0.115 | 1150 | 0.2459 | 0.8830 |
| 0.2072 | 0.116 | 1160 | 0.2309 | 0.8929 |
| 0.2331 | 0.117 | 1170 | 0.2942 | 0.8835 |
| 0.2361 | 0.118 | 1180 | 0.2517 | 0.8958 |
| 0.3166 | 0.119 | 1190 | 0.2590 | 0.8947 |
| 0.2891 | 0.12 | 1200 | 0.2725 | 0.8787 |
| 0.3136 | 0.121 | 1210 | 0.2321 | 0.8969 |
| 0.2569 | 0.122 | 1220 | 0.2489 | 0.8929 |
| 0.262 | 0.123 | 1230 | 0.2493 | 0.8869 |
| 0.311 | 0.124 | 1240 | 0.2285 | 0.8996 |
| 0.2848 | 0.125 | 1250 | 0.2407 | 0.8877 |
| 0.2321 | 0.126 | 1260 | 0.2276 | 0.8887 |
| 0.2398 | 0.127 | 1270 | 0.2598 | 0.8879 |
| 0.2399 | 0.128 | 1280 | 0.2331 | 0.8850 |
| 0.3352 | 0.129 | 1290 | 0.2384 | 0.8908 |
| 0.3042 | 0.13 | 1300 | 0.2161 | 0.9016 |
| 0.22 | 0.131 | 1310 | 0.2493 | 0.8815 |
| 0.2821 | 0.132 | 1320 | 0.2159 | 0.8989 |
| 0.2903 | 0.133 | 1330 | 0.2258 | 0.8990 |
| 0.3207 | 0.134 | 1340 | 0.2223 | 0.9039 |
| 0.204 | 0.135 | 1350 | 0.2109 | 0.9000 |
| 0.2346 | 0.136 | 1360 | 0.2305 | 0.8923 |
| 0.2775 | 0.137 | 1370 | 0.2150 | 0.9013 |
| 0.2689 | 0.138 | 1380 | 0.2313 | 0.9031 |
| 0.2346 | 0.139 | 1390 | 0.2181 | 0.9017 |
| 0.2454 | 0.14 | 1400 | 0.2273 | 0.9002 |
| 0.2867 | 0.141 | 1410 | 0.2218 | 0.8954 |
| 0.3079 | 0.142 | 1420 | 0.2302 | 0.8858 |
| 0.2169 | 0.143 | 1430 | 0.2588 | 0.8806 |
| 0.3228 | 0.144 | 1440 | 0.2274 | 0.8998 |
| 0.3602 | 0.145 | 1450 | 0.2293 | 0.8955 |
| 0.2999 | 0.146 | 1460 | 0.2218 | 0.8977 |
| 0.2667 | 0.147 | 1470 | 0.2312 | 0.8941 |
| 0.2569 | 0.148 | 1480 | 0.2269 | 0.8991 |
| 0.1956 | 0.149 | 1490 | 0.2411 | 0.8928 |
| 0.3543 | 0.15 | 1500 | 0.2184 | 0.8998 |
| 0.2969 | 0.151 | 1510 | 0.2328 | 0.8989 |
| 0.297 | 0.152 | 1520 | 0.2190 | 0.9038 |
| 0.258 | 0.153 | 1530 | 0.2292 | 0.9037 |
| 0.2046 | 0.154 | 1540 | 0.2245 | 0.8946 |
| 0.224 | 0.155 | 1550 | 0.2213 | 0.9004 |
| 0.2647 | 0.156 | 1560 | 0.2498 | 0.8844 |
| 0.2191 | 0.157 | 1570 | 0.2382 | 0.9008 |
| 0.2515 | 0.158 | 1580 | 0.2387 | 0.8952 |
| 0.2661 | 0.159 | 1590 | 0.2342 | 0.8864 |
| 0.2301 | 0.16 | 1600 | 0.2692 | 0.8745 |
| 0.2119 | 0.161 | 1610 | 0.2365 | 0.8897 |
| 0.1666 | 0.162 | 1620 | 0.2417 | 0.8896 |
| 0.2507 | 0.163 | 1630 | 0.2416 | 0.8873 |
| 0.2006 | 0.164 | 1640 | 0.2659 | 0.8839 |
| 0.1649 | 0.165 | 1650 | 0.2301 | 0.8972 |
| 0.2099 | 0.166 | 1660 | 0.2514 | 0.8956 |
| 0.3191 | 0.167 | 1670 | 0.2337 | 0.8846 |
| 0.2718 | 0.168 | 1680 | 0.2297 | 0.9123 |
| 0.2827 | 0.169 | 1690 | 0.2338 | 0.8931 |
| 0.2433 | 0.17 | 1700 | 0.2308 | 0.8927 |
| 0.2719 | 0.171 | 1710 | 0.2331 | 0.8946 |
| 0.2151 | 0.172 | 1720 | 0.2131 | 0.9052 |
| 0.2758 | 0.173 | 1730 | 0.2272 | 0.8989 |
| 0.3078 | 0.174 | 1740 | 0.2180 | 0.9072 |
| 0.3012 | 0.175 | 1750 | 0.2258 | 0.9034 |
| 0.3162 | 0.176 | 1760 | 0.2213 | 0.9057 |
| 0.2551 | 0.177 | 1770 | 0.2595 | 0.8818 |
| 0.2563 | 0.178 | 1780 | 0.2230 | 0.8973 |
| 0.2825 | 0.179 | 1790 | 0.2222 | 0.8957 |
| 0.1916 | 0.18 | 1800 | 0.2307 | 0.8981 |
| 0.2145 | 0.181 | 1810 | 0.2285 | 0.8975 |
| 0.2029 | 0.182 | 1820 | 0.2357 | 0.9003 |
| 0.1559 | 0.183 | 1830 | 0.2645 | 0.8880 |
| 0.3173 | 0.184 | 1840 | 0.2209 | 0.8992 |
| 0.2343 | 0.185 | 1850 | 0.2334 | 0.9043 |
| 0.233 | 0.186 | 1860 | 0.2241 | 0.9025 |
| 0.2697 | 0.187 | 1870 | 0.2150 | 0.8991 |
| 0.3023 | 0.188 | 1880 | 0.2312 | 0.8976 |
| 0.2052 | 0.189 | 1890 | 0.2053 | 0.8975 |
| 0.2601 | 0.19 | 1900 | 0.2042 | 0.8974 |
| 0.2966 | 0.191 | 1910 | 0.2142 | 0.8971 |
| 0.2338 | 0.192 | 1920 | 0.2185 | 0.9079 |
| 0.2392 | 0.193 | 1930 | 0.2141 | 0.9011 |
| 0.2909 | 0.194 | 1940 | 0.2037 | 0.9099 |
| 0.1561 | 0.195 | 1950 | 0.2209 | 0.8987 |
| 0.2166 | 0.196 | 1960 | 0.2053 | 0.9043 |
| 0.1753 | 0.197 | 1970 | 0.2334 | 0.8958 |
| 0.2572 | 0.198 | 1980 | 0.2016 | 0.9124 |
| 0.2519 | 0.199 | 1990 | 0.2043 | 0.9080 |
| 0.2751 | 0.2 | 2000 | 0.2013 | 0.8982 |
| 0.1827 | 0.201 | 2010 | 0.2211 | 0.8992 |
| 0.2584 | 0.202 | 2020 | 0.2040 | 0.9018 |
| 0.1674 | 0.203 | 2030 | 0.2070 | 0.9000 |
| 0.2112 | 0.204 | 2040 | 0.2133 | 0.9018 |
| 0.2124 | 0.205 | 2050 | 0.2254 | 0.8967 |
| 0.2657 | 0.206 | 2060 | 0.2175 | 0.8984 |
| 0.2703 | 0.207 | 2070 | 0.2126 | 0.8987 |
| 0.1997 | 0.208 | 2080 | 0.2199 | 0.9004 |
| 0.2749 | 0.209 | 2090 | 0.2135 | 0.9043 |
| 0.2035 | 0.21 | 2100 | 0.2126 | 0.9046 |
| 0.2048 | 0.211 | 2110 | 0.2226 | 0.8964 |
| 0.2843 | 0.212 | 2120 | 0.2087 | 0.9000 |
| 0.212 | 0.213 | 2130 | 0.2081 | 0.9021 |
| 0.2369 | 0.214 | 2140 | 0.2103 | 0.8995 |
| 0.3212 | 0.215 | 2150 | 0.2088 | 0.9001 |
| 0.2415 | 0.216 | 2160 | 0.2096 | 0.9004 |
| 0.2062 | 0.217 | 2170 | 0.1923 | 0.9117 |
| 0.18 | 0.218 | 2180 | 0.1974 | 0.9056 |
| 0.2727 | 0.219 | 2190 | 0.2039 | 0.9004 |
| 0.1637 | 0.22 | 2200 | 0.2147 | 0.9033 |
| 0.2226 | 0.221 | 2210 | 0.2168 | 0.9026 |
| 0.2141 | 0.222 | 2220 | 0.2268 | 0.8997 |
| 0.3045 | 0.223 | 2230 | 0.2309 | 0.8927 |
| 0.2436 | 0.224 | 2240 | 0.2149 | 0.8973 |
| 0.2004 | 0.225 | 2250 | 0.2109 | 0.9006 |
| 0.2007 | 0.226 | 2260 | 0.2045 | 0.9067 |
| 0.2847 | 0.227 | 2270 | 0.2053 | 0.9026 |
| 0.1972 | 0.228 | 2280 | 0.1985 | 0.9033 |
| 0.1911 | 0.229 | 2290 | 0.2042 | 0.9000 |
| 0.1927 | 0.23 | 2300 | 0.2245 | 0.8932 |
| 0.1972 | 0.231 | 2310 | 0.2107 | 0.9097 |
| 0.2774 | 0.232 | 2320 | 0.2030 | 0.9090 |
| 0.2769 | 0.233 | 2330 | 0.2125 | 0.9041 |
| 0.2035 | 0.234 | 2340 | 0.2065 | 0.9088 |
| 0.2681 | 0.235 | 2350 | 0.1956 | 0.9007 |
| 0.2544 | 0.236 | 2360 | 0.1895 | 0.9110 |
| 0.1944 | 0.237 | 2370 | 0.1969 | 0.9072 |
| 0.2021 | 0.238 | 2380 | 0.1973 | 0.9125 |
| 0.3147 | 0.239 | 2390 | 0.1996 | 0.9054 |
| 0.2124 | 0.24 | 2400 | 0.1967 | 0.8963 |
| 0.1894 | 0.241 | 2410 | 0.2093 | 0.9005 |
| 0.1422 | 0.242 | 2420 | 0.2005 | 0.9006 |
| 0.2342 | 0.243 | 2430 | 0.2304 | 0.9045 |
| 0.2423 | 0.244 | 2440 | 0.2087 | 0.9044 |
| 0.2633 | 0.245 | 2450 | 0.2067 | 0.8981 |
| 0.263 | 0.246 | 2460 | 0.1968 | 0.9032 |
| 0.261 | 0.247 | 2470 | 0.1978 | 0.9077 |
| 0.2244 | 0.248 | 2480 | 0.2112 | 0.8978 |
| 0.1525 | 0.249 | 2490 | 0.2126 | 0.8963 |
| 0.1654 | 0.25 | 2500 | 0.2239 | 0.9051 |
| 0.2889 | 0.251 | 2510 | 0.2156 | 0.9059 |
| 0.226 | 0.252 | 2520 | 0.2035 | 0.9063 |
| 0.167 | 0.253 | 2530 | 0.1978 | 0.9134 |
| 0.1725 | 0.254 | 2540 | 0.1920 | 0.9000 |
| 0.1468 | 0.255 | 2550 | 0.1920 | 0.9069 |
| 0.1826 | 0.256 | 2560 | 0.1983 | 0.9002 |
| 0.232 | 0.257 | 2570 | 0.1973 | 0.9057 |
| 0.24 | 0.258 | 2580 | 0.2118 | 0.9103 |
| 0.2233 | 0.259 | 2590 | 0.1918 | 0.9037 |
| 0.1876 | 0.26 | 2600 | 0.1801 | 0.9063 |
| 0.1969 | 0.261 | 2610 | 0.1913 | 0.9003 |
| 0.2566 | 0.262 | 2620 | 0.1808 | 0.9177 |
| 0.2822 | 0.263 | 2630 | 0.1794 | 0.9115 |
| 0.1906 | 0.264 | 2640 | 0.1774 | 0.9183 |
| 0.2331 | 0.265 | 2650 | 0.1866 | 0.9164 |
| 0.1635 | 0.266 | 2660 | 0.1899 | 0.9162 |
| 0.1811 | 0.267 | 2670 | 0.1890 | 0.9181 |
| 0.1816 | 0.268 | 2680 | 0.2162 | 0.9022 |
| 0.2075 | 0.269 | 2690 | 0.1994 | 0.9123 |
| 0.2319 | 0.27 | 2700 | 0.1975 | 0.9124 |
| 0.2151 | 0.271 | 2710 | 0.1890 | 0.9022 |
| 0.2136 | 0.272 | 2720 | 0.1855 | 0.9117 |
| 0.1694 | 0.273 | 2730 | 0.1956 | 0.9015 |
| 0.1677 | 0.274 | 2740 | 0.2091 | 0.8887 |
| 0.1263 | 0.275 | 2750 | 0.2078 | 0.9031 |
| 0.1912 | 0.276 | 2760 | 0.2167 | 0.9008 |
| 0.2041 | 0.277 | 2770 | 0.2054 | 0.9070 |
| 0.1954 | 0.278 | 2780 | 0.2008 | 0.9054 |
| 0.1986 | 0.279 | 2790 | 0.2041 | 0.9067 |
| 0.1575 | 0.28 | 2800 | 0.2093 | 0.8988 |
| 0.1828 | 0.281 | 2810 | 0.2275 | 0.8909 |
| 0.2934 | 0.282 | 2820 | 0.2133 | 0.8980 |
| 0.2458 | 0.283 | 2830 | 0.2197 | 0.8957 |
| 0.2335 | 0.284 | 2840 | 0.2097 | 0.9072 |
| 0.19 | 0.285 | 2850 | 0.2036 | 0.9082 |
| 0.2064 | 0.286 | 2860 | 0.1983 | 0.8998 |
| 0.192 | 0.287 | 2870 | 0.1960 | 0.9032 |
| 0.1733 | 0.288 | 2880 | 0.1828 | 0.9142 |
| 0.2318 | 0.289 | 2890 | 0.1845 | 0.9206 |
| 0.1763 | 0.29 | 2900 | 0.2025 | 0.9062 |
| 0.2 | 0.291 | 2910 | 0.1889 | 0.9070 |
| 0.2404 | 0.292 | 2920 | 0.1977 | 0.9050 |
| 0.2782 | 0.293 | 2930 | 0.1917 | 0.9114 |
| 0.2759 | 0.294 | 2940 | 0.1901 | 0.9139 |
| 0.2116 | 0.295 | 2950 | 0.1833 | 0.9087 |
| 0.1655 | 0.296 | 2960 | 0.1986 | 0.9119 |
| 0.2151 | 0.297 | 2970 | 0.1983 | 0.9171 |
| 0.3039 | 0.298 | 2980 | 0.1923 | 0.9140 |
| 0.1952 | 0.299 | 2990 | 0.2056 | 0.9111 |
| 0.1677 | 0.3 | 3000 | 0.1857 | 0.9192 |
| 0.2047 | 0.301 | 3010 | 0.1939 | 0.9021 |
| 0.2264 | 0.302 | 3020 | 0.1877 | 0.9002 |
| 0.1909 | 0.303 | 3030 | 0.2034 | 0.9057 |
| 0.1358 | 0.304 | 3040 | 0.2142 | 0.9085 |
| 0.1421 | 0.305 | 3050 | 0.2122 | 0.9009 |
| 0.2964 | 0.306 | 3060 | 0.1957 | 0.9064 |
| 0.2169 | 0.307 | 3070 | 0.1921 | 0.9131 |
| 0.1558 | 0.308 | 3080 | 0.2019 | 0.9072 |
| 0.1865 | 0.309 | 3090 | 0.2023 | 0.8989 |
| 0.2234 | 0.31 | 3100 | 0.2003 | 0.9055 |
| 0.1754 | 0.311 | 3110 | 0.1898 | 0.9181 |
| 0.1896 | 0.312 | 3120 | 0.1835 | 0.9132 |
| 0.1888 | 0.313 | 3130 | 0.1787 | 0.9208 |
| 0.2242 | 0.314 | 3140 | 0.1828 | 0.9108 |
| 0.187 | 0.315 | 3150 | 0.1895 | 0.9100 |
| 0.1717 | 0.316 | 3160 | 0.1863 | 0.9164 |
| 0.223 | 0.317 | 3170 | 0.1818 | 0.9179 |
| 0.1442 | 0.318 | 3180 | 0.1848 | 0.9136 |
| 0.2267 | 0.319 | 3190 | 0.1845 | 0.9192 |
| 0.1604 | 0.32 | 3200 | 0.1974 | 0.9102 |
| 0.2301 | 0.321 | 3210 | 0.1911 | 0.9137 |
| 0.1975 | 0.322 | 3220 | 0.1850 | 0.9161 |
| 0.2313 | 0.323 | 3230 | 0.1918 | 0.9120 |
| 0.2643 | 0.324 | 3240 | 0.1873 | 0.9115 |
| 0.1765 | 0.325 | 3250 | 0.1886 | 0.9154 |
| 0.2192 | 0.326 | 3260 | 0.1862 | 0.9064 |
| 0.1201 | 0.327 | 3270 | 0.2250 | 0.8932 |
| 0.2303 | 0.328 | 3280 | 0.1947 | 0.9034 |
| 0.1269 | 0.329 | 3290 | 0.2037 | 0.9030 |
| 0.1802 | 0.33 | 3300 | 0.1949 | 0.9119 |
| 0.1671 | 0.331 | 3310 | 0.2010 | 0.9050 |
| 0.2515 | 0.332 | 3320 | 0.1955 | 0.9044 |
| 0.2136 | 0.333 | 3330 | 0.1891 | 0.9107 |
| 0.1534 | 0.334 | 3340 | 0.1923 | 0.9032 |
| 0.2039 | 0.335 | 3350 | 0.1705 | 0.9161 |
| 0.2738 | 0.336 | 3360 | 0.1724 | 0.9186 |
| 0.2006 | 0.337 | 3370 | 0.1741 | 0.9268 |
| 0.2049 | 0.338 | 3380 | 0.1727 | 0.9161 |
| 0.1699 | 0.339 | 3390 | 0.1893 | 0.9138 |
| 0.1866 | 0.34 | 3400 | 0.1693 | 0.9197 |
| 0.257 | 0.341 | 3410 | 0.1800 | 0.9078 |
| 0.1452 | 0.342 | 3420 | 0.1712 | 0.9164 |
| 0.2233 | 0.343 | 3430 | 0.1769 | 0.9133 |
| 0.1997 | 0.344 | 3440 | 0.1831 | 0.9098 |
| 0.1339 | 0.345 | 3450 | 0.1821 | 0.9107 |
| 0.2007 | 0.346 | 3460 | 0.1940 | 0.8999 |
| 0.2419 | 0.347 | 3470 | 0.1693 | 0.9140 |
| 0.1754 | 0.348 | 3480 | 0.1741 | 0.9149 |
| 0.1273 | 0.349 | 3490 | 0.1712 | 0.9211 |
| 0.1888 | 0.35 | 3500 | 0.1832 | 0.9128 |
| 0.2153 | 0.351 | 3510 | 0.1898 | 0.9078 |
| 0.1493 | 0.352 | 3520 | 0.1716 | 0.9167 |
| 0.2246 | 0.353 | 3530 | 0.1721 | 0.9210 |
| 0.1476 | 0.354 | 3540 | 0.1708 | 0.9234 |
| 0.1698 | 0.355 | 3550 | 0.1719 | 0.9230 |
| 0.2313 | 0.356 | 3560 | 0.1819 | 0.9171 |
| 0.3101 | 0.357 | 3570 | 0.1864 | 0.9116 |
| 0.2021 | 0.358 | 3580 | 0.1763 | 0.9158 |
| 0.2117 | 0.359 | 3590 | 0.1871 | 0.9173 |
| 0.1743 | 0.36 | 3600 | 0.1844 | 0.9094 |
| 0.183 | 0.361 | 3610 | 0.1895 | 0.9126 |
| 0.2193 | 0.362 | 3620 | 0.1786 | 0.9135 |
| 0.1453 | 0.363 | 3630 | 0.1934 | 0.9109 |
| 0.1323 | 0.364 | 3640 | 0.1999 | 0.9135 |
| 0.1795 | 0.365 | 3650 | 0.1916 | 0.9216 |
| 0.1165 | 0.366 | 3660 | 0.1962 | 0.9222 |
| 0.226 | 0.367 | 3670 | 0.2196 | 0.9133 |
| 0.248 | 0.368 | 3680 | 0.2008 | 0.9111 |
| 0.2108 | 0.369 | 3690 | 0.1890 | 0.9082 |
| 0.1433 | 0.37 | 3700 | 0.1832 | 0.9127 |
| 0.1738 | 0.371 | 3710 | 0.1740 | 0.9137 |
| 0.221 | 0.372 | 3720 | 0.1629 | 0.9179 |
| 0.178 | 0.373 | 3730 | 0.1615 | 0.9220 |
| 0.2048 | 0.374 | 3740 | 0.1808 | 0.9170 |
| 0.2401 | 0.375 | 3750 | 0.1703 | 0.9095 |
| 0.1976 | 0.376 | 3760 | 0.1761 | 0.9123 |
| 0.1341 | 0.377 | 3770 | 0.1714 | 0.9224 |
| 0.1765 | 0.378 | 3780 | 0.1913 | 0.9233 |
| 0.2242 | 0.379 | 3790 | 0.1781 | 0.9181 |
| 0.1909 | 0.38 | 3800 | 0.1756 | 0.9211 |
| 0.1685 | 0.381 | 3810 | 0.1710 | 0.9229 |
| 0.1406 | 0.382 | 3820 | 0.1719 | 0.9222 |
| 0.1688 | 0.383 | 3830 | 0.1672 | 0.9242 |
| 0.1694 | 0.384 | 3840 | 0.1723 | 0.9282 |
| 0.2343 | 0.385 | 3850 | 0.1700 | 0.9275 |
| 0.1821 | 0.386 | 3860 | 0.1748 | 0.9185 |
| 0.2181 | 0.387 | 3870 | 0.1714 | 0.9209 |
| 0.1535 | 0.388 | 3880 | 0.1681 | 0.9278 |
| 0.1608 | 0.389 | 3890 | 0.1648 | 0.9302 |
| 0.2485 | 0.39 | 3900 | 0.1724 | 0.9223 |
| 0.119 | 0.391 | 3910 | 0.1766 | 0.9201 |
| 0.1651 | 0.392 | 3920 | 0.1614 | 0.9322 |
| 0.2045 | 0.393 | 3930 | 0.1736 | 0.9166 |
| 0.1796 | 0.394 | 3940 | 0.1757 | 0.9190 |
| 0.1544 | 0.395 | 3950 | 0.1803 | 0.9182 |
| 0.2425 | 0.396 | 3960 | 0.1864 | 0.9100 |
| 0.1745 | 0.397 | 3970 | 0.1735 | 0.9163 |
| 0.1606 | 0.398 | 3980 | 0.1608 | 0.9227 |
| 0.2025 | 0.399 | 3990 | 0.1673 | 0.9231 |
| 0.183 | 0.4 | 4000 | 0.1889 | 0.9143 |
| 0.2068 | 0.401 | 4010 | 0.1810 | 0.9173 |
| 0.1911 | 0.402 | 4020 | 0.1798 | 0.9104 |
| 0.1679 | 0.403 | 4030 | 0.1597 | 0.9250 |
| 0.2467 | 0.404 | 4040 | 0.1658 | 0.9250 |
| 0.1673 | 0.405 | 4050 | 0.1685 | 0.9184 |
| 0.1845 | 0.406 | 4060 | 0.1615 | 0.9282 |
| 0.125 | 0.407 | 4070 | 0.1826 | 0.9189 |
| 0.2811 | 0.408 | 4080 | 0.1889 | 0.9210 |
| 0.1235 | 0.409 | 4090 | 0.1731 | 0.9222 |
| 0.1398 | 0.41 | 4100 | 0.1690 | 0.9221 |
| 0.1965 | 0.411 | 4110 | 0.1717 | 0.9197 |
| 0.1447 | 0.412 | 4120 | 0.1779 | 0.9207 |
| 0.1595 | 0.413 | 4130 | 0.1803 | 0.9206 |
| 0.1733 | 0.414 | 4140 | 0.1865 | 0.9277 |
| 0.1496 | 0.415 | 4150 | 0.2079 | 0.9065 |
| 0.1834 | 0.416 | 4160 | 0.1836 | 0.9202 |
| 0.1969 | 0.417 | 4170 | 0.1679 | 0.9204 |
| 0.2122 | 0.418 | 4180 | 0.1578 | 0.9202 |
| 0.178 | 0.419 | 4190 | 0.1536 | 0.9247 |
| 0.1972 | 0.42 | 4200 | 0.1503 | 0.9273 |
| 0.1551 | 0.421 | 4210 | 0.1580 | 0.9258 |
| 0.1475 | 0.422 | 4220 | 0.1681 | 0.9207 |
| 0.1725 | 0.423 | 4230 | 0.1680 | 0.9227 |
| 0.1415 | 0.424 | 4240 | 0.1672 | 0.9232 |
| 0.1735 | 0.425 | 4250 | 0.1737 | 0.9195 |
| 0.178 | 0.426 | 4260 | 0.1647 | 0.9229 |
| 0.1759 | 0.427 | 4270 | 0.1675 | 0.9249 |
| 0.1894 | 0.428 | 4280 | 0.1569 | 0.9232 |
| 0.2786 | 0.429 | 4290 | 0.1570 | 0.9251 |
| 0.1458 | 0.43 | 4300 | 0.1832 | 0.9160 |
| 0.147 | 0.431 | 4310 | 0.1921 | 0.9116 |
| 0.174 | 0.432 | 4320 | 0.1689 | 0.9203 |
| 0.1917 | 0.433 | 4330 | 0.1568 | 0.9271 |
| 0.1849 | 0.434 | 4340 | 0.1635 | 0.9296 |
| 0.1382 | 0.435 | 4350 | 0.1636 | 0.9217 |
| 0.1725 | 0.436 | 4360 | 0.1680 | 0.9242 |
| 0.1363 | 0.437 | 4370 | 0.1659 | 0.9241 |
| 0.1623 | 0.438 | 4380 | 0.1749 | 0.9217 |
| 0.2707 | 0.439 | 4390 | 0.1768 | 0.9268 |
| 0.1426 | 0.44 | 4400 | 0.1680 | 0.9256 |
| 0.1773 | 0.441 | 4410 | 0.1701 | 0.9271 |
| 0.1622 | 0.442 | 4420 | 0.1778 | 0.9201 |
| 0.2435 | 0.443 | 4430 | 0.1820 | 0.9158 |
| 0.1851 | 0.444 | 4440 | 0.1695 | 0.9268 |
| 0.1584 | 0.445 | 4450 | 0.1734 | 0.9181 |
| 0.1308 | 0.446 | 4460 | 0.1631 | 0.9110 |
| 0.1896 | 0.447 | 4470 | 0.1645 | 0.9166 |
| 0.145 | 0.448 | 4480 | 0.1630 | 0.9244 |
| 0.2251 | 0.449 | 4490 | 0.1677 | 0.9229 |
| 0.1803 | 0.45 | 4500 | 0.1673 | 0.9213 |
| 0.1949 | 0.451 | 4510 | 0.1626 | 0.9220 |
| 0.2165 | 0.452 | 4520 | 0.1618 | 0.9257 |
| 0.2049 | 0.453 | 4530 | 0.1567 | 0.9324 |
| 0.1477 | 0.454 | 4540 | 0.1591 | 0.9244 |
| 0.1904 | 0.455 | 4550 | 0.1583 | 0.9345 |
