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hkivancoral/hushem_5x_beit_base_sgd_0001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_0001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4647
- Accuracy: 0.2791
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5793 | 1.0 | 28 | 1.5789 | 0.2558 |
| 1.5183 | 2.0 | 56 | 1.5712 | 0.2558 |
| 1.5213 | 3.0 | 84 | 1.5641 | 0.2558 |
| 1.4605 | 4.0 | 112 | 1.5574 | 0.2558 |
| 1.4855 | 5.0 | 140 | 1.5511 | 0.2558 |
| 1.4714 | 6.0 | 168 | 1.5448 | 0.2558 |
| 1.5489 | 7.0 | 196 | 1.5392 | 0.2791 |
| 1.4903 | 8.0 | 224 | 1.5342 | 0.2791 |
| 1.4325 | 9.0 | 252 | 1.5290 | 0.2791 |
| 1.4353 | 10.0 | 280 | 1.5246 | 0.2558 |
| 1.4693 | 11.0 | 308 | 1.5207 | 0.2558 |
| 1.4343 | 12.0 | 336 | 1.5162 | 0.2558 |
| 1.4713 | 13.0 | 364 | 1.5122 | 0.2558 |
| 1.4732 | 14.0 | 392 | 1.5085 | 0.2558 |
| 1.517 | 15.0 | 420 | 1.5050 | 0.2558 |
| 1.4521 | 16.0 | 448 | 1.5018 | 0.2558 |
| 1.4309 | 17.0 | 476 | 1.4988 | 0.2558 |
| 1.4246 | 18.0 | 504 | 1.4964 | 0.2558 |
| 1.4231 | 19.0 | 532 | 1.4937 | 0.2558 |
| 1.4691 | 20.0 | 560 | 1.4912 | 0.2558 |
| 1.4305 | 21.0 | 588 | 1.4888 | 0.2558 |
| 1.4575 | 22.0 | 616 | 1.4865 | 0.2558 |
| 1.4268 | 23.0 | 644 | 1.4845 | 0.2558 |
| 1.3904 | 24.0 | 672 | 1.4827 | 0.2558 |
| 1.4432 | 25.0 | 700 | 1.4808 | 0.2558 |
| 1.4078 | 26.0 | 728 | 1.4793 | 0.2558 |
| 1.382 | 27.0 | 756 | 1.4777 | 0.2558 |
| 1.3894 | 28.0 | 784 | 1.4764 | 0.2558 |
| 1.4046 | 29.0 | 812 | 1.4751 | 0.2558 |
| 1.4273 | 30.0 | 840 | 1.4741 | 0.2791 |
| 1.3786 | 31.0 | 868 | 1.4730 | 0.2791 |
| 1.3777 | 32.0 | 896 | 1.4719 | 0.2791 |
| 1.3887 | 33.0 | 924 | 1.4708 | 0.2791 |
| 1.3651 | 34.0 | 952 | 1.4700 | 0.2791 |
| 1.4904 | 35.0 | 980 | 1.4692 | 0.2791 |
| 1.3288 | 36.0 | 1008 | 1.4686 | 0.2791 |
| 1.3653 | 37.0 | 1036 | 1.4680 | 0.2791 |
| 1.3833 | 38.0 | 1064 | 1.4673 | 0.2791 |
| 1.3973 | 39.0 | 1092 | 1.4668 | 0.2791 |
| 1.4044 | 40.0 | 1120 | 1.4663 | 0.2791 |
| 1.3896 | 41.0 | 1148 | 1.4659 | 0.2791 |
| 1.3676 | 42.0 | 1176 | 1.4656 | 0.2791 |
| 1.3444 | 43.0 | 1204 | 1.4654 | 0.2791 |
| 1.3782 | 44.0 | 1232 | 1.4651 | 0.2791 |
| 1.44 | 45.0 | 1260 | 1.4650 | 0.2791 |
| 1.383 | 46.0 | 1288 | 1.4648 | 0.2791 |
| 1.3752 | 47.0 | 1316 | 1.4648 | 0.2791 |
| 1.343 | 48.0 | 1344 | 1.4647 | 0.2791 |
| 1.3923 | 49.0 | 1372 | 1.4647 | 0.2791 |
| 1.429 | 50.0 | 1400 | 1.4647 | 0.2791 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
paolinox/mobilenet-finetuned-food101
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilenet-finetuned-food101
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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5518
- Accuracy: 0.821
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.9575 | 0.153 |
| 1.9536 | 2.0 | 12 | 1.8509 | 0.265 |
| 1.9536 | 3.0 | 18 | 1.7003 | 0.451 |
| 1.7915 | 4.0 | 24 | 1.5181 | 0.578 |
| 1.4994 | 5.0 | 30 | 1.3609 | 0.631 |
| 1.4994 | 6.0 | 36 | 1.2321 | 0.669 |
| 1.2203 | 7.0 | 42 | 1.0696 | 0.69 |
| 1.2203 | 8.0 | 48 | 0.9676 | 0.723 |
| 1.0215 | 9.0 | 54 | 0.8888 | 0.729 |
| 0.8462 | 10.0 | 60 | 0.8380 | 0.74 |
| 0.8462 | 11.0 | 66 | 0.7461 | 0.778 |
| 0.744 | 12.0 | 72 | 0.6724 | 0.792 |
| 0.744 | 13.0 | 78 | 0.7314 | 0.769 |
| 0.6496 | 14.0 | 84 | 0.6831 | 0.77 |
| 0.6143 | 15.0 | 90 | 0.5937 | 0.81 |
| 0.6143 | 16.0 | 96 | 0.6217 | 0.793 |
| 0.5468 | 17.0 | 102 | 0.5965 | 0.788 |
| 0.5468 | 18.0 | 108 | 0.5944 | 0.813 |
| 0.5428 | 19.0 | 114 | 0.5869 | 0.812 |
| 0.5193 | 20.0 | 120 | 0.5565 | 0.82 |
| 0.5193 | 21.0 | 126 | 0.6155 | 0.803 |
| 0.4902 | 22.0 | 132 | 0.5685 | 0.817 |
| 0.4902 | 23.0 | 138 | 0.6097 | 0.789 |
| 0.4869 | 24.0 | 144 | 0.6002 | 0.8 |
| 0.4745 | 25.0 | 150 | 0.5569 | 0.814 |
| 0.4745 | 26.0 | 156 | 0.5414 | 0.821 |
| 0.4653 | 27.0 | 162 | 0.5806 | 0.807 |
| 0.4653 | 28.0 | 168 | 0.5663 | 0.807 |
| 0.4543 | 29.0 | 174 | 0.5412 | 0.825 |
| 0.4575 | 30.0 | 180 | 0.5518 | 0.821 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"beignets",
"bruschetta",
"chicken_wings",
"hamburger",
"pork_chop",
"prime_rib",
"ramen"
] |
paolinox/mobilevit-finetuned-food101
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilevit-finetuned-food101
This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4191
- Accuracy: 0.874
## Model description
More information needed
## Intended uses & 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9487 | 0.98 | 23 | 1.9476 | 0.151 |
| 1.9273 | 2.0 | 47 | 1.9070 | 0.24 |
| 1.8561 | 2.98 | 70 | 1.8401 | 0.448 |
| 1.7788 | 4.0 | 94 | 1.7301 | 0.612 |
| 1.6586 | 4.98 | 117 | 1.5863 | 0.676 |
| 1.4603 | 6.0 | 141 | 1.4199 | 0.72 |
| 1.3027 | 6.98 | 164 | 1.2215 | 0.734 |
| 1.1717 | 8.0 | 188 | 1.0581 | 0.759 |
| 0.9601 | 8.98 | 211 | 0.9013 | 0.769 |
| 0.8482 | 10.0 | 235 | 0.7866 | 0.791 |
| 0.7276 | 10.98 | 258 | 0.7112 | 0.803 |
| 0.6449 | 12.0 | 282 | 0.6132 | 0.835 |
| 0.6279 | 12.98 | 305 | 0.6069 | 0.83 |
| 0.5982 | 14.0 | 329 | 0.5637 | 0.832 |
| 0.5766 | 14.98 | 352 | 0.5149 | 0.857 |
| 0.5345 | 16.0 | 376 | 0.5392 | 0.837 |
| 0.494 | 16.98 | 399 | 0.5017 | 0.848 |
| 0.4953 | 18.0 | 423 | 0.5002 | 0.846 |
| 0.5118 | 18.98 | 446 | 0.4782 | 0.856 |
| 0.4708 | 20.0 | 470 | 0.4898 | 0.858 |
| 0.4774 | 20.98 | 493 | 0.4769 | 0.851 |
| 0.4848 | 22.0 | 517 | 0.4665 | 0.841 |
| 0.4533 | 22.98 | 540 | 0.4890 | 0.837 |
| 0.4449 | 24.0 | 564 | 0.4558 | 0.857 |
| 0.4205 | 24.98 | 587 | 0.4767 | 0.857 |
| 0.4417 | 26.0 | 611 | 0.4476 | 0.853 |
| 0.4333 | 26.98 | 634 | 0.4853 | 0.834 |
| 0.4545 | 28.0 | 658 | 0.4573 | 0.847 |
| 0.4489 | 28.98 | 681 | 0.4659 | 0.845 |
| 0.4172 | 29.36 | 690 | 0.4191 | 0.874 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"beignets",
"bruschetta",
"chicken_wings",
"hamburger",
"pork_chop",
"prime_rib",
"ramen"
] |
Andron00e/ViTForImageClassification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViTForImageClassification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1199
- Accuracy: 0.9678
## Model description
[A detailed description of model architecture can be found here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/modeling_vit.py#L756)
## Training and evaluation data
[CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom)
## Training procedure
Straightforward tuning of all model's parameters.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 128
- 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2995 | 0.27 | 100 | 0.3419 | 0.9108 |
| 0.2289 | 0.53 | 200 | 0.2482 | 0.9288 |
| 0.1811 | 0.8 | 300 | 0.2139 | 0.9357 |
| 0.0797 | 1.07 | 400 | 0.1813 | 0.946 |
| 0.1128 | 1.33 | 500 | 0.1741 | 0.9452 |
| 0.086 | 1.6 | 600 | 0.1659 | 0.9513 |
| 0.0815 | 1.87 | 700 | 0.1468 | 0.9547 |
| 0.048 | 2.13 | 800 | 0.1393 | 0.9592 |
| 0.021 | 2.4 | 900 | 0.1399 | 0.9603 |
| 0.0271 | 2.67 | 1000 | 0.1334 | 0.9642 |
| 0.0231 | 2.93 | 1100 | 0.1228 | 0.9658 |
| 0.0101 | 3.2 | 1200 | 0.1229 | 0.9673 |
| 0.0041 | 3.47 | 1300 | 0.1189 | 0.9675 |
| 0.0043 | 3.73 | 1400 | 0.1165 | 0.9683 |
| 0.0067 | 4.0 | 1500 | 0.1145 | 0.9697 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
paolinox/segformer-finetuned-food101
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-food101
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3478
- Accuracy: 0.888
## Model description
More information needed
## Intended uses & 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0272 | 0.98 | 23 | 1.8039 | 0.329 |
| 1.5806 | 2.0 | 47 | 1.2465 | 0.608 |
| 1.0564 | 2.98 | 70 | 0.7507 | 0.756 |
| 0.7358 | 4.0 | 94 | 0.6263 | 0.784 |
| 0.6482 | 4.98 | 117 | 0.5551 | 0.795 |
| 0.5692 | 6.0 | 141 | 0.5849 | 0.794 |
| 0.5552 | 6.98 | 164 | 0.4931 | 0.831 |
| 0.4956 | 8.0 | 188 | 0.5166 | 0.83 |
| 0.4748 | 8.98 | 211 | 0.4808 | 0.834 |
| 0.424 | 10.0 | 235 | 0.4238 | 0.852 |
| 0.4314 | 10.98 | 258 | 0.4858 | 0.838 |
| 0.4071 | 12.0 | 282 | 0.4304 | 0.858 |
| 0.3928 | 12.98 | 305 | 0.4621 | 0.851 |
| 0.3695 | 14.0 | 329 | 0.4398 | 0.859 |
| 0.3704 | 14.98 | 352 | 0.4172 | 0.855 |
| 0.3299 | 16.0 | 376 | 0.4225 | 0.856 |
| 0.3391 | 16.98 | 399 | 0.4165 | 0.855 |
| 0.3023 | 18.0 | 423 | 0.3828 | 0.869 |
| 0.3318 | 18.98 | 446 | 0.4190 | 0.861 |
| 0.2994 | 20.0 | 470 | 0.4190 | 0.861 |
| 0.323 | 20.98 | 493 | 0.4034 | 0.866 |
| 0.2883 | 22.0 | 517 | 0.4083 | 0.874 |
| 0.2959 | 22.98 | 540 | 0.4202 | 0.862 |
| 0.2665 | 24.0 | 564 | 0.3740 | 0.881 |
| 0.2765 | 24.98 | 587 | 0.4123 | 0.866 |
| 0.2728 | 26.0 | 611 | 0.3763 | 0.868 |
| 0.2817 | 26.98 | 634 | 0.3939 | 0.864 |
| 0.2467 | 28.0 | 658 | 0.3938 | 0.87 |
| 0.2772 | 28.98 | 681 | 0.4013 | 0.866 |
| 0.2243 | 29.36 | 690 | 0.3478 | 0.888 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"beignets",
"bruschetta",
"chicken_wings",
"hamburger",
"pork_chop",
"prime_rib",
"ramen"
] |
hkivancoral/hushem_5x_beit_base_sgd_0001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_0001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3804
- Accuracy: 0.4048
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4632 | 1.0 | 28 | 1.5173 | 0.2143 |
| 1.3741 | 2.0 | 56 | 1.5094 | 0.2381 |
| 1.4021 | 3.0 | 84 | 1.5010 | 0.2381 |
| 1.3681 | 4.0 | 112 | 1.4942 | 0.2381 |
| 1.4122 | 5.0 | 140 | 1.4872 | 0.2381 |
| 1.3657 | 6.0 | 168 | 1.4803 | 0.2619 |
| 1.3993 | 7.0 | 196 | 1.4742 | 0.2619 |
| 1.3652 | 8.0 | 224 | 1.4681 | 0.2619 |
| 1.3615 | 9.0 | 252 | 1.4624 | 0.2619 |
| 1.3492 | 10.0 | 280 | 1.4574 | 0.2619 |
| 1.3205 | 11.0 | 308 | 1.4526 | 0.2619 |
| 1.3552 | 12.0 | 336 | 1.4476 | 0.2619 |
| 1.3393 | 13.0 | 364 | 1.4435 | 0.2619 |
| 1.3397 | 14.0 | 392 | 1.4389 | 0.2619 |
| 1.3561 | 15.0 | 420 | 1.4347 | 0.2619 |
| 1.3361 | 16.0 | 448 | 1.4313 | 0.2619 |
| 1.3287 | 17.0 | 476 | 1.4281 | 0.2857 |
| 1.3138 | 18.0 | 504 | 1.4246 | 0.3095 |
| 1.3241 | 19.0 | 532 | 1.4213 | 0.3095 |
| 1.3033 | 20.0 | 560 | 1.4184 | 0.3095 |
| 1.3163 | 21.0 | 588 | 1.4155 | 0.3095 |
| 1.3116 | 22.0 | 616 | 1.4126 | 0.3095 |
| 1.3228 | 23.0 | 644 | 1.4101 | 0.3095 |
| 1.3214 | 24.0 | 672 | 1.4076 | 0.3333 |
| 1.2818 | 25.0 | 700 | 1.4051 | 0.3333 |
| 1.2948 | 26.0 | 728 | 1.4029 | 0.3333 |
| 1.3231 | 27.0 | 756 | 1.4008 | 0.3333 |
| 1.2969 | 28.0 | 784 | 1.3988 | 0.3333 |
| 1.2659 | 29.0 | 812 | 1.3969 | 0.3333 |
| 1.2426 | 30.0 | 840 | 1.3952 | 0.3571 |
| 1.2934 | 31.0 | 868 | 1.3935 | 0.3810 |
| 1.2777 | 32.0 | 896 | 1.3917 | 0.4048 |
| 1.2767 | 33.0 | 924 | 1.3904 | 0.4048 |
| 1.3162 | 34.0 | 952 | 1.3892 | 0.4048 |
| 1.2726 | 35.0 | 980 | 1.3880 | 0.4048 |
| 1.294 | 36.0 | 1008 | 1.3868 | 0.4048 |
| 1.2554 | 37.0 | 1036 | 1.3858 | 0.4048 |
| 1.2838 | 38.0 | 1064 | 1.3848 | 0.4048 |
| 1.2842 | 39.0 | 1092 | 1.3839 | 0.4048 |
| 1.2721 | 40.0 | 1120 | 1.3832 | 0.4048 |
| 1.2562 | 41.0 | 1148 | 1.3826 | 0.4048 |
| 1.2576 | 42.0 | 1176 | 1.3821 | 0.4048 |
| 1.3 | 43.0 | 1204 | 1.3815 | 0.4048 |
| 1.273 | 44.0 | 1232 | 1.3811 | 0.4048 |
| 1.2913 | 45.0 | 1260 | 1.3808 | 0.4048 |
| 1.2814 | 46.0 | 1288 | 1.3806 | 0.4048 |
| 1.2272 | 47.0 | 1316 | 1.3805 | 0.4048 |
| 1.2516 | 48.0 | 1344 | 1.3804 | 0.4048 |
| 1.2555 | 49.0 | 1372 | 1.3804 | 0.4048 |
| 1.3084 | 50.0 | 1400 | 1.3804 | 0.4048 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
edwinpalegre/ee8225-group4-vit-trashnet-enhanced
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ee8225-group4-vit-trashnet-enhanced
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 edwinpalegre/trashnet-enhanced dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0793
- Accuracy: 0.9817
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0603 | 0.4 | 100 | 0.1482 | 0.9623 |
| 0.0363 | 0.8 | 200 | 0.1123 | 0.9698 |
| 0.0306 | 1.2 | 300 | 0.1069 | 0.9721 |
| 0.023 | 1.61 | 400 | 0.1188 | 0.9706 |
| 0.0172 | 2.01 | 500 | 0.1019 | 0.9734 |
| 0.0161 | 2.41 | 600 | 0.1112 | 0.9746 |
| 0.0163 | 2.81 | 700 | 0.0874 | 0.9801 |
| 0.0024 | 3.21 | 800 | 0.0793 | 0.9817 |
| 0.0133 | 3.61 | 900 | 0.0831 | 0.9812 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"biodegradable",
"cardboard",
"glass",
"metal",
"paper",
"plastic",
"trash"
] |
hkivancoral/hushem_5x_beit_base_sgd_0001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4856
- Accuracy: 0.3171
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5711 | 1.0 | 28 | 1.6258 | 0.2439 |