| 0.1627 | 0.456 | 4560 | 0.1638 | 0.9298 |
| 0.1821 | 0.457 | 4570 | 0.1717 | 0.9287 |
| 0.18 | 0.458 | 4580 | 0.1688 | 0.9224 |
| 0.2186 | 0.459 | 4590 | 0.1681 | 0.9244 |
| 0.1787 | 0.46 | 4600 | 0.1608 | 0.9291 |
| 0.2132 | 0.461 | 4610 | 0.1654 | 0.9283 |
| 0.1414 | 0.462 | 4620 | 0.1648 | 0.9253 |
| 0.1264 | 0.463 | 4630 | 0.1746 | 0.9188 |
| 0.1449 | 0.464 | 4640 | 0.1772 | 0.9210 |
| 0.1751 | 0.465 | 4650 | 0.1656 | 0.9198 |
| 0.1781 | 0.466 | 4660 | 0.1793 | 0.9162 |
| 0.1882 | 0.467 | 4670 | 0.1687 | 0.9191 |
| 0.1604 | 0.468 | 4680 | 0.1742 | 0.9258 |
| 0.2189 | 0.469 | 4690 | 0.1765 | 0.9222 |
| 0.194 | 0.47 | 4700 | 0.1653 | 0.9208 |
| 0.1702 | 0.471 | 4710 | 0.1658 | 0.9229 |
| 0.201 | 0.472 | 4720 | 0.1620 | 0.9264 |
| 0.1531 | 0.473 | 4730 | 0.1626 | 0.9254 |
| 0.1781 | 0.474 | 4740 | 0.1637 | 0.9247 |
| 0.1926 | 0.475 | 4750 | 0.1667 | 0.9222 |
| 0.2163 | 0.476 | 4760 | 0.1614 | 0.9252 |
| 0.1598 | 0.477 | 4770 | 0.1622 | 0.9212 |
| 0.1312 | 0.478 | 4780 | 0.1572 | 0.9289 |
| 0.1815 | 0.479 | 4790 | 0.1566 | 0.9279 |
| 0.2202 | 0.48 | 4800 | 0.1610 | 0.9273 |
| 0.136 | 0.481 | 4810 | 0.1567 | 0.9304 |
| 0.1911 | 0.482 | 4820 | 0.1506 | 0.9281 |
| 0.1387 | 0.483 | 4830 | 0.1473 | 0.9283 |
| 0.1189 | 0.484 | 4840 | 0.1538 | 0.9257 |
| 0.1182 | 0.485 | 4850 | 0.1686 | 0.9197 |
| 0.1295 | 0.486 | 4860 | 0.1777 | 0.9206 |
| 0.2251 | 0.487 | 4870 | 0.1632 | 0.9221 |
| 0.1973 | 0.488 | 4880 | 0.1584 | 0.9300 |
| 0.2054 | 0.489 | 4890 | 0.1594 | 0.9247 |
| 0.1955 | 0.49 | 4900 | 0.1611 | 0.9301 |
| 0.2192 | 0.491 | 4910 | 0.1673 | 0.9243 |
| 0.1694 | 0.492 | 4920 | 0.1630 | 0.9280 |
| 0.2275 | 0.493 | 4930 | 0.1643 | 0.9226 |
| 0.208 | 0.494 | 4940 | 0.1749 | 0.9184 |
| 0.1429 | 0.495 | 4950 | 0.1709 | 0.9191 |
| 0.1433 | 0.496 | 4960 | 0.1712 | 0.9189 |
| 0.1275 | 0.497 | 4970 | 0.1773 | 0.9143 |
| 0.1427 | 0.498 | 4980 | 0.1758 | 0.9207 |
| 0.1689 | 0.499 | 4990 | 0.1744 | 0.9225 |
| 0.199 | 0.5 | 5000 | 0.1703 | 0.9264 |
| 0.1973 | 0.501 | 5010 | 0.1711 | 0.9285 |
| 0.1671 | 0.502 | 5020 | 0.1698 | 0.9238 |
| 0.2287 | 0.503 | 5030 | 0.1716 | 0.9228 |
| 0.1917 | 0.504 | 5040 | 0.1602 | 0.9249 |
| 0.1709 | 0.505 | 5050 | 0.1573 | 0.9295 |
| 0.2076 | 0.506 | 5060 | 0.1606 | 0.9215 |
| 0.1645 | 0.507 | 5070 | 0.1582 | 0.9242 |
| 0.1879 | 0.508 | 5080 | 0.1550 | 0.9261 |
| 0.1447 | 0.509 | 5090 | 0.1557 | 0.9327 |
| 0.1519 | 0.51 | 5100 | 0.1641 | 0.9299 |
| 0.1607 | 0.511 | 5110 | 0.1720 | 0.9255 |
| 0.2512 | 0.512 | 5120 | 0.1690 | 0.9242 |
| 0.1772 | 0.513 | 5130 | 0.1571 | 0.9312 |
| 0.1358 | 0.514 | 5140 | 0.1597 | 0.9253 |
| 0.1785 | 0.515 | 5150 | 0.1548 | 0.9314 |
| 0.1668 | 0.516 | 5160 | 0.1568 | 0.9303 |
| 0.1876 | 0.517 | 5170 | 0.1571 | 0.9273 |
| 0.1599 | 0.518 | 5180 | 0.1584 | 0.9366 |
| 0.1567 | 0.519 | 5190 | 0.1627 | 0.9251 |
| 0.1491 | 0.52 | 5200 | 0.1681 | 0.9208 |
| 0.184 | 0.521 | 5210 | 0.1608 | 0.9276 |
| 0.193 | 0.522 | 5220 | 0.1542 | 0.9215 |
| 0.2304 | 0.523 | 5230 | 0.1491 | 0.9323 |
| 0.1729 | 0.524 | 5240 | 0.1493 | 0.9343 |
| 0.1812 | 0.525 | 5250 | 0.1494 | 0.9324 |
| 0.1675 | 0.526 | 5260 | 0.1564 | 0.9277 |
| 0.2164 | 0.527 | 5270 | 0.1533 | 0.9282 |
| 0.1708 | 0.528 | 5280 | 0.1524 | 0.9309 |
| 0.1307 | 0.529 | 5290 | 0.1515 | 0.9343 |
| 0.2109 | 0.53 | 5300 | 0.1595 | 0.9280 |
| 0.1505 | 0.531 | 5310 | 0.1633 | 0.9245 |
| 0.1652 | 0.532 | 5320 | 0.1594 | 0.9328 |
| 0.1794 | 0.533 | 5330 | 0.1707 | 0.9232 |
| 0.2188 | 0.534 | 5340 | 0.1654 | 0.9255 |
| 0.1683 | 0.535 | 5350 | 0.1587 | 0.9292 |
| 0.1709 | 0.536 | 5360 | 0.1651 | 0.9293 |
| 0.1794 | 0.537 | 5370 | 0.1606 | 0.9297 |
| 0.1277 | 0.538 | 5380 | 0.1546 | 0.9242 |
| 0.1787 | 0.539 | 5390 | 0.1599 | 0.9291 |
| 0.1492 | 0.54 | 5400 | 0.1710 | 0.9287 |
| 0.117 | 0.541 | 5410 | 0.1709 | 0.9292 |
| 0.139 | 0.542 | 5420 | 0.1679 | 0.9251 |
| 0.2192 | 0.543 | 5430 | 0.1666 | 0.9268 |
| 0.1548 | 0.544 | 5440 | 0.1641 | 0.9292 |
| 0.1613 | 0.545 | 5450 | 0.1628 | 0.9316 |
| 0.2514 | 0.546 | 5460 | 0.1607 | 0.9295 |
| 0.1481 | 0.547 | 5470 | 0.1569 | 0.9277 |
| 0.16 | 0.548 | 5480 | 0.1554 | 0.9331 |
| 0.1249 | 0.549 | 5490 | 0.1516 | 0.9340 |
| 0.1975 | 0.55 | 5500 | 0.1526 | 0.9320 |
| 0.1831 | 0.551 | 5510 | 0.1800 | 0.9185 |
| 0.2268 | 0.552 | 5520 | 0.1644 | 0.9231 |
| 0.0959 | 0.553 | 5530 | 0.1595 | 0.9247 |
| 0.1268 | 0.554 | 5540 | 0.1619 | 0.9272 |
| 0.186 | 0.555 | 5550 | 0.1566 | 0.9284 |
| 0.2403 | 0.556 | 5560 | 0.1632 | 0.9259 |
| 0.1602 | 0.557 | 5570 | 0.1639 | 0.9250 |
| 0.1926 | 0.558 | 5580 | 0.1629 | 0.9288 |
| 0.1992 | 0.559 | 5590 | 0.1595 | 0.9288 |
| 0.1652 | 0.56 | 5600 | 0.1508 | 0.9349 |
| 0.1072 | 0.561 | 5610 | 0.1442 | 0.9338 |
| 0.184 | 0.562 | 5620 | 0.1456 | 0.9333 |
| 0.2096 | 0.563 | 5630 | 0.1472 | 0.9373 |
| 0.1582 | 0.564 | 5640 | 0.1514 | 0.9358 |
| 0.1963 | 0.565 | 5650 | 0.1498 | 0.9354 |
| 0.1916 | 0.566 | 5660 | 0.1507 | 0.9303 |
| 0.1429 | 0.567 | 5670 | 0.1487 | 0.9386 |
| 0.1419 | 0.568 | 5680 | 0.1495 | 0.9374 |
| 0.1167 | 0.569 | 5690 | 0.1495 | 0.9398 |
| 0.1305 | 0.57 | 5700 | 0.1522 | 0.9398 |
| 0.1949 | 0.571 | 5710 | 0.1519 | 0.9354 |
| 0.269 | 0.572 | 5720 | 0.1536 | 0.9356 |
| 0.1536 | 0.573 | 5730 | 0.1467 | 0.9407 |
| 0.1426 | 0.574 | 5740 | 0.1443 | 0.9406 |
| 0.1979 | 0.575 | 5750 | 0.1661 | 0.9278 |
| 0.2082 | 0.576 | 5760 | 0.1558 | 0.9304 |
| 0.147 | 0.577 | 5770 | 0.1475 | 0.9350 |
| 0.2214 | 0.578 | 5780 | 0.1434 | 0.9326 |
| 0.1147 | 0.579 | 5790 | 0.1392 | 0.9431 |
| 0.0913 | 0.58 | 5800 | 0.1447 | 0.9370 |
| 0.1517 | 0.581 | 5810 | 0.1472 | 0.9375 |
| 0.1945 | 0.582 | 5820 | 0.1488 | 0.9365 |
| 0.136 | 0.583 | 5830 | 0.1538 | 0.9319 |
| 0.285 | 0.584 | 5840 | 0.1550 | 0.9318 |
| 0.2128 | 0.585 | 5850 | 0.1479 | 0.9351 |
| 0.1587 | 0.586 | 5860 | 0.1420 | 0.9378 |
| 0.1735 | 0.587 | 5870 | 0.1450 | 0.9375 |
| 0.137 | 0.588 | 5880 | 0.1467 | 0.9348 |
| 0.1344 | 0.589 | 5890 | 0.1420 | 0.9378 |
| 0.174 | 0.59 | 5900 | 0.1432 | 0.9330 |
| 0.1697 | 0.591 | 5910 | 0.1482 | 0.9322 |
| 0.1392 | 0.592 | 5920 | 0.1521 | 0.9283 |
| 0.1561 | 0.593 | 5930 | 0.1519 | 0.9297 |
| 0.1289 | 0.594 | 5940 | 0.1511 | 0.9277 |
| 0.1832 | 0.595 | 5950 | 0.1488 | 0.9271 |
| 0.0969 | 0.596 | 5960 | 0.1465 | 0.9301 |
| 0.1519 | 0.597 | 5970 | 0.1507 | 0.9263 |
| 0.115 | 0.598 | 5980 | 0.1521 | 0.9331 |
| 0.1303 | 0.599 | 5990 | 0.1544 | 0.9315 |
| 0.1779 | 0.6 | 6000 | 0.1552 | 0.9316 |
| 0.1911 | 0.601 | 6010 | 0.1573 | 0.9271 |
| 0.2195 | 0.602 | 6020 | 0.1441 | 0.9329 |
| 0.1524 | 0.603 | 6030 | 0.1380 | 0.9317 |
| 0.098 | 0.604 | 6040 | 0.1404 | 0.9346 |
| 0.1873 | 0.605 | 6050 | 0.1493 | 0.9327 |
| 0.1983 | 0.606 | 6060 | 0.1579 | 0.9261 |
| 0.1419 | 0.607 | 6070 | 0.1571 | 0.9280 |
| 0.1758 | 0.608 | 6080 | 0.1465 | 0.9340 |
| 0.1593 | 0.609 | 6090 | 0.1449 | 0.9295 |
| 0.1465 | 0.61 | 6100 | 0.1440 | 0.9257 |
| 0.1285 | 0.611 | 6110 | 0.1377 | 0.9374 |
| 0.1849 | 0.612 | 6120 | 0.1420 | 0.9359 |
| 0.1417 | 0.613 | 6130 | 0.1467 | 0.9391 |
| 0.1379 | 0.614 | 6140 | 0.1438 | 0.9340 |
| 0.1905 | 0.615 | 6150 | 0.1469 | 0.9334 |
| 0.1737 | 0.616 | 6160 | 0.1452 | 0.9383 |
| 0.1208 | 0.617 | 6170 | 0.1460 | 0.9401 |
| 0.1876 | 0.618 | 6180 | 0.1544 | 0.9314 |
| 0.2501 | 0.619 | 6190 | 0.1527 | 0.9384 |
| 0.1855 | 0.62 | 6200 | 0.1439 | 0.9373 |
| 0.1433 | 0.621 | 6210 | 0.1394 | 0.9290 |
| 0.2147 | 0.622 | 6220 | 0.1334 | 0.9309 |
| 0.1806 | 0.623 | 6230 | 0.1329 | 0.9383 |
| 0.1517 | 0.624 | 6240 | 0.1365 | 0.9363 |
| 0.1423 | 0.625 | 6250 | 0.1465 | 0.9306 |
| 0.1765 | 0.626 | 6260 | 0.1549 | 0.9241 |
| 0.2143 | 0.627 | 6270 | 0.1490 | 0.9274 |
| 0.1689 | 0.628 | 6280 | 0.1358 | 0.9349 |
| 0.1412 | 0.629 | 6290 | 0.1354 | 0.9355 |
| 0.1701 | 0.63 | 6300 | 0.1358 | 0.9349 |
| 0.1317 | 0.631 | 6310 | 0.1421 | 0.9304 |
| 0.1668 | 0.632 | 6320 | 0.1413 | 0.9330 |
| 0.1933 | 0.633 | 6330 | 0.1371 | 0.9386 |
| 0.1499 | 0.634 | 6340 | 0.1354 | 0.9430 |
| 0.1796 | 0.635 | 6350 | 0.1352 | 0.9426 |
| 0.1234 | 0.636 | 6360 | 0.1369 | 0.9388 |
| 0.1951 | 0.637 | 6370 | 0.1419 | 0.9396 |
| 0.0861 | 0.638 | 6380 | 0.1508 | 0.9325 |
| 0.1367 | 0.639 | 6390 | 0.1439 | 0.9401 |
| 0.1637 | 0.64 | 6400 | 0.1382 | 0.9387 |
| 0.1064 | 0.641 | 6410 | 0.1278 | 0.9421 |
| 0.1211 | 0.642 | 6420 | 0.1287 | 0.9462 |
| 0.1922 | 0.643 | 6430 | 0.1337 | 0.9418 |
| 0.1345 | 0.644 | 6440 | 0.1381 | 0.9411 |
| 0.1152 | 0.645 | 6450 | 0.1362 | 0.9398 |
| 0.1691 | 0.646 | 6460 | 0.1441 | 0.9347 |
| 0.1432 | 0.647 | 6470 | 0.1363 | 0.9353 |
| 0.1131 | 0.648 | 6480 | 0.1323 | 0.9358 |
| 0.1506 | 0.649 | 6490 | 0.1337 | 0.9349 |
| 0.2489 | 0.65 | 6500 | 0.1320 | 0.9422 |
| 0.1354 | 0.651 | 6510 | 0.1327 | 0.9491 |
| 0.1034 | 0.652 | 6520 | 0.1311 | 0.9428 |
| 0.1797 | 0.653 | 6530 | 0.1327 | 0.9454 |
| 0.2172 | 0.654 | 6540 | 0.1299 | 0.9434 |
| 0.1505 | 0.655 | 6550 | 0.1297 | 0.9370 |
| 0.1851 | 0.656 | 6560 | 0.1307 | 0.9366 |
| 0.2165 | 0.657 | 6570 | 0.1306 | 0.9359 |
| 0.1523 | 0.658 | 6580 | 0.1293 | 0.9358 |
| 0.1752 | 0.659 | 6590 | 0.1303 | 0.9368 |
| 0.1435 | 0.66 | 6600 | 0.1295 | 0.9395 |
| 0.1347 | 0.661 | 6610 | 0.1285 | 0.9364 |
| 0.1555 | 0.662 | 6620 | 0.1359 | 0.9342 |
| 0.0997 | 0.663 | 6630 | 0.1373 | 0.9332 |
| 0.1844 | 0.664 | 6640 | 0.1401 | 0.9390 |
| 0.1551 | 0.665 | 6650 | 0.1408 | 0.9388 |
| 0.1633 | 0.666 | 6660 | 0.1352 | 0.9358 |
| 0.1112 | 0.667 | 6670 | 0.1358 | 0.9343 |
| 0.1259 | 0.668 | 6680 | 0.1359 | 0.9361 |
| 0.2496 | 0.669 | 6690 | 0.1351 | 0.9425 |
| 0.1874 | 0.67 | 6700 | 0.1437 | 0.9394 |
| 0.1842 | 0.671 | 6710 | 0.1392 | 0.9388 |
| 0.106 | 0.672 | 6720 | 0.1393 | 0.9357 |
| 0.1232 | 0.673 | 6730 | 0.1429 | 0.9376 |
| 0.1449 | 0.674 | 6740 | 0.1407 | 0.9389 |
| 0.1366 | 0.675 | 6750 | 0.1363 | 0.9377 |
| 0.1654 | 0.676 | 6760 | 0.1375 | 0.9371 |
| 0.1281 | 0.677 | 6770 | 0.1445 | 0.9329 |
| 0.2304 | 0.678 | 6780 | 0.1422 | 0.9319 |
| 0.1593 | 0.679 | 6790 | 0.1420 | 0.9322 |
| 0.1615 | 0.68 | 6800 | 0.1389 | 0.9401 |
| 0.1609 | 0.681 | 6810 | 0.1397 | 0.9420 |
| 0.1807 | 0.682 | 6820 | 0.1373 | 0.9362 |
| 0.2436 | 0.683 | 6830 | 0.1361 | 0.9356 |
| 0.1432 | 0.684 | 6840 | 0.1371 | 0.9349 |
| 0.1015 | 0.685 | 6850 | 0.1370 | 0.9363 |
| 0.1378 | 0.686 | 6860 | 0.1320 | 0.9366 |
| 0.1594 | 0.687 | 6870 | 0.1341 | 0.9377 |
| 0.13 | 0.688 | 6880 | 0.1390 | 0.9355 |
| 0.1325 | 0.689 | 6890 | 0.1366 | 0.9361 |
| 0.1238 | 0.69 | 6900 | 0.1373 | 0.9375 |
| 0.1305 | 0.691 | 6910 | 0.1393 | 0.9418 |
| 0.215 | 0.692 | 6920 | 0.1405 | 0.9371 |
| 0.228 | 0.693 | 6930 | 0.1387 | 0.9373 |
| 0.1245 | 0.694 | 6940 | 0.1347 | 0.9366 |
| 0.1754 | 0.695 | 6950 | 0.1336 | 0.9390 |
| 0.1382 | 0.696 | 6960 | 0.1362 | 0.9349 |
| 0.1866 | 0.697 | 6970 | 0.1378 | 0.9327 |
| 0.2051 | 0.698 | 6980 | 0.1316 | 0.9388 |
| 0.0997 | 0.699 | 6990 | 0.1339 | 0.9332 |
| 0.1574 | 0.7 | 7000 | 0.1430 | 0.9255 |
| 0.1643 | 0.701 | 7010 | 0.1454 | 0.9231 |
| 0.2168 | 0.702 | 7020 | 0.1444 | 0.9262 |
| 0.1226 | 0.703 | 7030 | 0.1383 | 0.9343 |
| 0.0938 | 0.704 | 7040 | 0.1385 | 0.9332 |
| 0.1476 | 0.705 | 7050 | 0.1411 | 0.9322 |
| 0.1174 | 0.706 | 7060 | 0.1386 | 0.9316 |
| 0.16 | 0.707 | 7070 | 0.1384 | 0.9338 |
| 0.21 | 0.708 | 7080 | 0.1407 | 0.9322 |
| 0.1288 | 0.709 | 7090 | 0.1447 | 0.9371 |
| 0.1894 | 0.71 | 7100 | 0.1410 | 0.9370 |
| 0.15 | 0.711 | 7110 | 0.1349 | 0.9342 |
| 0.0823 | 0.712 | 7120 | 0.1346 | 0.9363 |
| 0.2081 | 0.713 | 7130 | 0.1349 | 0.9338 |
| 0.1875 | 0.714 | 7140 | 0.1348 | 0.9316 |
| 0.237 | 0.715 | 7150 | 0.1362 | 0.9316 |
| 0.139 | 0.716 | 7160 | 0.1382 | 0.9319 |
| 0.0989 | 0.717 | 7170 | 0.1365 | 0.9339 |
| 0.1211 | 0.718 | 7180 | 0.1333 | 0.9314 |
| 0.1755 | 0.719 | 7190 | 0.1315 | 0.9355 |
| 0.1848 | 0.72 | 7200 | 0.1327 | 0.9301 |
| 0.1119 | 0.721 | 7210 | 0.1339 | 0.9329 |
| 0.1475 | 0.722 | 7220 | 0.1306 | 0.9318 |
| 0.1113 | 0.723 | 7230 | 0.1330 | 0.9317 |
| 0.118 | 0.724 | 7240 | 0.1348 | 0.9312 |
| 0.1506 | 0.725 | 7250 | 0.1362 | 0.9306 |
| 0.1317 | 0.726 | 7260 | 0.1334 | 0.9300 |
| 0.1135 | 0.727 | 7270 | 0.1435 | 0.9380 |
| 0.1604 | 0.728 | 7280 | 0.1452 | 0.9358 |
| 0.1608 | 0.729 | 7290 | 0.1360 | 0.9362 |
| 0.1082 | 0.73 | 7300 | 0.1373 | 0.9360 |
| 0.2255 | 0.731 | 7310 | 0.1451 | 0.9290 |
| 0.1271 | 0.732 | 7320 | 0.1433 | 0.9315 |
| 0.1215 | 0.733 | 7330 | 0.1366 | 0.9307 |
| 0.1288 | 0.734 | 7340 | 0.1354 | 0.9357 |
| 0.1317 | 0.735 | 7350 | 0.1436 | 0.9332 |
| 0.1921 | 0.736 | 7360 | 0.1455 | 0.9322 |
| 0.1373 | 0.737 | 7370 | 0.1378 | 0.9372 |
| 0.218 | 0.738 | 7380 | 0.1345 | 0.9359 |
| 0.2011 | 0.739 | 7390 | 0.1330 | 0.9335 |
| 0.2116 | 0.74 | 7400 | 0.1353 | 0.9277 |
| 0.1246 | 0.741 | 7410 | 0.1345 | 0.9290 |
| 0.1687 | 0.742 | 7420 | 0.1345 | 0.9234 |
| 0.1417 | 0.743 | 7430 | 0.1344 | 0.9288 |
| 0.1429 | 0.744 | 7440 | 0.1320 | 0.9293 |
| 0.1559 | 0.745 | 7450 | 0.1325 | 0.9284 |
| 0.168 | 0.746 | 7460 | 0.1354 | 0.9336 |
| 0.1854 | 0.747 | 7470 | 0.1341 | 0.9340 |
| 0.1707 | 0.748 | 7480 | 0.1366 | 0.9333 |
| 0.1608 | 0.749 | 7490 | 0.1376 | 0.9328 |
| 0.1845 | 0.75 | 7500 | 0.1428 | 0.9263 |
| 0.2005 | 0.751 | 7510 | 0.1446 | 0.9330 |
| 0.1182 | 0.752 | 7520 | 0.1424 | 0.9403 |
| 0.1394 | 0.753 | 7530 | 0.1362 | 0.9384 |
| 0.1735 | 0.754 | 7540 | 0.1351 | 0.9382 |
| 0.1381 | 0.755 | 7550 | 0.1318 | 0.9372 |
| 0.1901 | 0.756 | 7560 | 0.1303 | 0.9343 |
| 0.1661 | 0.757 | 7570 | 0.1359 | 0.9324 |
| 0.142 | 0.758 | 7580 | 0.1348 | 0.9343 |
| 0.1809 | 0.759 | 7590 | 0.1355 | 0.9353 |
| 0.1708 | 0.76 | 7600 | 0.1377 | 0.9307 |
| 0.1324 | 0.761 | 7610 | 0.1395 | 0.9277 |
| 0.1288 | 0.762 | 7620 | 0.1459 | 0.9281 |
| 0.1496 | 0.763 | 7630 | 0.1528 | 0.9291 |
| 0.1184 | 0.764 | 7640 | 0.1461 | 0.9283 |
| 0.1629 | 0.765 | 7650 | 0.1371 | 0.9362 |
| 0.203 | 0.766 | 7660 | 0.1357 | 0.9398 |
| 0.1362 | 0.767 | 7670 | 0.1335 | 0.9372 |
| 0.0951 | 0.768 | 7680 | 0.1326 | 0.9350 |
| 0.1582 | 0.769 | 7690 | 0.1383 | 0.9379 |
| 0.1218 | 0.77 | 7700 | 0.1348 | 0.9408 |
| 0.1607 | 0.771 | 7710 | 0.1324 | 0.9394 |
| 0.1555 | 0.772 | 7720 | 0.1349 | 0.9386 |
| 0.1988 | 0.773 | 7730 | 0.1337 | 0.9363 |
| 0.0823 | 0.774 | 7740 | 0.1333 | 0.9371 |
| 0.1827 | 0.775 | 7750 | 0.1319 | 0.9366 |
| 0.1844 | 0.776 | 7760 | 0.1315 | 0.9373 |
| 0.1711 | 0.777 | 7770 | 0.1320 | 0.9394 |
| 0.1501 | 0.778 | 7780 | 0.1331 | 0.9398 |
| 0.1481 | 0.779 | 7790 | 0.1352 | 0.9372 |
| 0.1123 | 0.78 | 7800 | 0.1329 | 0.9361 |
| 0.2192 | 0.781 | 7810 | 0.1329 | 0.9325 |
| 0.1706 | 0.782 | 7820 | 0.1346 | 0.9303 |
| 0.1741 | 0.783 | 7830 | 0.1337 | 0.9328 |
| 0.1383 | 0.784 | 7840 | 0.1313 | 0.9401 |
| 0.1406 | 0.785 | 7850 | 0.1328 | 0.9395 |
| 0.1339 | 0.786 | 7860 | 0.1324 | 0.9380 |
| 0.0995 | 0.787 | 7870 | 0.1330 | 0.9366 |
| 0.1629 | 0.788 | 7880 | 0.1348 | 0.9399 |
| 0.0979 | 0.789 | 7890 | 0.1367 | 0.9399 |
| 0.1297 | 0.79 | 7900 | 0.1373 | 0.9430 |
| 0.1375 | 0.791 | 7910 | 0.1380 | 0.9395 |
| 0.1866 | 0.792 | 7920 | 0.1360 | 0.9448 |
| 0.1756 | 0.793 | 7930 | 0.1311 | 0.9379 |
| 0.0814 | 0.794 | 7940 | 0.1288 | 0.9360 |
| 0.178 | 0.795 | 7950 | 0.1293 | 0.9392 |
| 0.126 | 0.796 | 7960 | 0.1276 | 0.9398 |
| 0.1311 | 0.797 | 7970 | 0.1270 | 0.9406 |
| 0.1421 | 0.798 | 7980 | 0.1310 | 0.9406 |
| 0.208 | 0.799 | 7990 | 0.1307 | 0.9411 |
| 0.1573 | 0.8 | 8000 | 0.1293 | 0.9387 |
| 0.1541 | 0.801 | 8010 | 0.1278 | 0.9380 |
| 0.1684 | 0.802 | 8020 | 0.1261 | 0.9380 |
| 0.1055 | 0.803 | 8030 | 0.1255 | 0.9370 |
| 0.126 | 0.804 | 8040 | 0.1274 | 0.9435 |
| 0.1832 | 0.805 | 8050 | 0.1305 | 0.9398 |
| 0.1268 | 0.806 | 8060 | 0.1311 | 0.9384 |
| 0.131 | 0.807 | 8070 | 0.1306 | 0.9400 |
| 0.1816 | 0.808 | 8080 | 0.1322 | 0.9349 |
| 0.0741 | 0.809 | 8090 | 0.1349 | 0.9356 |
| 0.1177 | 0.81 | 8100 | 0.1365 | 0.9341 |
| 0.1746 | 0.811 | 8110 | 0.1366 | 0.9347 |
| 0.1427 | 0.812 | 8120 | 0.1320 | 0.9379 |
| 0.1598 | 0.813 | 8130 | 0.1266 | 0.9407 |
| 0.1426 | 0.814 | 8140 | 0.1254 | 0.9409 |
| 0.2106 | 0.815 | 8150 | 0.1269 | 0.9360 |
| 0.166 | 0.816 | 8160 | 0.1303 | 0.9393 |
| 0.125 | 0.817 | 8170 | 0.1289 | 0.9407 |
| 0.2108 | 0.818 | 8180 | 0.1292 | 0.9357 |
| 0.178 | 0.819 | 8190 | 0.1291 | 0.9368 |
| 0.2 | 0.82 | 8200 | 0.1301 | 0.9377 |
| 0.1461 | 0.821 | 8210 | 0.1294 | 0.9415 |
| 0.1479 | 0.822 | 8220 | 0.1267 | 0.9401 |
| 0.1738 | 0.823 | 8230 | 0.1262 | 0.9415 |
| 0.1045 | 0.824 | 8240 | 0.1282 | 0.9431 |
| 0.1864 | 0.825 | 8250 | 0.1295 | 0.9431 |
| 0.2176 | 0.826 | 8260 | 0.1300 | 0.9415 |
| 0.2055 | 0.827 | 8270 | 0.1295 | 0.9373 |
| 0.1623 | 0.828 | 8280 | 0.1310 | 0.9378 |
| 0.1975 | 0.829 | 8290 | 0.1301 | 0.9377 |
| 0.1808 | 0.83 | 8300 | 0.1312 | 0.9398 |
| 0.1056 | 0.831 | 8310 | 0.1352 | 0.9398 |
| 0.1676 | 0.832 | 8320 | 0.1356 | 0.9372 |
| 0.1836 | 0.833 | 8330 | 0.1363 | 0.9376 |
| 0.1194 | 0.834 | 8340 | 0.1356 | 0.9377 |
| 0.1249 | 0.835 | 8350 | 0.1338 | 0.9389 |
| 0.1779 | 0.836 | 8360 | 0.1338 | 0.9405 |
| 0.1166 | 0.837 | 8370 | 0.1341 | 0.9375 |
| 0.1178 | 0.838 | 8380 | 0.1327 | 0.9383 |
| 0.1075 | 0.839 | 8390 | 0.1311 | 0.9392 |
| 0.138 | 0.84 | 8400 | 0.1333 | 0.9375 |
| 0.2505 | 0.841 | 8410 | 0.1394 | 0.9392 |
| 0.1832 | 0.842 | 8420 | 0.1443 | 0.9346 |
| 0.1129 | 0.843 | 8430 | 0.1445 | 0.9321 |
| 0.1495 | 0.844 | 8440 | 0.1398 | 0.9373 |
| 0.1613 | 0.845 | 8450 | 0.1338 | 0.9384 |
| 0.1919 | 0.846 | 8460 | 0.1308 | 0.9417 |
| 0.1861 | 0.847 | 8470 | 0.1315 | 0.9428 |
| 0.1136 | 0.848 | 8480 | 0.1311 | 0.9426 |
| 0.1306 | 0.849 | 8490 | 0.1305 | 0.9418 |
| 0.1185 | 0.85 | 8500 | 0.1291 | 0.9395 |
| 0.123 | 0.851 | 8510 | 0.1286 | 0.9403 |
| 0.124 | 0.852 | 8520 | 0.1293 | 0.9398 |
| 0.1152 | 0.853 | 8530 | 0.1302 | 0.9432 |
| 0.1907 | 0.854 | 8540 | 0.1311 | 0.9396 |
| 0.1352 | 0.855 | 8550 | 0.1315 | 0.9414 |
| 0.1529 | 0.856 | 8560 | 0.1332 | 0.9394 |
| 0.2119 | 0.857 | 8570 | 0.1317 | 0.9412 |
| 0.0997 | 0.858 | 8580 | 0.1307 | 0.9387 |
| 0.1687 | 0.859 | 8590 | 0.1313 | 0.9385 |
| 0.1966 | 0.86 | 8600 | 0.1300 | 0.9390 |
| 0.1452 | 0.861 | 8610 | 0.1274 | 0.9379 |
| 0.1389 | 0.862 | 8620 | 0.1276 | 0.9392 |
| 0.1257 | 0.863 | 8630 | 0.1282 | 0.9412 |
| 0.1493 | 0.864 | 8640 | 0.1269 | 0.9374 |
| 0.2041 | 0.865 | 8650 | 0.1290 | 0.9386 |
| 0.1235 | 0.866 | 8660 | 0.1295 | 0.9412 |
| 0.0813 | 0.867 | 8670 | 0.1305 | 0.9407 |
| 0.1382 | 0.868 | 8680 | 0.1329 | 0.9423 |
| 0.1035 | 0.869 | 8690 | 0.1347 | 0.9387 |
| 0.1524 | 0.87 | 8700 | 0.1309 | 0.9414 |
| 0.1245 | 0.871 | 8710 | 0.1288 | 0.9400 |
| 0.1096 | 0.872 | 8720 | 0.1283 | 0.9392 |
| 0.1561 | 0.873 | 8730 | 0.1275 | 0.9408 |
| 0.1319 | 0.874 | 8740 | 0.1264 | 0.9413 |
| 0.1425 | 0.875 | 8750 | 0.1263 | 0.9367 |
| 0.1551 | 0.876 | 8760 | 0.1278 | 0.9380 |
| 0.187 | 0.877 | 8770 | 0.1291 | 0.9370 |
| 0.1376 | 0.878 | 8780 | 0.1316 | 0.9342 |
| 0.1772 | 0.879 | 8790 | 0.1328 | 0.9361 |
| 0.1657 | 0.88 | 8800 | 0.1315 | 0.9378 |
| 0.1423 | 0.881 | 8810 | 0.1325 | 0.9422 |
| 0.1429 | 0.882 | 8820 | 0.1309 | 0.9376 |
| 0.1431 | 0.883 | 8830 | 0.1296 | 0.9363 |
| 0.1234 | 0.884 | 8840 | 0.1275 | 0.9352 |
| 0.1377 | 0.885 | 8850 | 0.1282 | 0.9370 |
| 0.1325 | 0.886 | 8860 | 0.1288 | 0.9374 |
| 0.1386 | 0.887 | 8870 | 0.1282 | 0.9405 |
| 0.1631 | 0.888 | 8880 | 0.1276 | 0.9390 |
| 0.2117 | 0.889 | 8890 | 0.1261 | 0.9376 |
| 0.139 | 0.89 | 8900 | 0.1252 | 0.9350 |
| 0.1719 | 0.891 | 8910 | 0.1258 | 0.9342 |
| 0.1408 | 0.892 | 8920 | 0.1261 | 0.9342 |
| 0.1657 | 0.893 | 8930 | 0.1272 | 0.9375 |
| 0.1558 | 0.894 | 8940 | 0.1279 | 0.9355 |
| 0.1084 | 0.895 | 8950 | 0.1287 | 0.9403 |
| 0.1264 | 0.896 | 8960 | 0.1307 | 0.9383 |
| 0.1607 | 0.897 | 8970 | 0.1310 | 0.9386 |
| 0.1423 | 0.898 | 8980 | 0.1312 | 0.9370 |
| 0.1114 | 0.899 | 8990 | 0.1322 | 0.9385 |
| 0.1389 | 0.9 | 9000 | 0.1324 | 0.9376 |
| 0.1933 | 0.901 | 9010 | 0.1318 | 0.9382 |
| 0.1274 | 0.902 | 9020 | 0.1307 | 0.9423 |
| 0.1647 | 0.903 | 9030 | 0.1283 | 0.9428 |
| 0.1518 | 0.904 | 9040 | 0.1271 | 0.9433 |
| 0.1801 | 0.905 | 9050 | 0.1275 | 0.9409 |
| 0.1996 | 0.906 | 9060 | 0.1271 | 0.9367 |
| 0.1646 | 0.907 | 9070 | 0.1274 | 0.9369 |
| 0.1331 | 0.908 | 9080 | 0.1285 | 0.9364 |
| 0.1095 | 0.909 | 9090 | 0.1295 | 0.9349 |
| 0.1143 | 0.91 | 9100 | 0.1284 | 0.9401 |
| 0.1485 | 0.911 | 9110 | 0.1276 | 0.9403 |
| 0.0699 | 0.912 | 9120 | 0.1272 | 0.9423 |
| 0.1465 | 0.913 | 9130 | 0.1273 | 0.9413 |
| 0.1555 | 0.914 | 9140 | 0.1275 | 0.9413 |
| 0.088 | 0.915 | 9150 | 0.1278 | 0.9412 |
| 0.1705 | 0.916 | 9160 | 0.1276 | 0.9391 |
| 0.1643 | 0.917 | 9170 | 0.1278 | 0.9375 |
| 0.1684 | 0.918 | 9180 | 0.1288 | 0.9383 |
| 0.2409 | 0.919 | 9190 | 0.1300 | 0.9389 |
| 0.1119 | 0.92 | 9200 | 0.1287 | 0.9382 |
| 0.1179 | 0.921 | 9210 | 0.1281 | 0.9394 |
| 0.1527 | 0.922 | 9220 | 0.1285 | 0.9409 |
| 0.1124 | 0.923 | 9230 | 0.1289 | 0.9394 |
| 0.1162 | 0.924 | 9240 | 0.1294 | 0.9384 |
| 0.1859 | 0.925 | 9250 | 0.1293 | 0.9388 |
| 0.1007 | 0.926 | 9260 | 0.1296 | 0.9372 |
| 0.1072 | 0.927 | 9270 | 0.1289 | 0.9360 |
| 0.1343 | 0.928 | 9280 | 0.1299 | 0.9354 |
| 0.1395 | 0.929 | 9290 | 0.1299 | 0.9373 |
| 0.1865 | 0.93 | 9300 | 0.1296 | 0.9376 |
| 0.0891 | 0.931 | 9310 | 0.1299 | 0.9376 |
| 0.12 | 0.932 | 9320 | 0.1309 | 0.9366 |
| 0.1031 | 0.933 | 9330 | 0.1322 | 0.9382 |
| 0.1011 | 0.934 | 9340 | 0.1342 | 0.9387 |
| 0.124 | 0.935 | 9350 | 0.1343 | 0.9387 |
| 0.124 | 0.936 | 9360 | 0.1337 | 0.9403 |
| 0.1346 | 0.937 | 9370 | 0.1331 | 0.9382 |
| 0.1656 | 0.938 | 9380 | 0.1318 | 0.9397 |
| 0.1446 | 0.939 | 9390 | 0.1321 | 0.9407 |
| 0.1621 | 0.94 | 9400 | 0.1321 | 0.9397 |
| 0.1469 | 0.941 | 9410 | 0.1330 | 0.9407 |
| 0.1492 | 0.942 | 9420 | 0.1346 | 0.9403 |
| 0.1746 | 0.943 | 9430 | 0.1343 | 0.9403 |
| 0.1282 | 0.944 | 9440 | 0.1329 | 0.9418 |
| 0.1443 | 0.945 | 9450 | 0.1316 | 0.9413 |
| 0.1655 | 0.946 | 9460 | 0.1305 | 0.9417 |
| 0.1749 | 0.947 | 9470 | 0.1295 | 0.9417 |
| 0.1642 | 0.948 | 9480 | 0.1291 | 0.9407 |
| 0.1155 | 0.949 | 9490 | 0.1288 | 0.9407 |
| 0.1252 | 0.95 | 9500 | 0.1284 | 0.9421 |
| 0.0801 | 0.951 | 9510 | 0.1288 | 0.9421 |
| 0.1442 | 0.952 | 9520 | 0.1290 | 0.9421 |
| 0.1711 | 0.953 | 9530 | 0.1292 | 0.9421 |
| 0.1003 | 0.954 | 9540 | 0.1293 | 0.9405 |
| 0.1406 | 0.955 | 9550 | 0.1290 | 0.9396 |
| 0.1614 | 0.956 | 9560 | 0.1290 | 0.9381 |
| 0.1685 | 0.957 | 9570 | 0.1292 | 0.9381 |
| 0.1286 | 0.958 | 9580 | 0.1291 | 0.9381 |
| 0.16 | 0.959 | 9590 | 0.1292 | 0.9365 |
| 0.1299 | 0.96 | 9600 | 0.1296 | 0.9374 |
| 0.0625 | 0.961 | 9610 | 0.1302 | 0.9353 |
| 0.1405 | 0.962 | 9620 | 0.1303 | 0.9353 |
| 0.1742 | 0.963 | 9630 | 0.1296 | 0.9374 |
| 0.2382 | 0.964 | 9640 | 0.1291 | 0.9390 |
| 0.1113 | 0.965 | 9650 | 0.1289 | 0.9381 |
| 0.1784 | 0.966 | 9660 | 0.1289 | 0.9381 |
| 0.1758 | 0.967 | 9670 | 0.1293 | 0.9381 |
| 0.127 | 0.968 | 9680 | 0.1301 | 0.9391 |
| 0.1734 | 0.969 | 9690 | 0.1304 | 0.9391 |
| 0.1404 | 0.97 | 9700 | 0.1303 | 0.9381 |
| 0.1352 | 0.971 | 9710 | 0.1305 | 0.9391 |
| 0.1539 | 0.972 | 9720 | 0.1304 | 0.9391 |
| 0.1027 | 0.973 | 9730 | 0.1309 | 0.9391 |
| 0.1598 | 0.974 | 9740 | 0.1307 | 0.9391 |
| 0.1606 | 0.975 | 9750 | 0.1305 | 0.9391 |
| 0.1401 | 0.976 | 9760 | 0.1305 | 0.9391 |
| 0.1927 | 0.977 | 9770 | 0.1304 | 0.9391 |
| 0.1041 | 0.978 | 9780 | 0.1306 | 0.9391 |
| 0.152 | 0.979 | 9790 | 0.1304 | 0.9391 |
| 0.1174 | 0.98 | 9800 | 0.1302 | 0.9391 |
| 0.1271 | 0.981 | 9810 | 0.1298 | 0.9391 |
| 0.1694 | 0.982 | 9820 | 0.1295 | 0.9391 |
| 0.1516 | 0.983 | 9830 | 0.1295 | 0.9391 |
| 0.1234 | 0.984 | 9840 | 0.1294 | 0.9391 |
| 0.1082 | 0.985 | 9850 | 0.1294 | 0.9391 |
| 0.1929 | 0.986 | 9860 | 0.1294 | 0.9391 |
| 0.1788 | 0.987 | 9870 | 0.1293 | 0.9391 |
| 0.125 | 0.988 | 9880 | 0.1291 | 0.9391 |
| 0.1335 | 0.989 | 9890 | 0.1292 | 0.9391 |
| 0.1345 | 0.99 | 9900 | 0.1293 | 0.9391 |
| 0.1502 | 0.991 | 9910 | 0.1294 | 0.9391 |
| 0.1444 | 0.992 | 9920 | 0.1296 | 0.9391 |
| 0.1139 | 0.993 | 9930 | 0.1296 | 0.9391 |
| 0.1599 | 0.994 | 9940 | 0.1295 | 0.9391 |
| 0.1023 | 0.995 | 9950 | 0.1295 | 0.9391 |
| 0.0991 | 0.996 | 9960 | 0.1295 | 0.9391 |
| 0.117 | 0.997 | 9970 | 0.1295 | 0.9391 |
| 0.134 | 0.998 | 9980 | 0.1295 | 0.9391 |
| 0.1423 | 0.999 | 9990 | 0.1295 | 0.9391 |
| 0.0845 | 1.0 | 10000 | 0.1295 | 0.9391 |
### Framework versions
- Transformers 4.44.1
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"90",
"180",
"270"
] |
Mitsua/mitsua-japanese-tagger
|
# Model Card for Mitsua Japanese Tagger
[ミツアちゃん](https://elanmitsua.com/)と日本語で楽しくタグ付けするためのモデルです。本モデルは以下のステップでスクラッチ学習しました。
1. [100万枚のカラーフラクタル図形の画像](https://huggingface.co/datasets/Mitsua/color-multi-fractal-db-1k)で事前学習しました。事前学習モデルを[Swin Base Multi Fractal 1k](https://huggingface.co/Mitsua/swin-base-multi-fractal-1k)として公開しました。
2. オプトインの許諾済みデータ、オープンライセンスのデータ、パブリックドメインのデータでファインチューニングしました。
本モデルの学習では学習済みの基盤モデルは使用しておらず、ライセンスされていないデータや、AI生成画像などのライセンスされていないデータで学習したAIモデルの出力も学習データとして使用していません。
本モデルは、CC BY-NCライセンスに基づき、非商用の目的で使用していただく事が可能です。商用利用についてはinfo [at] elanmitsua.comまでお問い合わせください。
Swin Transformer model for Japanese image tagging, trained solely on opt-in licensed data, openly licensed data and public domain data. This is finetuned checkpoint from [Swin Base Multi Fractal 1k](https://huggingface.co/Mitsua/swin-base-multi-fractal-1k), which is trained solely on [formula driven fractal images](https://huggingface.co/datasets/Mitsua/color-multi-fractal-db-1k).
This model is licensed under CC BY-NC and is freely used for non-commercial, research and educational purposes. For commercial use, please contact us: info [at] elanmitsua.com
## Model Details
- **Developed by:** [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- **Model type:** Multi label classification
- **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
- For commercial use, please contact us: info [at] elanmitsua.com
## Usage
```python
from transformers import pipeline
pipe = pipeline("image-classification", model="Mitsua/mitsua-japanese-tagger")
ret = pipe("test.jpg", function_to_apply="sigmoid", top_k=100)
print(ret)
```
## Official Public Characters
We have obtained official permission to train these Japanese fictional characters.
The dataset includes official images and fan arts from opt-in contributors.
公式の許可を得て、以下のキャラクターの公式提供画像及びオプトイン参加者のファンアートを学習しています。
- [つくよみちゃん](https://tyc.rei-yumesaki.net/)
- [東北ずん子・ずんだもんプロジェクト](https://zunko.jp/)
- [紡ネン](https://tsumuginen.com/)
- [フィーちゃん](https://u-stella.co.jp/gallery/ccd-0500/)
# Mitsua Contributors Credit (Opt-in)
- 霧太郎/HAnS N Erhard, pikurusu39, Hussini, 灯坂アキラ, ムスビイト, ネセヨレワ, 亞襲, E-Ken, とまこ, Nr. N, RI-YAnks, mkbt, 夢観士, 最中亜梨香/中森あか, KIrishusei, 長岡キヘイ, username_Kk32056, 相生創, amabox, 柊 華久椰, nog, 加熱九真, 嘯(しゃお), 夢前黎, みきうさぎ, るな, テラ リソース / Tera Resource (素材系サークル), 力ナディス, 野々村のの, とあ, Roach=Jinx, ging ging.jpeg, 莉子, 毛玉, 寝てる猫, ぽーたー, やえした みえ, mizuchi, 262111, 乙幡皇斗羽, とどめの35番, 明煉瓦, ゆう, 桐生星斗(投稿物生成物使用自由), WAYA, rcc, ask, L, 弐人, Sulphuriy, 602e, 石川すゐす, cha, 中屋, IRICOMIX, 琵來山まろり(画像加工可), とりとめ, 鏡双司, えれいた, mariedoi, あると, aaa05302, netai98, らどん, 脂質, ろすえん, 善良, つあ🌠, UranosEBi, YR, lenbrant, 長谷川, 輝竜司 / citrocube, 詩原るいか, 末広うた, 翠泉, 月波 清火, ゆぬ, 駒込ぴぺっこ, 原動機, ふわふわわ
敬称略/Honorific titles are omitted.
## Training Data
Our dataset is a mix of opt-in licensed data and openly licensed data.
Pre-filtering based on metadata and captions are applied to exclude potential rights-infringing, harmful or NSFW data.
For pre-filtering data, we built 146,041 words database which contains artist names, celebrity names, fictional character names, trademarks and bad words, based on Wikidata licensed under CC0.
We pre-process with face-blurring.
- "Mitsua Likes" Dataset : Our licensed data from opt-in contributors
- [Contributors Credit](https://elanmitsua.notion.site/Mitsua-Contributors-Credit-c67a12b795bc4f30807649588bfd4822) (Attribution)
- All training data can be browsed on our Discord server "[Mitsua Contributors](https://discord.gg/7VTGRweTUg)"
- All contributors were screened upon entry and all submitted images were human verified.
- AI generated contents detector is used to exclude potential AI generated images.
- "3R" and "3RG" licensed images and its captions are used to train this model.
- [Poly Haven](https://polyhaven.com/) HDRI images licensed under CC0 are used to augment background composition.
- [Localized Narratives](https://google.github.io/localized-narratives/) (CC BY 4.0)
- Jordi Pont-Tuset, Jasper Uijlings, Soravit Changpinyo, Radu Soricut, and Vittorio Ferrari, "Connecting Vision and Language with Localized Narratives" ECCV (Spotlight), 2020
- A subset of images licensed under CC BY 2.0 are used for training.
- Finally 642,789 images are used for training. [All attributons are found here](localized_narratives_attributon.csv).
- [STAIR Captions](http://captions.stair.center/) (CC BY 4.0)
- Yuya Yoshikawa, Yutaro Shigeto, and Akikazu Takeuchi, “STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset”, Annual Meeting of the Association for Computational Linguistics (ACL), Short Paper, 2017.
- A subset of images licensed under CC BY 2.0, CC BY-SA 2.0 are used for training.
- Finally 26,164 images are used for training. [All attributons are found here](stair_captions_attribution.csv).
- Wikidata Dataset (CC0)
- We built new dataset based on Wikidata structured data licensed under CC0 and Wikimedia Commons CC0 / public domain images.
- We used "[depicts](https://www.wikidata.org/wiki/Property:P180)" and "[made from material](https://www.wikidata.org/wiki/Property:P186)" property for training.
- To check if it is in the public domain, Wikimedia Commons category tags and Wikidata artist property were used together.
- Only images that were in the public domain in at least all of the source of origin country, Japan, EU, and the United States were used.
- Finally 267,573 images are used for training. [All attributions are found here](wikimedia_commons_pd_attribution.csv).
* Even if the dataset itself is CC-licensed, we did not use it if the image contained in the dataset is not properly licensed, is based on unauthorized use of copyrighted works, or is based on the synthetic data output of other pretrained models.
* English captions are translated into Japanese using [ElanMT](https://huggingface.co/Mitsua/elan-mt-bt-en-ja) model which is trained solely on openly licensed corpus.