| 1.5362 | 2.0 | 56 | 1.6161 | 0.2439 |
| 1.5243 | 3.0 | 84 | 1.6077 | 0.2439 |
| 1.5675 | 4.0 | 112 | 1.5988 | 0.2439 |
| 1.5133 | 5.0 | 140 | 1.5920 | 0.2439 |
| 1.5639 | 6.0 | 168 | 1.5854 | 0.2439 |
| 1.555 | 7.0 | 196 | 1.5785 | 0.2439 |
| 1.5064 | 8.0 | 224 | 1.5727 | 0.2439 |
| 1.4878 | 9.0 | 252 | 1.5672 | 0.2439 |
| 1.5121 | 10.0 | 280 | 1.5615 | 0.2439 |
| 1.4492 | 11.0 | 308 | 1.5578 | 0.2439 |
| 1.5023 | 12.0 | 336 | 1.5529 | 0.2439 |
| 1.5035 | 13.0 | 364 | 1.5492 | 0.2439 |
| 1.4801 | 14.0 | 392 | 1.5454 | 0.2439 |
| 1.4838 | 15.0 | 420 | 1.5419 | 0.2683 |
| 1.4587 | 16.0 | 448 | 1.5385 | 0.2683 |
| 1.4655 | 17.0 | 476 | 1.5343 | 0.2683 |
| 1.4244 | 18.0 | 504 | 1.5315 | 0.2927 |
| 1.4339 | 19.0 | 532 | 1.5284 | 0.2927 |
| 1.4266 | 20.0 | 560 | 1.5249 | 0.2927 |
| 1.4474 | 21.0 | 588 | 1.5220 | 0.2927 |
| 1.4652 | 22.0 | 616 | 1.5188 | 0.3171 |
| 1.4621 | 23.0 | 644 | 1.5163 | 0.3171 |
| 1.4655 | 24.0 | 672 | 1.5146 | 0.3171 |
| 1.4192 | 25.0 | 700 | 1.5130 | 0.3171 |
| 1.4459 | 26.0 | 728 | 1.5105 | 0.3171 |
| 1.469 | 27.0 | 756 | 1.5090 | 0.3171 |
| 1.3585 | 28.0 | 784 | 1.5067 | 0.3171 |
| 1.4084 | 29.0 | 812 | 1.5049 | 0.3171 |
| 1.4047 | 30.0 | 840 | 1.5031 | 0.3171 |
| 1.4414 | 31.0 | 868 | 1.5013 | 0.3171 |
| 1.3836 | 32.0 | 896 | 1.4995 | 0.3171 |
| 1.3896 | 33.0 | 924 | 1.4979 | 0.3171 |
| 1.4222 | 34.0 | 952 | 1.4964 | 0.3171 |
| 1.4396 | 35.0 | 980 | 1.4952 | 0.3171 |
| 1.3891 | 36.0 | 1008 | 1.4939 | 0.3171 |
| 1.393 | 37.0 | 1036 | 1.4925 | 0.3171 |
| 1.3697 | 38.0 | 1064 | 1.4914 | 0.3171 |
| 1.4252 | 39.0 | 1092 | 1.4901 | 0.3171 |
| 1.365 | 40.0 | 1120 | 1.4892 | 0.3171 |
| 1.4164 | 41.0 | 1148 | 1.4883 | 0.3171 |
| 1.3854 | 42.0 | 1176 | 1.4876 | 0.3171 |
| 1.3744 | 43.0 | 1204 | 1.4870 | 0.3171 |
| 1.4041 | 44.0 | 1232 | 1.4865 | 0.3171 |
| 1.3952 | 45.0 | 1260 | 1.4861 | 0.3171 |
| 1.3758 | 46.0 | 1288 | 1.4858 | 0.3171 |
| 1.3986 | 47.0 | 1316 | 1.4857 | 0.3171 |
| 1.3628 | 48.0 | 1344 | 1.4856 | 0.3171 |
| 1.4108 | 49.0 | 1372 | 1.4856 | 0.3171 |
| 1.4199 | 50.0 | 1400 | 1.4856 | 0.3171 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_sgd_00001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5922
- Accuracy: 0.2667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4867 | 1.0 | 27 | 1.6071 | 0.2667 |
| 1.5392 | 2.0 | 54 | 1.6064 | 0.2667 |
| 1.5844 | 3.0 | 81 | 1.6056 | 0.2667 |
| 1.5797 | 4.0 | 108 | 1.6050 | 0.2667 |
| 1.5108 | 5.0 | 135 | 1.6044 | 0.2667 |
| 1.5236 | 6.0 | 162 | 1.6037 | 0.2667 |
| 1.5199 | 7.0 | 189 | 1.6031 | 0.2667 |
| 1.544 | 8.0 | 216 | 1.6026 | 0.2667 |
| 1.5317 | 9.0 | 243 | 1.6020 | 0.2667 |
| 1.537 | 10.0 | 270 | 1.6014 | 0.2667 |
| 1.5415 | 11.0 | 297 | 1.6010 | 0.2667 |
| 1.5478 | 12.0 | 324 | 1.6004 | 0.2667 |
| 1.4666 | 13.0 | 351 | 1.6000 | 0.2667 |
| 1.5352 | 14.0 | 378 | 1.5995 | 0.2667 |
| 1.478 | 15.0 | 405 | 1.5990 | 0.2667 |
| 1.5333 | 16.0 | 432 | 1.5986 | 0.2667 |
| 1.5245 | 17.0 | 459 | 1.5982 | 0.2667 |
| 1.5379 | 18.0 | 486 | 1.5978 | 0.2667 |
| 1.52 | 19.0 | 513 | 1.5975 | 0.2667 |
| 1.5508 | 20.0 | 540 | 1.5971 | 0.2667 |
| 1.5421 | 21.0 | 567 | 1.5967 | 0.2667 |
| 1.4919 | 22.0 | 594 | 1.5963 | 0.2667 |
| 1.483 | 23.0 | 621 | 1.5960 | 0.2667 |
| 1.5087 | 24.0 | 648 | 1.5957 | 0.2667 |
| 1.5236 | 25.0 | 675 | 1.5954 | 0.2667 |
| 1.5228 | 26.0 | 702 | 1.5951 | 0.2667 |
| 1.5439 | 27.0 | 729 | 1.5949 | 0.2667 |
| 1.5272 | 28.0 | 756 | 1.5946 | 0.2667 |
| 1.5029 | 29.0 | 783 | 1.5943 | 0.2667 |
| 1.5695 | 30.0 | 810 | 1.5941 | 0.2667 |
| 1.5057 | 31.0 | 837 | 1.5939 | 0.2667 |
| 1.5092 | 32.0 | 864 | 1.5937 | 0.2667 |
| 1.575 | 33.0 | 891 | 1.5935 | 0.2667 |
| 1.5175 | 34.0 | 918 | 1.5934 | 0.2667 |
| 1.4801 | 35.0 | 945 | 1.5932 | 0.2667 |
| 1.4771 | 36.0 | 972 | 1.5930 | 0.2667 |
| 1.5042 | 37.0 | 999 | 1.5929 | 0.2667 |
| 1.5372 | 38.0 | 1026 | 1.5928 | 0.2667 |
| 1.5158 | 39.0 | 1053 | 1.5927 | 0.2667 |
| 1.4902 | 40.0 | 1080 | 1.5926 | 0.2667 |
| 1.4904 | 41.0 | 1107 | 1.5925 | 0.2667 |
| 1.4817 | 42.0 | 1134 | 1.5924 | 0.2667 |
| 1.5064 | 43.0 | 1161 | 1.5923 | 0.2667 |
| 1.4625 | 44.0 | 1188 | 1.5923 | 0.2667 |
| 1.5064 | 45.0 | 1215 | 1.5923 | 0.2667 |
| 1.4956 | 46.0 | 1242 | 1.5922 | 0.2667 |
| 1.502 | 47.0 | 1269 | 1.5922 | 0.2667 |
| 1.495 | 48.0 | 1296 | 1.5922 | 0.2667 |
| 1.4896 | 49.0 | 1323 | 1.5922 | 0.2667 |
| 1.5118 | 50.0 | 1350 | 1.5922 | 0.2667 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_sgd_00001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_00001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5367
- Accuracy: 0.2667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5006 | 1.0 | 27 | 1.5552 | 0.2667 |
| 1.5759 | 2.0 | 54 | 1.5543 | 0.2667 |
| 1.5707 | 3.0 | 81 | 1.5535 | 0.2667 |
| 1.578 | 4.0 | 108 | 1.5527 | 0.2667 |
| 1.5119 | 5.0 | 135 | 1.5520 | 0.2667 |
| 1.5352 | 6.0 | 162 | 1.5512 | 0.2667 |
| 1.5348 | 7.0 | 189 | 1.5504 | 0.2667 |
| 1.5693 | 8.0 | 216 | 1.5497 | 0.2667 |
| 1.5386 | 9.0 | 243 | 1.5490 | 0.2667 |
| 1.5189 | 10.0 | 270 | 1.5483 | 0.2667 |
| 1.5597 | 11.0 | 297 | 1.5477 | 0.2667 |
| 1.5706 | 12.0 | 324 | 1.5471 | 0.2667 |
| 1.5157 | 13.0 | 351 | 1.5465 | 0.2667 |
| 1.5457 | 14.0 | 378 | 1.5458 | 0.2667 |
| 1.5087 | 15.0 | 405 | 1.5453 | 0.2667 |
| 1.5323 | 16.0 | 432 | 1.5447 | 0.2667 |
| 1.5363 | 17.0 | 459 | 1.5442 | 0.2667 |
| 1.5615 | 18.0 | 486 | 1.5437 | 0.2667 |
| 1.5236 | 19.0 | 513 | 1.5433 | 0.2667 |
| 1.566 | 20.0 | 540 | 1.5428 | 0.2667 |
| 1.5446 | 21.0 | 567 | 1.5424 | 0.2667 |
| 1.5289 | 22.0 | 594 | 1.5419 | 0.2667 |
| 1.4823 | 23.0 | 621 | 1.5415 | 0.2667 |
| 1.5025 | 24.0 | 648 | 1.5411 | 0.2667 |
| 1.5362 | 25.0 | 675 | 1.5407 | 0.2667 |
| 1.5593 | 26.0 | 702 | 1.5404 | 0.2667 |
| 1.5515 | 27.0 | 729 | 1.5401 | 0.2667 |
| 1.5275 | 28.0 | 756 | 1.5397 | 0.2667 |
| 1.5171 | 29.0 | 783 | 1.5394 | 0.2667 |
| 1.5816 | 30.0 | 810 | 1.5391 | 0.2667 |
| 1.5294 | 31.0 | 837 | 1.5389 | 0.2667 |
| 1.5276 | 32.0 | 864 | 1.5386 | 0.2667 |
| 1.5584 | 33.0 | 891 | 1.5384 | 0.2667 |
| 1.5549 | 34.0 | 918 | 1.5382 | 0.2667 |
| 1.4864 | 35.0 | 945 | 1.5380 | 0.2667 |
| 1.4851 | 36.0 | 972 | 1.5378 | 0.2667 |
| 1.4835 | 37.0 | 999 | 1.5376 | 0.2667 |
| 1.5708 | 38.0 | 1026 | 1.5374 | 0.2667 |
| 1.5448 | 39.0 | 1053 | 1.5373 | 0.2667 |
| 1.4945 | 40.0 | 1080 | 1.5372 | 0.2667 |
| 1.486 | 41.0 | 1107 | 1.5371 | 0.2667 |
| 1.5082 | 42.0 | 1134 | 1.5370 | 0.2667 |
| 1.5323 | 43.0 | 1161 | 1.5369 | 0.2667 |
| 1.4965 | 44.0 | 1188 | 1.5368 | 0.2667 |
| 1.5407 | 45.0 | 1215 | 1.5368 | 0.2667 |
| 1.5084 | 46.0 | 1242 | 1.5368 | 0.2667 |
| 1.5191 | 47.0 | 1269 | 1.5367 | 0.2667 |
| 1.5617 | 48.0 | 1296 | 1.5367 | 0.2667 |
| 1.4992 | 49.0 | 1323 | 1.5367 | 0.2667 |
| 1.4782 | 50.0 | 1350 | 1.5367 | 0.2667 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_sgd_00001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_00001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5681
- Accuracy: 0.2558
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5838 | 1.0 | 28 | 1.5858 | 0.2558 |
| 1.5323 | 2.0 | 56 | 1.5850 | 0.2558 |
| 1.5483 | 3.0 | 84 | 1.5842 | 0.2558 |
| 1.4864 | 4.0 | 112 | 1.5834 | 0.2558 |
| 1.5286 | 5.0 | 140 | 1.5827 | 0.2558 |
| 1.5129 | 6.0 | 168 | 1.5819 | 0.2558 |
| 1.6083 | 7.0 | 196 | 1.5812 | 0.2558 |
| 1.5405 | 8.0 | 224 | 1.5806 | 0.2558 |
| 1.5045 | 9.0 | 252 | 1.5799 | 0.2558 |
| 1.4827 | 10.0 | 280 | 1.5793 | 0.2558 |
| 1.5466 | 11.0 | 308 | 1.5787 | 0.2558 |
| 1.502 | 12.0 | 336 | 1.5780 | 0.2558 |
| 1.5701 | 13.0 | 364 | 1.5775 | 0.2558 |
| 1.5522 | 14.0 | 392 | 1.5769 | 0.2558 |
| 1.6273 | 15.0 | 420 | 1.5763 | 0.2558 |
| 1.5496 | 16.0 | 448 | 1.5758 | 0.2558 |
| 1.5263 | 17.0 | 476 | 1.5753 | 0.2558 |
| 1.5326 | 18.0 | 504 | 1.5748 | 0.2558 |
| 1.5229 | 19.0 | 532 | 1.5744 | 0.2558 |
| 1.6308 | 20.0 | 560 | 1.5739 | 0.2558 |
| 1.5402 | 21.0 | 588 | 1.5734 | 0.2558 |
| 1.5767 | 22.0 | 616 | 1.5730 | 0.2558 |
| 1.546 | 23.0 | 644 | 1.5726 | 0.2558 |
| 1.4997 | 24.0 | 672 | 1.5722 | 0.2558 |
| 1.5699 | 25.0 | 700 | 1.5719 | 0.2558 |
| 1.5518 | 26.0 | 728 | 1.5715 | 0.2558 |
| 1.5078 | 27.0 | 756 | 1.5712 | 0.2558 |
| 1.509 | 28.0 | 784 | 1.5709 | 0.2558 |
| 1.5496 | 29.0 | 812 | 1.5706 | 0.2558 |
| 1.5569 | 30.0 | 840 | 1.5704 | 0.2558 |
| 1.5113 | 31.0 | 868 | 1.5701 | 0.2558 |
| 1.5157 | 32.0 | 896 | 1.5699 | 0.2558 |
| 1.5362 | 33.0 | 924 | 1.5696 | 0.2558 |
| 1.4946 | 34.0 | 952 | 1.5694 | 0.2558 |
| 1.6128 | 35.0 | 980 | 1.5692 | 0.2558 |
| 1.4515 | 36.0 | 1008 | 1.5691 | 0.2558 |
| 1.4956 | 37.0 | 1036 | 1.5689 | 0.2558 |
| 1.5189 | 38.0 | 1064 | 1.5688 | 0.2558 |
| 1.571 | 39.0 | 1092 | 1.5687 | 0.2558 |
| 1.549 | 40.0 | 1120 | 1.5685 | 0.2558 |
| 1.524 | 41.0 | 1148 | 1.5684 | 0.2558 |
| 1.5138 | 42.0 | 1176 | 1.5684 | 0.2558 |
| 1.4952 | 43.0 | 1204 | 1.5683 | 0.2558 |
| 1.5406 | 44.0 | 1232 | 1.5682 | 0.2558 |
| 1.6126 | 45.0 | 1260 | 1.5682 | 0.2558 |
| 1.5484 | 46.0 | 1288 | 1.5682 | 0.2558 |
| 1.5268 | 47.0 | 1316 | 1.5681 | 0.2558 |
| 1.4882 | 48.0 | 1344 | 1.5681 | 0.2558 |
| 1.5345 | 49.0 | 1372 | 1.5681 | 0.2558 |
| 1.5815 | 50.0 | 1400 | 1.5681 | 0.2558 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_sgd_00001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_00001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4856
- Accuracy: 0.3095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5648 | 1.0 | 28 | 1.5024 | 0.3095 |
| 1.5958 | 2.0 | 56 | 1.5016 | 0.3095 |
| 1.5478 | 3.0 | 84 | 1.5008 | 0.3095 |
| 1.6175 | 4.0 | 112 | 1.5001 | 0.3095 |
| 1.5019 | 5.0 | 140 | 1.4994 | 0.3095 |
| 1.5612 | 6.0 | 168 | 1.4987 | 0.3095 |
| 1.5556 | 7.0 | 196 | 1.4981 | 0.3095 |
| 1.5275 | 8.0 | 224 | 1.4974 | 0.3095 |
| 1.529 | 9.0 | 252 | 1.4968 | 0.3095 |
| 1.5306 | 10.0 | 280 | 1.4962 | 0.3095 |
| 1.5486 | 11.0 | 308 | 1.4956 | 0.3095 |
| 1.5567 | 12.0 | 336 | 1.4950 | 0.3095 |
| 1.5578 | 13.0 | 364 | 1.4945 | 0.3095 |
| 1.5601 | 14.0 | 392 | 1.4939 | 0.3095 |
| 1.5869 | 15.0 | 420 | 1.4934 | 0.3095 |
| 1.5292 | 16.0 | 448 | 1.4929 | 0.3095 |
| 1.584 | 17.0 | 476 | 1.4924 | 0.3095 |
| 1.5709 | 18.0 | 504 | 1.4919 | 0.3095 |
| 1.5246 | 19.0 | 532 | 1.4915 | 0.3095 |
| 1.508 | 20.0 | 560 | 1.4911 | 0.3095 |
| 1.5627 | 21.0 | 588 | 1.4907 | 0.3095 |
| 1.543 | 22.0 | 616 | 1.4904 | 0.3095 |
| 1.5306 | 23.0 | 644 | 1.4900 | 0.3095 |
| 1.5347 | 24.0 | 672 | 1.4896 | 0.3095 |
| 1.5296 | 25.0 | 700 | 1.4893 | 0.3095 |
| 1.5722 | 26.0 | 728 | 1.4889 | 0.3095 |
| 1.6103 | 27.0 | 756 | 1.4886 | 0.3095 |
| 1.5352 | 28.0 | 784 | 1.4883 | 0.3095 |
| 1.5133 | 29.0 | 812 | 1.4880 | 0.3095 |
| 1.4677 | 30.0 | 840 | 1.4878 | 0.3095 |
| 1.5424 | 31.0 | 868 | 1.4876 | 0.3095 |
| 1.5132 | 32.0 | 896 | 1.4873 | 0.3095 |
| 1.5611 | 33.0 | 924 | 1.4871 | 0.3095 |
| 1.5494 | 34.0 | 952 | 1.4869 | 0.3095 |
| 1.5087 | 35.0 | 980 | 1.4867 | 0.3095 |
| 1.5719 | 36.0 | 1008 | 1.4865 | 0.3095 |
| 1.5037 | 37.0 | 1036 | 1.4864 | 0.3095 |
| 1.5457 | 38.0 | 1064 | 1.4863 | 0.3095 |
| 1.5227 | 39.0 | 1092 | 1.4861 | 0.3095 |
| 1.5024 | 40.0 | 1120 | 1.4860 | 0.3095 |
| 1.5112 | 41.0 | 1148 | 1.4859 | 0.3095 |
| 1.4872 | 42.0 | 1176 | 1.4858 | 0.3095 |
| 1.5623 | 43.0 | 1204 | 1.4858 | 0.3095 |
| 1.5147 | 44.0 | 1232 | 1.4857 | 0.3095 |
| 1.5196 | 45.0 | 1260 | 1.4857 | 0.3095 |
| 1.5574 | 46.0 | 1288 | 1.4856 | 0.3095 |
| 1.5277 | 47.0 | 1316 | 1.4856 | 0.3095 |
| 1.602 | 48.0 | 1344 | 1.4856 | 0.3095 |
| 1.5259 | 49.0 | 1372 | 1.4856 | 0.3095 |
| 1.5075 | 50.0 | 1400 | 1.4856 | 0.3095 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_sgd_00001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6137
- Accuracy: 0.2439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5748 | 1.0 | 28 | 1.6349 | 0.2439 |
| 1.5498 | 2.0 | 56 | 1.6339 | 0.2439 |
| 1.5458 | 3.0 | 84 | 1.6329 | 0.2439 |
| 1.5997 | 4.0 | 112 | 1.6319 | 0.2439 |
| 1.5518 | 5.0 | 140 | 1.6310 | 0.2439 |
| 1.6078 | 6.0 | 168 | 1.6301 | 0.2439 |
| 1.6054 | 7.0 | 196 | 1.6292 | 0.2439 |
| 1.5635 | 8.0 | 224 | 1.6284 | 0.2439 |
| 1.5412 | 9.0 | 252 | 1.6276 | 0.2439 |
| 1.5684 | 10.0 | 280 | 1.6268 | 0.2439 |
| 1.5211 | 11.0 | 308 | 1.6261 | 0.2439 |
| 1.5857 | 12.0 | 336 | 1.6254 | 0.2439 |
| 1.5804 | 13.0 | 364 | 1.6248 | 0.2439 |
| 1.5778 | 14.0 | 392 | 1.6241 | 0.2439 |
| 1.5905 | 15.0 | 420 | 1.6235 | 0.2439 |
| 1.5552 | 16.0 | 448 | 1.6228 | 0.2439 |
| 1.5712 | 17.0 | 476 | 1.6222 | 0.2439 |
| 1.5113 | 18.0 | 504 | 1.6216 | 0.2439 |
| 1.5441 | 19.0 | 532 | 1.6210 | 0.2439 |
| 1.547 | 20.0 | 560 | 1.6205 | 0.2439 |
| 1.5712 | 21.0 | 588 | 1.6200 | 0.2439 |
| 1.595 | 22.0 | 616 | 1.6195 | 0.2439 |
| 1.6001 | 23.0 | 644 | 1.6190 | 0.2439 |
| 1.6008 | 24.0 | 672 | 1.6185 | 0.2439 |
| 1.5469 | 25.0 | 700 | 1.6181 | 0.2439 |
| 1.567 | 26.0 | 728 | 1.6177 | 0.2439 |
| 1.618 | 27.0 | 756 | 1.6173 | 0.2439 |
| 1.4849 | 28.0 | 784 | 1.6170 | 0.2439 |
| 1.5706 | 29.0 | 812 | 1.6166 | 0.2439 |
| 1.5269 | 30.0 | 840 | 1.6163 | 0.2439 |
| 1.588 | 31.0 | 868 | 1.6160 | 0.2439 |
| 1.5207 | 32.0 | 896 | 1.6157 | 0.2439 |
| 1.5395 | 33.0 | 924 | 1.6155 | 0.2439 |
| 1.5482 | 34.0 | 952 | 1.6152 | 0.2439 |
| 1.6004 | 35.0 | 980 | 1.6150 | 0.2439 |
| 1.5389 | 36.0 | 1008 | 1.6148 | 0.2439 |
| 1.5566 | 37.0 | 1036 | 1.6146 | 0.2439 |
| 1.54 | 38.0 | 1064 | 1.6145 | 0.2439 |
| 1.5715 | 39.0 | 1092 | 1.6143 | 0.2439 |
| 1.5148 | 40.0 | 1120 | 1.6142 | 0.2439 |
| 1.5688 | 41.0 | 1148 | 1.6141 | 0.2439 |
| 1.5803 | 42.0 | 1176 | 1.6140 | 0.2439 |
| 1.5477 | 43.0 | 1204 | 1.6139 | 0.2439 |
| 1.5623 | 44.0 | 1232 | 1.6138 | 0.2439 |
| 1.5648 | 45.0 | 1260 | 1.6137 | 0.2439 |
| 1.5331 | 46.0 | 1288 | 1.6137 | 0.2439 |
| 1.5791 | 47.0 | 1316 | 1.6137 | 0.2439 |
| 1.5282 | 48.0 | 1344 | 1.6137 | 0.2439 |
| 1.5715 | 49.0 | 1372 | 1.6137 | 0.2439 |
| 1.5955 | 50.0 | 1400 | 1.6137 | 0.2439 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2430
- Accuracy: 0.4444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5782 | 1.0 | 27 | 1.4061 | 0.2444 |
| 1.4004 | 2.0 | 54 | 1.4559 | 0.2444 |
| 1.3873 | 3.0 | 81 | 1.4120 | 0.2444 |
| 1.3666 | 4.0 | 108 | 1.6275 | 0.2444 |
| 1.3597 | 5.0 | 135 | 1.4398 | 0.2444 |
| 1.2814 | 6.0 | 162 | 1.5328 | 0.2444 |
| 1.2056 | 7.0 | 189 | 1.5389 | 0.2 |
| 1.1635 | 8.0 | 216 | 1.5332 | 0.2444 |
| 1.1235 | 9.0 | 243 | 1.6681 | 0.2444 |
| 1.1484 | 10.0 | 270 | 1.6176 | 0.2667 |
| 1.1757 | 11.0 | 297 | 1.6312 | 0.2444 |
| 1.1297 | 12.0 | 324 | 1.5067 | 0.2444 |
| 1.1448 | 13.0 | 351 | 1.5657 | 0.2444 |
| 1.1725 | 14.0 | 378 | 1.5184 | 0.1556 |
| 1.1591 | 15.0 | 405 | 1.5790 | 0.2444 |
| 1.1549 | 16.0 | 432 | 1.5501 | 0.2444 |
| 1.0865 | 17.0 | 459 | 1.5776 | 0.2444 |
| 1.1351 | 18.0 | 486 | 1.6195 | 0.3111 |
| 1.0974 | 19.0 | 513 | 1.5360 | 0.2444 |
| 1.0992 | 20.0 | 540 | 1.5742 | 0.3111 |
| 1.0894 | 21.0 | 567 | 1.4918 | 0.3778 |
| 1.0557 | 22.0 | 594 | 1.5742 | 0.2444 |
| 1.0574 | 23.0 | 621 | 1.5043 | 0.4222 |
| 1.0148 | 24.0 | 648 | 1.3535 | 0.4222 |
| 1.1133 | 25.0 | 675 | 1.4897 | 0.4 |
| 1.02 | 26.0 | 702 | 1.4554 | 0.4222 |
| 1.0107 | 27.0 | 729 | 1.4238 | 0.4 |
| 0.9307 | 28.0 | 756 | 1.7644 | 0.3556 |
| 0.8335 | 29.0 | 783 | 2.0253 | 0.3556 |
| 0.8203 | 30.0 | 810 | 1.7990 | 0.3556 |
| 0.7263 | 31.0 | 837 | 1.6909 | 0.3778 |
| 0.8387 | 32.0 | 864 | 1.4758 | 0.4 |
| 0.6837 | 33.0 | 891 | 2.1584 | 0.3556 |
| 0.7155 | 34.0 | 918 | 1.7102 | 0.3778 |
| 0.6349 | 35.0 | 945 | 1.1875 | 0.4667 |
| 0.6331 | 36.0 | 972 | 1.9965 | 0.4222 |
| 0.5871 | 37.0 | 999 | 1.7881 | 0.4 |
| 0.595 | 38.0 | 1026 | 1.7629 | 0.4 |
| 0.5266 | 39.0 | 1053 | 1.6720 | 0.4222 |
| 0.4985 | 40.0 | 1080 | 2.3229 | 0.4222 |
| 0.4855 | 41.0 | 1107 | 1.6470 | 0.4444 |
| 0.503 | 42.0 | 1134 | 1.7515 | 0.4667 |
| 0.4432 | 43.0 | 1161 | 2.0538 | 0.4222 |
| 0.3668 | 44.0 | 1188 | 2.1471 | 0.4444 |
| 0.3654 | 45.0 | 1215 | 2.0004 | 0.4444 |
| 0.3317 | 46.0 | 1242 | 2.1973 | 0.4444 |
| 0.2413 | 47.0 | 1269 | 2.2882 | 0.4444 |
| 0.2395 | 48.0 | 1296 | 2.2389 | 0.4444 |
| 0.2502 | 49.0 | 1323 | 2.2430 | 0.4444 |
| 0.237 | 50.0 | 1350 | 2.2430 | 0.4444 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7933
- Accuracy: 0.4444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4827 | 1.0 | 27 | 1.4775 | 0.2444 |
| 1.4158 | 2.0 | 54 | 1.4002 | 0.2667 |
| 1.3654 | 3.0 | 81 | 1.4674 | 0.2444 |
| 1.4175 | 4.0 | 108 | 1.4412 | 0.2444 |
| 1.394 | 5.0 | 135 | 1.3951 | 0.2667 |
| 1.2686 | 6.0 | 162 | 1.3983 | 0.2444 |
| 1.2556 | 7.0 | 189 | 1.4175 | 0.2889 |
| 1.2245 | 8.0 | 216 | 1.4754 | 0.2222 |
| 1.1427 | 9.0 | 243 | 1.5387 | 0.2 |
| 1.1659 | 10.0 | 270 | 1.3896 | 0.3333 |
| 1.2047 | 11.0 | 297 | 1.6922 | 0.2444 |
| 1.1384 | 12.0 | 324 | 1.4940 | 0.2667 |
| 1.1563 | 13.0 | 351 | 1.3730 | 0.2889 |
| 1.1141 | 14.0 | 378 | 1.4944 | 0.2222 |
| 1.0922 | 15.0 | 405 | 1.4049 | 0.2222 |
| 1.0475 | 16.0 | 432 | 1.2541 | 0.4 |
| 0.9208 | 17.0 | 459 | 1.2993 | 0.4222 |
| 0.9847 | 18.0 | 486 | 1.4111 | 0.4889 |
| 0.9327 | 19.0 | 513 | 1.3175 | 0.2889 |
| 0.8591 | 20.0 | 540 | 1.2892 | 0.3111 |
| 0.7605 | 21.0 | 567 | 1.6440 | 0.2667 |
| 0.7953 | 22.0 | 594 | 1.6915 | 0.3778 |
| 0.7644 | 23.0 | 621 | 1.6017 | 0.4667 |
| 0.7884 | 24.0 | 648 | 1.4064 | 0.2444 |
| 0.6883 | 25.0 | 675 | 1.9722 | 0.3111 |
| 0.7747 | 26.0 | 702 | 1.9209 | 0.4889 |
| 0.7012 | 27.0 | 729 | 2.2074 | 0.5333 |
| 0.6951 | 28.0 | 756 | 2.4602 | 0.3556 |
| 0.6581 | 29.0 | 783 | 2.1544 | 0.4222 |
| 0.6529 | 30.0 | 810 | 2.0677 | 0.3556 |
| 0.533 | 31.0 | 837 | 2.1507 | 0.3778 |
| 0.6648 | 32.0 | 864 | 2.1628 | 0.4222 |
| 0.6094 | 33.0 | 891 | 2.5365 | 0.3778 |
| 0.5601 | 34.0 | 918 | 2.8323 | 0.4222 |
| 0.519 | 35.0 | 945 | 2.4166 | 0.4 |
| 0.5988 | 36.0 | 972 | 2.6302 | 0.4444 |
| 0.5359 | 37.0 | 999 | 2.9183 | 0.3778 |
| 0.5451 | 38.0 | 1026 | 2.8746 | 0.5111 |
| 0.5087 | 39.0 | 1053 | 2.7419 | 0.4667 |
| 0.4563 | 40.0 | 1080 | 3.1565 | 0.4222 |
| 0.5182 | 41.0 | 1107 | 3.1768 | 0.4444 |
| 0.4348 | 42.0 | 1134 | 3.2761 | 0.4222 |
| 0.4504 | 43.0 | 1161 | 3.4108 | 0.4667 |
| 0.417 | 44.0 | 1188 | 3.5781 | 0.4444 |
| 0.4297 | 45.0 | 1215 | 3.6284 | 0.4444 |
| 0.3399 | 46.0 | 1242 | 3.7187 | 0.4444 |
| 0.3846 | 47.0 | 1269 | 3.7298 | 0.4667 |
| 0.3494 | 48.0 | 1296 | 3.7854 | 0.4444 |
| 0.3468 | 49.0 | 1323 | 3.7933 | 0.4444 |
| 0.3313 | 50.0 | 1350 | 3.7933 | 0.4444 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
sh-zheng/vit-base-patch16-224-in21k-fintuned-SurfaceRoughness
|
## Vision Transformer (Fine-Tuned model)
refer to https://huggingface.co/google/vit-base-patch16-224 for model detail and how to use
## Model Description
Predict surface roughness category using snips taken from google maps aerial view. There are 3 categories: surface roughness B, surface roughness C, surface roughness D as defined in ASCE 7-16 section 26.7.2.
|
[
"roughnessb",
"roughnessc",
"roughnessd"
] |
hkivancoral/hushem_5x_beit_base_rms_001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4768
- Accuracy: 0.6279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.51 | 1.0 | 28 | 1.6351 | 0.2558 |
| 1.3869 | 2.0 | 56 | 1.4127 | 0.2558 |
| 1.3848 | 3.0 | 84 | 1.3895 | 0.2558 |
| 1.4113 | 4.0 | 112 | 1.3824 | 0.2558 |
| 1.3569 | 5.0 | 140 | 1.4121 | 0.2326 |
| 1.4625 | 6.0 | 168 | 1.3739 | 0.2326 |
| 1.3804 | 7.0 | 196 | 1.2185 | 0.5349 |
| 1.1352 | 8.0 | 224 | 1.1411 | 0.4884 |
| 1.0899 | 9.0 | 252 | 1.2426 | 0.3953 |
| 1.0945 | 10.0 | 280 | 1.1820 | 0.3488 |
| 1.1149 | 11.0 | 308 | 1.4574 | 0.3023 |
| 0.9942 | 12.0 | 336 | 1.4728 | 0.3256 |
| 1.0204 | 13.0 | 364 | 0.9801 | 0.5581 |
| 0.9987 | 14.0 | 392 | 1.0096 | 0.5349 |
| 1.0664 | 15.0 | 420 | 1.0007 | 0.5814 |
| 0.9463 | 16.0 | 448 | 1.2188 | 0.3953 |
| 0.9756 | 17.0 | 476 | 1.1284 | 0.5116 |
| 0.9698 | 18.0 | 504 | 1.4394 | 0.4419 |
| 1.061 | 19.0 | 532 | 1.1162 | 0.4884 |
| 0.8426 | 20.0 | 560 | 1.9296 | 0.3721 |
| 0.876 | 21.0 | 588 | 1.0070 | 0.5581 |
| 0.8908 | 22.0 | 616 | 1.2196 | 0.5349 |
| 0.8599 | 23.0 | 644 | 0.9502 | 0.6047 |
| 0.8338 | 24.0 | 672 | 0.8737 | 0.6279 |
| 0.785 | 25.0 | 700 | 1.1006 | 0.5814 |
| 0.82 | 26.0 | 728 | 1.0398 | 0.5814 |
| 0.8016 | 27.0 | 756 | 1.6671 | 0.3256 |
| 0.8574 | 28.0 | 784 | 1.1704 | 0.6279 |
| 0.8104 | 29.0 | 812 | 1.0502 | 0.6279 |
| 0.7421 | 30.0 | 840 | 0.9270 | 0.5814 |
| 0.7093 | 31.0 | 868 | 1.8057 | 0.4186 |
| 0.7469 | 32.0 | 896 | 0.9665 | 0.5814 |
| 0.7175 | 33.0 | 924 | 0.8190 | 0.6512 |
| 0.7129 | 34.0 | 952 | 1.0680 | 0.6279 |
| 0.7793 | 35.0 | 980 | 1.0966 | 0.5581 |
| 0.6879 | 36.0 | 1008 | 0.9990 | 0.5814 |
| 0.7016 | 37.0 | 1036 | 1.7556 | 0.4884 |
| 0.6238 | 38.0 | 1064 | 1.5792 | 0.4651 |
| 0.6025 | 39.0 | 1092 | 1.1502 | 0.6047 |
| 0.7264 | 40.0 | 1120 | 1.3317 | 0.5349 |
| 0.6063 | 41.0 | 1148 | 1.5492 | 0.5116 |
| 0.5816 | 42.0 | 1176 | 1.5787 | 0.5814 |
| 0.4627 | 43.0 | 1204 | 1.1301 | 0.6047 |
| 0.4652 | 44.0 | 1232 | 1.5008 | 0.6279 |
| 0.3885 | 45.0 | 1260 | 1.3167 | 0.6279 |
| 0.4003 | 46.0 | 1288 | 1.3851 | 0.6512 |
| 0.3882 | 47.0 | 1316 | 1.4601 | 0.6047 |
| 0.353 | 48.0 | 1344 | 1.4699 | 0.6279 |
| 0.3487 | 49.0 | 1372 | 1.4768 | 0.6279 |
| 0.2789 | 50.0 | 1400 | 1.4768 | 0.6279 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5372
- Accuracy: 0.7619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4749 | 1.0 | 28 | 1.3999 | 0.2381 |
| 1.39 | 2.0 | 56 | 1.4010 | 0.2619 |
| 1.4057 | 3.0 | 84 | 1.3886 | 0.2381 |
| 1.3953 | 4.0 | 112 | 1.3773 | 0.2381 |
| 1.3855 | 5.0 | 140 | 1.3607 | 0.2619 |
| 1.3721 | 6.0 | 168 | 1.1238 | 0.5 |
| 1.2199 | 7.0 | 196 | 1.2305 | 0.4762 |
| 1.1505 | 8.0 | 224 | 0.9832 | 0.4762 |
| 1.1076 | 9.0 | 252 | 0.9145 | 0.5476 |
| 1.04 | 10.0 | 280 | 0.9689 | 0.5476 |
| 0.9947 | 11.0 | 308 | 0.8866 | 0.6429 |
| 1.0266 | 12.0 | 336 | 0.8639 | 0.6905 |
| 0.9955 | 13.0 | 364 | 0.8959 | 0.6190 |
| 0.9564 | 14.0 | 392 | 0.8608 | 0.6667 |
| 0.9123 | 15.0 | 420 | 0.7711 | 0.6905 |
| 0.9391 | 16.0 | 448 | 0.7070 | 0.7619 |
| 0.9117 | 17.0 | 476 | 0.7366 | 0.7619 |
| 0.902 | 18.0 | 504 | 0.7650 | 0.7143 |
| 0.8479 | 19.0 | 532 | 0.7181 | 0.7381 |
| 0.8138 | 20.0 | 560 | 0.8337 | 0.6667 |
| 0.7593 | 21.0 | 588 | 0.8325 | 0.6905 |
| 0.8558 | 22.0 | 616 | 0.7211 | 0.8095 |
| 0.8609 | 23.0 | 644 | 0.7758 | 0.7619 |
| 0.7997 | 24.0 | 672 | 0.8535 | 0.7143 |
| 0.6915 | 25.0 | 700 | 0.8962 | 0.7381 |
| 0.7445 | 26.0 | 728 | 0.7116 | 0.7619 |
| 0.6818 | 27.0 | 756 | 0.9464 | 0.5714 |
| 0.6812 | 28.0 | 784 | 0.6802 | 0.7143 |
| 0.662 | 29.0 | 812 | 1.0464 | 0.5476 |
| 0.6161 | 30.0 | 840 | 0.7154 | 0.7857 |
| 0.5942 | 31.0 | 868 | 0.6122 | 0.7619 |
| 0.571 | 32.0 | 896 | 0.6263 | 0.7857 |
| 0.5357 | 33.0 | 924 | 0.8564 | 0.8095 |
| 0.4815 | 34.0 | 952 | 0.9986 | 0.7381 |
| 0.5261 | 35.0 | 980 | 0.9173 | 0.8095 |
| 0.3508 | 36.0 | 1008 | 1.0846 | 0.7619 |
| 0.3469 | 37.0 | 1036 | 0.9412 | 0.8333 |
| 0.3024 | 38.0 | 1064 | 0.9602 | 0.8333 |
| 0.2908 | 39.0 | 1092 | 1.1234 | 0.8333 |
| 0.2222 | 40.0 | 1120 | 1.1275 | 0.8095 |
| 0.2149 | 41.0 | 1148 | 1.4618 | 0.7381 |
| 0.2207 | 42.0 | 1176 | 1.3470 | 0.7857 |
| 0.094 | 43.0 | 1204 | 1.5389 | 0.7619 |
| 0.1227 | 44.0 | 1232 | 1.3819 | 0.7857 |
| 0.0713 | 45.0 | 1260 | 1.5287 | 0.7619 |
| 0.0383 | 46.0 | 1288 | 1.5676 | 0.8095 |
| 0.0259 | 47.0 | 1316 | 1.4966 | 0.7857 |
| 0.023 | 48.0 | 1344 | 1.5355 | 0.7619 |
| 0.0304 | 49.0 | 1372 | 1.5372 | 0.7619 |
| 0.0233 | 50.0 | 1400 | 1.5372 | 0.7619 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
aldogeova/isa-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. -->
# isa-vit_model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0370
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0947 | 3.85 | 500 | 0.0370 | 0.9850 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
hkivancoral/hushem_5x_beit_base_rms_001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4962
- Accuracy: 0.3415
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.501 | 1.0 | 28 | 1.3919 | 0.2439 |
| 1.3993 | 2.0 | 56 | 1.4008 | 0.2683 |
| 1.4258 | 3.0 | 84 | 1.4098 | 0.2439 |
| 1.4011 | 4.0 | 112 | 1.3674 | 0.2683 |
| 1.4153 | 5.0 | 140 | 1.3306 | 0.2683 |
| 1.3649 | 6.0 | 168 | 1.4784 | 0.2195 |
| 1.3525 | 7.0 | 196 | 1.2906 | 0.4390 |
| 1.3374 | 8.0 | 224 | 1.1798 | 0.5122 |
| 1.2661 | 9.0 | 252 | 1.3479 | 0.5122 |
| 1.3011 | 10.0 | 280 | 1.3054 | 0.4878 |
| 1.2212 | 11.0 | 308 | 1.1612 | 0.5122 |
| 1.2579 | 12.0 | 336 | 1.2572 | 0.2683 |
| 1.2438 | 13.0 | 364 | 1.1160 | 0.4634 |
| 1.2218 | 14.0 | 392 | 1.1291 | 0.4878 |
| 1.2455 | 15.0 | 420 | 1.4587 | 0.4390 |