## Disclaimer
- 免責事項：認識結果は不正確で、有害であったりバイアスがかかっている可能性があります。本モデルは比較的小規模でライセンスされたデータのみで達成可能な性能を調査するために開発されたモデルであり、認識の正確性が必要なユースケースでの使用には適していません。絵藍ミツアプロジェクト及び株式会社アブストラクトエンジンはCC BY-NC 4.0ライセンス第5条に基づき、本モデルの使用によって生じた直接的または間接的な損失に対して、一切の責任を負いません。
- Disclaimer: The recognition result may be very incorrect, harmful or biased. The model was developed to investigate achievable performance with only a relatively small, licensed data, and is not suitable for use cases requiring high recognition accuracy. Under Section 5 of the CC BY-NC 4.0 License, ELAN MITSUA Project / Abstract Engine is not responsible for any direct or indirect loss caused by the use of the model.
|
[
"人物",
"vrm",
"上半身",
"ショートカット",
"透明",
"パーティクル",
"丘の上",
"白い眉毛",
"内股",
"ガラス壁",
"学校の制服",
"女装",
"デフォルメキャラ",
"バレエ",
"ツインテール",
"顔は正面",
"コート",
"ベッド",
"ビール",
"曇り空",
"ゆるい眉",
"淡い色合い",
"シルエット",
"牙",
"しっかりした眉毛",
"長髪",
"黒い眉毛",
"まとめ髪",
"センター分けの前髪",
"電灯",
"静物画",
"寒い",
"ハーフ",
"カウチ",
"金属",
"ダム",
"植物",
"塔",
"狐の獣人",
"素描作品",
"小学生女子",
"耳が見える髪型",
"ホーム",
"踏ん張っている",
"スラっと伸びた足",
"サングラス",
"赤いドレス",
"道路",
"ネコミミ",
"グランドピアノ",
"ゴージャス",
"チェーン",
"ダブルブレスト",
"朝夕",
"黒ズボン",
"池",
"街灯",
"左目が隠れている",
"衣装",
"黒のスニーカー",
"紺色の目",
"前髪は目までの長さ",
"左目隠れ",
"マニキュアをしている",
"エメラルドのような瞳",
"立体",
"ローブ",
"雪景色",
"童顔",
"山",
"切りそろえた前髪",
"明るい茶髪",
"茶色のズボン",
"黄色と白の毛並みの肌",
"紺テーマ",
"青色のネイル",
"おさげ髪",
"黄色い髪",
"前髪は眉の少し上まで",
"茂み",
"超露出オーバー",
"character::東北ずん子",
"ひんやり",
"両腕を上げている",
"筋肉質",
"黒い袖口",
"character::雨照リー",
"寝る",
"白いボタン",
"線路",
"ほうれい線",
"耳",
"中性コントラスト",
"鮮やかな金髪",
"スティック",
"左脚で立つ",
"名画パロディ",
"茶色のアイライン",
"長いм字型の前髪",
"中学生か高校生くらい学生服",
"瓦",
"金縁",
"窓",
"霧",
"腰で縛っている",
"後髪の毛先が青",
"白い足袋",
"黒い短いスボン",
"薄暗い",
"カーペット",
"上を見上げる",
"白い上着",
"耳が出ている",
"セルシェーディング",
"風景",
"金色の装飾",
"掌に水色と黒のグローブ",
"オレンジの作業服",
"ハイキック",
"構造",
"耳飾り",
"緑っぽい黒髪",
"びっくり",
"結んだ髪",
"禁忌アニマル",
"アウトライン",
"おじさん",
"演説する",
"壺",
"鉄道",
"ドーナツ",
"ツリー",
"セミロング",
"左腕は後ろへ引く",
"ヨーロッパ風",
"茶色のローファー",
"水着",
"ボサボサの髪",
"仰け反っている",
"ギター",
"ストレートのロングヘア",
"戦闘",
"左腕を前へ出す",
"展望",
"前列４人しゃがむ",
"りんごほっぺ",
"祭壇画",
"m字バング",
"チャイナ服",
"ストリート系",
"困り眉",
"農場",
"金色のボタン",
"上着の前が開いている",
"右腕は体の横",
"茶色のブーツ",
"木板床",
"ロープ",
"右を見ている",
"ワイヤー",
"吸血鬼",
"黒いヘアバンド",
"崖",
"黒いタイツ",
"幻想的",
"写真フレーム",
"白いtシャツ",
"短い髪",
"バレエのポーズ",
"テーブル",
"右分け前髪",
"素肌",
"寒色",
"男子学生",
"目にかかる前髪",
"地雷系",
"斜めアングル",
"見下ろす",
"跳ねっ毛のやや長髪",
"自画像",
"デフォルメ",
"ツリ眉",
"白いセーラー服",
"アオリの構図",
"肘を曲げている",
"黒い長ズボン",
"ダイナミック",
"マント",
"カラフルな絆創膏",
"ワークショップ",
"青い靴",
"アニメ",
"character::皇ミカド",
"遊泳",
"野菜",
"肩編み込みの前髪",
"横たわる",
"立ち上げ前髪",
"黄色背景",
"短いポニーテール",
"お辞儀",
"画面左を向いている",
"左を見ている",
"ケーキ",
"出会い",
"エビ反り",
"羽根",
"桜の花",
"伝える",
"両手を上げている",
"アーチ",
"空手",
"トンネル",
"まっすぐ",
"フラットな塗り",
"メカ",
"拍手している",
"ハート型",
"にっこり",
"右脚を前へ出す",
"左腕を後ろへ引く",
"座席",
"イエスの幼少時代",
"長い丈のパンツ",
"白背景",
"被写界深度",
"科学者",
"ポップで目立つ衣装",
"蛍光灯",
"電話",
"ヴェネツィア",
"廃墟",
"茶色い机",
"不安",
"ぱっつんの横髪",
"共演する",
"白髪",
"グラフィティ",
"選手",
"白襟",
"ヤング",
"マイク",
"アイボリー背景",
"こちらを見ていない",
"焦茶色の目",
"片足で立っている",
"目つきの悪い",
"紙",
"黒いセーラー襟",
"銀のバックル",
"腰を落とす",
"ニーソ",
"白いワンピース",
"黒いハイネック",
"ウサギ",
"細かい睫毛",
"黒い手",
"女性型アンドロイド",
"白黒",
"メイクをしている",
"夏服",
"涙袋メイク",
"谷",
"浮く",
"読書",
"掌を握っている",
"包帯",
"ブリッコのポーズ",
"グーの手",
"短髪",
"白い着物の女の子",
"背中を丸める",
"歩道",
"目と眉毛の距離が近い",
"黒い袖",
"ノスタルジー",
"不健康な目元",
"９人のアイドル",
"正面顔",
"ゆるキャラ",
"海",
"楽器",
"黄色系で統一された",
"少し赤らむ頬",
"ロングドレス",
"粗い弱カラーハーフトーン",
"緑がかった黒髪",
"恋",
"灰色のショートカット",
"オレンジのネイル",
"エアインテーク",
"紫色の目",
"3頭身",
"夕方",
"駐車場",
"みつあ学園",
"ピンクの靴",
"キックボクシング",
"オレンジ",
"血管",
"クリスマス",
"白い半袖のシャツ",
"学生",
"縦２列に並んだボタン",
"私服",
"上半身を捻る",
"ウサ耳",
"逆さま",
"紫色のネイル",
"輝き",
"character::無表情で意思疎通するマン",
"グラデーションのかかった髪",
"指ぬきグローブ",
"黒いズボン",
"日没",
"ハートがついた黒いチョーカー",
"青のインナーカラー",
"空を飛ぶ",
"女性の水着",
"うつ伏せ",
"片眼鏡",
"character::姫石ミルキー",
"アイテム",
"短い袖",
"木材",
"前髪は眉の少し下まで",
"黒いショールカラー",
"右手に持っている",
"メルヘン",
"装甲",
"格闘技",
"character::ヴァッサル(すけさく)",
"グラフィティアート",
"右前の和服",
"右前の上着",
"横から",
"おでこ",
"肩まであるボブカット",
"前髪は眉のあたりまで",
"セルルック",
"小指に指輪",
"宴会",
"リラックス",
"地面を見ている",
"右腕を前へ出す",
"餅",
"character::チョップスティックラーメンモンスター",
"character::絵藍ミツア",
"グロー",
"男の肖像",
"ファミリーレストランのウエイトレスの姿",
"喫茶店の店員の姿",
"五本指",
"プール",
"白いロングブーツ",
"昆虫",
"黄色の肩章",
"ノートパソコン",
"長い前髪",
"ハーフサイドアップ",
"工業用",
"女の子座り",
"ゴシック",
"緑のロングコート",
"猫目",
"黒いジャケット",
"灰色ロングヘア",
"煙",
"黒いtシャツ",
"黄色の目",
"黄色い首",
"海岸",
"チャイナボタン",
"やや前のめり",
"ゴシックのくすんだ水色のワンピース",
"チョキの手",
"尻尾髪",
"異世界ファッション",
"鏡",
"加算用素材",
"ピンクの目",
"金の装飾",
"白エプロン",
"港",
"中庭",
"化粧襟",
"横から見た",
"丸みのある髪型",
"紫色の眉毛",
"横分け",
"顔は下を向いている",
"ダンス",
"花瓶",
"右前襟ジャケット",
"暗い背景",
"ファンタジーの人物",
"フード",
"ラーメンを食べる",
"さらしを胴体に巻いている",
"さらし布",
"前髪に三房のアホ毛",
"長い耳",
"暗い",
"手すり",
"白いサンダル",
"短いツインテール",
"ローアングル",
"ピンク色の花",
"白いまつ毛",
"ダンサー",
"ぱっちりとした目",
"男性よりの中性",
"エメラルドグリーンのマニキュア",
"青背景",
"手を合わせている",
"森の中",
"立ち絵",
"絶対領域",
"拒否する",
"槍",
"傘",
"白いソックス",
"少しツリ目",
"甘い",
"セーラー襟",
"赤い頬",
"青が基調",
"相撲の見合うポーズ",
"胸元に透かしのある黒いリボン",
"前髪長め",
"小物",
"右前",
"character::ぺルジスタント・リペア・ソッティーレリバー",
"黒い靴底",
"魅了",
"黒背景",
"図形",
"赤い靴",
"星空",
"左手を腰に当てる",
"ラフなタッチ",
"カーニバル",
"眩しい",
"和服風の衣服",
"看板",
"落ち着いた金髪",
"発光",
"眉毛",
"金の眉毛",
"白い靴紐",
"ウーマン",
"黒いサンダル",
"銀目",
"character::シルベモンシロ",
"黒い翼",
"前髪パッツン短髪",
"青銅",
"不思議な踊り",
"棒",
"食卓",
"character::シャロー・ウンディーネ",
"紫色のマニキュア",
"アンビエント・オクルージョン",
"漫符",
"顔が見えない",
"プリズムカラーのハイライト",
"ビクトリア朝時代の女性",
"足を組む",
"石畳",
"ブーツ",
"黄色髪",
"オレンジ色が混ざった金髪",
"振り向く",
"赤毛",
"絵の具のチューブのアクセサリー",
"ローポリゴン",
"酔っぱらい",
"オレンジ色の目",
"紫のエプロン",
"ちょうちょのコート",
"広角レンズ",
"倒れている",
"ヴァイオリン",
"スクワット",
"ピンク色のリボンタイ",
"白いノースリーブ",
"character::桜井ミズ",
"ヘルメット",
"トラック",
"立て襟",
"チョウ",
"黒い靴",
"おでこがでている",
"女子学生",
"右腕を上げている",
"身長190cm",
"小人",
"ビン",
"まつ毛が多い",
"猫",
"レース模様",
"木のフローリング",
"車",
"青い丸椅子",
"ずんホラ",
"目元のタトゥー",
"泥",
"赤いパーカー",
"紺アウトライン",
"天井",
"緑のドレス",
"ヘアアクセサリー",
"薄紫の眉",
"ボブヘア",
"細い金属フレームの丸メガネ",
"右近下駄",
"茶色のロングブーツ",
"パイプ",
"音楽",
"チュチュ",
"茶色のタイツ",
"靴下なし",
"オレンジ髪のツインテール",
"白いスカート",
"スニーカー",
"木漏れ日",
"話し合う",
"黒いネクタイ",
"小悪魔",
"サイボーグ",
"黒手袋",
"パッツン前髪",
"怯える",
"スポーティー",
"立って構えている",
"椅子",
"はね",
"ピンクの唇",
"どんぐり眼",
"ピンヒール",
"魔女の帽子",
"儀式",
"狐の尻尾",
"泳いでいる",
"ベスト",
"深緑色の目",
"ラブ",
"会話",
"テント",
"character::テトラ・タイター",
"店舗",
"椅子に座るポーズ",
"白肌",
"黒のブーツ",
"宇宙人",
"コーヒー",
"怖がっている",
"斜め前から",
"正午",
"腰痛改善ストレッチ",
"助け",
"ベリショ",
"グレーのシャツ",
"ふわふわの髪",
"斜め前から見た",
"ラケット",
"黒色の靴",
"ペールブルーの目",
"ひとつ結び",
"青テーマ",
"縮こまって上目遣いに見る",
"夕焼け",
"妖精ずんだもん",
"character::彷徨うリョウ",
"アイドルの踊り",
"黒いワンピース",
"赤ちゃん",
"肌が出た胸元",
"右腕を上げる",
"耽美",
"非対称な前髪",
"片足で立つ",
"緑色のズボン",
"夕日",
"くせ毛",
"大きなm字バング",
"証明写真つきの名札",
"デジタルエフェクト",
"ヘソ出し",
"赤オレンジの薄いリップグロス",
"焦げ茶色のまつ毛",
"丸い",
"黒い靴ひも",
"月",
"かき氷",
"男の娘",
"両膝を曲げている",
"目玉クリップに挟まれた白紙",
"回転",
"両目隠れ",
"左を向いている",
"紫の服",
"バックプレート",
"のけぞっている",
"花弁が5枚の花",
"はね毛",
"上まぶたが真っ直ぐのジト目",
"茶色のメッシュ",
"真珠",
"写本",
"ラマスーン・ウンディーネ",
"白いひじ",
"白い肘",
"手袋",
"大きく胸を反らしている",
"胡散臭い男性",
"灰色のロングヘア",
"ケーブル",
"バニーガール",
"手を合わせる",
"プレゼント",
"一本立ち",
"茶色い眉毛",
"神話画",
"ミニスカート",
"アゲハ蝶モチーフのベスト",
"黒襟",
"黒いサングラス",
"フロントに黒いボタンが3個",
"画面右を向いている",
"街中",
"インド風",
"ローコントラスト",
"白色のシャツ",
"アニマル",
"水色の目",
"決めポーズ",
"電柱",
"黒い指ぬきグローブ",
"サリーを着た女性",
"サンタガール",
"茶色いロングブーツ",
"医療",
"寒色系のメイク",
"両腕は体の横",
"用具",
"フリル",
"首を傾ける",
"三つ編み前髪",
"ゲームエフェクト",
"待って",
"開襟シャツ",
"シャッター",
"青いネクタイ",
"ドリンク",
"イヤリング",
"ジャンプする",
"ヨーロッパ",
"屈む",
"青緑の目",
"右腕を下ろしている",
"長袖パーカー",
"学ラン",
"同一人物",
"オレンジ色の左手",
"ガーターベルト",
"浜",
"白フリル付き半袖のシャツ",
"ビネット",
"雲",
"近未来",
"航空機",
"アルファベット",
"籠",
"紫のタイトな服",
"ブルーのインナーカラー",
"sf義手",
"フォーク",
"膝から上",
"大人っぽいヘアスタイル",
"anistropickuwahara背景",
"黒いタンクトップ",
"交通",
"手を差し出す",
"カートゥーン",
"冬服",
"銀髪のショートヘア",
"黒いリボンタイ",
"左手首にアクセサリー",
"赤いハート",
"ナイフ",
"白い靴",
"ローポリ",
"儚い",
"口の中が青",
"青紫色の口紅",
"乗馬",
"肩出しトップス",
"結び髪",
"ピンクのインナーカラー",
"左回転している",
"黒のハイソックス",
"kuwahara背景",
"皮革",
"白のセーラー服",
"百合",
"character::ノーミード・アラクネ",
"潰れた文字",
"左手を上げている",
"バレエの動作",
"グレーのストライプのネクタイ",
"白い運動靴",
"男性の水着",
"水色背景",
"紺色のスカート",
"肘当て",
"首にピンクのリボン",
"左肘を曲げている",
"character::紡ネン",
"左目の下にフェイスペイント",
"日中",
"黒のプリーツスカート",
"前を占めている白いライダースジャケット",
"ペン",
"横髪",
"マジック",
"別れで手を振る",
"長い袖",
"列車",
"腰を捻る",
"カートゥーン風",
"🐹",
"サイバーガール",
"ハイライトのない髪",
"テレビ",
"lic背景",
"朝・午後",
"バンテージ",
"ショール",
"ノースリーブのタートルネック",
"赤い髪飾り",
"右前腕を上へ上げる",
"フェアリー",
"ロマンティック",
"肩と背中を露出",
"火",
"水",
"目元の皺",
"楕円形",
"ブッシュ",
"白い着物",
"シニヨン",
"薄紫色の目",
"ダークグレー背景",
"フェンス・柵",
"明るめの黒のスーツ",
"コンテンポラリーダンス",
"デザイン",
"膝まであるロングスカート",
"フーディー",
"レンズフレア",
"中見",
"灰色のプレートアーマー",
"元気",
"ピンクの花の模様",
"プラチナブロンド",
"短めのポニテ",
"灰色の着物",
"プリーツスカート",
"背景",
"水色のリングのフェイスペイント",
"小屋",
"首に黒いリボン",
"通り",
"頬杖",
"前かがみになっている",
"飲み物",
"絵藍ミツアの服",
"行動不能",
"絵画",
"パフスリーブ",
"黒いセーラー服",
"左手にマイクを持っている",
"ローマ",
"白い雲",
"黒メイド服の長袖",
"猫背",
"人型",
"縦襟",
"魚",
"アニマル柄tシャツ",
"へそ出し",
"スペクタクル",
"記号的表現",
"青白い肌",
"冷却",
"恐怖",
"入学",
"サスペンス",
"ホラー",
"紋章",
"黒いスペード",
"前傾姿勢",
"ティッシュ",
"臀部の筋肉のストレッチ",
"黒いコルセット",
"黄緑色の瞳",
"ロングポニーテール",
"脚を開いている",
"フィンガーレスグローブ",
"メダル",
"足での構え",
"顔",
"メッシュ",
"右脚で立つ",
"仮面",
"ガラス窓",
"短いボブカット",
"腰を曲げる",
"緑の服",
"パステル",
"オリーブ",
"合唱する",
"ピンクのメッシュが入った髪",
"強アンビエント・オクルージョン",
"金眼",
"左手首にブレスレット",
"別離",
"ロングショット",
"右肘は曲がっている",
"センター分け前髪",
"白い睫毛",
"赤いリップ",
"character::19歳つくよみちゃん",
"character::雨照サク",
"紫の目",
"跳ね髪",
"紺色のリボン",
"絨毯",
"クレオパトラ",
"本棚",
"透かし模様",
"しろ",
"茶色と黄色のメッシュ髪",
"黒いトランプのマークの長いソックス",
"成人",
"水色テーマ",
"合わせ目のある肌",
"左頬に縦方向の傷",
"デコルテの見えるレオタード",
"木のテーブル",
"飲食",
"水色の肌",
"長い襟足",
"悪魔",
"主線なし",
"アイスクリーム",
"柱",
"青い空",
"左腕を引く",
"明確で低コントラスト",
"character::シルエット影子",
"口ひげ",
"モノキニ",
"デコルテが見えている",
"コックコート",
"王冠を頭に乗せている",
"電線",
"前のめり",
"高い蹴り",
"ゴシックパンク",
"手を差し伸べる",
"ハイライトのつよい睫毛",
"金の額縁",
"白縁",
"毛皮",
"髪のような黄色の触覚",
"カーブ",
"強い女の子",
"高コントラスト",
"台所",
"蓋",
"ロボ娘",
"左手を前へ出す",
"手書き文字",
"清楚",
"夕暮れ",
"ショートブーツ",
"フレスコ画",
"ピースサイン",
"ディフュージョン",
"ノースリーブ",
"灰色の靴下",
"ウエストアップ",
"編み込まれた前髪",
"右手を上げる",
"ディスプレイ",
"田園",
"新聞",
"路地",
"胸から上",
"バット",
"ポートレイト",
"ボディスーツ",
"黒色の目",
"黒色の手袋",
"丸顔",
"エジプト系ヘア",
"ウセク",
"ストーン",
"毛布",
"右前のカーディガン",
"銀髪ロングの少女",
"部分的に曇り",
"粉雪",
"発表会に出る女の子",
"垂らしたサイドヘア",
"麺料理",
"黒い片眼鏡",
"白い花",
"大聖堂",
"矢",
"黒いドレス",
"赤紫の髪",
"青色",
"体育座り",
"のり",
"指フレーム",
"スプーン",
"ビル",
"短いボブヘア",
"カラフルな手",
"ボンタン",
"赤い靴下",
"牛乳を注ぐ女",
"ツーサイドアップ",
"順光",
"茶色のショートブーツ",
"中腰で左足を前に突き出し",
"凍えるような",
"裏が黄土色の黒いリバーシブルコート",
"貴族の男性",
"ディアンドル",
"赤いボタンが８個",
"合図する",
"おにぎり",
"ワンピース",
"赤のリップ",
"ティーカップ",
"閉じた口",
"下ろしたフード",
"バルコニー",
"祈祷している",
"掌をひらひらと回転させる",
"水色の髪",
"ファイル",
"黒のリボン",
"人工光",
"ネコ耳",
"獸耳",
"サッカー",
"焦げ茶色の袖",
"緑色の靴",
"金箔",
"水色とピンクのアイシャドウ",
"機械脚",
"女学生",
"庭",
"青髪",
"aラインワンピース",
"水色の袖口",
"水色の襟",
"機械腕",
"白いショートパンツ",
"サッカーボール",
"緑の人差し指",
"筆致",
"モニター",
"青いロボット",
"運動",
"お人形のような少女",
"character::グラツィア・アラクネ",
"桔梗の髪飾り",
"フィギュア",
"白いドレス",
"三頭身",
"左腕は降ろしている",
"両腕を横へ広げる",
"表裏で色が違う手袋",
"左腕を横へ伸ばす",
"立っている",
"右手を挙げている",
"右前襟の着物",
"character::おにぎり先生",
"桃色の長袖の中華服",
"白い袖口",
"トゥーンレンダリング",
"跳ねた前髪",
"俯瞰の構図",
"両手を上げる",
"しいたけ目",
"弱発光",
"三つ編み",
"ストロー",
"vroidの足",
"喜びのポーズ",
"屏風",
"悔しがっている",
"思案",
"白のソックス",
"鉄棒",
"腕時計",
"家具",
"紫背景",
"黒と緑の手袋",
"半袖の白いシャツ",
"景観",
"高校生",
"黒いカソック",
"軍事的な踊り",
"見え隠れ",
"黒いショートブーツ",
"銀世界",
"ピンクと白の靴",
"タレ目",
"ポット",
"幸福",
"つやつやの髪",
"神秘",
"袖をまくった",
"若者",
"上を向いている",
"レース",
"黒服",
"ボロボロのローブ",
"肩紐",
"赤と白のスニーカー",
"ログハウスの電話ボックス",
"背中に沢山の刺繍",
"砂浜",
"ジャンパースカート",
"家庭科ドラゴン",
"逆立ちしてる",
"逃げる",
"司会する",
"降り注ぐ",
"食べ物",
"ダークな雰囲気",
"足を開いている",
"踊り子",
"赤いタイトな服",
"握りこぶし",
"桃色の帯",
"枯草",
"印象派",
"ミントグリーンのおさげ",
"ブラー",
"舞台",
"雪山",
"脚がない",
"ゲーム",
"紫色の腰エプロン",
"バロック絵画",
"元気な女の子",
"水色髪",
"フリルの腕輪",
"ケモノ耳",
"拍手",
"ビキニ",
"匍匐前進",
"広い袖口",
"恋慕",
"獣脚",
"地獄",
"肝試し",
"ミントグリーンの髪",
"黒いベスト",
"寓意",
"パープルの目",
"建築",
"2本の角",
"砂",
"潜水",
"黒のボディスーツ",
"虹色のハイライトの目",
"黒いコート",
"まごころ",
"厚底の靴",
"白い扇子",
"ボブ",
"若い女性",
"玉座に座っている",
"ニーパッド",
"足を広げて立つ",
"クラシカルロリィタ",
"小型犬",
"立ち姿",
"横書き",
"確認し合う",
"魔法少女",
"斜め掛けのカバン",
"目の中心にハイライト",
"両腕を前へ伸ばす",
"光",
"灰色のフリル",
"跪く",
"インテリア",
"走行",
"肩甲骨を寄せる",
"character::841ちゃん",
"右手にハートのステッキを持つ",
"朝から午後",
"1人",
"芸術",
"ベージュテーマ",
"四足歩行",
"横になっている",
"海の中",
"おっとっと",
"マスク",
"白フリル付きエプロン",
"ハイライトのない目",
"右手を前に出す",
"首元が黒い",
"おむすび",
"ジャケット",
"学ランの男性",
"男の学生服",
"黒い下着",
"半水面",
"前兆",
"呼ぶ",
"飛び込む",
"食器棚",
"甘味",
"トマト",
"手",
"赤いメッシュが入った黒髪",
"朝と午後",
"時計",
"ロンドン",
"灰色の服",
"岸壁",
"花弁",
"イーゼル",
"灰色の長袖のカーディガン",
"作業場",
"灰色の目",
"薄茶色の目",
"鳥居",
"ヒールのない靴",
"運河",
"両腕を下ろしている",
"大きく斬り込んだ",
"くすみブルーのシンプルな瞳",
"斜め顔",
"ジュエル",
"ポジティブ",
"ハート",
"黒い瞳孔",
"コンビ",
"ドッグ・カラー",
"竜",
"目が見えず歩く",
"情景",
"タータン模様のパンツ",
"襷掛けの赤いリボン",
"ポスト",
"腕がない",
"後ろから",
"星々",
"二つのおさげ",
"ドクロのお面",
"黄緑色のベスト",
"ダークブラウンの髪",
"光源",
"真ん中分けの前髪",
"うなじソケット",
"グレースケール",
"放棄",
"腕を前に",
"ガーリー",
"煌めき",
"ギザ歯",
"黒い帯",
"茶色のニットベスト",
"character::歌闇ミホ",
"淡い色彩のキャラクター",
"演舞",
"ドラゴン",
"水彩",
"たれ目",
"足を開いて立つ",
"駅",
"character::シルベモルフォ",
"四頭身",
"帆船",
"白いコート",
"股間を少し開いている",
"河川",
"上半身は横向き",
"緑のパーカー",
"黄色テーマ",
"白のティーポット",
"初期フランドル派",
"電球",
"右手を前へ出す",
"真ん中が長い前髪",
"噴水",
"胸元にピンクのリボン",
"前立てが緑色",
"左足を前に出す",
"低木",
"制服",
"機器",
"黄色系の茶髪",
"ヴィジュアル系",
"藤色のカーディガン",
"キュート",
"右腕は後ろへ引く",
"舗装",
"character::東北きりたん",
"顔の見えない",
"玄関",
"空",
"イラスト",
"character::ペネリちゃん",
"木の枝",
"薄いグラデーションの肌",
"tバック",
"左手はお腹の前",
"花びらの形をしたスカート",
"肩をだしたドレス",
"上体反らし",
"黒いタートルネック",
"車輪",
"漫画風",
"白鳥のポーズ",
"バレエの手",
"胸元に赤いリボン",
"横を見る",
"不思議の国のアリスモチーフ",
"アリスモチーフの少女",
"もちもちの頬",
"グラディエーター",
"量産型地雷女子",
"金の飾り",
"川",
"右腕は体の横へ引く",
"カジュアルファッション",
"怖がる",
"後ろに三つ編み",
"舞踏会",
"ウサギ人",
"遠景",
"魔術的な儀式",
"シークレットサービス",
"腰掛ける",
"星",
"黒髪のオールバック",
"桜色の唇",
"鎧",
"少女漫画",
"つやのある黒い靴",
"ボンキュッボン",
"学校の机",
"太鼓",
"右前の洋服",
"青い燕尾服",
"座る",
"構造物",
"短毛種の犬",
"目元が隠れている",
"左腕を下ろしている",
"首をかしげている",
"テラコッタ",
"オスケモ",
"細身の少年",
"座り跪ずき",
"左肘は曲がっている",
"マスコットキャラクター",
"短めの黒のビスチェ",
"上半身は斜め45度",
"公衆電話の中の人",
"青いワンピースドレス",
"ピアノの椅子",
"スター",
"赤い親指",
"羽ばたき",
"長めのショートカット",
"ラペルが白黒のストライプ",
"茶色い瞳",
"群れ",
"サイドアップヘア",
"指が5本ある",
"右膝は曲がっている",
"頬杖をついている",
"左腕を曲げている",
"身長18センチメートルのヤミーヤンデル",
"シャーベットカラー",
"七三分けの前髪",
"枝豆おにぎり",
"照明",
"白と青",
"カジュアルファッションのコーディネート",
"都会の若い娘",
"胸元に黒いリボン",
"ピンクのハート",
"襟足が長いショートヘア",
"つまさき立ちのケモノ",
"狐の獣人の裸足",
"青紫の髪",
"両手を広げる",
"サイドテール",
"character::中国うさぎ",
"真っ直ぐ逆立ちしてる",
"黄金のわっか",
"子ども",
"ダークグレーの髪",
"踏んじばっている",
"お菓子",
"焦げ茶色の眉毛",
"ロマンチック",
"寝転がる",
"グラデーション",
"立つ",
"右脚は後ろへ引く",
"赤いワンピース",
"両手で本を読む",
"横になる",
"清潔感",
"右足に重心をかけている",
"エアインテーク前髪",
"白い膝裏",
"黒い靴紐",
"アナログ画",
"踊る",
"ジャージを着た桜井ミズ",
"西洋の童話",
"白tシャツ",
"黒いワンピースのドレス",
"水の鏡",
"前蹴り",
"黒い特殊部隊の少年",
"ラブリー",
"文様",
"下まつ毛",
"赤髪",
"灰色の水着のシルベモンシロ",
"オレンジ色の靴",
"鹿",
"鉄棒にぶら下がっている",
"斜面",
"ゆめかわいい",
"滝",
"前髪パッツン",
"ダンスグループ",
"白いズボン",
"一部曇り",
"特殊モデルの男性",
"白い真珠のネックレス",
"足を組んでいる",
"黒ロングヘア",
"つやつやの銀髪",
"踴り合う",
"目の中央にハイライト",
"街",
"両手を顔に添える",
"生足",
"長ズボン",
"頬に手を添える",
"太いベルト",
"後ろにそのまま転んだ状態",
"猫耳のような髪",
"黒とピンクのメッシュ髪",
"花束",
"背中を丸めている",
"濫",
"暗くて顔が見えない",
"頭が下で腰が上",
"エプロン",
"装飾写本",
"チェック模様のリボン",
"character::シルエット影男",
"オレンジ色の眉毛",
"腕捲り",
"パステルピンク",
"砂利",
"顔は横を向いている",
"陶器",
"青みピンク",
"ミディアムヘア",
"天国",
"黒いミニスカート",
"ぼろぼろの布",
"黄金の腕輪",
"背景画",
"バラをイメージしたドレス",
"character::歌涙サコ",
"左で分けている前髪",
"黒いメガネ",
"黒が基調のコート",
"本",
"犬を追いかける",
"白い長袖シャツ腕まくり裾出し",
"黒い犬鼻",
"プレゼントボックス",
"青い左太もも",
"リンゴ",
"飛び越える",
"冷たいデザート",
"脱出",
"仰向けにエビ反っている",
"sdキャラ",
"仰向けに反っている",
"バイザー",
"動物に擬態",
"絹",
"白いライダースーツ",
"ベッドに背中から倒れ込んだ",