| 1.2528 | 16.0 | 448 | 1.3009 | 0.5122 |
| 1.2445 | 17.0 | 476 | 1.1915 | 0.5122 |
| 1.1729 | 18.0 | 504 | 1.3461 | 0.4390 |
| 1.2917 | 19.0 | 532 | 1.3956 | 0.3659 |
| 1.2335 | 20.0 | 560 | 1.1161 | 0.4146 |
| 1.1787 | 21.0 | 588 | 1.4220 | 0.4390 |
| 1.1076 | 22.0 | 616 | 1.2157 | 0.5122 |
| 1.1837 | 23.0 | 644 | 1.2878 | 0.4634 |
| 1.065 | 24.0 | 672 | 1.3373 | 0.3659 |
| 1.0753 | 25.0 | 700 | 1.2968 | 0.4634 |
| 1.0288 | 26.0 | 728 | 1.2996 | 0.4146 |
| 1.0679 | 27.0 | 756 | 1.2975 | 0.3902 |
| 1.0591 | 28.0 | 784 | 1.3051 | 0.4634 |
| 1.0148 | 29.0 | 812 | 1.2575 | 0.5854 |
| 1.0668 | 30.0 | 840 | 1.3174 | 0.3415 |
| 0.9767 | 31.0 | 868 | 1.3259 | 0.4390 |
| 0.9254 | 32.0 | 896 | 1.3236 | 0.4878 |
| 0.9064 | 33.0 | 924 | 1.5265 | 0.3902 |
| 0.9504 | 34.0 | 952 | 1.2456 | 0.4390 |
| 0.8534 | 35.0 | 980 | 1.2811 | 0.5122 |
| 0.8361 | 36.0 | 1008 | 1.2101 | 0.6098 |
| 0.7846 | 37.0 | 1036 | 1.3727 | 0.4390 |
| 0.7661 | 38.0 | 1064 | 1.4030 | 0.4878 |
| 0.8237 | 39.0 | 1092 | 1.3385 | 0.4634 |
| 0.7652 | 40.0 | 1120 | 1.6174 | 0.4146 |
| 0.6764 | 41.0 | 1148 | 1.6358 | 0.4390 |
| 0.5675 | 42.0 | 1176 | 1.7675 | 0.4390 |
| 0.5777 | 43.0 | 1204 | 1.8573 | 0.4390 |
| 0.5704 | 44.0 | 1232 | 2.0252 | 0.3902 |
| 0.5677 | 45.0 | 1260 | 2.0725 | 0.3902 |
| 0.4676 | 46.0 | 1288 | 2.4159 | 0.3171 |
| 0.4167 | 47.0 | 1316 | 2.4083 | 0.3415 |
| 0.416 | 48.0 | 1344 | 2.4826 | 0.3415 |
| 0.3715 | 49.0 | 1372 | 2.4962 | 0.3415 |
| 0.368 | 50.0 | 1400 | 2.4962 | 0.3415 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_0001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_0001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6811
- Accuracy: 0.3556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4287 | 1.0 | 27 | 1.3926 | 0.2222 |
| 1.3907 | 2.0 | 54 | 1.3975 | 0.2667 |
| 1.358 | 3.0 | 81 | 1.5093 | 0.2444 |
| 1.3416 | 4.0 | 108 | 1.5118 | 0.2444 |
| 1.2056 | 5.0 | 135 | 1.4928 | 0.2444 |
| 1.1299 | 6.0 | 162 | 1.6562 | 0.2222 |
| 1.1641 | 7.0 | 189 | 1.5947 | 0.2444 |
| 1.1473 | 8.0 | 216 | 1.5964 | 0.2444 |
| 1.1298 | 9.0 | 243 | 1.7663 | 0.2444 |
| 1.1045 | 10.0 | 270 | 1.6309 | 0.3778 |
| 0.8985 | 11.0 | 297 | 1.6908 | 0.4 |
| 0.7744 | 12.0 | 324 | 1.3949 | 0.3556 |
| 0.7617 | 13.0 | 351 | 1.4646 | 0.3778 |
| 0.6843 | 14.0 | 378 | 1.5910 | 0.3778 |
| 0.6647 | 15.0 | 405 | 1.8050 | 0.4 |
| 0.6363 | 16.0 | 432 | 1.7016 | 0.3333 |
| 0.6362 | 17.0 | 459 | 1.8539 | 0.3778 |
| 0.6858 | 18.0 | 486 | 1.8678 | 0.3556 |
| 0.7039 | 19.0 | 513 | 1.5776 | 0.3556 |
| 0.6292 | 20.0 | 540 | 1.8552 | 0.3111 |
| 0.4567 | 21.0 | 567 | 1.7854 | 0.3556 |
| 0.5954 | 22.0 | 594 | 2.4822 | 0.3556 |
| 0.5737 | 23.0 | 621 | 2.0564 | 0.4 |
| 0.4941 | 24.0 | 648 | 1.9451 | 0.3111 |
| 0.523 | 25.0 | 675 | 2.0359 | 0.3778 |
| 0.5221 | 26.0 | 702 | 2.1184 | 0.4 |
| 0.4589 | 27.0 | 729 | 2.0471 | 0.3556 |
| 0.4473 | 28.0 | 756 | 2.5353 | 0.3556 |
| 0.4328 | 29.0 | 783 | 2.7479 | 0.3556 |
| 0.4259 | 30.0 | 810 | 2.2239 | 0.3778 |
| 0.3698 | 31.0 | 837 | 2.5363 | 0.3556 |
| 0.3577 | 32.0 | 864 | 2.5264 | 0.3556 |
| 0.3882 | 33.0 | 891 | 2.2649 | 0.3333 |
| 0.3526 | 34.0 | 918 | 2.6438 | 0.3556 |
| 0.2747 | 35.0 | 945 | 2.3584 | 0.3778 |
| 0.2842 | 36.0 | 972 | 2.8515 | 0.3556 |
| 0.2603 | 37.0 | 999 | 2.3416 | 0.3778 |
| 0.2268 | 38.0 | 1026 | 2.7485 | 0.3778 |
| 0.2 | 39.0 | 1053 | 3.3636 | 0.3333 |
| 0.2049 | 40.0 | 1080 | 3.1692 | 0.3333 |
| 0.1369 | 41.0 | 1107 | 3.3885 | 0.3556 |
| 0.1813 | 42.0 | 1134 | 3.3020 | 0.3333 |
| 0.1518 | 43.0 | 1161 | 2.8618 | 0.4 |
| 0.0986 | 44.0 | 1188 | 3.2902 | 0.3778 |
| 0.131 | 45.0 | 1215 | 3.3898 | 0.3333 |
| 0.0809 | 46.0 | 1242 | 3.5629 | 0.3333 |
| 0.048 | 47.0 | 1269 | 3.7516 | 0.3333 |
| 0.038 | 48.0 | 1296 | 3.6814 | 0.3556 |
| 0.0465 | 49.0 | 1323 | 3.6811 | 0.3556 |
| 0.0644 | 50.0 | 1350 | 3.6811 | 0.3556 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_0001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_0001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 6.9592
- Accuracy: 0.5111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3984 | 1.0 | 27 | 1.3816 | 0.2889 |
| 1.3107 | 2.0 | 54 | 1.6609 | 0.2889 |
| 1.229 | 3.0 | 81 | 1.5449 | 0.2889 |
| 1.3814 | 4.0 | 108 | 1.6341 | 0.2889 |
| 1.2031 | 5.0 | 135 | 1.4184 | 0.2667 |
| 1.1619 | 6.0 | 162 | 1.4603 | 0.2889 |
| 1.1757 | 7.0 | 189 | 1.4200 | 0.2889 |
| 1.1575 | 8.0 | 216 | 1.3581 | 0.2889 |
| 1.0419 | 9.0 | 243 | 1.5164 | 0.4 |
| 1.0334 | 10.0 | 270 | 1.3939 | 0.4889 |
| 0.799 | 11.0 | 297 | 1.4216 | 0.5333 |
| 0.7589 | 12.0 | 324 | 1.5018 | 0.5111 |
| 0.7466 | 13.0 | 351 | 1.2714 | 0.3778 |
| 0.7077 | 14.0 | 378 | 1.2899 | 0.4 |
| 0.7022 | 15.0 | 405 | 1.4427 | 0.3333 |
| 0.6019 | 16.0 | 432 | 1.5793 | 0.4 |
| 0.6413 | 17.0 | 459 | 1.5251 | 0.3111 |
| 0.6003 | 18.0 | 486 | 2.0148 | 0.4889 |
| 0.5924 | 19.0 | 513 | 2.2670 | 0.4889 |
| 0.5357 | 20.0 | 540 | 2.0323 | 0.3556 |
| 0.5196 | 21.0 | 567 | 2.5285 | 0.4889 |
| 0.5137 | 22.0 | 594 | 3.7709 | 0.4222 |
| 0.4488 | 23.0 | 621 | 3.1001 | 0.5111 |
| 0.4667 | 24.0 | 648 | 2.9452 | 0.4 |
| 0.3277 | 25.0 | 675 | 2.8861 | 0.4667 |
| 0.3619 | 26.0 | 702 | 3.3939 | 0.5111 |
| 0.3379 | 27.0 | 729 | 3.5247 | 0.5333 |
| 0.2572 | 28.0 | 756 | 4.2104 | 0.5111 |
| 0.2257 | 29.0 | 783 | 3.4821 | 0.4889 |
| 0.2189 | 30.0 | 810 | 3.8860 | 0.4667 |
| 0.1431 | 31.0 | 837 | 5.2772 | 0.4667 |
| 0.2402 | 32.0 | 864 | 6.2470 | 0.4222 |
| 0.122 | 33.0 | 891 | 5.2693 | 0.4 |
| 0.2017 | 34.0 | 918 | 6.0732 | 0.5111 |
| 0.0844 | 35.0 | 945 | 6.0091 | 0.5556 |
| 0.1316 | 36.0 | 972 | 6.1584 | 0.4889 |
| 0.0377 | 37.0 | 999 | 7.3245 | 0.4889 |
| 0.1128 | 38.0 | 1026 | 6.6950 | 0.4444 |
| 0.0551 | 39.0 | 1053 | 7.0821 | 0.5111 |
| 0.0382 | 40.0 | 1080 | 7.5961 | 0.4889 |
| 0.0547 | 41.0 | 1107 | 6.2914 | 0.5111 |
| 0.0128 | 42.0 | 1134 | 6.4101 | 0.4889 |
| 0.0359 | 43.0 | 1161 | 6.6377 | 0.5111 |
| 0.004 | 44.0 | 1188 | 6.6707 | 0.4889 |
| 0.0224 | 45.0 | 1215 | 7.0078 | 0.4889 |
| 0.0292 | 46.0 | 1242 | 6.9800 | 0.4889 |
| 0.0156 | 47.0 | 1269 | 6.9010 | 0.4889 |
| 0.0096 | 48.0 | 1296 | 6.9583 | 0.5111 |
| 0.0108 | 49.0 | 1323 | 6.9592 | 0.5111 |
| 0.0394 | 50.0 | 1350 | 6.9592 | 0.5111 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_0001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_0001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0271
- Accuracy: 0.6744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4187 | 1.0 | 28 | 1.4291 | 0.2558 |
| 1.401 | 2.0 | 56 | 1.4569 | 0.2558 |
| 1.367 | 3.0 | 84 | 1.2989 | 0.2791 |
| 1.3068 | 4.0 | 112 | 1.1706 | 0.5116 |
| 1.282 | 5.0 | 140 | 1.1869 | 0.5581 |
| 1.1177 | 6.0 | 168 | 0.8916 | 0.7442 |
| 0.8904 | 7.0 | 196 | 0.7798 | 0.7209 |
| 0.9449 | 8.0 | 224 | 0.6587 | 0.7674 |
| 0.8708 | 9.0 | 252 | 1.0524 | 0.5814 |
| 0.9352 | 10.0 | 280 | 0.7664 | 0.6744 |
| 0.8718 | 11.0 | 308 | 0.6191 | 0.7907 |
| 0.7977 | 12.0 | 336 | 1.1991 | 0.6512 |
| 0.8081 | 13.0 | 364 | 0.7062 | 0.7674 |
| 0.7399 | 14.0 | 392 | 0.7130 | 0.6744 |
| 0.8202 | 15.0 | 420 | 0.7484 | 0.6977 |
| 0.7069 | 16.0 | 448 | 0.6665 | 0.6977 |
| 0.6169 | 17.0 | 476 | 0.7828 | 0.6279 |
| 0.6766 | 18.0 | 504 | 0.9849 | 0.5814 |
| 0.6876 | 19.0 | 532 | 0.7015 | 0.7442 |
| 0.5123 | 20.0 | 560 | 0.9230 | 0.7442 |
| 0.4885 | 21.0 | 588 | 0.9671 | 0.6279 |
| 0.5212 | 22.0 | 616 | 1.2712 | 0.6744 |
| 0.5047 | 23.0 | 644 | 0.7902 | 0.6512 |
| 0.4047 | 24.0 | 672 | 1.3996 | 0.7209 |
| 0.361 | 25.0 | 700 | 1.1508 | 0.6279 |
| 0.362 | 26.0 | 728 | 1.0709 | 0.6279 |
| 0.3752 | 27.0 | 756 | 0.9894 | 0.6512 |
| 0.2958 | 28.0 | 784 | 1.2219 | 0.6279 |
| 0.3016 | 29.0 | 812 | 0.8154 | 0.6977 |
| 0.2083 | 30.0 | 840 | 1.2432 | 0.6047 |
| 0.2249 | 31.0 | 868 | 1.5401 | 0.6047 |
| 0.1443 | 32.0 | 896 | 1.3193 | 0.6279 |
| 0.1501 | 33.0 | 924 | 1.1707 | 0.6977 |
| 0.1715 | 34.0 | 952 | 1.1677 | 0.7442 |
| 0.2795 | 35.0 | 980 | 1.2992 | 0.6744 |
| 0.1174 | 36.0 | 1008 | 1.6643 | 0.6744 |
| 0.1132 | 37.0 | 1036 | 1.7522 | 0.6279 |
| 0.0738 | 38.0 | 1064 | 1.6182 | 0.6744 |
| 0.0433 | 39.0 | 1092 | 2.1223 | 0.6512 |
| 0.0483 | 40.0 | 1120 | 2.5522 | 0.5814 |
| 0.0333 | 41.0 | 1148 | 1.8374 | 0.6977 |
| 0.0107 | 42.0 | 1176 | 1.9629 | 0.6744 |
| 0.013 | 43.0 | 1204 | 1.6900 | 0.7209 |
| 0.0316 | 44.0 | 1232 | 2.1881 | 0.6512 |
| 0.0272 | 45.0 | 1260 | 1.8428 | 0.6744 |
| 0.0298 | 46.0 | 1288 | 1.7049 | 0.7674 |
| 0.0196 | 47.0 | 1316 | 1.9117 | 0.6744 |
| 0.0084 | 48.0 | 1344 | 2.0336 | 0.6744 |
| 0.0059 | 49.0 | 1372 | 2.0271 | 0.6744 |
| 0.0065 | 50.0 | 1400 | 2.0271 | 0.6744 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_0001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_0001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8159
- Accuracy: 0.7857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4247 | 1.0 | 28 | 1.3411 | 0.2381 |
| 1.3595 | 2.0 | 56 | 1.2501 | 0.4286 |
| 1.3116 | 3.0 | 84 | 1.5240 | 0.2381 |
| 1.303 | 4.0 | 112 | 1.0491 | 0.5238 |
| 1.1942 | 5.0 | 140 | 0.8861 | 0.7143 |
| 1.1712 | 6.0 | 168 | 0.9106 | 0.5238 |
| 0.977 | 7.0 | 196 | 1.1447 | 0.6905 |
| 0.9351 | 8.0 | 224 | 0.7191 | 0.7619 |
| 0.8453 | 9.0 | 252 | 1.3331 | 0.5714 |
| 0.8831 | 10.0 | 280 | 0.8305 | 0.6905 |
| 0.8349 | 11.0 | 308 | 0.6872 | 0.7619 |
| 0.845 | 12.0 | 336 | 0.7545 | 0.7619 |
| 0.784 | 13.0 | 364 | 0.7961 | 0.7857 |
| 0.7404 | 14.0 | 392 | 0.6338 | 0.8095 |
| 0.6277 | 15.0 | 420 | 0.7200 | 0.7143 |
| 0.6386 | 16.0 | 448 | 0.7383 | 0.8095 |
| 0.6167 | 17.0 | 476 | 0.5440 | 0.8095 |
| 0.5129 | 18.0 | 504 | 0.7061 | 0.7619 |
| 0.3836 | 19.0 | 532 | 0.7181 | 0.7381 |
| 0.3202 | 20.0 | 560 | 0.4277 | 0.8095 |
| 0.1958 | 21.0 | 588 | 1.1637 | 0.7381 |
| 0.2343 | 22.0 | 616 | 1.0581 | 0.8095 |
| 0.2016 | 23.0 | 644 | 0.8968 | 0.7857 |
| 0.116 | 24.0 | 672 | 1.0426 | 0.7857 |
| 0.1027 | 25.0 | 700 | 0.6841 | 0.8333 |
| 0.1133 | 26.0 | 728 | 0.8260 | 0.8095 |
| 0.1258 | 27.0 | 756 | 1.3215 | 0.7619 |
| 0.0595 | 28.0 | 784 | 1.0509 | 0.8810 |
| 0.0945 | 29.0 | 812 | 1.3868 | 0.7857 |
| 0.0022 | 30.0 | 840 | 1.7553 | 0.8095 |
| 0.0004 | 31.0 | 868 | 1.9423 | 0.7857 |
| 0.0466 | 32.0 | 896 | 2.0945 | 0.8095 |
| 0.0367 | 33.0 | 924 | 1.6928 | 0.8095 |
| 0.1032 | 34.0 | 952 | 1.3572 | 0.8571 |
| 0.0331 | 35.0 | 980 | 2.0437 | 0.8095 |
| 0.0001 | 36.0 | 1008 | 2.0414 | 0.8333 |
| 0.0286 | 37.0 | 1036 | 2.0546 | 0.7619 |
| 0.009 | 38.0 | 1064 | 2.8381 | 0.7857 |
| 0.0573 | 39.0 | 1092 | 2.4470 | 0.7857 |
| 0.0497 | 40.0 | 1120 | 1.8192 | 0.7857 |
| 0.0003 | 41.0 | 1148 | 2.1421 | 0.7143 |
| 0.0003 | 42.0 | 1176 | 2.2125 | 0.7381 |
| 0.0001 | 43.0 | 1204 | 2.1555 | 0.7619 |
| 0.0002 | 44.0 | 1232 | 1.8154 | 0.7381 |
| 0.0197 | 45.0 | 1260 | 1.7188 | 0.7381 |
| 0.0002 | 46.0 | 1288 | 1.6637 | 0.8095 |
| 0.0152 | 47.0 | 1316 | 1.6954 | 0.8095 |
| 0.0001 | 48.0 | 1344 | 1.8153 | 0.7857 |
| 0.0002 | 49.0 | 1372 | 1.8159 | 0.7857 |
| 0.0 | 50.0 | 1400 | 1.8159 | 0.7857 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_0001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4047
- Accuracy: 0.7073
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4155 | 1.0 | 28 | 1.3777 | 0.2683 |
| 1.3848 | 2.0 | 56 | 1.2989 | 0.2927 |
| 1.3314 | 3.0 | 84 | 1.2733 | 0.4878 |
| 1.2486 | 4.0 | 112 | 1.0811 | 0.5122 |
| 1.2007 | 5.0 | 140 | 0.9236 | 0.5854 |
| 1.05 | 6.0 | 168 | 1.1380 | 0.5122 |
| 1.0162 | 7.0 | 196 | 0.9574 | 0.5854 |
| 0.9476 | 8.0 | 224 | 1.4400 | 0.4878 |
| 0.903 | 9.0 | 252 | 0.9012 | 0.6341 |
| 0.9351 | 10.0 | 280 | 1.0183 | 0.6829 |
| 0.8113 | 11.0 | 308 | 0.9612 | 0.6585 |
| 0.8131 | 12.0 | 336 | 1.6631 | 0.4878 |
| 0.7921 | 13.0 | 364 | 0.9316 | 0.6829 |
| 0.8114 | 14.0 | 392 | 1.3372 | 0.5854 |
| 0.7382 | 15.0 | 420 | 1.4796 | 0.6341 |
| 0.7119 | 16.0 | 448 | 1.9753 | 0.5366 |
| 0.6933 | 17.0 | 476 | 1.3458 | 0.7073 |
| 0.591 | 18.0 | 504 | 1.3968 | 0.6585 |
| 0.6986 | 19.0 | 532 | 1.4904 | 0.6829 |
| 0.6832 | 20.0 | 560 | 1.7362 | 0.6585 |
| 0.5173 | 21.0 | 588 | 1.5475 | 0.7317 |
| 0.5116 | 22.0 | 616 | 1.9547 | 0.6585 |
| 0.4833 | 23.0 | 644 | 2.1246 | 0.6341 |
| 0.4295 | 24.0 | 672 | 1.9058 | 0.7317 |
| 0.4431 | 25.0 | 700 | 2.4495 | 0.6585 |
| 0.3801 | 26.0 | 728 | 1.6867 | 0.7561 |
| 0.4263 | 27.0 | 756 | 2.1056 | 0.6585 |
| 0.3209 | 28.0 | 784 | 2.6127 | 0.6098 |
| 0.29 | 29.0 | 812 | 2.2833 | 0.6341 |
| 0.2306 | 30.0 | 840 | 2.6477 | 0.6341 |
| 0.2318 | 31.0 | 868 | 2.2205 | 0.6829 |
| 0.1766 | 32.0 | 896 | 2.1057 | 0.8293 |
| 0.1861 | 33.0 | 924 | 2.9102 | 0.6341 |
| 0.2172 | 34.0 | 952 | 2.3319 | 0.7317 |
| 0.1336 | 35.0 | 980 | 2.7931 | 0.7073 |
| 0.128 | 36.0 | 1008 | 3.2544 | 0.6098 |
| 0.1009 | 37.0 | 1036 | 2.3057 | 0.7805 |
| 0.1495 | 38.0 | 1064 | 2.9047 | 0.7317 |
| 0.0845 | 39.0 | 1092 | 3.1290 | 0.7317 |
| 0.064 | 40.0 | 1120 | 2.9682 | 0.7561 |
| 0.0399 | 41.0 | 1148 | 2.9364 | 0.7561 |
| 0.0198 | 42.0 | 1176 | 4.0340 | 0.6585 |
| 0.0179 | 43.0 | 1204 | 3.2313 | 0.7317 |
| 0.0799 | 44.0 | 1232 | 3.4340 | 0.7317 |
| 0.0495 | 45.0 | 1260 | 3.8737 | 0.6829 |
| 0.041 | 46.0 | 1288 | 3.5139 | 0.6829 |
| 0.0058 | 47.0 | 1316 | 3.4146 | 0.7073 |
| 0.0141 | 48.0 | 1344 | 3.4016 | 0.7073 |
| 0.0316 | 49.0 | 1372 | 3.4047 | 0.7073 |
| 0.0269 | 50.0 | 1400 | 3.4047 | 0.7073 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_00001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5839
- Accuracy: 0.8222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5474 | 1.0 | 27 | 0.5910 | 0.8444 |
| 0.0818 | 2.0 | 54 | 0.5720 | 0.7778 |
| 0.0238 | 3.0 | 81 | 0.8576 | 0.7111 |
| 0.0074 | 4.0 | 108 | 0.5321 | 0.8667 |
| 0.0032 | 5.0 | 135 | 0.4605 | 0.8667 |
| 0.0017 | 6.0 | 162 | 0.6849 | 0.7778 |
| 0.0024 | 7.0 | 189 | 0.4973 | 0.8667 |
| 0.0008 | 8.0 | 216 | 0.4640 | 0.8667 |
| 0.0044 | 9.0 | 243 | 0.6817 | 0.8222 |
| 0.0005 | 10.0 | 270 | 0.5671 | 0.8222 |
| 0.0004 | 11.0 | 297 | 0.5195 | 0.8444 |
| 0.0002 | 12.0 | 324 | 0.7506 | 0.8222 |
| 0.0007 | 13.0 | 351 | 0.4960 | 0.8667 |
| 0.0004 | 14.0 | 378 | 0.4879 | 0.8667 |
| 0.0002 | 15.0 | 405 | 0.2878 | 0.8889 |
| 0.0004 | 16.0 | 432 | 0.5723 | 0.7778 |
| 0.0038 | 17.0 | 459 | 0.8796 | 0.8 |
| 0.0011 | 18.0 | 486 | 0.4544 | 0.8444 |
| 0.001 | 19.0 | 513 | 0.2346 | 0.8889 |
| 0.0001 | 20.0 | 540 | 0.6421 | 0.8444 |
| 0.0001 | 21.0 | 567 | 0.5172 | 0.8667 |
| 0.0012 | 22.0 | 594 | 0.4729 | 0.8222 |
| 0.0001 | 23.0 | 621 | 0.4318 | 0.8222 |
| 0.0001 | 24.0 | 648 | 0.4087 | 0.8222 |
| 0.0004 | 25.0 | 675 | 0.4267 | 0.8889 |
| 0.0001 | 26.0 | 702 | 0.4250 | 0.8667 |
| 0.0001 | 27.0 | 729 | 0.3081 | 0.8889 |
| 0.0001 | 28.0 | 756 | 0.4008 | 0.8222 |
| 0.0 | 29.0 | 783 | 0.3766 | 0.8444 |
| 0.0001 | 30.0 | 810 | 0.3622 | 0.9111 |
| 0.0 | 31.0 | 837 | 0.4006 | 0.8222 |
| 0.0001 | 32.0 | 864 | 0.4743 | 0.8444 |
| 0.0001 | 33.0 | 891 | 0.3292 | 0.8889 |
| 0.0001 | 34.0 | 918 | 1.1554 | 0.7556 |
| 0.0002 | 35.0 | 945 | 0.6888 | 0.8 |
| 0.0003 | 36.0 | 972 | 0.4504 | 0.8667 |
| 0.0001 | 37.0 | 999 | 0.4287 | 0.8667 |
| 0.0 | 38.0 | 1026 | 0.4528 | 0.8667 |
| 0.0001 | 39.0 | 1053 | 0.4353 | 0.8667 |
| 0.0 | 40.0 | 1080 | 0.4656 | 0.8444 |
| 0.0044 | 41.0 | 1107 | 0.4571 | 0.8222 |
| 0.0 | 42.0 | 1134 | 0.4813 | 0.8222 |
| 0.0004 | 43.0 | 1161 | 0.5618 | 0.8444 |
| 0.0 | 44.0 | 1188 | 0.5635 | 0.8444 |
| 0.0 | 45.0 | 1215 | 0.5635 | 0.8444 |
| 0.0061 | 46.0 | 1242 | 0.5733 | 0.8444 |
| 0.0 | 47.0 | 1269 | 0.5697 | 0.8444 |
| 0.0001 | 48.0 | 1296 | 0.5838 | 0.8222 |
| 0.0001 | 49.0 | 1323 | 0.5839 | 0.8222 |
| 0.0 | 50.0 | 1350 | 0.5839 | 0.8222 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_00001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_00001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8184
- Accuracy: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6865 | 1.0 | 27 | 0.7969 | 0.7556 |
| 0.1615 | 2.0 | 54 | 0.9353 | 0.7778 |
| 0.041 | 3.0 | 81 | 1.0745 | 0.6444 |
| 0.0119 | 4.0 | 108 | 1.0481 | 0.7333 |
| 0.0095 | 5.0 | 135 | 0.6063 | 0.8667 |
| 0.0013 | 6.0 | 162 | 0.6520 | 0.8444 |
| 0.0015 | 7.0 | 189 | 0.7604 | 0.8667 |
| 0.0013 | 8.0 | 216 | 0.7595 | 0.8444 |
| 0.0008 | 9.0 | 243 | 0.8299 | 0.8444 |
| 0.0008 | 10.0 | 270 | 0.6509 | 0.8444 |
| 0.0009 | 11.0 | 297 | 0.7989 | 0.8444 |
| 0.0002 | 12.0 | 324 | 0.8458 | 0.8444 |
| 0.0005 | 13.0 | 351 | 0.6321 | 0.8667 |
| 0.0002 | 14.0 | 378 | 0.6972 | 0.8444 |
| 0.0002 | 15.0 | 405 | 0.7426 | 0.8667 |
| 0.0005 | 16.0 | 432 | 0.9776 | 0.8 |
| 0.0023 | 17.0 | 459 | 1.0180 | 0.8 |
| 0.0003 | 18.0 | 486 | 1.1105 | 0.7778 |
| 0.0006 | 19.0 | 513 | 0.9919 | 0.7556 |
| 0.0002 | 20.0 | 540 | 1.0177 | 0.8 |
| 0.0012 | 21.0 | 567 | 0.9992 | 0.8444 |
| 0.0003 | 22.0 | 594 | 0.9760 | 0.8444 |
| 0.0047 | 23.0 | 621 | 0.9891 | 0.8 |
| 0.0061 | 24.0 | 648 | 0.9730 | 0.8222 |
| 0.0002 | 25.0 | 675 | 0.8247 | 0.8222 |
| 0.0001 | 26.0 | 702 | 0.8270 | 0.8667 |
| 0.0001 | 27.0 | 729 | 0.7978 | 0.8222 |
| 0.0 | 28.0 | 756 | 0.8136 | 0.8444 |
| 0.0001 | 29.0 | 783 | 0.8553 | 0.8444 |
| 0.0001 | 30.0 | 810 | 0.9423 | 0.8444 |
| 0.0001 | 31.0 | 837 | 0.9286 | 0.8222 |
| 0.0001 | 32.0 | 864 | 0.9464 | 0.8222 |
| 0.0002 | 33.0 | 891 | 0.8713 | 0.8444 |
| 0.0001 | 34.0 | 918 | 0.8762 | 0.8444 |
| 0.0001 | 35.0 | 945 | 0.9092 | 0.8667 |
| 0.0 | 36.0 | 972 | 0.9547 | 0.8444 |
| 0.0 | 37.0 | 999 | 0.9283 | 0.8444 |
| 0.0 | 38.0 | 1026 | 0.8639 | 0.8444 |
| 0.0001 | 39.0 | 1053 | 0.8477 | 0.8667 |
| 0.0 | 40.0 | 1080 | 0.8432 | 0.8667 |
| 0.0 | 41.0 | 1107 | 0.8325 | 0.8667 |
| 0.0 | 42.0 | 1134 | 0.7851 | 0.8667 |
| 0.0003 | 43.0 | 1161 | 0.7875 | 0.8667 |
| 0.0 | 44.0 | 1188 | 0.7888 | 0.8667 |
| 0.0001 | 45.0 | 1215 | 0.8006 | 0.8889 |
| 0.0001 | 46.0 | 1242 | 0.8075 | 0.8889 |
| 0.0001 | 47.0 | 1269 | 0.8158 | 0.8889 |
| 0.0 | 48.0 | 1296 | 0.8184 | 0.8667 |
| 0.0002 | 49.0 | 1323 | 0.8184 | 0.8667 |
| 0.0001 | 50.0 | 1350 | 0.8184 | 0.8667 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_00001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_00001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5241
- Accuracy: 0.9070
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7661 | 1.0 | 28 | 0.5413 | 0.7907 |
| 0.1086 | 2.0 | 56 | 0.1845 | 0.9535 |
| 0.0112 | 3.0 | 84 | 0.3881 | 0.9070 |
| 0.0076 | 4.0 | 112 | 0.3276 | 0.9070 |
| 0.0043 | 5.0 | 140 | 0.4248 | 0.9070 |
| 0.0016 | 6.0 | 168 | 0.2522 | 0.9302 |
| 0.0099 | 7.0 | 196 | 0.2768 | 0.9070 |
| 0.0014 | 8.0 | 224 | 0.2639 | 0.9302 |
| 0.0013 | 9.0 | 252 | 0.3818 | 0.9070 |
| 0.0009 | 10.0 | 280 | 0.1248 | 0.9535 |
| 0.0006 | 11.0 | 308 | 0.2509 | 0.9070 |
| 0.0003 | 12.0 | 336 | 0.2923 | 0.9070 |
| 0.001 | 13.0 | 364 | 0.5107 | 0.8837 |
| 0.0019 | 14.0 | 392 | 0.3339 | 0.9535 |
| 0.0002 | 15.0 | 420 | 0.3891 | 0.9070 |
| 0.0003 | 16.0 | 448 | 0.4248 | 0.9070 |
| 0.0005 | 17.0 | 476 | 0.2832 | 0.9535 |
| 0.0003 | 18.0 | 504 | 0.3491 | 0.9070 |
| 0.0002 | 19.0 | 532 | 0.4104 | 0.9070 |
| 0.0001 | 20.0 | 560 | 0.4255 | 0.9070 |
| 0.0009 | 21.0 | 588 | 0.4651 | 0.9070 |
| 0.0015 | 22.0 | 616 | 0.4792 | 0.9070 |
| 0.0001 | 23.0 | 644 | 0.4509 | 0.9070 |
| 0.0006 | 24.0 | 672 | 0.5680 | 0.9302 |
| 0.0001 | 25.0 | 700 | 0.3224 | 0.9070 |
| 0.0001 | 26.0 | 728 | 0.3096 | 0.9302 |
| 0.0001 | 27.0 | 756 | 0.6066 | 0.9070 |
| 0.0001 | 28.0 | 784 | 0.3940 | 0.9070 |
| 0.0001 | 29.0 | 812 | 0.3550 | 0.9070 |
| 0.0 | 30.0 | 840 | 0.4157 | 0.9070 |
| 0.0001 | 31.0 | 868 | 0.4340 | 0.9070 |
| 0.0166 | 32.0 | 896 | 0.6996 | 0.9070 |
| 0.0001 | 33.0 | 924 | 0.5595 | 0.9070 |
| 0.0 | 34.0 | 952 | 0.3606 | 0.9070 |
| 0.0001 | 35.0 | 980 | 0.4821 | 0.9070 |
| 0.0013 | 36.0 | 1008 | 0.4503 | 0.9070 |
| 0.0001 | 37.0 | 1036 | 0.4301 | 0.9070 |
| 0.0001 | 38.0 | 1064 | 0.4884 | 0.9070 |
| 0.0 | 39.0 | 1092 | 0.4958 | 0.9070 |
| 0.0009 | 40.0 | 1120 | 0.5821 | 0.9070 |
| 0.0001 | 41.0 | 1148 | 0.4696 | 0.9070 |
| 0.0 | 42.0 | 1176 | 0.4577 | 0.9070 |
| 0.0 | 43.0 | 1204 | 0.4998 | 0.9070 |
| 0.0 | 44.0 | 1232 | 0.5154 | 0.9070 |
| 0.0001 | 45.0 | 1260 | 0.5227 | 0.9070 |
| 0.0003 | 46.0 | 1288 | 0.5170 | 0.9070 |
| 0.0001 | 47.0 | 1316 | 0.5187 | 0.9070 |
| 0.0001 | 48.0 | 1344 | 0.5241 | 0.9070 |
| 0.0 | 49.0 | 1372 | 0.5241 | 0.9070 |
| 0.0 | 50.0 | 1400 | 0.5241 | 0.9070 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_00001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_00001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4242
- Accuracy: 0.9048
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7963 | 1.0 | 28 | 0.5873 | 0.8095 |
| 0.1378 | 2.0 | 56 | 0.2600 | 0.9048 |
| 0.0372 | 3.0 | 84 | 0.1249 | 0.9286 |
| 0.0142 | 4.0 | 112 | 0.1881 | 0.9048 |
| 0.0031 | 5.0 | 140 | 0.2720 | 0.9524 |
| 0.0011 | 6.0 | 168 | 0.2309 | 0.9286 |
| 0.0018 | 7.0 | 196 | 0.3809 | 0.9048 |
| 0.0008 | 8.0 | 224 | 0.3332 | 0.9048 |
| 0.0014 | 9.0 | 252 | 0.3365 | 0.8810 |
| 0.0123 | 10.0 | 280 | 0.2089 | 0.9286 |
| 0.0005 | 11.0 | 308 | 0.1962 | 0.9286 |
| 0.0038 | 12.0 | 336 | 0.2845 | 0.9048 |
| 0.0078 | 13.0 | 364 | 0.2498 | 0.9048 |
| 0.001 | 14.0 | 392 | 0.0353 | 1.0 |
| 0.0002 | 15.0 | 420 | 0.1604 | 0.9286 |
| 0.0003 | 16.0 | 448 | 0.6770 | 0.8810 |
| 0.0002 | 17.0 | 476 | 0.3566 | 0.9048 |
| 0.0001 | 18.0 | 504 | 0.1974 | 0.8810 |
| 0.0004 | 19.0 | 532 | 0.0247 | 1.0 |
| 0.0001 | 20.0 | 560 | 0.0905 | 0.9286 |
| 0.0001 | 21.0 | 588 | 0.1806 | 0.9286 |
| 0.0011 | 22.0 | 616 | 0.2156 | 0.9524 |
| 0.0007 | 23.0 | 644 | 0.4203 | 0.9286 |
| 0.0002 | 24.0 | 672 | 0.2731 | 0.9286 |
| 0.0054 | 25.0 | 700 | 0.2589 | 0.8810 |
| 0.0001 | 26.0 | 728 | 0.2893 | 0.9048 |
| 0.0 | 27.0 | 756 | 0.3737 | 0.8810 |
| 0.0002 | 28.0 | 784 | 0.3310 | 0.9048 |
| 0.0001 | 29.0 | 812 | 0.2394 | 0.9048 |
| 0.0 | 30.0 | 840 | 0.2320 | 0.9048 |
| 0.0001 | 31.0 | 868 | 0.2751 | 0.9048 |
| 0.0012 | 32.0 | 896 | 0.2756 | 0.9048 |
| 0.0 | 33.0 | 924 | 0.1983 | 0.9048 |
| 0.0001 | 34.0 | 952 | 0.1565 | 0.9048 |
| 0.0 | 35.0 | 980 | 0.1912 | 0.9048 |
| 0.0001 | 36.0 | 1008 | 0.2103 | 0.9048 |
| 0.0 | 37.0 | 1036 | 0.1693 | 0.9048 |
| 0.0 | 38.0 | 1064 | 0.1895 | 0.9048 |
| 0.0 | 39.0 | 1092 | 0.2300 | 0.9048 |
| 0.0018 | 40.0 | 1120 | 0.7391 | 0.9048 |
| 0.0 | 41.0 | 1148 | 0.6660 | 0.9048 |
| 0.0 | 42.0 | 1176 | 0.5981 | 0.9048 |
| 0.0001 | 43.0 | 1204 | 0.6379 | 0.9048 |
| 0.0001 | 44.0 | 1232 | 0.5736 | 0.9048 |
| 0.0002 | 45.0 | 1260 | 0.4940 | 0.9048 |
| 0.0001 | 46.0 | 1288 | 0.4348 | 0.9048 |
| 0.0001 | 47.0 | 1316 | 0.4551 | 0.9048 |
| 0.0 | 48.0 | 1344 | 0.4241 | 0.9048 |
| 0.0026 | 49.0 | 1372 | 0.4242 | 0.9048 |
| 0.0 | 50.0 | 1400 | 0.4242 | 0.9048 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_5x_beit_base_rms_00001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_rms_00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1845
- Accuracy: 0.8049
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9367 | 1.0 | 28 | 0.6533 | 0.7073 |
| 0.1926 | 2.0 | 56 | 0.5512 | 0.7805 |
| 0.047 | 3.0 | 84 | 0.6007 | 0.8049 |
| 0.0193 | 4.0 | 112 | 0.2590 | 0.9024 |
| 0.0089 | 5.0 | 140 | 0.4654 | 0.8293 |
| 0.0038 | 6.0 | 168 | 0.5932 | 0.8293 |
| 0.0017 | 7.0 | 196 | 0.6877 | 0.8293 |
| 0.0014 | 8.0 | 224 | 0.7982 | 0.8049 |
| 0.0007 | 9.0 | 252 | 0.6044 | 0.8293 |
| 0.0007 | 10.0 | 280 | 0.6788 | 0.8537 |
| 0.0003 | 11.0 | 308 | 0.6662 | 0.8537 |
| 0.0003 | 12.0 | 336 | 0.6588 | 0.8537 |
| 0.0002 | 13.0 | 364 | 0.6343 | 0.8293 |
| 0.0046 | 14.0 | 392 | 1.0649 | 0.7805 |
| 0.0012 | 15.0 | 420 | 0.7359 | 0.8293 |
| 0.0005 | 16.0 | 448 | 0.7345 | 0.8293 |
| 0.0066 | 17.0 | 476 | 0.7816 | 0.8537 |
| 0.0014 | 18.0 | 504 | 0.6553 | 0.8780 |
| 0.0003 | 19.0 | 532 | 0.5879 | 0.8780 |
| 0.0001 | 20.0 | 560 | 0.6539 | 0.8537 |
| 0.0001 | 21.0 | 588 | 0.5762 | 0.8293 |
| 0.0006 | 22.0 | 616 | 0.3307 | 0.8293 |
| 0.0001 | 23.0 | 644 | 0.6447 | 0.8293 |
| 0.0002 | 24.0 | 672 | 0.7471 | 0.8537 |
| 0.0002 | 25.0 | 700 | 0.6200 | 0.8537 |
| 0.0001 | 26.0 | 728 | 0.9057 | 0.8537 |
| 0.0001 | 27.0 | 756 | 0.8578 | 0.8537 |
| 0.0004 | 28.0 | 784 | 0.7354 | 0.8537 |
| 0.0001 | 29.0 | 812 | 0.8285 | 0.8537 |
| 0.0004 | 30.0 | 840 | 0.7442 | 0.8780 |
| 0.0001 | 31.0 | 868 | 0.9315 | 0.8049 |
| 0.0002 | 32.0 | 896 | 1.0255 | 0.8049 |
| 0.0 | 33.0 | 924 | 1.0401 | 0.7805 |
| 0.0001 | 34.0 | 952 | 1.0520 | 0.8293 |
| 0.0004 | 35.0 | 980 | 0.9869 | 0.8537 |
| 0.0 | 36.0 | 1008 | 0.9764 | 0.8537 |
| 0.0001 | 37.0 | 1036 | 0.9356 | 0.8537 |
| 0.0001 | 38.0 | 1064 | 1.1522 | 0.8049 |
| 0.0 | 39.0 | 1092 | 1.0978 | 0.8049 |
| 0.0005 | 40.0 | 1120 | 1.0647 | 0.8293 |
| 0.0003 | 41.0 | 1148 | 1.2331 | 0.8049 |