"ぴょん",
"指ぬき手袋",
"緑っぽい銀髪",
"アニメ風の食べ物",
"夜",
"白い筒状の容器",
"英国風の雰囲気",
"character::ミセバヤ-vox",
"character::マシェリちゃん",
"オレンジ色",
"漢服",
"左腿を前へ出す",
"泣きぼくろ",
"ゾンビのポーズ",
"地雷系女子",
"スタジオ",
"短袖",
"ウサギの尻尾",
"サラサラの髪",
"ピルエット",
"左腕を前へ伸ばす",
"やみの雰囲気",
"偉そう",
"日本舞踊",
"股関節を開く",
"緑のフードを被っている",
"前髪あり",
"前屈み",
"赤いメガネ",
"ワイングラス",
"スポーティーな服装",
"シンプルな塗り",
"首の後ろにリボン結び",
"中量タイプ",
"薄紫の髪",
"character::東北イタコ",
"動画素材",
"チェック模様のパジャマ",
"ショートパンツ",
"ジャグ",
"青い口",
"降雪",
"茶色のコルセット",
"白い跳ねっ毛のやや長髪",
"茶色の跳ね髪",
"逃亡",
"下を向いている",
"オーバーニーのブーツ",
"ブリキ",
"人間",
"ドラマチック",
"男装",
"バス",
"グレイ型宇宙人",
"しんしんと降り積もる",
"単色",
"茶色いマント",
"三角錐",
"モンスター",
"ソックスガーター",
"ボブカット",
"ノースリーブワンピース",
"水色のベスト",
"新緑",
"中華風の服",
"王冠をかぶっている",
"図書館",
"黄色い作業着",
"顔に痣",
"神殿",
"レトロモダンな雰囲気の女の子",
"下から",
"斜めに切られた前髪",
"右腿を前へ出す",
"潜る",
"白いプルメリアの花飾り",
"魔術",
"三角座り",
"画面中央に水面がある",
"舞い落ちる",
"水色のリボンタイ",
"カラーイラスト",
"ランプ",
"特殊モデルの女性",
"街並み",
"煽り",
"魔法陣",
"堕天使",
"明確で高いコントラスト",
"髪が跳ねている",
"ふたばのアホ毛",
"おろし髪",
"雨",
"赤背景",
"青と白のブローチ",
"砦",
"垂れ目",
"茶色の二―ソックス",
"男性化",
"白と黄色の花",
"テカテカの金属",
"駅員",
"デッサンの授業を受ける生徒たち",
"バランスをとっている",
"ネクタイ",
"ハートウィングモンスター",
"黒いローブ",
"細いアホ毛",
"頬杖をつく",
"左手にハンドマイクを持つ",
"アンダーパス",
"赤色の花",
"無地の腕章",
"tポーズ",
"グレーのチューブトップ風ワンピース",
"アイドル",
"袖の無い衣装",
"床を叩いている",
"男子児童",
"妖美",
"四角い輪",
"赤いベスト",
"人形",
"黒い髪部分はオールバック",
"無性別",
"水浅葱の髪",
"ボール",
"左腕を横に",
"バロック",
"頭を上げている",
"紺色の毛皮",
"金色の丸いボタン",
"前髪を真ん中分け",
"開いた目",
"泥だらけのサッカーボール",
"灰色の肌",
"多色髪",
"顔アップ",
"ぱっちりした目",
"立って読書",
"可愛いポーズ",
"鷲",
"桟橋",
"シルフ・グラツィア・ソッティーレリバー",
"v字襟",
"ガウン",
"ラベンダーの髪",
"ブドウ",
"巻物",
"太陽",
"雪が降る",
"真っ黒な目",
"短い丈の服",
"左足で立つ",
"うれしい",
"黒地にピンクのライン",
"黒いハイソックス",
"薄緑の髪",
"トイレ",
"ラインが入った水色のネクタイ",
"朝日",
"お祭り実行委員",
"character::宇宙星羅",
"sfチックなファッションで",
"ペンライトを持つ2人",
"キリン",
"斜め後ろから見た",
"胸を強調したジャンパースカート",
"緑と青の洞窟",
"右手を上げて挨拶する",
"ブリンブリン",
"まつ毛",
"デジタルイラストの背景",
"赤いフェイスペイント",
"宗教画",
"ピンクの羽根",
"水色のモノクル",
"バニースーツ",
"左腕をおろしている",
"キツめの眉",
"バイヨーポッド01",
"培養ポッド",
"帽子",
"茶色い犬",
"杖",
"黒髪に青みがかったハイライト",
"ろうそく",
"スケッチブック",
"おへそ",
"ピンクの鼻",
"マニエリスム",
"愛嬌を振りまく",
"アゲハ",
"褐色肌",
"万歳",
"右足は前に踏み出している",
"右足の膝は曲げている",
"白黒ツートンカラー髪の男の子",
"左右白黒の髪",
"vroidの手",
"銀色のフレームのメガネ",
"黒猫の獣人",
"ビキニスタイル",
"黒色のズボン",
"短い前髪",
"紫色のリップ",
"腕まくりをしているシャツ",
"青いインナーカラー",
"ケージ",
"髪の内側が黄緑色",
"左腕は体の横へ引く",
"水色のネイル",
"グレーの靴下",
"チョーク",
"瓶",
"ハイソックス",
"落ち着いた色合い",
"両前腕を上へ上げる",
"サンドイッチ",
"母親",
"茶色のジャンパースカート",
"首を少し傾けている",
"黒い警官帽",
"足を広げて立っている",
"頭上に浮かぶ青い菱形の線",
"過鋳造",
"黒い目",
"見下す",
"柔らかな色合い",
"夜景",
"変装",
"オレンジ色のライン装飾",
"オーク・パネル",
"スープ",
"オレンジ色のハイカットスニーカー",
"エレガント",
"しゃがんでいる",
"日の出",
"トップスと同じ色のマニキュア",
"character::漏足デルノ",
"統率者と従者",
"character::桜井リネ",
"跪いている",
"植木鉢",
"両手を斜め上へ上げる",
"パン",
"茶色の長手袋",
"無垢",
"弱ディフュージョン",
"丘",
"ハイビスカス模様のアロハシャツ",
"🧹",
"星型のハイライト",
"カタカナ",
"ベージュ色のスニーカー",
"白いゆったりしたカーディガン",
"character::フィーちゃん",
"青色のメガネ",
"袖がつまったビショップスリーブ",
"オレンジテーマ",
"ピンク髪",
"黒いインナーのズボン",
"枕",
"パールネックレス",
"はしご",
"冬季",
"驚き",
"調理実習",
"バック転",
"デコルテ",
"グレーのインナー",
"少し浅い被写界深度",
"女性の胸",
"黒いウサギ耳のカチューシャ",
"右半身を特に伸ばしている",
"白雪",
"かき氷の容器",
"ライオン",
"踊り子の衣装",
"お腹が見えている",
"ピストルマグポーチ",
"無造作ヘア",
"ハイネック",
"ストライプのネクタイ",
"水色のメッシュ",
"両肘が曲がっている",
"冬期",
"しわのある男性",
"白いレース袖",
"金属棒",
"晴天",
"黒い指抜きグローブ",
"ドット絵",
"体操",
"硬貨",
"並んだ鳥居",
"右を向いている",
"牛柄のtシャツ",
"ドヤ顔",
"青緑の髪",
"キツネ",
"黒いストッキング",
"ファンシーな服装の少年",
"ゆるふわショートヘア",
"ファンタジー",
"大きな足",
"ターコイズブルーの髪色",
"ローティーンぐらいの",
"緑のインナー",
"グリル",
"トレー",
"ボンネット",
"白と赤のリボン",
"上半身を捻っている",
"コンポジット",
"茶色背景",
"ぽっちゃり",
"幽霊の女性",
"三角の天冠",
"髪",
"ヒールのない黒のブーツ",
"左腕は前へ出す",
"ゾンビ少女",
"変化",
"コールド",
"カソックを着用した男性",
"裸足",
"薄いクリーム色のシャツ",
"肩紐がついたノースリーブ",
"右手を伸ばす",
"召喚",
"講演者",
"右膝を曲げている",
"左腕を前に出す",
"王子様風のジャケット",
"白い水着姿",
"黒い縁取り",
"白水着上",
"茶色のネクタイ",
"青紫のビスチェ",
"角っ子",
"コズミック",
"ミニ丈の和服",
"パス",
"二つの蝋燭の髪飾り",
"ユキ",
"character::漂井もここ",
"両手は体の横",
"岩",
"右手に扇子を持っている",
"絵具のアクセサリー",
"ぴかぴか",
"白い体",
"セツ",
"くすんだ青い髪",
"左脚にペイント",
"ボウル",
"許しを乞う",
"黒いレギンス",
"黒髪",
"オレンジ髪",
"喜劇",
"薄いピンクのトップス",
"ずんだもんの衣装",
"脱走",
"斜めから見た",
"紫色の長袖ニット",
"中腰で手を振る",
"黄色のパーカー紐",
"金縁のついた",
"黄金のベルト",
"一つ結び",
"青い顔",
"緑のvネック",
"設備",
"和室",
"ベージュ色のショートパンツ",
"氷の結晶",
"体操の演技前のポーズ",
"s字型になる立ち方",
"アンティーク調の椅子",
"襟",
"部屋",
"ハンバーガー",
"ハート頭の兵隊",
"上向きのまつ毛",
"グレーモデル",
"ピンク色が基調",
"粗い強カラーハーフトーン",
"太い紺アウトライン",
"太い女の子",
"生脚",
"西洋人",
"紺背景",
"非現実的",
"カジュアルパンクファッションの女性",
"サンタブーツ",
"パレット",
"オフの日のヤミーヤンデル",
"細身体型",
"黒が基調の服",
"両足を揃えている",
"黒い仮面",
"新雪",
"風景画",
"白いキャミソール",
"黒色のジャージ",
"暖炉",
"ゆるキャラボディ",
"雪片",
"ステッカー",
"墨色の目",
"雪花",
"板状結晶",
"同一人物が2人",
"晴れ",
"凍える",
"氷雪",
"黒が基調の衣装",
"首元に黒いリボン",
"死",
"デバフ",
"雪華",
"雪の花",
"茶色の革靴",
"赤と金色のジャケット",
"カップ",
"つめたい",
"耳出し",
"明るいベージュの袖なしニット",
"こおり",
"氷板",
"氷晶",
"楽しい",
"ウィンターシーズン",
"灰雪",
"対称軸が6つの図形",
"ジト目",
"ホリデーシーズン",
"冬の思い出",
"むつのはな",
"雪華模様",
"対象図形",
"乾き雪",
"好青年",
"腰に大きな黒いリボン",
"水瓶",
"グラデーションマップ",
"ショー",
"手紙",
"朝・秋",
"腰を前へ曲げる",
"雪が降り注ぐ",
"八重歯",
"サイドステップ",
"洗礼者ヨハネ",
"黒のスーツ",
"馬車",
"アイススケート",
"机",
"ピクセルアート",
"チョウチョ",
"宙に浮かぶ",
"ウインク",
"両腕を前へ出す",
"雪の原",
"木の幹",
"氷",
"エングレービング",
"白い大地",
"自然光",
"ティール背景",
"織物",
"大きな結晶",
"グリーティングカードの背景",
"きっと息が白くなる",
"冬の絵ハガキの背景",
"雪の結晶が見える",
"雪のパーティクル",
"デジタル動画素材",
"六角形をベースに",
"白と水色と青い",
"エルフ耳",
"見渡す限りの雪",
"ずんずんつもる",
"コンポジット済",
"ややシンプル",
"半透明の雪",
"回転する雪",
"空と大地",
"スカイダイビング",
"追いかけっこ",
"テニスでスマッシュを打った",
"みつあ学園の制服",
"黒いハーフパンツ",
"白い地面",
"灰色のローブ",
"白いゴム手袋",
"赤リボンの結び目",
"白目部分が無い目",
"ダークメルヘン",
"日の出の夕暮れ",
"肩幅で立っている",
"おぼろげ",
"白いフリル",
"茶色皮革",
"タワー",
"水色のラインペイント",
"勝利",
"大きな丸い黒縁メガネ",
"絵文字",
"左肘を直角に曲げる",
"ロングへア",
"大理石",
"メイドさん",
"黒タイツ",
"不敵な笑み",
"クラシカルメイド",
"ハートのステッキを持っている",
"緑色のサロペット",
"両二の腕を横へ上げる",
"cgレンダリング",
"蟹みたいなヘアスタイルの青年",
"股を少し開いている",
"大きい胸",
"サキュバス",
"cg",
"巨大な手",
"携帯電話",
"幅の狭い肩",
"水属性",
"眉の下まである前髪",
"ステンドグラス",
"ヘソ",
"スペード頭の兵隊",
"穴あきグローブ",
"現代的な衣装",
"家屋",
"赤いタイトミニスカート",
"信号機",
"ミニスカ女子",
"水路",
"ブラシ",
"長袖の白シャツ",
"十字架アンクのネックレス",
"対角線構図",
"てふてふ",
"腰にゼンマイがついている",
"彫刻",
"幸運",
"紫のツインテール",
"深緑色の上着",
"ヒップホップダンス",
"トッピング",
"日の出の夕日",
"大きめのイラストtシャツ",
"濃い灰色のトップス",
"人との会話",
"緑色のサスペンダー",
"片足立ち",
"宙に浮く",
"黒い胸当て",
"羊飼い",
"緑のカーテン",
"灰色のビキニ",
"カソック",
"木製品",
"デフォルメの手",
"握った手",
"少し前のめり",
"靴下",
"丸いクリスタル",
"灰色のシャツ",
"白い裾上げしたズボン",
"灰色のロングジャケット",
"コメディ",
"セクシーな英語の先生",
"左手は左腿の上",
"アヒル",
"手は人差し指と中指を伸ばす",
"通行",
"長袖",
"両肘は曲がっている",
"character::飯田武竜絵寿子",
"水に浸かる",
"口元にほくろがある",
"恋愛",
"黄色いアゲハ蝶モチーフのベスト",
"白色からピンク色のグラデーション髪",
"胸元が大きく開いた黒インナー",
"日の出-日没",
"未来人",
"ドキドキ",
"素足",
"緑のtシャツ",
"労働",
"白い胸当て",
"低等身",
"両腕を前に出し伸ばす",
"みつあみ",
"カラフルなばんそうこう",
"ヘビ",
"ピンクのライン入りの黒いハイソックス",
"背中の剣を構えている",
"鎖骨",
"開いた黒ジャケット",
"おだんご髪",
"character::もちもっちー",
"ライトパープルのtシャツ",
"目の真ん中にハイライト",
"メイドの少女",
"浴室",
"座法",
"浮遊感",
"白い少女",
"湖",
"左手にブレスレット",
"手を広げる",
"ゴシックロリィタ",
"灰色の眉毛",
"ローライズ",
"両手を顔に",
"お茶",
"獣",
"両手を体の右側に構える",
"左手",
"tシャツ",
"サラマンダー・アラクネ",
"黒いヒガンバナの柄のチャイナ服",
"幅の広いカチューシャ",
"防具用のカチューシャ",
"コンテ",
"クッション",
"動画エフェクト",
"波打ち際",
"ピンク色のインナーカラ―",
"淡い紫色のウサミミメイド",
"中性的",
"正座して俯いての祈り",
"柵",
"ひらがな",
"両足で立っている",
"灰色のソックス",
"都市風景",
"ややたれ目",
"物語の登場人物",
"宇宙",
"銀髪の少女",
"ピンク背景",
"ピンク色の肉球",
"白い袖",
"ガイノイド",
"右足を前に出している",
"白の肩出しトップス",
"左側頭部にワンサイドアップ",
"左膝は曲がっている",
"赤いハイヒール",
"大きめの金のイヤリング",
"艶やかな黒髪",
"3人",
"紺色の服",
"赤と白のまっすぐなストロー",
"希望",
"2対の翅",
"胡蝶の夢",
"4枚の翅",
"学校の椅子",
"汗",
"中年男性",
"ノースリーブの白い服",
"キリッとした眉",
"左腕を斜め下へ伸ばす",
"パティスリー",
"金",
"ヒエロニムス",
"クレヨン",
"グレーのロングブーツ",
"うす紫のブラウス",
"モニュメント",
"青い法被",
"紫色の帯",
"キラキラ",
"緑の靴裏",
"青いズボン",
"赤い縁取り",
"金属製のカチューシャ",
"赤毛の女の子",
"皺のある手",
"褐色の肌",
"黒い翼がモチーフの衣装",
"赤毛のアン",
"ピザ",
"木",
"ジーンズ",
"プールサイドから上がる姿勢",
"人魚の姿勢",
"建設",
"マスコットキャラ",
"花が赤い",
"内観",
"銀と赤のオッドアイ",
"薄紫のリップ",
"黒色のヘッドセット",
"デフォルメされた羽ばたき",
"片目隠れ",
"バタフライエフェクト",
"標本の蝶が飛んでいる",
"顔は前方を向いている",
"襷掛け",
"決意",
"羊",
"アール・ヌーヴォー",
"立体絵画",
"首元に赤いリボン",
"オレンジ色のネクタイピン",
"アンビエント オクルージョン",
"チアガールが足を上げている",
"祭りでビールを配る",
"お茶会",
"手を少し上げる",
"両手は胸の前",
"きらり",
"クレーン",
"ゴーグル",
"ペトロ",
"フレスコ",
"和服",
"襟口の部分のみピンク色",
"ピンクのスタンドカラー",
"チョコミントカラーの服",
"お腹にポケット",
"サイド分け",
"右腕を股間方向に下げ",
"俯瞰構図",
"お腹部分にポケット",
"時祷書",
"sfチックなハイヒール",
"中性",
"白のサイハイソックス",
"赤い野菜",
"一つくくりの髪",
"茶色いジャケット",
"頭部のみ",
"力強い",
"緑のハイヒール",
"絵が描かれた紙",
"病気のエルフ",
"充血した目",
"お姉さん",
"顔にペイント",
"企み",
"アサシン",
"価値がある",
"愛情",
"前を閉じた上着",
"ハーフアップの髪型",
"襟で口元が隠れている",
"紺色のパーカー",
"印刷物",
"パーカー",
"液体",
"充実",
"ぱっつんロングヘア",
"ひらめき",
"キラン",
"右目隠れ前髪",
"丸い顔",
"白い眼鏡",
"ベージュのベスト",
"手前で振り返る絵藍ミツア",
"歌",
"高い襟",
"ピンクの足",
"赤いtシャツ",
"飲食店",
"冷蔵庫",
"格闘家",
"イタリア・ルネサンス",
"ロボ1009のtシャツ",
"sf風のプリーツスカート",
"粘土",
"シンプル",
"セーラー服の男の子",
"窓枠のない半円窓",
"左腿を上げる",
"緑色の尻尾",
"気高い",
"快晴",
"バタフライ",
"かがやき",
"斜めカットの前髪",
"煌めき背景",
"船",
"明滅",
"ざっくりヘア",
"垂れ目気味",
"芽",
"彩度が低い背景",
"レアアイテム",
"ちらちら",
"character::桜井アイト",
"なにかある",
"冴えている",
"単色背景",
"素手",
"大人",
"うなじにソケットがある",
"店員の名札",
"テニス",
"水平に見た",
"ダイナミックポーズ",
"豪華な服装",
"太い指",
"幼稚園のベル",
"傷がある",
"人々を元気づける",
"両肘を曲げる",
"キラメキの背景",
"光あふれる",
"身長175cm",
"ハートのトランプ兵",
"短い靴下",
"宇宙アイドル",
"sf風衣装",
"緑のインナーカラー",
"跨ごうとしている",
"襟足が長い髪",
"ライム背景",
"ふわふわした雰囲気の男性",
"羽耳",
"スポーツが趣味の男性",
"夢中",
"フリルのある上品なシャツ",
"色々な色",
"右ストレートパンチ",
"女性のショートヘア",
"発動",
"愛らしい",
"機械",
"白い猫耳",
"カーディナルのネイル",
"女装した男性",
"スイカ色のサリ",
"千鳥柄のジャケット",
"鋭い目つき",
"左手を顔の近くに",
"チョコレートがかかったアイス",
"左足は後方に伸ばしている",
"パステル画",
"ハーフアップ",
"木製テーブル",
"ピンク色の丈の短いtシャツ",
"上半身を倒している",
"ぼさっとしたショートヘア",
"紺色髪",
"濃いピンクの髪",
"しましまの靴下",
"濡れ透け",
"金と赤い宝石の髪装飾",
"告白",
"チョーカー",
"大きい耳",
"野球",
"頭は正面を向く",
"水平線",
"紺色の甚平",
"ふんわりした雰囲気",
"ツインテールの女の子",
"聖職者の少年",
"町並み",
"雑草",
"剣士",
"健康的な肌",
"魅惑",
"全身図",
"明星",
"フカン構図",
"ご飯",
"character::黒崎クロサ",
"星の背景",
"りんご",
"屋台",
"デジタルイラスト",
"キラメキ",
"金の薔薇",
"遠征",
"慕う",
"ココロ",
"不滅",
"思いやり",
"枯葉",
"羽毛",
"シャンデリア",
"自動車",
"片思い",
"リオデジャネイロ",
"ハンガー",
"バレンタインのモチーフ",
"お慕い申しております",
"デフォルメされた心臓",
"シンメトリーの図形",
"クピドの矢じり",
"どっきどき",
"善なるもの",
"公園",
"ｌｏｖｅ",
"はぁと",
"キュンキュン",
"審判",
"一目ぼれ",
"ブルッヘ",
"ミニアチュール",
"恋心",
"灰色の背景",
"相思相愛",
"油絵具",
"ウェーブ髪",
"ハート図形の群れ",
"ファンの気持ち",
"乙女チック",
"乙女ちっく",
"らぶりー",
"甘々",
"晴れた空",
"散策",
"3dモデル",
"テーブルクロス",
"黒いベルト",
"輪廻転生",
"生まれ変わる",
"プラント",
"ガラス戸",
"無害",
"祭壇",
"カエル",
"美の象徴",
"触覚がある",
"魂の象徴",
"宝石",
"白いレインブーツ",
"市街地",
"漆喰",
"近景",
"黒い主線",
"荷物",
"結婚",
"スライス",
"チーズ",
"石垣",
"インナーカラー",
"蝶の群れ",
"木版画",
"ハートのトランプ",
"ガブリエル",
"左向き",
"ビーズ",
"白い綿の服",
"ガッシュ",
"ブクブク",
"プロジェクター",
"ロボット",
"自画像 ",
"シェルター",
"木炭",
"あわ",
"コイン",
"受胎告知",
"ヴェネツィア派",
"バブル",
"右向き",
"5人",
"アシンメトリーな前髪",
"徒歩",
"結界",
"名盤",
"ツインテールの黒髪",
"ダートロード",
"市場",
"5頭身",
"象牙",
"考え事",
"乗客",
"野原",
"緑色のリボン",
"アイコン",
"玉座",
"サンタ帽",
"おやつ",
"プリズム風の明暗境界線",
"デフォルメされた星",
"スポットライト風",
"虹色の境界色",
"象",
"ストレートヘア",
"軍服",
"少し上から見た",
"デシタルペイント",
"木の床",
"灰色ローブ",
"持ち物",
"満月",
"気球",
"ハイライトの無い目",
"十字架",
"強発光",
"シャーペン",
"日陰",
"トランク",
"黄緑色のコート",
"復活",
"パーティクル用素材",
"シダ",
"扉",
"装飾品",
"牧草",
"輝く",
"バケツ",
"タオル",
"ストッキングみたいな薄手のロング手袋",
"小川",
"農家",
"司祭",
"修道士",
"オランダ黄金時代の絵画",
"無人",
"顔アイコン",
"男の子",
"中腰",
"チョキ",
"画用木炭",
"ガラス瓶",
"ミカエル",
"両手で持っている",
"ギミック",
"一点透視図法",
"マグダラのマリア",
"詠唱",
"魔除け",
"橋",
"レンガの壁",
"城壁",
"遠距離攻撃",
"里山",
"フィルタ写真背景",
"朝焼け",
"魔法円",
"黄緑色の枝豆カチューシャ",
"ペンタグラム",
"ソーサラー",
"水中",
"あげは蝶",
"防御呪文",
"攻撃呪文",
"揚羽蝶",
"tシャツ",
"手描きの読めない魔法文字",
"魔法陣エフェクト背景",
"同心円の模様",
"魔法陣が輝く",
"光る地面",
"白色のセーラー服",
"フィルター済写真背景",
"縞模様",
"白いインナー",
"赤色の肌",
"タバコ",
"金属製品",
"濁点",
"石灰岩",
"フィルター写真背景",
"藪",
"オッドアイ",
"下から見た",
"東方三博士の礼拝",
"頭蓋",
"玉",
"メタリックな立方体が一つ",
"金属質の立方体",
"銅版画",
"フィルタ済写真背景",
"地平線",
"アンティーク・ジュエリー",
"学生服",
"寝室",
"鶏肉",
"農村",
"車道",
"正面画",
"自己表現",
"プラットフォーム",
"サクラ",
"サーフボード",
"ラフな和服",
"猫耳",
"赤色の靴",
"タップ",
"青いグラデーション",
"運動場",
"遊歩道",
"支線",
"空と海",
"コウモリ",
"ウール",
"ボクシング",
"スーツ",
"ガラス製品",
"水彩紙",
"草地",
"観葉植物",
"ラファエル",
"ブーケ",
"岩石",
"キリストの磔刑",
"安らぎ",
"盛期ルネサンス",
"シンメトリー",
"食器",
"球状",
"植物学",
"歩行",
"やすらぎ",
"塗り残しがある",
"樹枝状結晶",
"植木",
"スターダム",
"卓上ライト",
"グラデーションの髪",
"フードを被っている",
"風車",
"木壁",
"泡沫",
"魚類",
"城塞",
"深海",
"骨",
"干し草",
"天板",
"太い主線",
"センター分け",
"コラージュ",
"米",
"学び",
"笛",
"高層ビル",
"ヤギ",
"リトグラフ",
"ローツインテール",
"なめらか",
"トレイル",
"ガラス",
"薄ピンク色のワンピースドレス",
"細かい髪の書き込み",
"馬術",
"ピアノ",
"集団肖像画",
"スケートボード",
"球",
"黒いスーツ",
"海底",
"城",
"道具",
"カードゲーム",
"ファンタジックなドレス",
"受難",
"つるつる",
"囲い",
"シャボン",
"遊具",
"鉄",
"地下道",
"少しカーブした雪原",
"緑色のセーラースカーフ",
"テラス",
"コントラストが強め",
"無表情",
"聖家族",
"トリプティック",
"空港",
"気泡",
"レモン",
"ほぼ正面",
"避難所",
"都会",
"ロト",
"店内",
"楽譜",
"防空壕",
"やや俯瞰で見た",
"プレート",
"軽食",
"浅瀬",
"あぶく",
"きりたんの髪飾り",
"森",
"みずみずしい",
"教会堂",
"グラデーションがある",
"盾",
"反射がある",
"農地",
"薄い緑のチュチュ",
"ピンク色のパンプス",
"夜間",
"溺れる",
"ぱっつん",
"ロマン主義",
"ゾウ",
"レリーフ",
"簪",
"年代記",
"家々",
"ぷりぷり",
"グラファイト",
"黒いアゲハ蝶",
"騎馬像",
"グローブ",
"立っている姿勢",
"無意識",
"オートバイ",
"白い光源が映りこむ",
"中央が帯状に黒い",
"シンプルな球体",
"背面",
"城跡",
"ダストビン",
"立法",
"ローファー",
"潜航",
"液体の中",
"あわあわ",
"城郭",
"ぼこぼこ",
"漁船",
"character::お絵描き青子",
"湧き出す多くの泡のパーティクル",
"少しモーションブラー",
"液体の中の一人称視点",
"景色",
"水の中での一人称",
"水滴にも見える",
"はっきりした泡",
"水の中の泡",
"半透明の泡",
"水中の気体",
"風",
"複合板結晶",
"単色塗り",
"シロップがかかっていないかき氷",
"ショートヘア",
"人外",
"無色のシロップのかき氷",
"あこがれ",
"医師",
"グラデーションがある塗り",
"黒いオールバック",
"ペンダント",
"水域",
"エフェクト素材",
"平坦な塗り",
"人が写っていない",
"手を振る",
"きりたん砲",
"散歩道",
"おいしい",
"四角い立体",
"ボールドライン",
"光があふれる",
"大きなパーティクル素材",
"自信にあふれた",
"希望にあふれた",
"モノクロフィルム",
"ツリ目",
"海中",
"和食",
"ピッチャー",
"木柵",
"白い跳ねっ毛",
"雲背景",
"クリアなハート",
"ジュース",
"牛乳",
"ずんだアロー",
"カーテン",
"リュート",
"音楽家",
"雲の背景",
"岩場",
"黒いモノクル",
"誰もいない",
"肘掛け",
"黒い丸眼鏡",
"防波堤",
"ユニフォーム",
"真ん中分け",
"銀",
"江戸時代",
"塑像",
"荷車",
"波止場",
"ダビデ",
"掛軸",
"上目遣い",
"海苔",
"光輪",
"緑背景",
"水墨画",
"超高層ビル",
"隠し通路",
"オーク材",
"白いページ",
"山林",
"カッターボート",
"頬線",
"エステル",
"嵐",
"ハイヒール",
"道路沿い",
"喫煙",
"人民",
"バックパック",
"住まい",
"ゼラチン・シルバー・プリント",
"道路標識",
"薄明",
"荒野",
"ヘッドセット",
"フォーマル",
"惑星",
"傾いた",
"両手",
"白い和服",
"救い",
"紫テーマ",
"道路樹",
"パケット",
"青いシャツ",
"煙突",
"膝を曲げている",
"ロココ",
"電子機器",
"収穫",
"リビングルーム",
"背中",
"戦車",
"舟",
"ワンピースを着た女性",
"史跡",
"羽根が舞う",
"キリスト",
"漁港",
"引き出し",
"予兆",
"体が黒い",
"困り顔",
"刃物",
"赤い髪留め",
"聖霊",
"芯の強さ",
"紙幣",
"茶髪",
"弱アンビエント・オクルージョン",
"天井灯",
"ニンジン",
"蝶",
"村落",
"紅い髪留め",
"人物写真",
"福音記者ヨハネ",
"デザート",
"芸術家",
"墨書",
"かわいい",
"地中海",
"オレンジ色の鰭",
"祝福",
"鉢植え",
"エッチング",
"パイル",
"樽",
"character::大江戸ちゃんこ",
"木棒",
"分け目",
"ちびキャラ",
"パリ",
"デジタルイラスト背景",
"テクスチャー",
"焦げ茶色の輪郭線",
"風景の背景",
"vサイン",
"腕",
"ホットドッグ",
"髭",
"黒シャツ",
"柔軟体操",
"フルート",
"青いtシャツ",
"釘",
"広葉樹",
"係留",
"板敷き",
"樹皮",
"港湾",
"ナプキン",
"裏地が黄色の黒いコート",
"走る",
"ブロッコリー",
"戦士",
"イチゴ",
"ワインボトル",
"白い机",
"日向",
"石造り",
"女の子の部屋",
"日本家屋",
"キリストの哀悼",
"桃色の頬",
"乾燥葉",
"はさみ",
"青い床",
"カモ",
"全身画",
"ズボンをはいている",
"マッシュルーム",
"洋食",
"sdイラスト",
"シマウマ",
"黒い手袋",
"暗がり",
"聖人",
"トロリー",
"黄色い壁",
"暖色系",
"一軒家",
"松",
"ナザレのヨセフ",
"ネガフィルム",
"ジャガイモ",
"ベージュ背景",
"温かい",
"テニスラケット",
"東方の三博士",
"ワゴン",
"タマネギ",
"テーブルグラス",
"石室",
"児童公園",
"日本の田舎",
"デコ出し",
"耳が見えている",
"金属フェンス",
"もふもふ",
"タペストリー",
"動物園",
"白く丸い皿",
"クッキー",
"親指を曲げている",
"リス",
"薬指を曲げている",
"小指を曲げている",
"茶色い目",
"イノセント",
"ヒューマノイド",
"青いドレス",
"ブタ",
"アスファルトの道路",
"草本",
"羽",
"流れる水",
"何もかかっていないかき氷",
"履物",
"屋外",
"ピース",
"バラ",
"家畜",
"きりたんぽ",
"正六面体",
"細密肖像画",
"セバスティアヌス",
"静寂",
"日本の風景",
"段ボール箱",
"青空の背景",
"左脚を前へ出す",
"広角レンズ風に湾曲",
"チャーシュー",
"深い森",
"キリストの降誕",
"かきあげ前髪",
"プラッター",
"赤いどんぶりに入ったラーメン",
"黒い海苔",
"テニスコート",
"小刀",
"色白",
"ペルシア絨毯",
"厚紙",
"グラデーションの空",
"車椅子",
"加護",
"井戸",
"アルバム",
"送電",
"ペールオレンジの手",
"内巻きの髪",
"縦長",
"アレクサンドリアのカタリナ",
"落とし物",
"翼の一部",
"ポリプティック",
"係留設備",
"仏教",
"少し傾いた",
"オウム",
"絵の具チューブのヘアアクセサリー",