| 0.0 | 42.0 | 1176 | 1.3110 | 0.8049 |
| 0.0 | 43.0 | 1204 | 1.2050 | 0.8049 |
| 0.0 | 44.0 | 1232 | 1.1647 | 0.8049 |
| 0.0002 | 45.0 | 1260 | 1.2154 | 0.8049 |
| 0.0001 | 46.0 | 1288 | 1.2000 | 0.8049 |
| 0.0001 | 47.0 | 1316 | 1.1915 | 0.8049 |
| 0.0 | 48.0 | 1344 | 1.1844 | 0.8049 |
| 0.0001 | 49.0 | 1372 | 1.1845 | 0.8049 |
| 0.0 | 50.0 | 1400 | 1.1845 | 0.8049 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
xiaopch/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: 0.0520
- Accuracy: 0.9837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2341 | 1.0 | 190 | 0.1160 | 0.9593 |
| 0.1813 | 2.0 | 380 | 0.0715 | 0.9752 |
| 0.1401 | 3.0 | 570 | 0.0520 | 0.9837 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
xiaopch/vit-base-patch16-224-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. -->
# vit-base-patch16-224-finetuned
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: 1.1532
- Accuracy: 0.6747
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8046 | 1.0 | 35 | 1.5308 | 0.6004 |
| 1.1931 | 2.0 | 70 | 1.2080 | 0.6526 |
| 1.0292 | 3.0 | 105 | 1.1532 | 0.6747 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"spodoptera_litura",
"aphid",
"beet_armyworm",
"borer",
"chemical_fertilizer",
"cnidocampa_flavescens",
"corn_borer",
"cotton_bollworm",
"fhb",
"grasshopper",
"longhorn_beetle",
"oriental_fruit_fly",
"pesticides",
"plutella_xylostella",
"rice_planthopper",
"rice_stem_borer",
"rolled_leaf_borer"
] |
hkivancoral/smids_1x_beit_base_adamax_001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2333
- Accuracy: 0.8531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6722 | 1.0 | 76 | 0.7068 | 0.7346 |
| 0.5257 | 2.0 | 152 | 0.7906 | 0.6978 |
| 0.3766 | 3.0 | 228 | 0.4936 | 0.8030 |
| 0.4703 | 4.0 | 304 | 0.5194 | 0.8047 |
| 0.3758 | 5.0 | 380 | 0.4944 | 0.8047 |
| 0.3685 | 6.0 | 456 | 0.4662 | 0.8364 |
| 0.2812 | 7.0 | 532 | 0.5286 | 0.8314 |
| 0.2831 | 8.0 | 608 | 0.4636 | 0.8331 |
| 0.2359 | 9.0 | 684 | 0.5034 | 0.8063 |
| 0.1426 | 10.0 | 760 | 0.5477 | 0.8280 |
| 0.2668 | 11.0 | 836 | 0.6880 | 0.8130 |
| 0.182 | 12.0 | 912 | 0.6113 | 0.8280 |
| 0.1925 | 13.0 | 988 | 0.5781 | 0.8280 |
| 0.1404 | 14.0 | 1064 | 0.8189 | 0.8114 |
| 0.0795 | 15.0 | 1140 | 0.8425 | 0.8230 |
| 0.0585 | 16.0 | 1216 | 0.6551 | 0.8481 |
| 0.0935 | 17.0 | 1292 | 0.7044 | 0.8347 |
| 0.0369 | 18.0 | 1368 | 0.9110 | 0.8414 |
| 0.0816 | 19.0 | 1444 | 0.9853 | 0.8414 |
| 0.063 | 20.0 | 1520 | 0.7577 | 0.8464 |
| 0.0166 | 21.0 | 1596 | 0.8613 | 0.8381 |
| 0.0172 | 22.0 | 1672 | 0.7211 | 0.8548 |
| 0.0101 | 23.0 | 1748 | 0.9887 | 0.8297 |
| 0.059 | 24.0 | 1824 | 1.1066 | 0.8414 |
| 0.0163 | 25.0 | 1900 | 0.8966 | 0.8481 |
| 0.0425 | 26.0 | 1976 | 0.9615 | 0.8364 |
| 0.0118 | 27.0 | 2052 | 1.0527 | 0.8481 |
| 0.0022 | 28.0 | 2128 | 1.0163 | 0.8464 |
| 0.0009 | 29.0 | 2204 | 1.0736 | 0.8514 |
| 0.0005 | 30.0 | 2280 | 1.0490 | 0.8531 |
| 0.0032 | 31.0 | 2356 | 1.1469 | 0.8514 |
| 0.0106 | 32.0 | 2432 | 1.1588 | 0.8497 |
| 0.06 | 33.0 | 2508 | 1.1292 | 0.8514 |
| 0.0041 | 34.0 | 2584 | 1.0765 | 0.8531 |
| 0.0193 | 35.0 | 2660 | 1.2132 | 0.8548 |
| 0.0004 | 36.0 | 2736 | 1.1489 | 0.8481 |
| 0.0134 | 37.0 | 2812 | 1.2292 | 0.8464 |
| 0.0047 | 38.0 | 2888 | 1.1921 | 0.8514 |
| 0.0036 | 39.0 | 2964 | 1.2034 | 0.8464 |
| 0.0001 | 40.0 | 3040 | 1.1597 | 0.8481 |
| 0.0065 | 41.0 | 3116 | 1.1753 | 0.8548 |
| 0.0001 | 42.0 | 3192 | 1.1808 | 0.8548 |
| 0.0 | 43.0 | 3268 | 1.1898 | 0.8564 |
| 0.0001 | 44.0 | 3344 | 1.2021 | 0.8581 |
| 0.0061 | 45.0 | 3420 | 1.2174 | 0.8564 |
| 0.0 | 46.0 | 3496 | 1.2210 | 0.8548 |
| 0.0025 | 47.0 | 3572 | 1.2289 | 0.8531 |
| 0.0025 | 48.0 | 3648 | 1.2311 | 0.8548 |
| 0.0023 | 49.0 | 3724 | 1.2330 | 0.8531 |
| 0.0044 | 50.0 | 3800 | 1.2333 | 0.8531 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4865
- Accuracy: 0.7903
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9007 | 1.0 | 75 | 0.8802 | 0.5191 |
| 0.7789 | 2.0 | 150 | 0.8973 | 0.5424 |
| 0.8219 | 3.0 | 225 | 0.7607 | 0.6406 |
| 0.7838 | 4.0 | 300 | 0.7358 | 0.6522 |
| 0.6602 | 5.0 | 375 | 0.6978 | 0.6672 |
| 0.7026 | 6.0 | 450 | 0.6685 | 0.6955 |
| 0.6394 | 7.0 | 525 | 0.7731 | 0.6589 |
| 0.6471 | 8.0 | 600 | 0.6234 | 0.7138 |
| 0.5881 | 9.0 | 675 | 0.6358 | 0.7205 |
| 0.5254 | 10.0 | 750 | 0.5746 | 0.7671 |
| 0.5153 | 11.0 | 825 | 0.5501 | 0.7704 |
| 0.5459 | 12.0 | 900 | 0.5543 | 0.7687 |
| 0.5526 | 13.0 | 975 | 0.5321 | 0.7737 |
| 0.5236 | 14.0 | 1050 | 0.5404 | 0.7937 |
| 0.4317 | 15.0 | 1125 | 0.6220 | 0.7604 |
| 0.4195 | 16.0 | 1200 | 0.5679 | 0.7854 |
| 0.3753 | 17.0 | 1275 | 0.6021 | 0.7687 |
| 0.3821 | 18.0 | 1350 | 0.5958 | 0.7854 |
| 0.3599 | 19.0 | 1425 | 0.6478 | 0.7837 |
| 0.2813 | 20.0 | 1500 | 0.6634 | 0.7671 |
| 0.224 | 21.0 | 1575 | 0.6766 | 0.7820 |
| 0.2635 | 22.0 | 1650 | 0.6781 | 0.7870 |
| 0.1832 | 23.0 | 1725 | 0.8041 | 0.7604 |
| 0.1751 | 24.0 | 1800 | 0.8069 | 0.7671 |
| 0.2421 | 25.0 | 1875 | 0.8820 | 0.7737 |
| 0.2115 | 26.0 | 1950 | 0.8838 | 0.7970 |
| 0.1798 | 27.0 | 2025 | 0.8954 | 0.7787 |
| 0.1341 | 28.0 | 2100 | 1.0505 | 0.7987 |
| 0.0669 | 29.0 | 2175 | 1.2992 | 0.7770 |
| 0.0892 | 30.0 | 2250 | 1.1168 | 0.7987 |
| 0.1159 | 31.0 | 2325 | 1.2066 | 0.7870 |
| 0.1289 | 32.0 | 2400 | 1.5859 | 0.7687 |
| 0.0687 | 33.0 | 2475 | 1.1777 | 0.7887 |
| 0.0226 | 34.0 | 2550 | 1.4423 | 0.7854 |
| 0.04 | 35.0 | 2625 | 1.4594 | 0.7870 |
| 0.0552 | 36.0 | 2700 | 1.3867 | 0.7820 |
| 0.0439 | 37.0 | 2775 | 1.4599 | 0.7720 |
| 0.0308 | 38.0 | 2850 | 1.4968 | 0.7903 |
| 0.0564 | 39.0 | 2925 | 1.5256 | 0.7953 |
| 0.0227 | 40.0 | 3000 | 1.4454 | 0.7953 |
| 0.0214 | 41.0 | 3075 | 1.3100 | 0.8087 |
| 0.0167 | 42.0 | 3150 | 1.4699 | 0.7987 |
| 0.0299 | 43.0 | 3225 | 1.4525 | 0.7903 |
| 0.0171 | 44.0 | 3300 | 1.3889 | 0.8053 |
| 0.011 | 45.0 | 3375 | 1.3819 | 0.7920 |
| 0.014 | 46.0 | 3450 | 1.5122 | 0.7903 |
| 0.0198 | 47.0 | 3525 | 1.4328 | 0.7920 |
| 0.0085 | 48.0 | 3600 | 1.5057 | 0.7920 |
| 0.0028 | 49.0 | 3675 | 1.4856 | 0.7903 |
| 0.0049 | 50.0 | 3750 | 1.4865 | 0.7903 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
andrecastro/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: 0.0271
- Accuracy: 0.9967
## Model description
More information needed
## Intended uses & 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0898 | 1.0 | 327 | 0.0707 | 0.9757 |
| 0.0221 | 2.0 | 654 | 0.0278 | 0.9920 |
| 0.06 | 3.0 | 981 | 0.0345 | 0.9913 |
| 0.0094 | 4.0 | 1309 | 0.0300 | 0.9947 |
| 0.0004 | 5.0 | 1636 | 0.0398 | 0.9942 |
| 0.0035 | 6.0 | 1963 | 0.0136 | 0.9975 |
| 0.0246 | 7.0 | 2290 | 0.0339 | 0.9940 |
| 0.0012 | 8.0 | 2618 | 0.0316 | 0.9958 |
| 0.0 | 9.0 | 2945 | 0.0302 | 0.9964 |
| 0.0 | 10.0 | 3272 | 0.0201 | 0.9973 |
| 0.0003 | 11.0 | 3599 | 0.0222 | 0.9955 |
| 0.0 | 12.0 | 3927 | 0.0218 | 0.9962 |
| 0.0001 | 13.0 | 4254 | 0.0293 | 0.9962 |
| 0.0002 | 14.0 | 4581 | 0.0272 | 0.9962 |
| 0.0 | 14.99 | 4905 | 0.0271 | 0.9967 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"anormal_total",
"normal_total"
] |
hkivancoral/smids_1x_beit_base_adamax_001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6207
- Accuracy: 0.8067
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.961 | 1.0 | 75 | 0.9579 | 0.5083 |
| 0.8519 | 2.0 | 150 | 0.8223 | 0.555 |
| 0.8429 | 3.0 | 225 | 0.8258 | 0.5417 |
| 0.8689 | 4.0 | 300 | 1.1933 | 0.5183 |
| 0.7212 | 5.0 | 375 | 0.6887 | 0.7133 |
| 0.649 | 6.0 | 450 | 0.7128 | 0.6567 |
| 0.6409 | 7.0 | 525 | 0.6763 | 0.71 |
| 0.5869 | 8.0 | 600 | 0.5948 | 0.7383 |
| 0.5565 | 9.0 | 675 | 0.6418 | 0.695 |
| 0.5839 | 10.0 | 750 | 0.6087 | 0.7267 |
| 0.5293 | 11.0 | 825 | 0.5977 | 0.7267 |
| 0.4762 | 12.0 | 900 | 0.5491 | 0.7783 |
| 0.4499 | 13.0 | 975 | 0.5838 | 0.7517 |
| 0.4302 | 14.0 | 1050 | 0.5473 | 0.77 |
| 0.4099 | 15.0 | 1125 | 0.5508 | 0.755 |
| 0.3178 | 16.0 | 1200 | 0.5699 | 0.78 |
| 0.341 | 17.0 | 1275 | 0.6033 | 0.7933 |
| 0.2555 | 18.0 | 1350 | 0.6573 | 0.7767 |
| 0.3366 | 19.0 | 1425 | 0.5611 | 0.7933 |
| 0.1724 | 20.0 | 1500 | 0.7339 | 0.7933 |
| 0.2297 | 21.0 | 1575 | 0.8132 | 0.78 |
| 0.2293 | 22.0 | 1650 | 0.7112 | 0.7833 |
| 0.1656 | 23.0 | 1725 | 0.8681 | 0.7767 |
| 0.1488 | 24.0 | 1800 | 0.9454 | 0.79 |
| 0.1667 | 25.0 | 1875 | 0.9934 | 0.7767 |
| 0.0534 | 26.0 | 1950 | 0.9484 | 0.7767 |
| 0.1635 | 27.0 | 2025 | 1.0833 | 0.77 |
| 0.0554 | 28.0 | 2100 | 1.1552 | 0.8017 |
| 0.0938 | 29.0 | 2175 | 1.0865 | 0.7917 |
| 0.1141 | 30.0 | 2250 | 1.3605 | 0.7883 |
| 0.0561 | 31.0 | 2325 | 1.2003 | 0.8033 |
| 0.064 | 32.0 | 2400 | 1.3257 | 0.7933 |
| 0.0695 | 33.0 | 2475 | 1.6036 | 0.7883 |
| 0.0143 | 34.0 | 2550 | 1.5166 | 0.7717 |
| 0.0099 | 35.0 | 2625 | 1.5177 | 0.7833 |
| 0.046 | 36.0 | 2700 | 1.6809 | 0.7983 |
| 0.0535 | 37.0 | 2775 | 1.6548 | 0.7783 |
| 0.0142 | 38.0 | 2850 | 1.9052 | 0.7867 |
| 0.0043 | 39.0 | 2925 | 1.8855 | 0.785 |
| 0.0169 | 40.0 | 3000 | 1.8422 | 0.7983 |
| 0.0085 | 41.0 | 3075 | 1.6803 | 0.8033 |
| 0.0125 | 42.0 | 3150 | 1.4852 | 0.8033 |
| 0.0037 | 43.0 | 3225 | 1.5490 | 0.7883 |
| 0.0153 | 44.0 | 3300 | 1.3985 | 0.81 |
| 0.0066 | 45.0 | 3375 | 1.5369 | 0.8083 |
| 0.0076 | 46.0 | 3450 | 1.5177 | 0.7983 |
| 0.0089 | 47.0 | 3525 | 1.6039 | 0.7883 |
| 0.0027 | 48.0 | 3600 | 1.6013 | 0.8067 |
| 0.0003 | 49.0 | 3675 | 1.6182 | 0.8067 |
| 0.0026 | 50.0 | 3750 | 1.6207 | 0.8067 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7646
- Accuracy: 0.775
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9222 | 1.0 | 75 | 0.8216 | 0.5567 |
| 0.8457 | 2.0 | 150 | 0.8398 | 0.57 |
| 0.8147 | 3.0 | 225 | 0.7493 | 0.6333 |
| 0.7701 | 4.0 | 300 | 0.7606 | 0.6117 |
| 0.8026 | 5.0 | 375 | 0.8189 | 0.565 |
| 0.6963 | 6.0 | 450 | 0.6808 | 0.665 |
| 0.7638 | 7.0 | 525 | 0.6641 | 0.7017 |
| 0.6601 | 8.0 | 600 | 0.6495 | 0.6833 |
| 0.6719 | 9.0 | 675 | 0.7134 | 0.66 |
| 0.5461 | 10.0 | 750 | 0.5791 | 0.7483 |
| 0.547 | 11.0 | 825 | 0.5859 | 0.7633 |
| 0.4912 | 12.0 | 900 | 0.5937 | 0.735 |
| 0.5352 | 13.0 | 975 | 0.5233 | 0.7667 |
| 0.4434 | 14.0 | 1050 | 0.5543 | 0.7617 |
| 0.4927 | 15.0 | 1125 | 0.7581 | 0.6767 |
| 0.4312 | 16.0 | 1200 | 0.5587 | 0.7667 |
| 0.3899 | 17.0 | 1275 | 0.6422 | 0.7633 |
| 0.3786 | 18.0 | 1350 | 0.6068 | 0.7783 |
| 0.4006 | 19.0 | 1425 | 0.6778 | 0.7617 |
| 0.3094 | 20.0 | 1500 | 0.6494 | 0.775 |
| 0.3319 | 21.0 | 1575 | 0.6363 | 0.765 |
| 0.2928 | 22.0 | 1650 | 0.7276 | 0.7817 |
| 0.2846 | 23.0 | 1725 | 0.8156 | 0.7733 |
| 0.1736 | 24.0 | 1800 | 0.7838 | 0.785 |
| 0.2416 | 25.0 | 1875 | 0.8283 | 0.775 |
| 0.1805 | 26.0 | 1950 | 0.8042 | 0.7867 |
| 0.1895 | 27.0 | 2025 | 1.0411 | 0.7933 |
| 0.0832 | 28.0 | 2100 | 1.0766 | 0.7983 |
| 0.099 | 29.0 | 2175 | 1.1178 | 0.7683 |
| 0.0916 | 30.0 | 2250 | 1.3040 | 0.775 |
| 0.128 | 31.0 | 2325 | 1.2237 | 0.7983 |
| 0.0775 | 32.0 | 2400 | 1.1999 | 0.79 |
| 0.0706 | 33.0 | 2475 | 1.4034 | 0.78 |
| 0.0546 | 34.0 | 2550 | 1.4009 | 0.785 |
| 0.0453 | 35.0 | 2625 | 1.2357 | 0.7917 |
| 0.0136 | 36.0 | 2700 | 1.4685 | 0.79 |
| 0.0534 | 37.0 | 2775 | 1.8215 | 0.7717 |
| 0.0751 | 38.0 | 2850 | 1.6150 | 0.7833 |
| 0.0013 | 39.0 | 2925 | 1.7207 | 0.7917 |
| 0.0466 | 40.0 | 3000 | 1.4737 | 0.785 |
| 0.0122 | 41.0 | 3075 | 1.5635 | 0.7783 |
| 0.0071 | 42.0 | 3150 | 1.6935 | 0.7783 |
| 0.0119 | 43.0 | 3225 | 1.6935 | 0.7833 |
| 0.0065 | 44.0 | 3300 | 1.7015 | 0.7883 |
| 0.0254 | 45.0 | 3375 | 1.7329 | 0.7867 |
| 0.0205 | 46.0 | 3450 | 1.6886 | 0.785 |
| 0.0082 | 47.0 | 3525 | 1.7094 | 0.7833 |
| 0.0134 | 48.0 | 3600 | 1.7793 | 0.78 |
| 0.005 | 49.0 | 3675 | 1.7866 | 0.7767 |
| 0.0132 | 50.0 | 3750 | 1.7646 | 0.775 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
jsalasr/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: 0.0367
- Accuracy: 0.9879
## Model description
More information needed
## Intended uses & 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1993 | 0.9904 | 77 | 0.0961 | 0.9617 |
| 0.1729 | 1.9936 | 155 | 0.1151 | 0.9486 |
| 0.1509 | 2.9968 | 233 | 0.0603 | 0.9748 |
| 0.1081 | 4.0 | 311 | 0.0367 | 0.9879 |
| 0.1195 | 4.9904 | 388 | 0.0936 | 0.9627 |
| 0.0674 | 5.9936 | 466 | 0.0370 | 0.9849 |
| 0.0629 | 6.9968 | 544 | 0.0400 | 0.9839 |
| 0.0718 | 8.0 | 622 | 0.0496 | 0.9839 |
| 0.0335 | 8.9904 | 699 | 0.0533 | 0.9819 |
| 0.0843 | 9.9035 | 770 | 0.0550 | 0.9809 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
[
"clear",
"cloudy"
] |
HarshaSingamshetty1/roof_classification_rearrange_labels
|
<!-- 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. -->
# HarshaSingamshetty1/roof_classification_rearrange_labels
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.3721
- Train Accuracy: 0.4404
- Validation Loss: 1.6641
- Validation Accuracy: 0.4000
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.0005, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 3.2127 | 0.1021 | 2.8916 | 0.1340 | 0 |
| 2.7296 | 0.1255 | 2.7126 | 0.1213 | 1 |
| 2.3888 | 0.2468 | 2.3456 | 0.2489 | 2 |
| 2.1480 | 0.2702 | 2.1604 | 0.2830 | 3 |
| 2.0789 | 0.3170 | 2.0942 | 0.3106 | 4 |
| 1.8117 | 0.3851 | 1.8224 | 0.3766 | 5 |
| 1.6477 | 0.3426 | 1.8774 | 0.3596 | 6 |
| 1.5677 | 0.4404 | 1.7042 | 0.4362 | 7 |
| 1.4018 | 0.4660 | 1.4974 | 0.4553 | 8 |
| 1.3721 | 0.4404 | 1.6641 | 0.4000 | 9 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"certainteed_heather blend_landmark",
"gaf_golden harvest_timberline american harvest",
"tamko_weathered wood_heritage",
"gaf_mission brown_timberline hdz",
"atlas_tan_pinnacle pristine",
"owens corning_desert rose_truedefinition duration",
"iko_cornerstone_dynasty",
"owen’s corning_midnight plum_duration",
"gaf_cedar falls_timberline american harvest",
"owens corning_amber_trudefinition duration",
"owens corning_estate gray_supreme",
"certainteed_weathered wood_presidential shake",
"iko_weathered wood_cambridge",
"atlas_cool sand_pinnacle sun",
"owens corning_driftwood_trudefinition duration",
"certainteed_max def weathered wood_landmark pro",
"atlas_coastal granite_pinnacle pristine",
"gaf_charcoal_timberline hdz",
"atlas_cool driftwood_pinnacle sun",
"atlas_cool costal cliffs_pinnacle sun",
"atlas_weathered wood_pinnacle pristine",
"atlas_oyster_pinnacle pristine",
"owens corning_sedona canyon_duration designer",
"gaf_weathered wood_timberline hdz",
"tamko_thunderstorm grey_titan xt",
"gaf_shakewood_timberline hdz",
"malarkey_weather wood_highlander nex",
"iko_dual black_cambridge",
"certainteed_weathered wood_landmark",
"atlas_cool coral canyon_pinnacle sun",
"atlas_summer storm_pinnacle pristine",
"gaf_pewter gray_timberline hdz",
"atlas_hickory_pinnacle pristine",
"gaf_charcoal_grand sequoia",
"gaf_driftwood_timberline hdz"
] |
PK-B/roof_classification_rearrange_labels
|
<!-- 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. -->
# PK-B/roof_classification_rearrange_labels
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: 0.7457
- Validation Loss: 0.9674
- Train Accuracy: 0.8106
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 18770, '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': 0.0001}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 3.3662 | 3.0784 | 0.3894 | 0 |
| 2.8003 | 2.5991 | 0.5830 | 1 |
| 2.3450 | 2.2234 | 0.6766 | 2 |
| 1.9717 | 1.8939 | 0.7532 | 3 |
| 1.6915 | 1.6970 | 0.7468 | 4 |
| 1.4260 | 1.3627 | 0.8553 | 5 |
| 1.1972 | 1.3024 | 0.8064 | 6 |
| 1.0469 | 1.0933 | 0.8532 | 7 |
| 0.8685 | 1.0638 | 0.8 | 8 |
| 0.7457 | 0.9674 | 0.8106 | 9 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"certainteed_heather blend_landmark",
"gaf_golden harvest_timberline american harvest",
"tamko_weathered wood_heritage",
"gaf_mission brown_timberline hdz",
"atlas_tan_pinnacle pristine",
"owens corning_desert rose_truedefinition duration",
"iko_cornerstone_dynasty",
"owen’s corning_midnight plum_duration",
"gaf_cedar falls_timberline american harvest",
"owens corning_amber_trudefinition duration",
"owens corning_estate gray_supreme",
"certainteed_weathered wood_presidential shake",
"iko_weathered wood_cambridge",
"atlas_cool sand_pinnacle sun",
"owens corning_driftwood_trudefinition duration",
"certainteed_max def weathered wood_landmark pro",
"atlas_coastal granite_pinnacle pristine",
"gaf_charcoal_timberline hdz",
"atlas_cool driftwood_pinnacle sun",
"atlas_cool costal cliffs_pinnacle sun",
"atlas_weathered wood_pinnacle pristine",
"atlas_oyster_pinnacle pristine",
"owens corning_sedona canyon_duration designer",
"gaf_weathered wood_timberline hdz",
"tamko_thunderstorm grey_titan xt",
"gaf_shakewood_timberline hdz",
"malarkey_weather wood_highlander nex",
"iko_dual black_cambridge",
"certainteed_weathered wood_landmark",
"atlas_cool coral canyon_pinnacle sun",
"atlas_summer storm_pinnacle pristine",
"gaf_pewter gray_timberline hdz",
"atlas_hickory_pinnacle pristine",
"gaf_charcoal_grand sequoia",
"gaf_driftwood_timberline hdz"
] |
hkivancoral/smids_1x_beit_base_adamax_001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1833
- Accuracy: 0.7633
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9804 | 1.0 | 75 | 0.8561 | 0.5383 |
| 0.8823 | 2.0 | 150 | 0.7905 | 0.5767 |
| 0.8002 | 3.0 | 225 | 0.7961 | 0.5633 |
| 0.8142 | 4.0 | 300 | 0.8679 | 0.6133 |
| 0.6765 | 5.0 | 375 | 0.6964 | 0.6817 |
| 0.652 | 6.0 | 450 | 0.6686 | 0.7 |
| 0.6785 | 7.0 | 525 | 0.6625 | 0.7067 |
| 0.5659 | 8.0 | 600 | 0.6154 | 0.7217 |
| 0.6383 | 9.0 | 675 | 0.6262 | 0.7117 |
| 0.5991 | 10.0 | 750 | 0.5856 | 0.7633 |
| 0.4627 | 11.0 | 825 | 0.5901 | 0.7633 |
| 0.5021 | 12.0 | 900 | 0.5968 | 0.7433 |
| 0.5421 | 13.0 | 975 | 0.5857 | 0.74 |
| 0.3951 | 14.0 | 1050 | 0.5723 | 0.7733 |
| 0.4943 | 15.0 | 1125 | 0.6046 | 0.7533 |
| 0.4076 | 16.0 | 1200 | 0.6196 | 0.7567 |
| 0.379 | 17.0 | 1275 | 0.5906 | 0.7817 |
| 0.3759 | 18.0 | 1350 | 0.5998 | 0.775 |
| 0.3383 | 19.0 | 1425 | 0.6508 | 0.7567 |
| 0.2622 | 20.0 | 1500 | 0.6675 | 0.775 |
| 0.316 | 21.0 | 1575 | 0.7118 | 0.785 |
| 0.2478 | 22.0 | 1650 | 0.7508 | 0.78 |
| 0.2696 | 23.0 | 1725 | 0.7052 | 0.7733 |
| 0.1441 | 24.0 | 1800 | 0.8658 | 0.7783 |
| 0.1966 | 25.0 | 1875 | 0.9393 | 0.7417 |
| 0.1228 | 26.0 | 1950 | 1.0783 | 0.7567 |
| 0.2151 | 27.0 | 2025 | 1.0051 | 0.7533 |
| 0.1799 | 28.0 | 2100 | 1.0898 | 0.755 |
| 0.1053 | 29.0 | 2175 | 1.0567 | 0.7533 |
| 0.122 | 30.0 | 2250 | 1.1544 | 0.7583 |
| 0.1375 | 31.0 | 2325 | 1.3014 | 0.7617 |
| 0.0659 | 32.0 | 2400 | 1.6359 | 0.765 |
| 0.0997 | 33.0 | 2475 | 1.4213 | 0.7717 |
| 0.0852 | 34.0 | 2550 | 1.6657 | 0.7467 |
| 0.0752 | 35.0 | 2625 | 1.5943 | 0.7733 |
| 0.0405 | 36.0 | 2700 | 1.5865 | 0.7583 |
| 0.0174 | 37.0 | 2775 | 1.8002 | 0.7533 |
| 0.0364 | 38.0 | 2850 | 1.6078 | 0.7583 |
| 0.0269 | 39.0 | 2925 | 2.0543 | 0.7667 |
| 0.0034 | 40.0 | 3000 | 2.1698 | 0.7517 |
| 0.0428 | 41.0 | 3075 | 1.8011 | 0.74 |
| 0.0355 | 42.0 | 3150 | 2.1588 | 0.7567 |
| 0.0068 | 43.0 | 3225 | 2.0789 | 0.7617 |
| 0.013 | 44.0 | 3300 | 2.0235 | 0.76 |
| 0.0102 | 45.0 | 3375 | 1.9567 | 0.7567 |
| 0.0216 | 46.0 | 3450 | 1.9788 | 0.765 |
| 0.0016 | 47.0 | 3525 | 2.1056 | 0.765 |
| 0.0046 | 48.0 | 3600 | 2.1156 | 0.7633 |
| 0.0115 | 49.0 | 3675 | 2.2014 | 0.7617 |
| 0.0156 | 50.0 | 3750 | 2.1833 | 0.7633 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_0001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_0001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8043
- Accuracy: 0.9032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3546 | 1.0 | 76 | 0.4865 | 0.7930 |
| 0.2399 | 2.0 | 152 | 0.3349 | 0.8731 |
| 0.136 | 3.0 | 228 | 0.2999 | 0.8831 |
| 0.1619 | 4.0 | 304 | 0.4346 | 0.8698 |
| 0.1213 | 5.0 | 380 | 0.4295 | 0.8748 |
| 0.0741 | 6.0 | 456 | 0.4439 | 0.8881 |
| 0.0995 | 7.0 | 532 | 0.5033 | 0.8815 |
| 0.0126 | 8.0 | 608 | 0.4887 | 0.8982 |
| 0.0174 | 9.0 | 684 | 0.6241 | 0.8848 |
| 0.0036 | 10.0 | 760 | 0.5630 | 0.8898 |
| 0.0047 | 11.0 | 836 | 0.6256 | 0.8898 |
| 0.025 | 12.0 | 912 | 0.5949 | 0.8982 |
| 0.0037 | 13.0 | 988 | 0.6192 | 0.8898 |
| 0.0095 | 14.0 | 1064 | 0.6191 | 0.8982 |
| 0.0074 | 15.0 | 1140 | 0.6693 | 0.8948 |
| 0.0061 | 16.0 | 1216 | 0.6785 | 0.8915 |
| 0.0003 | 17.0 | 1292 | 0.6825 | 0.8898 |
| 0.0001 | 18.0 | 1368 | 0.7695 | 0.8865 |
| 0.0107 | 19.0 | 1444 | 0.6909 | 0.8965 |
| 0.0125 | 20.0 | 1520 | 0.7272 | 0.8915 |
| 0.0016 | 21.0 | 1596 | 0.7585 | 0.8848 |
| 0.0028 | 22.0 | 1672 | 0.7524 | 0.8898 |
| 0.0017 | 23.0 | 1748 | 0.8165 | 0.8865 |
| 0.0046 | 24.0 | 1824 | 0.7698 | 0.8848 |
| 0.004 | 25.0 | 1900 | 0.8060 | 0.8915 |
| 0.003 | 26.0 | 1976 | 0.7525 | 0.8998 |
| 0.0039 | 27.0 | 2052 | 0.8271 | 0.8848 |
| 0.0001 | 28.0 | 2128 | 0.7809 | 0.8965 |
| 0.0001 | 29.0 | 2204 | 0.8142 | 0.8948 |
| 0.0 | 30.0 | 2280 | 0.7973 | 0.8881 |
| 0.0023 | 31.0 | 2356 | 0.7501 | 0.8998 |
| 0.0061 | 32.0 | 2432 | 0.7903 | 0.8932 |
| 0.0085 | 33.0 | 2508 | 0.7939 | 0.8932 |
| 0.0036 | 34.0 | 2584 | 0.7959 | 0.8982 |
| 0.0089 | 35.0 | 2660 | 0.7729 | 0.8982 |
| 0.0 | 36.0 | 2736 | 0.8000 | 0.8948 |
| 0.0038 | 37.0 | 2812 | 0.7757 | 0.8998 |
| 0.0028 | 38.0 | 2888 | 0.7902 | 0.8898 |
| 0.0024 | 39.0 | 2964 | 0.7785 | 0.9048 |
| 0.0001 | 40.0 | 3040 | 0.7668 | 0.9082 |
| 0.0052 | 41.0 | 3116 | 0.7725 | 0.9048 |
| 0.0 | 42.0 | 3192 | 0.7888 | 0.9032 |
| 0.0 | 43.0 | 3268 | 0.7934 | 0.9032 |
| 0.0 | 44.0 | 3344 | 0.7962 | 0.9032 |
| 0.0053 | 45.0 | 3420 | 0.8046 | 0.9032 |
| 0.0 | 46.0 | 3496 | 0.7994 | 0.9032 |
| 0.003 | 47.0 | 3572 | 0.8008 | 0.9032 |
| 0.0032 | 48.0 | 3648 | 0.8023 | 0.9032 |
| 0.0018 | 49.0 | 3724 | 0.8041 | 0.9032 |
| 0.0052 | 50.0 | 3800 | 0.8043 | 0.9032 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_0001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_0001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7859
- Accuracy: 0.8902
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.357 | 1.0 | 75 | 0.2942 | 0.8852 |
| 0.196 | 2.0 | 150 | 0.2977 | 0.8769 |
| 0.1343 | 3.0 | 225 | 0.3454 | 0.8835 |
| 0.1165 | 4.0 | 300 | 0.4770 | 0.8586 |
| 0.0357 | 5.0 | 375 | 0.3863 | 0.8819 |
| 0.0407 | 6.0 | 450 | 0.5588 | 0.8785 |
| 0.0487 | 7.0 | 525 | 0.5410 | 0.8769 |
| 0.0422 | 8.0 | 600 | 0.5327 | 0.8835 |
| 0.0252 | 9.0 | 675 | 0.5671 | 0.8885 |
| 0.0072 | 10.0 | 750 | 0.5229 | 0.8852 |
| 0.0013 | 11.0 | 825 | 0.5397 | 0.9018 |
| 0.0233 | 12.0 | 900 | 0.6716 | 0.8902 |
| 0.0031 | 13.0 | 975 | 0.6232 | 0.8935 |
| 0.0106 | 14.0 | 1050 | 0.6722 | 0.8835 |
| 0.0052 | 15.0 | 1125 | 0.5873 | 0.9101 |
| 0.0117 | 16.0 | 1200 | 0.6014 | 0.8935 |
| 0.0056 | 17.0 | 1275 | 0.6190 | 0.8952 |
| 0.018 | 18.0 | 1350 | 0.6714 | 0.8902 |
| 0.0034 | 19.0 | 1425 | 0.6903 | 0.8918 |
| 0.0034 | 20.0 | 1500 | 0.6789 | 0.8902 |
| 0.0018 | 21.0 | 1575 | 0.7049 | 0.8852 |
| 0.0015 | 22.0 | 1650 | 0.8451 | 0.8802 |
| 0.0032 | 23.0 | 1725 | 0.6725 | 0.8885 |
| 0.0116 | 24.0 | 1800 | 0.7163 | 0.8952 |
| 0.0001 | 25.0 | 1875 | 0.6827 | 0.8918 |
| 0.004 | 26.0 | 1950 | 0.7084 | 0.8885 |
| 0.012 | 27.0 | 2025 | 0.7239 | 0.8968 |
| 0.0099 | 28.0 | 2100 | 0.7371 | 0.8918 |
| 0.0044 | 29.0 | 2175 | 0.7635 | 0.8869 |
| 0.0039 | 30.0 | 2250 | 0.7043 | 0.8918 |
| 0.0035 | 31.0 | 2325 | 0.7276 | 0.8902 |
| 0.0 | 32.0 | 2400 | 0.7428 | 0.8935 |
| 0.0 | 33.0 | 2475 | 0.7968 | 0.8852 |
| 0.014 | 34.0 | 2550 | 0.7553 | 0.8918 |
| 0.0048 | 35.0 | 2625 | 0.7230 | 0.8968 |
| 0.0029 | 36.0 | 2700 | 0.7674 | 0.8869 |
| 0.0 | 37.0 | 2775 | 0.7425 | 0.8918 |
| 0.0023 | 38.0 | 2850 | 0.7970 | 0.8902 |
| 0.0047 | 39.0 | 2925 | 0.8047 | 0.8869 |
| 0.0021 | 40.0 | 3000 | 0.7994 | 0.8885 |
| 0.0 | 41.0 | 3075 | 0.7761 | 0.8852 |
| 0.0025 | 42.0 | 3150 | 0.7890 | 0.8885 |
| 0.0046 | 43.0 | 3225 | 0.7889 | 0.8885 |
| 0.0 | 44.0 | 3300 | 0.7915 | 0.8852 |
| 0.0047 | 45.0 | 3375 | 0.7967 | 0.8885 |
| 0.0 | 46.0 | 3450 | 0.7946 | 0.8869 |
| 0.002 | 47.0 | 3525 | 0.7884 | 0.8885 |
| 0.0 | 48.0 | 3600 | 0.7873 | 0.8885 |
| 0.0 | 49.0 | 3675 | 0.7859 | 0.8902 |
| 0.0 | 50.0 | 3750 | 0.7859 | 0.8902 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_0001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_0001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6695
- Accuracy: 0.9133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3952 | 1.0 | 75 | 0.2900 | 0.885 |
| 0.2706 | 2.0 | 150 | 0.2858 | 0.895 |
| 0.1431 | 3.0 | 225 | 0.3509 | 0.8717 |
| 0.0811 | 4.0 | 300 | 0.2863 | 0.9183 |
| 0.0342 | 5.0 | 375 | 0.4874 | 0.8783 |
| 0.0334 | 6.0 | 450 | 0.4282 | 0.9117 |
| 0.0097 | 7.0 | 525 | 0.4216 | 0.92 |
| 0.0417 | 8.0 | 600 | 0.4392 | 0.91 |
| 0.0057 | 9.0 | 675 | 0.4243 | 0.9183 |