"ダンジョンの入口",
"日",
"ロボットの立ち絵落書き",
"翼を形成する",
"白い雲の背景",
"のどかな景色",
"巨大建造物",
"前髪なし",
"けばけば",
"頭",
"時計塔",
"白い半そでシャツ",
"演劇",
"手でハートを作る",
"公共施設",
"大型船舶",
"雨雲",
"ナンバープレート",
"ハートの手",
"character::北海道めろん",
"ウェヌス",
"主教",
"ロバ",
"カラーハーフトーン",
"ハウスプラント",
"テニス選手",
"ウエストショット",
"飲料水",
"顔料",
"発電施設",
"広幅結晶",
"道路交通",
"青緑色の目",
"梨",
"肉",
"シンプル羽根背景",
"食事処",
"キャラクター",
"人の集団",
"ハグ",
"港湾施設",
"デジタルアート",
"祈り",
"暗い空",
"モノトーン",
"タイル",
"配管",
"character::四国めたん",
"フクロウ",
"ガーランド",
"絹織物",
"ヘリコプター",
"character::沖縄あわも",
"マネキン",
"睡眠",
"口を閉じた笑顔",
"雪",
"ワークベンチ",
"ダイヤモンド",
"ドキュメンタリー写真",
"character::おえかきジョー",
"茶色い床",
"中華料理",
"十字架像",
"狩猟",
"黒い線画",
"赤ベルベット",
"赤い目",
"開いた口",
"白と青の髪",
"ダイナミック横毛",
"新古典主義",
"カスパール",
"コップ",
"レッドヴェルヴェット",
"赤色ベロア",
"赤ビロード",
"白い家具",
"セーヌ川",
"教会",
"白いマウス",
"開いたノートパソコン",
"スノーボード",
"イコン",
"雄牛",
"白波",
"レレドス",
"杉の森",
"入浴",
"被服",
"キラキラの瞳",
"ピンクの布団",
"横転した",
"クリスマスツリー",
"オリーブ色背景",
"勉強部屋",
"ウォーキングショット",
"歩き撮り",
"ピンクの引き出し",
"白い本棚",
"ステレンスのサッシ",
"バレェ",
"青藤色のカーテン",
"植え込み",
"青空背景",
"イタリア料理",
"浮世絵",
"左手を垂直に下げる",
"ラクダ",
"アカデミック美術",
"深紅背景",
"リテイブル",
"タイツ",
"釣り",
"木戸",
"スーツケース",
"バイオレット背景",
"デジタルペイント背景",
"ヴェドゥータ",
"藍色背景",
"信頼",
"赤い布",
"住宅街",
"ハイコントラスト",
"ベラム",
"笑い眉",
"ネイビー背景",
"白布",
"歩行者視点",
"アナログ風",
"クロード・モネによる絵画",
"オレンジ色のカボチャ",
"貝殻",
"アッシジのフランチェスコ",
"仰向け",
"桜吹雪",
"リアリズム",
"地下シェルター",
"門",
"農民",
"木箱",
"納屋",
"正確ではない深度マップ",
"銀色背景",
"アクア背景",
"ボトル",
"栗色背景",
"マギの礼拝",
"赤紫背景",
"モーターボート",
"ジャック・オー・ランタン",
"クリスティーナ",
"プロペラ",
"モモ",
"左に流した長い前髪",
"カーキ背景",
"お嬢様",
"ドミニコ",
"再生可能エネルギー",
"モアイ",
"色鉛筆",
"アレゴリー",
"暮らしの風景",
"少し下から見た",
"暮らしの情景",
"持ち棒",
"ややアオリで見た",
"低彩度",
"持ち手が白い",
"単体の素材",
"紫のアウトライン",
"魚介類",
"アマゾン熱帯雨林",
"片目隠し",
"コデックス",
"光と闇",
"クジャク",
"フライドポテト",
"弱ビネット",
"半ズボン",
"虫類",
"道路構造",
"やかん",
"珊瑚色背景",
"日常の景色",
"バリケード",
"日本食",
"光背",
"防霜ファン",
"大きな月",
"黒い靴下",
"写実主義",
"墨汁",
"イースター島",
"日常風景",
"抜け道",
"海苔トッピングがあるラーメン",
"アウトラインのある3d",
"リネン",
"ダークグレーの床",
"金羊毛",
"車両",
"イサク",
"ターバン",
"灰色の床",
"光と影",
"左手をリラックスしている",
"モーセ",
"サクランボ",
"レジャーボート",
"儚い印象",
"エアコン",
"馬",
"ラファエル前派",
"桜の花びら",
"character::九州そら",
"黒い輪郭線",
"モノクル",
"照明器具",
"食べ物屋",
"鬱蒼とした森",
"バルバラ",
"マタイ",
"コントラスト",
"防衛施設",
"ユニコーン",
"雌牛",
"太陽の光",
"苔むした",
"アスファルトの道",
"シャトー",
"おかず",
"モンマス",
"サン・マルコ広場",
"秋",
"球体関節",
"帆布",
"テニスボール",
"プラスチックカバー",
"砂紋",
"3枚の絵画",
"入道雲",
"くすみカラー",
"加算用素材の背景",
"ネクロポリス",
"片メカクレ",
"青色のインナーカラー",
"斜め構図",
"クピードー",
"船員",
"オリエンタリズム",
"羊飼いの礼拝",
"右手をグーにしている",
"右手を握りこんでいる",
"怒り眉",
"白テーマ",
"白の水着下",
"屋外背景",
"採掘場",
"ブラジル",
"航空写真",
"観光客",
"トランペット",
"木の丸太",
"黄色い宝石",
"サイドディッシュ",
"エフェクトの背景",
"ハーフツインテール",
"2段ベッド",
"チェック柄のピンク色のプリーツスカート",
"洗浄",
"sd絵",
"フェリペ4世",
"連作画",
"ハンドスクロール",
"強い日差し",
"へそ",
"花冠",
"イラスト背景",
"入れ物",
"枝",
"飲水",
"国章",
"鐘楼",
"茶色い木のデスク",
"カフェの小物",
"黒テーマ",
"右前のベスト",
"ゼウス",
"露出オーバー",
"挨拶",
"猟犬",
"ハープ",
"道路車両",
"漁師",
"黒いモニター",
"童話的",
"弾薬庫",
"紙コップ",
"足に絆創膏を貼っている",
"奥行きがある雲",
"ロングスカート",
"公衆衛生保全",
"水資源の管理",
"街の新陳代謝",
"水資源管理",
"ペラペラ",
"ゲオルギオス",
"聖母戴冠",
"レーマー",
"明暗境界線",
"地下都市",
"犬",
"日影",
"兵器庫",
"泊地",
"チャペル",
"緑色のヘタ",
"墓地群",
"街角",
"揚げ物",
"坑口",
"プラカード",
"白い襟",
"閉じた笑い口",
"やや遠くにいる",
"見開いた目",
"北方ルネサンス",
"消火器",
"アポローン",
"採掘坑",
"白いワンピースを着た女性",
"侵入口",
"モンスターのねぐら",
"歩いている",
"地下への入口",
"弾薬貯蔵施設",
"兵器貯蔵庫",
"坑道跡",
"絵巻物",
"キアゲハ",
"家庭内労働者",
"ディアーナ",
"街の公園",
"怒っている",
"逆光",
"美術作品集",
"中華そば",
"砂時計",
"小雨が降る",
"アウグスティヌス",
"鋼",
"宝珠",
"木製ベンチ",
"深淵の森",
"鬱蒼と生い茂る山",
"新体操",
"コラージュ画像",
"戦争画",
"幼児期",
"優しい色合い",
"田舎の道路と家",
"ズック",
"ラム ",
"芝",
"ロザリオ",
"バックス",
"芝生",
"ニース",
"フィリポ",
"ベッドシート",
"網目紙",
"影がかった顔",
"側溝",
"ドレーパリー",
"白いストロー",
"テーブルウェア",
"ピンクのドレス",
"メカクレ",
"大アントニオス",
"カップケーキ",
"ナポレオン・ボナパルト",
"ポスト印象派",
"庭石",
"最後の審判",
"裏地が赤い黒いマント",
"等幅ペン",
"ニワトリ",
"良い香り",
"明るい",
"黄色いアゲハ蝶",
"ペット",
"内側が赤い椅子",
"大西洋",
"ランプポスト",
"建築図面",
"繁華街",
"高齢者",
"赤色の口",
"レンガ造りの建物",
"全身",
"自然",
"スケッチ",
"エジプトへの逃避",
"テイクアウトカップ",
"丸い体",
"囚人",
"建築設計",
"和紙",
"バルタザール",
"開いたカーテン",
"トビア",
"聖痕",
"セピアカラー",
"カーテンは開いている",
"木造家屋",
"血液",
"もくもくした雲",
"屋内の背景",
"半魚人",
"アームチェア",
"水注",
"旧客",
"自然の景観",
"大きな目",
"キャリッジ",
"静寂と風音",
"欄干",
"ユディト",
"畳",
"食肉",
"黒髪黒セーラー服の少女",
"木影（こかげ）",
"琺瑯",
"石橋",
"版画",
"脱出口",
"受胎告知 ",
"キリスト降架",
"コラージュ写真",
"冷たい背景",
"銅合金",
"テーブル・プレート",
"ペンブルック",
"サテュロス",
"セラミックス",
"流し前髪",
"性別不詳",
"唯一神",
"点描",
"球面",
"地面に反射している",
"主食",
"青い表紙の本",
"ラファエロ・サンティによる絵画作品",
"画筆",
"古墳時代",
"天使",
"メロン味のかき氷",
"道化師",
"裁縫",
"洪水",
"スノーボーダー",
"緑色のパンプス",
"ハート型の鰭",
"胸元が大きく開いた服",
"黒い襟",
"木造建て",
"下睫毛",
"学校",
"回転体",
"回転機構",
"壁掛け",
"ローテーションメカニズム",
"グリーンエネルギー",
"三枚の風車ブレード",
"風車の翼",
"柱体",
"左前の洋服",
"カラーサークルのチョーカー",
"磔",
"バッファロー",
"黒tシャツ",
"幻想的の背景",
"インターカム",
"カーナーヴォン",
"チャネリング",
"地球儀",
"薄い青の背景",
"捕手",
"小柄",
"かつら",
"アダム",
"ラザロ",
"クセのある金髪ショートカットヘア",
"頭に惑星軌道風の輪っか",
"斜め下から見た",
"灰色のtシャツ",
"上から光が当たる",
"ハートが2つ",
"埴輪は宇宙服",
"長い横髪",
"周囲は森",
"パーティクル背景",
"赤と金色の椅子",
"美味しいもの",
"ピン",
"抽象絵画",
"ジョージ・ワシントン",
"茶色い机の上にある",
"最後の晩餐",
"麦わら帽子",
"緑の目",
"絵の具の髪飾り",
"キリストの洗礼",
"トンド",
"ガラス台",
"パブリックアート",
"ヴァニタス",
"ロングボート",
"穏やかな表情",
"席",
"バスのりば",
"彫刻版画",
"獣人",
"クローク",
"やや内巻きの髪",
"現実主義",
"果実",
"被り物",
"クリシュナ",
"ロードピープル",
"鏡像",
"カーディフ",
"桃",
"口紅",
"ルクレティア",
"レンヌ",
"シルクハット",
"氷山",
"ヨアキム",
"古典主義",
"フェリペ2世",
"宮殿",
"メルキオール",
"サロメ",
"瞳孔がない瞳",
"アダムとイヴ",
"裏壁",
"鐘",
"トレース用",
"鎖",
"サンパウロ州",
"有翼祭壇",
"間取り図",
"ジャン1世",
"天文学者",
"白いトップス",
"十字架を担うキリスト",
"テムズ川",
"記念貨幣",
"野外彫刻",
"平野",
"錦絵",
"耐火粘土",
"柱頭",
"リオデジャネイロ州",
"ニューポート",
"ダーク",
"畜牛",
"うねり",
"プラム",
"使徒",
"アダムとエバ",
"ハドソン川",
"大型建築",
"歌唱",
"墓穴",
"ホロフェルネス",
"朝",
"スザンナ",
"死神",
"エデンの園",
"ストローラー",
"雪で覆われた斜面",
"コンウィ",
"水車小屋",
"刺繡",
"ラグラン",
"ヒトの頭",
"ドア",
"エリザベス1世",
"ベル",
"ニュンペー",
"チャールズ1世",
"市壁",
"緑のシャツ",
"アベリストウィス",
"漁",
"擬人化",
"詩",
"睫毛が長い",
"聖母の被昇天",
"緑テーマ",
"緑豊かな野原",
"十字",
"パークベンチ",
"羽根ペン",
"サンギュイン",
"碑文",
"大時計",
"キリスト教における神",
"サイバー",
"エジプトへの逃避途上の休息",
"ヘーラクレース",
"複製作品",
"ダブルデッカーバス",
"テーブルランプ",
"ヘアバンド",
"泣く",
"両性具有",
"パイプ喫煙",
"城門",
"絵画風写真背景",
"character::マンマルモニモニ",
"マクシミリアン1世",
"木造ホーム",
"ボタフォゴ",
"軍人",
"待ち針",
"クロス",
"コアフ",
"ディプティク",
"襞襟",
"木製机",
"高い鼻",
"ボードテキスト",
"ノートルダム大聖堂",
"プレートフード",
"ハーレム",
"ベール",
"スクワイア",
"白テーブルクロス",
"神殿奉献",
"自然主義",
"青い布",
"冠",
"アンリ4世",
"表現主義",
"聖爵",
"砂岩",
"レオナルド・ダ・ヴィンチによる絵画作品",
"ユーピテル",
"黄色のtシャツ",
"地形図",
"王",
"智天使",
"大人の女性",
"ハツカネズミ",
"オープンフィールド",
"王笏",
"母乳",
"チャリオット",
"ストリートスナップ",
"叙情詩集",
"吸食",
"サムソン",
"日傘",
"白いスニーカー",
"公現祭",
"スタイラス",
"ウェールズ人",
"ウェストコート",
"復活 ",
"快楽の園",
"市営バス",
"バイユー",
"酌取り",
"メルクリウス",
"character::つくよみちゃん",
"カート ",
"人工池",
"ヨハネス・フェルメールによる絵画",
"ルネサンス期のイタリア絵画",
"メナイ海峡",
"聖母教会",
"フィブラ",
"クリストフォロス",
"ラム",
"聖アントニウスの誘惑",
"ミステリアス",
"ジャイナ教",
"戦闘用ヘルメット",
"カナル・グランデ",
"ワイ川",
"ブナ材",
"聖会話",
"ロバート ",
"牧畜民",
"樹幹",
"ブローチ",
"青空",
"コンウィ城",
"ウェストミンスター寺院",
"たいまつ",
"ベルケマイヤー",
"西欧の服飾",
"聖詠",
"火器",
"ヨブ",
"民族服",
"金糸",
"木々",
"父なる神",
"オフィス・チェア",
"母乳栄養",
"月桂冠",
"カラビナ",
"鳥の翼",
"クラヴァット",
"アキレウス",
"コテジ",
"狩猟家",
"金色の目",
"チェプストウ",
"飾り額",
"浅浮き彫り",
"去勢牛",
"インク壺",
"ティンターン",
"聖セシリア",
"黒布",
"エリサベト",
"ビュラン",
"笑顔",
"小学５年生くらい",
"ヒトの頭蓋骨",
"彫刻群",
"ハドソン・リバー派",
"アルジャントゥイユ",
"カーナーヴォン城",
"シャード ",
"トローニー",
"ティンターン・アビイ",
"楮紙",
"聖バフォ教会",
"階段",
"帝国宝珠",
"エリザベト訪問",
"聖母の死",
"バラ属",
"チュニック",
"リンデン材",
"並木道",
"アラバスター",
"カール12世",
"ホームプレート",
"野外",
"東方三博士の礼拝 ",
"コロタイプ",
"ファームハウス",
"バースデーケーキ",
"コンウィ川",
"国際ゴシック",
"パーキングメーター",
"エロース",
"雷雨",
"羊飼いたちの訪問",
"皿",
"ペルセウス",
"カルカソンヌ美術館",
"虹",
"サンティアゴ・マタモロス",
"戦争",
"グスタフ3世",
"針葉樹材",
"重量計",
"ウマ科",
"キュイラス",
"肩出し",
"パストラル",
"バベルの塔",
"赤の火",
"ディー川",
"漕ぎ船",
"マリー・ド・メディシス",
"チェプストウ城",
"ソクラテス",
"ディナン",
"防護柵",
"ネックレス",
"ニコデモ",
"横臥",
"聖ウルスラ",
"ランベリス",
"動物学",
"接吻",
"絞首台",
"スノードン山",
"パイ",
"アドーニス",
"林",
"素朴派",
"portrait historié",
"ル・モン＝サン＝ミシェル",
"ジビエ",
"ハロルド2世",
"ハレー彗星",
"イスカリオテのユダ",
"大鎌",
"レオフウィン・ゴドウィンソン",
"ブローニュ伯ウスタシュ2世",
"ドレス",
"モット・アンド・ベーリー",
"バイユーのタペストリー",
"アングロ・サクソン美術",
"ギリス・ゴドウィンソン",
"エドワード懺悔王",
"バイユーのオド",
"ソルトセラー",
"作品シリーズ",
"クエノン川",
"クレタ・ルネサンス",
"黒いまつ毛",
"イエスの洗礼",
"デリラ",
"積み藁",
"神の子羊",
"ヘントの祭壇画",
"世界地図",
"ラーダー",
"メナイ・ブリッジ",
"ゼバスチアン・ミュンスター",
"ダンテ・アリギエーリ",
"マリア",
"ソープストーン",
"フィレンツェにある美術館",
"混合技法",
"ドイツの地図製作者",
"アヤメ属",
"帆",
"ティツィアーノ・ヴェチェッリオによる絵画",
"アプロディーテー",
"シエナのカタリナ",
"腋の下",
"ロングヘア",
"4人",
"油彩スケッチ",
"ポプラの木",
"犬の首輪",
"観想",
"カール11世",
"テンビー",
"銀糸",
"千代田区にある国宝",
"伴大納言絵詞",
"ユーノー",
"膝立ち",
"コントラポスト",
"夜警",
"アンフォラ",
"レガリア",
"頬被り",
"フィギュラティヴ・アート",
"グルートゥーズ写本",
"ポール・ロリマー",
"リース",
"マンドリン",
"微笑み",
"パリスの審判",
"馬術選手",
"バシネット",
"スザンナと長老たち",
"アンドロメダー",
"ラグラン城",
"アリアドネー",
"エトルタ",
"建築要素",
"アイコンタクト",
"俯瞰",
"神殿でのイエス・キリストのプレゼンテーション",
"アリマタヤのヨセフ",
"アハシュエロス",
"グラスリン川",
"ウィレム1世",
"サルコファガス",
"公共市場",
"元帥杖",
"コレクションや展示品",
"ネーデルラントの諺",
"オレンジ背景",
"キャッツキル山地",
"ネズミの相談",
"オランダの諺",
"ジャンヌ・ダルク",
"ジェッソ",
"腰",
"ハルトマン・シェーデル",
"セントロ地区",
"テティス",
"サスキア・ファン・オイレンブルフ",
"まんまる",
"セント・デイビッズ",
"ドルバダーン城",
"疎林",
"マリアナ・デ・アウストリア",
"ハーレフ城",
"ゲートハウス",
"ブレコン",
"印画紙",
"ミクストメディア",
"櫂",
"首輪",
"アベリストウィス城",
"カナの婚宴",
"クレオパトラ7世",
"ビアグラス",
"サンタ・マリア・デッラ・サルーテ聖堂",
"櫃",
"ランダフ地区",
"タケ亜科",
"肉体の難行",
"ウシャブティ",
"バストアップ",
"アンヌ・ドートリッシュ",
"ランダフ大聖堂",
"パウルス3世",
"口頭手続き",
"イサベル・クララ・エウヘニア",
"奈良県にある国宝",
"ペンブルック川",
"ベズゲレルト",
"ハヤブサ属",
"ウェルトウムス",
"ケモミミ",
"アバンギャルド",
"世の救い主",
"文学の登場人物",
"ホリーウェル",
"乳搾り女",
"踏板",
"肩",
"オリエンタルラグ",
"元帳",
"ヘリオグラフィー",
"翼",
"カーディフ城",
"穀物の収穫",
"荊冠のキリスト",
"フリードリヒ大王騎馬像",
"スランソニー修道院",
"パダルン湖",
"マリー・テレーズ・ドートリッシュ",
"グスタフ2世アドルフ",
"ベトゥス・ア・コエド",
"カプリッチョ ",
"金髪",
"黒いリボン",
"パイン材",
"支保工",
"慶祝",
"メロードの祭壇画",
"ロトの娘たち",
"cmnf",
"酒皶",
"男性の上半身裸",
"兄弟姉妹集団",
"ファウヌス",
"箱",
"ロゼッタ模様",
"美術モデル",
"踝",
"ダナエー",
"ティティアンヘア",
"パーティー ",
"過体重",
"フランドル派 ",
"ストワール",
"ウェヌスのえくぼ",
"セクシー",
"黄緑色の髪",
"臀裂",
"知恵の樹",
"乳輪",
"色々なドレス",
"黄緑テーマ",
"イチジクの葉",
"ピンクのリップ",
"黄緑背景",
"ポケット",
"裸",
"コンクリート",
"浮遊",
"冷たい",
"アホ毛",
"武器",
"ニーソックス",
"やや暗い",
"踊り",
"右メカクレ",
"切れ長の目",
"character::グノーム・アラクネ",
"白い服",
"浮いている",
"愛",
"カウボーイショット",
"屋根",
"カラフル",
"ボート",
"黒が基調",
"白手袋",
"黒い半ズボン",
"茶色のまつ毛",
"崇拝",
"異世界",
"アーモンド型の目",
"リラックス顔",
"パンプス",
"姫カット",
"しゃがむ",
"騎士",
"異形頭",
"白いタイツ",
"サンダル",
"剣",
"冬",
"パーの手",
"写真",
"いただきます",
"幼児",
"小道",
"望遠レンズ",
"指輪",
"横顔",
"少しデフォルメ",
"曇り",
"オフショルダー",
"前髪パッツン前髪",
"こちらを見ている",
"青い目",
"つやつや",
"弱カラーハーフトーン",
"カジュアル",
"セーター",
"交差",
"体術",
"腰から上",
"民族衣装",
"黒いブーツ",
"ハーフパンツ",
"上から",
"三白眼",
"食事",
"鳥",
"鉢",
"右脚を後ろへ引く",
"ジャンプ",
"夏",
"エフェクト",
"右肘を曲げる",
"とても浅い被写界深度",
"黒アウトライン",
"メイド服",
"朝-午後",
"涙袋",
"歌手",
"ベンチ",
"外ハネの髪",
"長めの前髪",
"格闘",
"アイボリーテーマ",
"グレイスケール",
"動物",
"黒い革靴",
"character::霧太郎",
"反射",
"草履",
"白い壁",
"貴族",
"石",
"フェンス",
"樹木",
"水面",
"草",
"長身の女性",
"背が高い",
"大学生～２８才くらい",
"黒縁眼鏡",
"ブレスレット",
"和風",
"鼻の上にハイライトがある",
"中学生か高校生くらい",
"皺",
"ジュリエットスリーブ",
"カップル",
"重ね着",
"エルフ",
"前髪テール",
"白い靴下",
"豊満な胸",
"タトゥー",
"懇願",
"刺繍",
"着物",
"ウェーブヘア",
"都市",
"アスファルト",
"ベルト",
"つり目",
"頭身が高い",
"バンザイ",
"灰色背景",
"料理",
"後ろ姿",
"ティールテーマ",
"上着の前が閉じている",
"2人",
"ダンスする",
"カメラ",
"真面目な顔",
"ヘアピン",
"vネック",
"塗料",
"スカート",
"レンガ",
"茶色のベルト",
"装備",
"アート",
"帯",
"ニーハイソックス",
"sf風",
"魔法",
"もみあげ",
"オールバック",
"室内",
"ジャージ",
"落書き",
"歌う",
"青年",
"つくよみちゃんの髪飾り",
"リボンタイ",
"和風ドレス",
"ピアス",
"お団子ヘア",
"卵",
"リップグロス",
"蛇",
"額縁",
"左肘を曲げる",
"女の子",
"あ口顔",
"立ちポーズ",
"町",
"左手を腰に当てている",
"バッグ",
"狐耳",
"黒いアイライン",
"中学生くらい",
"射撃の構え",
"黒靴",
"ベレー帽",
"閉じた目",
"シンプルな目",
"箸",
"ワイシャツ",
"メイク",
"キック",
"瞳は薄い緑",
"見上げる",
"ショートボブ",
"人外モデル",
"水彩風",
"半袖",
"花の髪飾り",
"腕まくり",
"７３分け",
"手のひらがエメラルドグリーンの黒い手袋",
"赤い服",
"容器",
"オリーブ背景",
"色白の肌",
"前屈",
"麻呂眉",
"花",
"紐靴",
"瞳の中心が白い",
"アーティファクト",
"サンライズサンセット",
"癖っ毛",
"人工遺物",
"イエス・キリスト",
"茶色の靴",
"黒いボタン",
"赤茶の髪",
"写真背景",
"エメラルド色のハイライト",
"バナナ",
"左膝を曲げる",
"泳ぐ",
"軽業",
"高彩度",
"葉",
"まっすぐな前髪",
"フィールド",
"長い髪",
"子供",
"純粋な空",
"白黒ツートンカラー髪",
"イラスト風3d",
"ボディースーツ",
"両肘を曲げている",
"日焼けした肌",
"聖母マリア",
"指テッポウを構えている",
"黒縁メガネ",
"記号",
"ぱっつん前髪",
"広場",
"肩パット付き白シャツ",
"前髪ぱっつん",
"ステージ衣装",
"観客",
"白衣",
"薄い灰色のフレームのメガネ",
"水彩画",
"吊り目",
"チアガール",
"強ディフュージョン",
"柔軟体操している",
"天使の輪",
"デジタルペイント",
"カチューシャ",
"二つ結び",
"赤いシャツ",
"セーラー服",
"洞窟",
"桜",
"ダブルボタン",
"建物",
"庭園",
"面目な顔",
"高貴",
"波",
"中学生",
"白い手袋",
"両膝は曲がっている",
"線画",
"character::原型ずんだもん",
"道",
"集団",
"シャツ",
"黒いスニーカー",
"レトロ",
"胸を張っている",
"瞳は灰色",
"ふわふわ",
"3.5頭身",
"妖精",
"ケモノ",
"成人男性",
"女性",
"ニュートラル顔",
"蜘蛛の巣",
"マニキュア",
"水泳",
"お願いしている",
"許しを乞うている",
"白いジャボタイ",
"つま先立ち",
"黄色のボタン",
"レディ",
"スカーフ",
"眼鏡",
"とても太い主線",
"コンピュータ",
"森林",
"指さし",
"character::ムスビイト",
"右目隠れ",
"兵士",
"漫画",
"腹筋を鍛えている",
"銀色の目",
"怒り顔",
"右手で紐を引っ張っている",
"絵の具チューブのヘアアクセ",
"赤いアイシャドウ",
"コック",
"袖なし",
"リボン",
"ラーメン",
"ラップ",
"銅像",
"装束",
"驚き顔",
"小学生",
"黒い長袖",
"旗",
"斜め前髪",
"はしゃぐ",
"猿",
"フィナーレ",
"2.5頭身",
"草原",
"昼",
"う口顔",
"白タイツ",
"アニメ塗り",
"メカ娘",
"ストレッチ",
"短パン",
"棚",
"街路",
"絆創膏",
"横を見ている",
"球体",
"悲しみ顔",
"ルネサンス",
"グレーの目",
"黒いショートパンツ",
"茶色の眉毛",
"おでこが出ている",
"赤テーマ",
"牛",
"両膝を曲げる",
"結婚式",
"ミツアアナトミー",
"斜め後ろから",
"白いベスト",
"紫目",
"魔法使い",
"斜めバング",
"前髪短い",
"♡",
"ファンシー",
"家族",
"バレリーナ",
"扇子",
"紫髪",
"アンドロイド",
"紫の眉毛",
"碧眼",
"裏地",
"黒い軍服",
"腰紐",
"ウサミミ",
"白いエプロン",
"黒い睫毛",
"腰を落としている",
"お口顔",
"ポスター",
"character::ずんだもん",
"住宅",
"ハイレグ",
"布",
"ブラウン背景",
"自転車",
"アオリ",
"反り返っている",
"左メカクレ",
"角",
"中性的な子供",
"前かがみ",
"中学生～高校生くらい",
"黒いインナー",
"そばかす",
"畑",
"ずんだもんの枝豆",
"黒いレース",
"クリア",
"膝丈",
"前を見ている",
"白いシャツ",
"手のひらは床に当てている",
"ピンクのリボン",
"高いコントラスト",
"モダンダンス",
"レストラン",
"白い瞳孔",
"マッチョ",
"cg背景",
"スキー",
"キャップ",
"モノクロ",
"弓",
"泡",
"character::ミニブルーロボ",
"ソファー",
"右腕を後ろへ引く",
"パステルカラー",
"ボタン",
"おさげ",
"右手を上げている",
"コルセット",
"肖像画",
"蜘蛛の襟章",
"太い眉毛",
"大きい目",
"アシンメトリーな髪型",
"現代",
"黒いスカート",
"色素の薄い肌",
"漢字",
"紺色のブレザー",
"宝石のような瞳",
"え口顔",
"胸像",
"夢",
"遺跡",
"黒目",
"オーバーキャスト",
"6個のボタン",
"黒いスラックス",
"黒の靴",
"老人",
"赤いリボン",
"左目閉じ",
"ハロウィーン",
"無精髭",
"ウサギ耳",
"聖書",
"釣り目",
"オフィス",
"獣耳",
"詰襟",
"白いワイシャツ",
"女子高生",
"ズボン",
"アイス",
"右膝を曲げる",
"黒のスカート",
"黒い肌",
"目元にしわ",
"アウトドア",
"虫",
"ホール",
"料理人",
"円形",
"ピンクテーマ",
"ブレザー",
"胸を張る",
"ダブルピース",
"霧太郎の服",
"黒い服",
"舞台挨拶",
"ハロウィン",
"ブラウンテーマ",
"左目だけの丸眼鏡",
"集合写真",
"い口顔",
"謝っている",
"ハート型のアホ毛",
"水色のインナーカラー",
"放棄された",
"仮装",
"下駄",
"クリーム",
"白い皿",
"足の裏側が緑",
"靴",
"肖像",
"左腕は体の横",
"黒いパーカー",
"小学生くらい",
"愛称はベルちゃん",
"腕短めのsdキャラ",
"四つん這い",
"衣類",
"ボブカットヘア",
"春",
"蝶ネクタイ",
"バストショット",
"オレンジのチーク",
"果物",
"黒いチョーカー",
"翼人",
"大きい瞳",
"撮影会",
"5等身",
"ぼやけている",
"黒い丸いモノクル",
"高校生くらい",
"男性",
"右目閉じ",
"長い睫毛",
"バナー",
"アニメの目",
"手が長めのsdキャラ",
"和装",
"黒いパンプス",
"細かいカラーハーフトーン",
"イエスの母マリア",
"中コントラスト",
"大人の男性",
"座っている",
"灰色のベスト",
"夜空",
"ジャンプしている",
"ケモ耳",
"薄い茶髪",
"筋肉",
"黒いフリル",
"目隠れ",
"外ハネ",
"シート",
"特殊モデル",
"切れ込みの入った袖",
"田舎",
"立襟",
"願っている",
"浅い被写界深度",
"地図",
"ハンサム",
"白い肌",
"うさ耳",
"少女漫画のような目",
"銀髪",
"銅",
"浴衣",
"ボサボサヘア",
"長いおさげ髪",
"木板",
"振り返る",
"白い翼",
"セミリアル",
"厚塗り",
"右肘を曲げている",
"屋内",
"飛行機",
"瞳は茶色",
"サイバーパンク",
"銃",
"ロイヤル",
"画家",
"魔女",
"胸ポケット",
"ドクロ",
"オレンジのリップ",
"低コントラスト",
"アーバン",
"黒いビクトリアン風スーツ",
"ヘチマカラー",
"赤いリボンタイ",
"左脚を後ろへ引く",
"金属ボタン",
"詰め襟",
"ステッキ",
"タンクトップ",
"魂",
"緑髪",
"character::ヤミーヤンデル",
"付け袖",
"ジャボタイ",
"猫口",
"マット",
"癖毛",
"文字",
"赤いネイル",
"ヴァンパイア",
"歩く",
"ポニーテール",
"強い",
"施設",
"赤いネクタイ",
"蚊を潰した",
"猫騙し",
"肩当",
"聖母子",
"フードを被っていない",
"白襟と白い胸元",
"横を向いている",
"茶色の目",
"黒い付け袖",
"岸",
"純白",
"土",
"タータン模様",
"上を見ている",
"右手を前に突き出し",
"左手少し曲げ",
"黒いクロスタイ",
"ワイン",
"壁",
"2本のピンクのライン",
"ヨガ",
"インク",
"後ろから見た",
"モブ",
"ポップ",
"巨乳",
"おもちゃ",
"倉庫",
"長いまつ毛"
] |
heado/ViT_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_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: 0.7872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 17 | 0.7872 |
| No log | 2.0 | 34 | 0.6297 |
| No log | 3.0 | 51 | 0.5765 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
[
"label_0",
"label_1",
"label_2"
] |
riyadifirman/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 [sxdave/plant_classification_model_1](https://huggingface.co/sxdave/plant_classification_model_1) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3907
- Accuracy: 0.9171
## Model description
More information needed
## Intended uses & 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
[
"unlabeled",
"basil",
"blueash",
"boxelder",
"cilantro",
"daisy",
"mint",
"oak%20leaves",
"oregano",
"parsely",
"poison%20ivy",
"poison%20oak",
"rose",
"tulip"
] |
djbp/swin-base-patch4-window7-224-in22k-cons_Classification_base_V10
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224-in22k-cons_Classification_base_V10
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3063
- Accuracy: 0.8819
- Auc Overall: 0.9577
- Auc Class 0: 0.9496
- Auc Class 1: 0.9589
- Auc Class 2: 0.9646
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
### Framework versions
- Transformers 4.44.2
- Pytorch 1.13.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"immovable",
"invalid",
"movable"
] |
franibm/autotrain-a1ahc-punm7
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.005880220327526331
f1: 1.0
precision: 1.0
recall: 1.0
auc: 1.0
accuracy: 1.0
|
[
"manipulated-images",
"non-manipulated-images"
] |
tangg555/clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist
This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0268
- Accuracy: 0.9917
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4312 | 0.9953 | 105 | 0.0539 | 0.9862 |
| 0.3224 | 2.0 | 211 | 0.0410 | 0.987 |
| 0.2945 | 2.9858 | 315 | 0.0268 | 0.9917 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
tangg555/clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist_linear_probe
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist_linear_probe
This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9687
- Accuracy: 0.9137
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.4414 | 0.9953 | 105 | 2.3838 | 0.102 |
| 2.2431 | 2.0 | 211 | 2.2100 | 0.2698 |
| 2.1251 | 2.9953 | 316 | 2.0662 | 0.4987 |
| 2.0119 | 4.0 | 422 | 1.9232 | 0.6905 |
| 1.9134 | 4.9953 | 527 | 1.8080 | 0.7645 |
| 1.8314 | 6.0 | 633 | 1.7059 | 0.8053 |
| 1.7816 | 6.9953 | 738 | 1.6175 | 0.8215 |
| 1.7076 | 8.0 | 844 | 1.5373 | 0.845 |
| 1.6632 | 8.9953 | 949 | 1.4675 | 0.8592 |
| 1.6188 | 10.0 | 1055 | 1.4062 | 0.863 |
| 1.5606 | 10.9953 | 1160 | 1.3510 | 0.8718 |
| 1.5185 | 12.0 | 1266 | 1.3031 | 0.8718 |
| 1.5007 | 12.9953 | 1371 | 1.2591 | 0.881 |
| 1.4573 | 14.0 | 1477 | 1.2201 | 0.8833 |
| 1.4474 | 14.9953 | 1582 | 1.1841 | 0.8875 |
| 1.4308 | 16.0 | 1688 | 1.1524 | 0.8925 |
| 1.4091 | 16.9953 | 1793 | 1.1246 | 0.8943 |
| 1.3683 | 18.0 | 1899 | 1.0986 | 0.8985 |
| 1.365 | 18.9953 | 2004 | 1.0752 | 0.9042 |
| 1.3635 | 20.0 | 2110 | 1.0563 | 0.9033 |
| 1.3422 | 20.9953 | 2215 | 1.0389 | 0.9043 |
| 1.3248 | 22.0 | 2321 | 1.0231 | 0.9083 |
| 1.2961 | 22.9953 | 2426 | 1.0100 | 0.9093 |
| 1.3136 | 24.0 | 2532 | 0.9986 | 0.9107 |
| 1.3067 | 24.9953 | 2637 | 0.9897 | 0.911 |
| 1.2984 | 26.0 | 2743 | 0.9818 | 0.9115 |
| 1.3045 | 26.9953 | 2848 | 0.9759 | 0.9132 |
| 1.291 | 28.0 | 2954 | 0.9717 | 0.9132 |
| 1.2731 | 28.9953 | 3059 | 0.9692 | 0.9142 |
| 1.3034 | 29.8578 | 3150 | 0.9687 | 0.9137 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
dukenmarga/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1383
- Accuracy: 0.6312
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.925 | 1.0 | 10 | 1.3570 | 0.4688 |
| 0.8379 | 2.0 | 20 | 1.1685 | 0.5875 |
| 0.6737 | 3.0 | 30 | 1.1795 | 0.6 |
| 0.4606 | 4.0 | 40 | 1.1383 | 0.6312 |
| 0.3416 | 5.0 | 50 | 1.2393 | 0.5687 |
| 0.2493 | 6.0 | 60 | 1.3971 | 0.5938 |
| 0.2341 | 7.0 | 70 | 1.3546 | 0.6062 |
| 0.1797 | 8.0 | 80 | 1.3681 | 0.5938 |
| 0.1221 | 9.0 | 90 | 1.6936 | 0.525 |
| 0.1077 | 10.0 | 100 | 1.7008 | 0.5375 |
| 0.0966 | 11.0 | 110 | 1.7380 | 0.525 |
| 0.1073 | 12.0 | 120 | 1.5617 | 0.575 |
| 0.0849 | 13.0 | 130 | 1.6178 | 0.6125 |
| 0.0704 | 14.0 | 140 | 1.6144 | 0.6125 |
| 0.0568 | 15.0 | 150 | 1.6111 | 0.6188 |
| 0.0555 | 16.0 | 160 | 1.5946 | 0.6 |
| 0.0498 | 17.0 | 170 | 1.6291 | 0.625 |
| 0.0464 | 18.0 | 180 | 1.6574 | 0.6188 |
| 0.0443 | 19.0 | 190 | 1.6740 | 0.6125 |
| 0.0429 | 20.0 | 200 | 1.6781 | 0.6125 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
amauriciogonzalez/dinov2-base-finetuned-har
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dinov2-base-finetuned-har
This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4424
- Accuracy: 0.8968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.9155 | 0.9910 | 83 | 0.6204 | 0.8053 |
| 0.749 | 1.9940 | 167 | 0.4433 | 0.8667 |
| 0.8197 | 2.9970 | 251 | 0.4826 | 0.8571 |
| 0.6854 | 4.0 | 335 | 0.4243 | 0.8725 |
| 0.7058 | 4.9910 | 418 | 0.4349 | 0.8593 |
| 0.6717 | 5.9940 | 502 | 0.4984 | 0.8434 |
| 0.6544 | 6.9970 | 586 | 0.4730 | 0.8545 |
| 0.5846 | 8.0 | 670 | 0.4631 | 0.8630 |
| 0.5207 | 8.9910 | 753 | 0.4072 | 0.8751 |
| 0.4977 | 9.9940 | 837 | 0.4627 | 0.8608 |
| 0.4974 | 10.9970 | 921 | 0.4600 | 0.8661 |
| 0.4502 | 12.0 | 1005 | 0.4548 | 0.8725 |
| 0.4051 | 12.9910 | 1088 | 0.4404 | 0.8709 |
| 0.3862 | 13.9940 | 1172 | 0.4498 | 0.8772 |
| 0.351 | 14.9970 | 1256 | 0.4859 | 0.8677 |
| 0.3807 | 16.0 | 1340 | 0.5189 | 0.8556 |
| 0.3538 | 16.9910 | 1423 | 0.4959 | 0.8646 |
| 0.3181 | 17.9940 | 1507 | 0.4831 | 0.8698 |
| 0.3225 | 18.9970 | 1591 | 0.4890 | 0.8804 |
| 0.3257 | 20.0 | 1675 | 0.4817 | 0.8735 |
| 0.2667 | 20.9910 | 1758 | 0.5199 | 0.8683 |
| 0.2863 | 21.9940 | 1842 | 0.4835 | 0.8683 |
| 0.2527 | 22.9970 | 1926 | 0.4764 | 0.8772 |
| 0.2657 | 24.0 | 2010 | 0.4651 | 0.8767 |
| 0.1995 | 24.9910 | 2093 | 0.5079 | 0.8693 |
| 0.2481 | 25.9940 | 2177 | 0.5112 | 0.8698 |
| 0.2072 | 26.9970 | 2261 | 0.5082 | 0.8831 |
| 0.2164 | 28.0 | 2345 | 0.5002 | 0.8730 |
| 0.2198 | 28.9910 | 2428 | 0.4785 | 0.8778 |
| 0.2137 | 29.9940 | 2512 | 0.5012 | 0.8889 |
| 0.1936 | 30.9970 | 2596 | 0.4961 | 0.8757 |
| 0.2255 | 32.0 | 2680 | 0.4987 | 0.8788 |
| 0.1818 | 32.9910 | 2763 | 0.4840 | 0.8852 |
| 0.1644 | 33.9940 | 2847 | 0.4694 | 0.8862 |
| 0.1799 | 34.9970 | 2931 | 0.4599 | 0.8915 |
| 0.1624 | 36.0 | 3015 | 0.5122 | 0.8852 |
| 0.157 | 36.9910 | 3098 | 0.4546 | 0.8899 |
| 0.2165 | 37.9940 | 3182 | 0.5097 | 0.8836 |
| 0.1565 | 38.9970 | 3266 | 0.4566 | 0.8952 |
| 0.1476 | 40.0 | 3350 | 0.4579 | 0.8915 |
| 0.1296 | 40.9910 | 3433 | 0.4595 | 0.8931 |
| 0.1159 | 41.9940 | 3517 | 0.4841 | 0.8884 |
| 0.1071 | 42.9970 | 3601 | 0.4730 | 0.8820 |
| 0.1017 | 44.0 | 3685 | 0.4470 | 0.8931 |
| 0.11 | 44.9910 | 3768 | 0.4557 | 0.8910 |
| 0.126 | 45.9940 | 3852 | 0.4585 | 0.8926 |
| 0.1079 | 46.9970 | 3936 | 0.4551 | 0.8905 |
| 0.1194 | 48.0 | 4020 | 0.4401 | 0.8947 |
| 0.11 | 48.9910 | 4103 | 0.4424 | 0.8968 |
| 0.1104 | 49.5522 | 4150 | 0.4414 | 0.8958 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"calling",
"clapping",
"cycling",
"dancing",
"drinking",
"eating",
"fighting",
"hugging",
"laughing",
"listening_to_music",
"running",
"sitting",
"sleeping",
"texting",
"using_laptop"
] |
amauriciogonzalez/dinov2-finetuned-har
|
<!-- This model card 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-finetuned-har
This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3078
- 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.9429 | 0.9910 | 83 | 0.5624 | 0.8328 |
| 0.7912 | 1.9940 | 167 | 0.4755 | 0.8587 |
| 0.7371 | 2.9970 | 251 | 0.4584 | 0.8550 |
| 0.5915 | 4.0 | 335 | 0.3870 | 0.8762 |
| 0.5635 | 4.9910 | 418 | 0.4037 | 0.8704 |
| 0.498 | 5.9940 | 502 | 0.3876 | 0.8804 |
| 0.4541 | 6.9970 | 586 | 0.3612 | 0.8884 |
| 0.3513 | 8.0 | 670 | 0.3240 | 0.9053 |
| 0.2963 | 8.9910 | 753 | 0.3176 | 0.9116 |
| 0.2815 | 9.9104 | 830 | 0.3078 | 0.9148 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"calling",
"clapping",
"cycling",
"dancing",
"drinking",
"eating",
"fighting",
"hugging",
"laughing",
"listening_to_music",
"running",
"sitting",
"sleeping",
"texting",
"using_laptop"
] |
sai17/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3663
- Accuracy: 0.4856
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.4389 | 0.1894 | 100 | 2.5163 | 0.4748 |
| 2.1742 | 0.3788 | 200 | 2.4580 | 0.4802 |
| 2.1934 | 0.5682 | 300 | 2.4167 | 0.4836 |
| 2.4634 | 0.7576 | 400 | 2.4232 | 0.4789 |
| 2.5892 | 0.9470 | 500 | 2.4008 | 0.4829 |
| 2.3142 | 1.1364 | 600 | 2.3910 | 0.4849 |
| 2.6178 | 1.3258 | 700 | 2.3910 | 0.4849 |
| 2.5871 | 1.5152 | 800 | 2.3954 | 0.4856 |
| 2.5426 | 1.7045 | 900 | 2.3848 | 0.4856 |
| 2.077 | 1.8939 | 1000 | 2.3795 | 0.4849 |
| 2.3489 | 2.0833 | 1100 | 2.3777 | 0.4849 |
| 2.6511 | 2.2727 | 1200 | 2.3717 | 0.4856 |
| 2.4127 | 2.4621 | 1300 | 2.3727 | 0.4856 |
| 2.4054 | 2.6515 | 1400 | 2.3753 | 0.4849 |
| 2.628 | 2.8409 | 1500 | 2.3736 | 0.4856 |
| 2.5406 | 3.0303 | 1600 | 2.3688 | 0.4856 |
| 2.4249 | 3.2197 | 1700 | 2.3726 | 0.4856 |
| 2.3137 | 3.4091 | 1800 | 2.3719 | 0.4856 |
| 2.4248 | 3.5985 | 1900 | 2.3667 | 0.4856 |
| 2.0676 | 3.7879 | 2000 | 2.3666 | 0.4856 |
| 2.2021 | 3.9773 | 2100 | 2.3663 | 0.4856 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"2018-panini-absolute",
"2018-donruss-elite",
"2015-panini-prizm-collegiate-draft-picks",
"2017-leaf-metal-u.s.-army-all-american-bowl---metal-autographs-blank-back-proofs-black-prismatic",
"2021-pro-set-metal---1989-autographs-xrc-wave-silver-prospect",
"2017-leaf-metal-draft---flashback-state-pride-autographs-super",
"2017-leaf-metal-u.s.-army-all-american-bowl---metal-autographs-flag-etch-red-prismatic",
"2017-leaf-metal-u.s.-army-all-american-bowl---metal-autographs-printing-plates-cyan",
"2006-donruss-elite",
"2005-upper-deck-afl",
"2018-panini-one---dual-patch-autographs-bronze",
"2017-leaf-metal-draft---armed-and-dangerous-autographs-silver",
"2001-finest",
"2015-topps-diamond---dual-diamond-autographs",
"2001-playoff-absolute-memorabilia",
"2021-pro-set-metal---1989-autograph-wave-ivory",
"2010-dav-tampa-bay-buccaneers",
"2015-bowman",
"2017-panini-classics",
"2018-panini-spectra",
"2024-wild-card-automania-american-football-preview---circle-sparkle-fireworks-4",
"2022-wild-card-auto-mania---diamond-pulsar-green",
"2013-big-33-maryland-high-school",
"2017-leaf-metal-draft---touchdown-kings-autographs-purple",
"2022-wild-card-auto-mania-american---diamond-modern-flag-(donut-circles)",
"2004-green-bay-packers-police---crime-stoppers-of-racine-county",
"2015-jogo-cfl-alumni-series-11",
"2005-the-danbury-mint-super-bowl-xxxix-champion-22kt-gold",
"2022-leaf-signature-series---autographs-gold",
"2022-panini-nfl-sticker-&-card-collection-(european-edition)",
"2011-panini-plates-&-patches",
"2005-hero-decks-new-england-patriots-football-heroes-playing-cards",
"2015-topps-diamond---rookie-autograph-jumbo-patches-gold-ink",
"2021-pro-set-metal---1989-autographs-xrc-mojo-ivory-variation-prospect",
"2001-upper-deck-pros-&-prospects",
"2022-wild-card-auto-mania---triangle-scribbles-lazer-black",
"2024-wild-card-automania-american-football-preview---hex-lazers-fireworks-2",
"2022-wild-card-auto-mania-american---circle-modern-flag-(crystal-holographic)",
"2005-topps-etopps-classic",
"2001-nflpa-player-of-the-day",
"2017-panini-pantheon",
"2024-wild-card-automania-american-football-preview---triangle-crystal-holographic-fireworks-5",
"2018-panini-prestige",
"2018-panini-one---award-winners",
"2023-bowman-university-chrome",
"2022-wild-card-auto-mania---triangle-sparkles-red",
"2023-clearly-donruss",
"2010-stirling-hamilton-tiger-cats",
"2002-donruss-classics",
"2012-extreme-cfl-grey-cup-100-years",
"2023-leaf-exotic",
"2017-leaf-trinity---signatures-patch-printing-plates-magenta-spectrum",
"2022-wild-card-auto-mania-american---diamond-modern-flag-(rainbow-board)",
"2006-leaf-limited",
"2013-green-bay-packers-police---larry-fritsch-cards,-llc,-stevens-point-town-of-hull-(portage-county)-fire-department",
"2024-wild-card-automania-american-football-preview---circle-rainbow-fireworks-4",
"2023-wild-card-5-card-draw---modern-blue-red-foil",
"2024-wild-card-automania-american-football-preview---square-mosaics-american-flag",
"2004-upper-deck-foundations",
"2015-topps-high-tek",
"2022-wild-card-auto-mania---triangle-scribbles-lazer-gold",
"2017-leaf-metal-draft---touchdown-kings-autographs-gold",
"2024-wild-card-automania-american-football-preview---circle-rainbow-constitution",
"2022-wild-card-auto-mania---circle-mirror-purple",
"2008-topps-philadelphia-eagles",
"2021-pro-set-metal---1989-autograph-crystals-purple",
"2016-panini-absolute",
"2021-pro-set-metal---1989-autographs-xrc-crystals-red-prospect",
"2017-leaf-metal-draft---touchdown-kings-autographs-green",
"2005-leaf-rookies-&-stars",
"2007-topps-cincinnati-bengals",
"2021-pro-set-metal---1989-autographs-xrc-rainbow-ivory-prospect",
"2021-pro-set-metal---1989-autograph-wave-orange",
"2024-wild-card-automania-american-football-preview---hex-rainbow-mt.-rushmore",
"2017-leaf-metal-draft---flashback-base-autographs-red",
"2018-leaf-ultimate-draft---flashback-autographs-1991-leaf-rookie-red",
"2024-wild-card-automania-american-football-preview---square-mosaics-fireworks-8",
"2017-leaf-metal-u.s.-army-all-american-bowl---patch-autographs-gold-football",
"2001-upper-deck-super-bowl-xxxv",
"2017-leaf-metal-u.s.-army-all-american-bowl---tour-autographs-gold",
"2024-wild-card-automania-american-football-preview---diamond-rainbow-eagle-2",
"2011-upper-deck",
"2016-panini-gala",
"2022-leaf-signature-series---dual-signature-outer-space",
"2024-wild-card-automania-american-football-preview---diamond-rainbow-constitution",
"2023-wild-card-5-card-draw---classic-tan-blue-foil",
"2024-wild-card-automania-american-football-preview---star-rainbow-eagle-3",
"2002-score-minnesota-vikings-stadium-giveaway",
"2024-wild-card-automania-american-football-preview---triangle-cross-hatch-fireworks-2",
"2019-leaf-metal-all-american-bowl---glove-autograph-purple",
"2023-wild-card-5-card-draw---contemporary-gold-red-foil",
"2023-leaf-trinity---trinity-duos-silver-spectrum-black",
"2024-wild-card-automania-american-football-preview---hex-lazers-fireworks-5",
"2007-green-bay-packers-police---watertown-police-department",
"2008-goal-line-hall-of-fame-art-collection",
"2006-finest",
"2008-green-bay-packers-police---your-local-law-enforcement-agency-and-the-green-bay-packers-organization",
"2001-green-bay-packers-police---thomas-bus-service,-ron’s-super-service,-racine-county-sheriff's-department",
"2010-green-bay-packers-police---brillion-and-reedsville-police-department",
"2021-pro-set-metal---1989-autographs-xrc-wave-ebony-variation-prospect",
"2012-leaf-valiant",
"2017-leaf-metal-u.s.-army-all-american-bowl---tour-autographs-silver",
"2004-upper-deck-dunkin'-donuts-new-york-giants",
"2010-topps-magic",
"2021-pro-set-metal---1989-autographs-xrc-wave-red,-white-&-blue-prospect",
"2008-nflpa-player-of-the-day",
"2017-leaf-metal-u.s.-army-all-american-bowl---patch-autographs-printing-plates-magenta",
"2017-leaf-metal-draft---flashback-base-autographs-super",
"2017-leaf-metal-draft---state-pride-autographs-gold",
"2022-wild-card-auto-mania---circle-mirror-red"
] |
al-css/PagesClassificationModel
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PagesClassificationModel
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the private_images_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0031
- Accuracy: 1.0
## Model description
This model clasificates Pages, from: Just Text, Tables and Text, and Just Tables.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"1",
"2"
] |
BTX24/convnextv2-base-22k-224-finetuned-tekno24
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnextv2-base-22k-224-finetuned-tekno24
This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9120
- Accuracy: 0.6138
- F1: 0.5996
- Precision: 0.5969
- Recall: 0.6138
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.3179 | 0.9968 | 78 | 1.2415 | 0.4207 | 0.3979 | 0.4642 | 0.4207 |
| 1.1998 | 1.9936 | 156 | 1.0769 | 0.5103 | 0.4525 | 0.5309 | 0.5103 |
| 1.168 | 2.9904 | 234 | 1.0573 | 0.5494 | 0.5033 | 0.5605 | 0.5494 |
| 1.1107 | 4.0 | 313 | 0.9924 | 0.5540 | 0.5163 | 0.5257 | 0.5540 |
| 1.1062 | 4.9968 | 391 | 1.0018 | 0.5747 | 0.5507 | 0.5660 | 0.5747 |
| 1.0331 | 5.9936 | 469 | 0.9901 | 0.5931 | 0.5768 | 0.6202 | 0.5931 |
| 1.0409 | 6.9904 | 547 | 0.9634 | 0.5747 | 0.5723 | 0.5722 | 0.5747 |
| 1.0176 | 8.0 | 626 | 0.9504 | 0.5931 | 0.5834 | 0.5814 | 0.5931 |
| 0.995 | 8.9968 | 704 | 0.9584 | 0.5908 | 0.5854 | 0.5853 | 0.5908 |
| 0.9937 | 9.9936 | 782 | 0.9339 | 0.6023 | 0.5934 | 0.5894 | 0.6023 |
| 0.9387 | 10.9904 | 860 | 0.9120 | 0.6138 | 0.5996 | 0.5969 | 0.6138 |
| 0.9324 | 11.9617 | 936 | 0.9135 | 0.5954 | 0.5879 | 0.5865 | 0.5954 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"kategori1",
"kategori2",
"kategori4",
"kategori5"
] |
rii92/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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2958
- Accuracy: 0.5125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.4813 | 0.4625 |
| No log | 2.0 | 80 | 1.3642 | 0.4938 |
| No log | 3.0 | 120 | 1.2958 | 0.5125 |
### Framework versions
- Transformers 4.44.1
- Pytorch 2.4.0+cpu
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
sruthianugraha/Biomedical_ViT
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- 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]
|
[
"benign",
"malignant",
"normal"
] |
ruben09/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0337
- Accuracy: 0.275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0836 | 1.0 | 10 | 2.0539 | 0.2062 |
| 2.0277 | 2.0 | 20 | 2.0386 | 0.2625 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
tangg555/clip-vit-large-patch14-finetuned-openai-clip-vit-large-patch14-mnist
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-vit-large-patch14-finetuned-openai-clip-vit-large-patch14-mnist
This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0258
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5191 | 1.0 | 422 | 0.0737 | 0.9802 |
| 0.4218 | 2.0 | 844 | 0.0505 | 0.9845 |
| 0.3987 | 3.0 | 1266 | 0.0497 | 0.9853 |
| 0.3776 | 4.0 | 1688 | 0.0466 | 0.985 |
| 0.3244 | 5.0 | 2110 | 0.0366 | 0.9897 |
| 0.3269 | 6.0 | 2532 | 0.0491 | 0.9867 |
| 0.3072 | 7.0 | 2954 | 0.0375 | 0.9885 |
| 0.2343 | 8.0 | 3376 | 0.0339 | 0.9908 |
| 0.2395 | 9.0 | 3798 | 0.0274 | 0.9912 |
| 0.222 | 10.0 | 4220 | 0.0258 | 0.9925 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
tangg555/clip-vit-base-patch32-finetuned-openai-clip-vit-base-patch32-mnist
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-vit-base-patch32-finetuned-openai-clip-vit-base-patch32-mnist
This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0201
- Accuracy: 0.9937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4813 | 1.0 | 422 | 0.1699 | 0.9447 |
| 0.4611 | 2.0 | 844 | 0.0592 | 0.9818 |
| 0.4193 | 3.0 | 1266 | 0.0584 | 0.9822 |
| 0.3782 | 4.0 | 1688 | 0.0669 | 0.9788 |
| 0.3293 | 5.0 | 2110 | 0.0349 | 0.9887 |
| 0.3383 | 6.0 | 2532 | 0.0349 | 0.9888 |
| 0.3291 | 7.0 | 2954 | 0.0381 | 0.9873 |
| 0.2783 | 8.0 | 3376 | 0.0225 | 0.9932 |
| 0.2631 | 9.0 | 3798 | 0.0217 | 0.9933 |
| 0.2815 | 10.0 | 4220 | 0.0201 | 0.9937 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
tangg555/clip-vit-large-patch14-336-finetuned-openai-clip-vit-large-patch14-336-mnist
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-vit-large-patch14-336-finetuned-openai-clip-vit-large-patch14-336-mnist
This model is a fine-tuned version of [openai/clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0173
- Accuracy: 0.9945
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4739 | 1.0 | 422 | 0.1506 | 0.9578 |
| 0.4608 | 2.0 | 844 | 0.0464 | 0.9857 |
| 0.3125 | 3.0 | 1266 | 0.0406 | 0.9878 |
| 0.3281 | 4.0 | 1688 | 0.0328 | 0.9895 |
| 0.3068 | 5.0 | 2110 | 0.0230 | 0.9933 |
| 0.3187 | 6.0 | 2532 | 0.0254 | 0.9918 |
| 0.2928 | 7.0 | 2954 | 0.0286 | 0.99 |
| 0.2336 | 8.0 | 3376 | 0.0296 | 0.9908 |
| 0.2472 | 9.0 | 3798 | 0.0217 | 0.994 |
| 0.2356 | 10.0 | 4220 | 0.0173 | 0.9945 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
tangg555/clip-vit-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-vit-base-patch16-finetuned-openai-clip-vit-base-patch16-mnist
This model is a fine-tuned version of [openai/clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0153
- Accuracy: 0.9958
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5063 | 1.0 | 422 | 0.0742 | 0.981 |
| 0.5043 | 2.0 | 844 | 0.0705 | 0.9762 |
| 0.3477 | 3.0 | 1266 | 0.0400 | 0.9883 |
| 0.3968 | 4.0 | 1688 | 0.0319 | 0.9895 |
| 0.4089 | 5.0 | 2110 | 0.0361 | 0.9893 |
| 0.3039 | 6.0 | 2532 | 0.0329 | 0.9882 |
| 0.293 | 7.0 | 2954 | 0.0244 | 0.9918 |
| 0.2723 | 8.0 | 3376 | 0.0241 | 0.9912 |
| 0.2441 | 9.0 | 3798 | 0.0164 | 0.9958 |
| 0.2394 | 10.0 | 4220 | 0.0153 | 0.9958 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
TalonMeyer/dvm-cars-vit-first-5k
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dvm-cars-vit-first-5k
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the TalonMeyer/dvm-cars-dataset-first-5k dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3711
- Accuracy: 0.4431
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.1701 | 1.0 | 251 | 2.9441 | 0.2994 |
| 2.5577 | 2.0 | 502 | 2.6693 | 0.3333 |
| 2.3469 | 3.0 | 753 | 2.5099 | 0.3593 |
| 2.1792 | 4.0 | 1004 | 2.4285 | 0.4032 |
| 2.0967 | 5.0 | 1255 | 2.4063 | 0.4152 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"kia rio 2006 black",
"kia rio 2006 blue",
"kia rio 2006 red",
"kia rio 2006 silver",
"kia rio 2011 black",
"kia rio 2011 blue",
"kia rio 2011 red",
"kia rio 2011 silver",
"kia rio 2011 unlisted",
"kia rio 2011 white",
"kia rio 2012 grey",
"kia rio 2015 black",
"kia rio 2015 blue",
"kia rio 2015 grey",
"kia rio 2015 red",
"kia rio 2015 silver",
"kia rio 2015 unlisted",
"kia rio 2015 white",
"kia rio 2015 yellow",
"kia rio 2016 black",
"kia rio 2016 blue",
"kia rio 2016 grey",
"kia rio 2016 red",
"kia rio 2016 silver",
"kia rio 2016 unlisted",
"kia rio 2016 white",
"kia rio 2016 yellow",
"kia rio 2019 black",
"kia rio 2019 blue",
"kia rio 2019 brown",
"kia rio 2019 grey",
"kia rio 2019 red",
"kia rio 2019 silver",
"kia rio 2019 white",
"kia rio 2019 yellow",
"kia soul 2009 beige",
"kia soul 2009 black",
"kia soul 2009 blue",
"kia soul 2009 orange",
"kia soul 2009 red",
"kia soul 2009 silver",
"kia soul 2009 unlisted",
"kia soul 2009 white",
"kia soul 2010 black",
"kia soul 2010 blue",
"kia soul 2010 brown",
"kia soul 2010 grey",
"kia soul 2010 red",
"kia soul 2010 silver",
"kia soul 2010 unlisted",
"kia soul 2010 white",
"kia soul 2011 black",
"kia soul 2011 blue",
"kia soul 2011 bronze",
"kia soul 2011 brown",
"kia soul 2011 green",
"kia soul 2011 grey",
"kia soul 2011 red",
"kia soul 2011 silver",
"kia soul 2011 unlisted",
"kia soul 2011 white",
"kia soul 2012 black",
"kia soul 2012 blue",
"kia soul 2012 green",
"kia soul 2012 red",
"kia soul 2012 silver",
"kia soul 2012 white",
"kia soul 2013 black",
"kia soul 2013 blue",
"kia soul 2013 bronze",
"kia soul 2013 red",
"kia soul 2013 silver",
"kia soul 2013 unlisted",
"kia soul 2013 white",
"kia soul 2014 black",
"kia soul 2014 blue",
"kia soul 2014 brown",
"kia soul 2014 green",
"kia soul 2014 multicolour",
"kia soul 2014 red",
"kia soul 2014 silver",
"kia soul 2014 unlisted",
"kia soul 2014 white",
"kia soul 2014 yellow",
"kia soul 2015 black",
"kia soul 2015 blue",
"kia soul 2015 green",
"kia soul 2015 grey",
"kia soul 2015 red",
"kia soul 2015 silver",
"kia soul 2015 unlisted",
"kia soul 2015 white",
"kia soul 2015 yellow",
"kia soul 2016 black",
"kia soul 2016 blue",
"kia soul 2016 grey",
"kia soul 2016 red",
"kia soul 2016 silver",
"kia soul 2016 white",
"kia soul 2016 yellow"
] |
raihanp/image_classification2
|
<!-- This model card 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_classification2
This model is a fine-tuned version of [dima806/facial_emotions_image_detection](https://huggingface.co/dima806/facial_emotions_image_detection) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9519
- Accuracy: 0.6687
## Model description
More information needed
## Intended uses & 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8187 | 1.0 | 80 | 1.7527 | 0.4813 |
| 1.52 | 2.0 | 160 | 1.3596 | 0.6312 |
| 1.4072 | 3.0 | 240 | 1.2119 | 0.5875 |
| 1.0868 | 4.0 | 320 | 1.0981 | 0.625 |
| 0.9286 | 5.0 | 400 | 1.0133 | 0.6625 |
| 0.9353 | 6.0 | 480 | 0.9711 | 0.625 |
| 0.7437 | 7.0 | 560 | 0.9389 | 0.6562 |
| 0.6774 | 8.0 | 640 | 0.9519 | 0.6687 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"angry",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise",
"contempt"
] |
dwililiya/emotion_recognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_recognition
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5235
- Accuracy: 0.4562
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3549 | 2.5 | 50 | 1.5704 | 0.4437 |
| 0.9647 | 5.0 | 100 | 1.5235 | 0.4562 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7"
] |
najmeh00/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. -->
# model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"label_23",
"label_24",
"label_25",
"label_26",
"label_27",
"label_28",
"label_29",
"label_30",
"label_31",
"label_32",
"label_33",
"label_34",
"label_35",
"label_36",
"label_37",
"label_38",
"label_39",
"label_40",
"label_41",
"label_42",
"label_43",
"label_44",
"label_45",
"label_46",
"label_47",
"label_48",
"label_49",
"label_50",
"label_51",
"label_52",
"label_53",
"label_54",
"label_55",
"label_56",
"label_57",
"label_58",
"label_59",
"label_60",
"label_61",
"label_62",
"label_63",
"label_64",
"label_65",
"label_66",
"label_67",
"label_68",
"label_69",
"label_70",
"label_71",
"label_72",
"label_73",
"label_74",
"label_75",
"label_76",
"label_77",
"label_78",
"label_79",
"label_80",
"label_81",
"label_82",
"label_83",
"label_84",
"label_85",
"label_86",
"label_87",
"label_88",
"label_89",
"label_90",
"label_91",
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wibidanes/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3144
- Accuracy: 0.5563
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8682 | 10.0 | 100 | 1.6262 | 0.5 |
| 1.3804 | 20.0 | 200 | 1.3624 | 0.575 |
| 1.1323 | 30.0 | 300 | 1.3339 | 0.5813 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
wiwiewei18/emotion_image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4645
- Accuracy: 0.45
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
DzakiArkaan/Emotion-Recognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Emotion-Recognition
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7248
- Accuracy: 0.35
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0336 | 1.0 | 40 | 1.9731 | 0.2313 |
| 1.7687 | 2.0 | 80 | 1.7835 | 0.3563 |
| 1.5551 | 3.0 | 120 | 1.7248 | 0.35 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
rohanmj99/vit-Facial-Expression-Recognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-Facial-Expression-Recognition
This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2606
- 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6309 | 0.3328 | 100 | 0.2618 | 0.9145 |
| 0.6165 | 0.6656 | 200 | 0.2600 | 0.9150 |
| 0.6283 | 0.9983 | 300 | 0.2659 | 0.9135 |
| 0.6171 | 1.3311 | 400 | 0.2561 | 0.9174 |
| 0.6112 | 1.6639 | 500 | 0.2606 | 0.9148 |
| 0.6081 | 1.9967 | 600 | 0.2624 | 0.9137 |
| 0.5885 | 2.3295 | 700 | 0.2671 | 0.9113 |
| 0.5975 | 2.6622 | 800 | 0.2572 | 0.9156 |
| 0.6067 | 2.9950 | 900 | 0.2683 | 0.9116 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"angry",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
DzakiArkaan/EmotionRecognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# EmotionRecognition
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: 1.6785
- 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9926 | 1.0 | 40 | 1.8900 | 0.3063 |
| 1.684 | 2.0 | 80 | 1.7338 | 0.4 |
| 1.4968 | 3.0 | 120 | 1.6785 | 0.3875 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7"
] |
FellOffTheStairs/Emotional_Recognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Emotional_Recognition
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
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