| 0.0023 | 10.0 | 750 | 0.5491 | 0.9 |
| 0.0342 | 11.0 | 825 | 0.4738 | 0.915 |
| 0.015 | 12.0 | 900 | 0.5105 | 0.9267 |
| 0.0179 | 13.0 | 975 | 0.6274 | 0.9083 |
| 0.0017 | 14.0 | 1050 | 0.5351 | 0.915 |
| 0.0012 | 15.0 | 1125 | 0.5446 | 0.905 |
| 0.0029 | 16.0 | 1200 | 0.5695 | 0.9067 |
| 0.0045 | 17.0 | 1275 | 0.5414 | 0.9133 |
| 0.0233 | 18.0 | 1350 | 0.7467 | 0.87 |
| 0.0001 | 19.0 | 1425 | 0.5934 | 0.9 |
| 0.0112 | 20.0 | 1500 | 0.5736 | 0.9067 |
| 0.0001 | 21.0 | 1575 | 0.6327 | 0.9033 |
| 0.0084 | 22.0 | 1650 | 0.5946 | 0.915 |
| 0.006 | 23.0 | 1725 | 0.5821 | 0.9133 |
| 0.0001 | 24.0 | 1800 | 0.6358 | 0.9 |
| 0.0 | 25.0 | 1875 | 0.5917 | 0.9117 |
| 0.0 | 26.0 | 1950 | 0.5998 | 0.9133 |
| 0.0002 | 27.0 | 2025 | 0.5967 | 0.915 |
| 0.0001 | 28.0 | 2100 | 0.5752 | 0.9117 |
| 0.0001 | 29.0 | 2175 | 0.6692 | 0.9 |
| 0.0044 | 30.0 | 2250 | 0.6493 | 0.9033 |
| 0.003 | 31.0 | 2325 | 0.6716 | 0.9117 |
| 0.0061 | 32.0 | 2400 | 0.7077 | 0.8983 |
| 0.0001 | 33.0 | 2475 | 0.6337 | 0.915 |
| 0.0003 | 34.0 | 2550 | 0.6698 | 0.9 |
| 0.0035 | 35.0 | 2625 | 0.6670 | 0.9033 |
| 0.0027 | 36.0 | 2700 | 0.6180 | 0.9067 |
| 0.0042 | 37.0 | 2775 | 0.6174 | 0.915 |
| 0.0023 | 38.0 | 2850 | 0.6161 | 0.9133 |
| 0.0001 | 39.0 | 2925 | 0.6601 | 0.91 |
| 0.0029 | 40.0 | 3000 | 0.6359 | 0.91 |
| 0.0 | 41.0 | 3075 | 0.6349 | 0.91 |
| 0.0022 | 42.0 | 3150 | 0.6576 | 0.9133 |
| 0.0028 | 43.0 | 3225 | 0.6662 | 0.9067 |
| 0.0 | 44.0 | 3300 | 0.6662 | 0.9083 |
| 0.0 | 45.0 | 3375 | 0.6797 | 0.9117 |
| 0.0 | 46.0 | 3450 | 0.6797 | 0.91 |
| 0.0043 | 47.0 | 3525 | 0.6738 | 0.91 |
| 0.0006 | 48.0 | 3600 | 0.6709 | 0.9133 |
| 0.0001 | 49.0 | 3675 | 0.6693 | 0.9133 |
| 0.0 | 50.0 | 3750 | 0.6695 | 0.9133 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_0001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_0001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1137
- Accuracy: 0.87
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3443 | 1.0 | 75 | 0.4137 | 0.8583 |
| 0.258 | 2.0 | 150 | 0.4036 | 0.8483 |
| 0.1343 | 3.0 | 225 | 0.4810 | 0.8533 |
| 0.0768 | 4.0 | 300 | 0.5625 | 0.86 |
| 0.0189 | 5.0 | 375 | 0.6619 | 0.8617 |
| 0.0435 | 6.0 | 450 | 0.6679 | 0.875 |
| 0.0162 | 7.0 | 525 | 0.7878 | 0.86 |
| 0.0677 | 8.0 | 600 | 0.7298 | 0.875 |
| 0.0423 | 9.0 | 675 | 0.8935 | 0.855 |
| 0.0172 | 10.0 | 750 | 0.8762 | 0.8717 |
| 0.001 | 11.0 | 825 | 0.8614 | 0.865 |
| 0.0092 | 12.0 | 900 | 0.8623 | 0.8717 |
| 0.0016 | 13.0 | 975 | 0.8916 | 0.87 |
| 0.0049 | 14.0 | 1050 | 0.8926 | 0.88 |
| 0.0101 | 15.0 | 1125 | 0.9303 | 0.8683 |
| 0.0014 | 16.0 | 1200 | 0.9140 | 0.8783 |
| 0.001 | 17.0 | 1275 | 0.9424 | 0.8817 |
| 0.0053 | 18.0 | 1350 | 0.8806 | 0.8817 |
| 0.0012 | 19.0 | 1425 | 0.9188 | 0.8917 |
| 0.0147 | 20.0 | 1500 | 0.9436 | 0.8767 |
| 0.0025 | 21.0 | 1575 | 0.9848 | 0.88 |
| 0.0092 | 22.0 | 1650 | 0.9945 | 0.8817 |
| 0.0279 | 23.0 | 1725 | 1.0063 | 0.875 |
| 0.0046 | 24.0 | 1800 | 1.0539 | 0.8767 |
| 0.0043 | 25.0 | 1875 | 1.0635 | 0.8717 |
| 0.0045 | 26.0 | 1950 | 1.0471 | 0.8733 |
| 0.0 | 27.0 | 2025 | 1.0128 | 0.8783 |
| 0.0004 | 28.0 | 2100 | 1.0296 | 0.8717 |
| 0.0001 | 29.0 | 2175 | 1.0117 | 0.875 |
| 0.0001 | 30.0 | 2250 | 1.0423 | 0.87 |
| 0.0073 | 31.0 | 2325 | 1.0722 | 0.87 |
| 0.0 | 32.0 | 2400 | 1.0662 | 0.8767 |
| 0.0 | 33.0 | 2475 | 1.0416 | 0.8717 |
| 0.0 | 34.0 | 2550 | 1.0959 | 0.8717 |
| 0.0034 | 35.0 | 2625 | 1.1220 | 0.87 |
| 0.0 | 36.0 | 2700 | 1.1441 | 0.8733 |
| 0.0 | 37.0 | 2775 | 1.1553 | 0.8733 |
| 0.0022 | 38.0 | 2850 | 1.1117 | 0.8767 |
| 0.0 | 39.0 | 2925 | 1.1002 | 0.8717 |
| 0.0 | 40.0 | 3000 | 1.1022 | 0.8683 |
| 0.003 | 41.0 | 3075 | 1.1129 | 0.8667 |
| 0.008 | 42.0 | 3150 | 1.1397 | 0.8667 |
| 0.0 | 43.0 | 3225 | 1.1224 | 0.87 |
| 0.0 | 44.0 | 3300 | 1.1186 | 0.8717 |
| 0.0 | 45.0 | 3375 | 1.1121 | 0.87 |
| 0.0001 | 46.0 | 3450 | 1.1134 | 0.87 |
| 0.0 | 47.0 | 3525 | 1.1172 | 0.8683 |
| 0.0001 | 48.0 | 3600 | 1.1134 | 0.87 |
| 0.0023 | 49.0 | 3675 | 1.1139 | 0.87 |
| 0.0022 | 50.0 | 3750 | 1.1137 | 0.87 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
xiaopch/vit-base-patch16-224-finetuned-for-agricultural
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-for-agricultural
This model is a fine-tuned version of [xiaopch/vit-base-patch16-224-finetuned](https://huggingface.co/xiaopch/vit-base-patch16-224-finetuned) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9246
- Accuracy: 0.7309
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9131 | 1.0 | 35 | 1.0878 | 0.6847 |
| 0.8066 | 2.0 | 70 | 0.9933 | 0.7189 |
| 0.7259 | 3.0 | 105 | 0.9445 | 0.7249 |
| 0.6719 | 4.0 | 140 | 0.9246 | 0.7309 |
| 0.6056 | 5.0 | 175 | 0.9258 | 0.7229 |
| 0.5576 | 6.0 | 210 | 0.9230 | 0.7309 |
| 0.5113 | 7.0 | 245 | 0.9152 | 0.7169 |
| 0.488 | 8.0 | 280 | 0.9119 | 0.7209 |
| 0.4822 | 9.0 | 315 | 0.9061 | 0.7269 |
| 0.4163 | 10.0 | 350 | 0.9039 | 0.7289 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"spodoptera_litura",
"aphid",
"beet_armyworm",
"borer",
"chemical_fertilizer",
"cnidocampa_flavescens",
"corn_borer",
"cotton_bollworm",
"fhb",
"grasshopper",
"longhorn_beetle",
"oriental_fruit_fly",
"pesticides",
"plutella_xylostella",
"rice_planthopper",
"rice_stem_borer",
"rolled_leaf_borer"
] |
canadianjosieharrison/swinv2-large-patch4-window12-192-22k-augmented
|
<!-- This model card 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-large-patch4-window12-192-22k-augmented
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3067
- Accuracy: 0.8723
## Model description
More information needed
## Intended uses & 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: 48
- eval_batch_size: 48
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 384
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.89 | 3 | 1.4847 | 0.5816 |
| No log | 1.78 | 6 | 0.9256 | 0.6950 |
| 1.2457 | 2.96 | 10 | 0.6017 | 0.7589 |
| 1.2457 | 3.85 | 13 | 0.3806 | 0.8723 |
| 1.2457 | 4.74 | 16 | 0.3866 | 0.8440 |
| 0.3656 | 5.93 | 20 | 0.3358 | 0.8794 |
| 0.3656 | 6.81 | 23 | 0.2803 | 0.8865 |
| 0.3656 | 8.0 | 27 | 0.3079 | 0.8723 |
| 0.2205 | 8.89 | 30 | 0.3067 | 0.8723 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"brick",
"metal",
"null",
"other",
"rustication",
"siding",
"stucco",
"wood"
] |
hkivancoral/smids_1x_beit_base_adamax_0001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8148
- Accuracy: 0.88
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3159 | 1.0 | 75 | 0.2787 | 0.8933 |
| 0.2494 | 2.0 | 150 | 0.2824 | 0.8917 |
| 0.1709 | 3.0 | 225 | 0.2857 | 0.89 |
| 0.0771 | 4.0 | 300 | 0.3708 | 0.8933 |
| 0.0554 | 5.0 | 375 | 0.4256 | 0.895 |
| 0.0571 | 6.0 | 450 | 0.4870 | 0.8867 |
| 0.0043 | 7.0 | 525 | 0.5217 | 0.9017 |
| 0.0346 | 8.0 | 600 | 0.5838 | 0.8983 |
| 0.0305 | 9.0 | 675 | 0.5589 | 0.89 |
| 0.0299 | 10.0 | 750 | 0.6507 | 0.8833 |
| 0.0112 | 11.0 | 825 | 0.7257 | 0.885 |
| 0.0571 | 12.0 | 900 | 0.6425 | 0.8933 |
| 0.0111 | 13.0 | 975 | 0.6434 | 0.885 |
| 0.0007 | 14.0 | 1050 | 0.6590 | 0.8917 |
| 0.0158 | 15.0 | 1125 | 0.6659 | 0.895 |
| 0.0001 | 16.0 | 1200 | 0.6546 | 0.8983 |
| 0.0007 | 17.0 | 1275 | 0.6736 | 0.8867 |
| 0.0231 | 18.0 | 1350 | 0.7021 | 0.8917 |
| 0.0081 | 19.0 | 1425 | 0.7031 | 0.8917 |
| 0.0001 | 20.0 | 1500 | 0.7077 | 0.8833 |
| 0.0034 | 21.0 | 1575 | 0.6794 | 0.885 |
| 0.0184 | 22.0 | 1650 | 0.7927 | 0.865 |
| 0.0002 | 23.0 | 1725 | 0.7523 | 0.8783 |
| 0.0048 | 24.0 | 1800 | 0.7237 | 0.885 |
| 0.0065 | 25.0 | 1875 | 0.7425 | 0.8867 |
| 0.0064 | 26.0 | 1950 | 0.7940 | 0.8833 |
| 0.0055 | 27.0 | 2025 | 0.7223 | 0.8983 |
| 0.0092 | 28.0 | 2100 | 0.7594 | 0.8933 |
| 0.0 | 29.0 | 2175 | 0.7361 | 0.89 |
| 0.0 | 30.0 | 2250 | 0.7567 | 0.89 |
| 0.017 | 31.0 | 2325 | 0.7474 | 0.8883 |
| 0.0029 | 32.0 | 2400 | 0.8687 | 0.8767 |
| 0.0165 | 33.0 | 2475 | 0.8109 | 0.8883 |
| 0.0031 | 34.0 | 2550 | 0.8076 | 0.885 |
| 0.0039 | 35.0 | 2625 | 0.8393 | 0.8833 |
| 0.0031 | 36.0 | 2700 | 0.8234 | 0.8817 |
| 0.0001 | 37.0 | 2775 | 0.8155 | 0.8833 |
| 0.0034 | 38.0 | 2850 | 0.8110 | 0.89 |
| 0.0036 | 39.0 | 2925 | 0.8344 | 0.8817 |
| 0.0002 | 40.0 | 3000 | 0.8172 | 0.8833 |
| 0.0025 | 41.0 | 3075 | 0.8298 | 0.8817 |
| 0.0021 | 42.0 | 3150 | 0.8481 | 0.8817 |
| 0.0001 | 43.0 | 3225 | 0.8405 | 0.8817 |
| 0.0035 | 44.0 | 3300 | 0.8375 | 0.8833 |
| 0.0006 | 45.0 | 3375 | 0.8281 | 0.885 |
| 0.0024 | 46.0 | 3450 | 0.8226 | 0.8833 |
| 0.0 | 47.0 | 3525 | 0.8109 | 0.8817 |
| 0.0 | 48.0 | 3600 | 0.8113 | 0.88 |
| 0.0026 | 49.0 | 3675 | 0.8154 | 0.88 |
| 0.0067 | 50.0 | 3750 | 0.8148 | 0.88 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_00001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6618
- Accuracy: 0.9032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4246 | 1.0 | 76 | 0.3687 | 0.8598 |
| 0.2552 | 2.0 | 152 | 0.2999 | 0.8798 |
| 0.1978 | 3.0 | 228 | 0.2886 | 0.8731 |
| 0.1972 | 4.0 | 304 | 0.2763 | 0.8865 |
| 0.1608 | 5.0 | 380 | 0.2799 | 0.8865 |
| 0.1346 | 6.0 | 456 | 0.3048 | 0.8815 |
| 0.0943 | 7.0 | 532 | 0.3402 | 0.8898 |
| 0.0622 | 8.0 | 608 | 0.3287 | 0.8915 |
| 0.0613 | 9.0 | 684 | 0.3634 | 0.8865 |
| 0.0585 | 10.0 | 760 | 0.3905 | 0.8881 |
| 0.0328 | 11.0 | 836 | 0.3830 | 0.8948 |
| 0.0344 | 12.0 | 912 | 0.4094 | 0.8915 |
| 0.053 | 13.0 | 988 | 0.4103 | 0.8932 |
| 0.0261 | 14.0 | 1064 | 0.4498 | 0.8932 |
| 0.0261 | 15.0 | 1140 | 0.4936 | 0.8915 |
| 0.0343 | 16.0 | 1216 | 0.4859 | 0.8932 |
| 0.0153 | 17.0 | 1292 | 0.5143 | 0.8815 |
| 0.0038 | 18.0 | 1368 | 0.5271 | 0.8865 |
| 0.0046 | 19.0 | 1444 | 0.5417 | 0.8898 |
| 0.0282 | 20.0 | 1520 | 0.5283 | 0.8948 |
| 0.0048 | 21.0 | 1596 | 0.5421 | 0.8965 |
| 0.0018 | 22.0 | 1672 | 0.5503 | 0.8898 |
| 0.0064 | 23.0 | 1748 | 0.5860 | 0.8848 |
| 0.0241 | 24.0 | 1824 | 0.5762 | 0.8948 |
| 0.0207 | 25.0 | 1900 | 0.5869 | 0.8915 |
| 0.0293 | 26.0 | 1976 | 0.5842 | 0.8948 |
| 0.0029 | 27.0 | 2052 | 0.6141 | 0.8932 |
| 0.0198 | 28.0 | 2128 | 0.6046 | 0.8982 |
| 0.0329 | 29.0 | 2204 | 0.6286 | 0.8948 |
| 0.0036 | 30.0 | 2280 | 0.6053 | 0.8948 |
| 0.0339 | 31.0 | 2356 | 0.6159 | 0.8881 |
| 0.0211 | 32.0 | 2432 | 0.6253 | 0.8932 |
| 0.0315 | 33.0 | 2508 | 0.6357 | 0.8915 |
| 0.0135 | 34.0 | 2584 | 0.6365 | 0.8932 |
| 0.0361 | 35.0 | 2660 | 0.6309 | 0.8965 |
| 0.0313 | 36.0 | 2736 | 0.6365 | 0.8965 |
| 0.0198 | 37.0 | 2812 | 0.6348 | 0.8965 |
| 0.0132 | 38.0 | 2888 | 0.6243 | 0.8948 |
| 0.0085 | 39.0 | 2964 | 0.6351 | 0.8948 |
| 0.001 | 40.0 | 3040 | 0.6372 | 0.8948 |
| 0.0149 | 41.0 | 3116 | 0.6607 | 0.8998 |
| 0.0056 | 42.0 | 3192 | 0.6570 | 0.9065 |
| 0.0011 | 43.0 | 3268 | 0.6635 | 0.8998 |
| 0.003 | 44.0 | 3344 | 0.6527 | 0.8982 |
| 0.041 | 45.0 | 3420 | 0.6537 | 0.8982 |
| 0.0011 | 46.0 | 3496 | 0.6576 | 0.8982 |
| 0.0196 | 47.0 | 3572 | 0.6599 | 0.8998 |
| 0.0117 | 48.0 | 3648 | 0.6620 | 0.9032 |
| 0.0018 | 49.0 | 3724 | 0.6617 | 0.9032 |
| 0.0144 | 50.0 | 3800 | 0.6618 | 0.9032 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_00001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_00001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7289
- Accuracy: 0.8852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4217 | 1.0 | 75 | 0.3814 | 0.8419 |
| 0.2549 | 2.0 | 150 | 0.3222 | 0.8735 |
| 0.2258 | 3.0 | 225 | 0.2946 | 0.8802 |
| 0.1818 | 4.0 | 300 | 0.2874 | 0.8935 |
| 0.1414 | 5.0 | 375 | 0.3063 | 0.8869 |
| 0.1276 | 6.0 | 450 | 0.3088 | 0.8835 |
| 0.1278 | 7.0 | 525 | 0.3231 | 0.8885 |
| 0.0712 | 8.0 | 600 | 0.3560 | 0.8869 |
| 0.0461 | 9.0 | 675 | 0.3613 | 0.8918 |
| 0.0475 | 10.0 | 750 | 0.3784 | 0.8952 |
| 0.0242 | 11.0 | 825 | 0.4079 | 0.8885 |
| 0.0506 | 12.0 | 900 | 0.4429 | 0.8869 |
| 0.0272 | 13.0 | 975 | 0.4714 | 0.8869 |
| 0.0444 | 14.0 | 1050 | 0.5396 | 0.8802 |
| 0.0206 | 15.0 | 1125 | 0.5526 | 0.8735 |
| 0.0163 | 16.0 | 1200 | 0.5286 | 0.8852 |
| 0.0204 | 17.0 | 1275 | 0.5940 | 0.8819 |
| 0.0355 | 18.0 | 1350 | 0.5758 | 0.8769 |
| 0.0442 | 19.0 | 1425 | 0.5804 | 0.8785 |
| 0.0309 | 20.0 | 1500 | 0.5941 | 0.8819 |
| 0.0106 | 21.0 | 1575 | 0.6105 | 0.8802 |
| 0.0257 | 22.0 | 1650 | 0.6126 | 0.8835 |
| 0.0159 | 23.0 | 1725 | 0.6156 | 0.8852 |
| 0.0399 | 24.0 | 1800 | 0.6198 | 0.8785 |
| 0.0047 | 25.0 | 1875 | 0.6196 | 0.8819 |
| 0.0247 | 26.0 | 1950 | 0.6464 | 0.8835 |
| 0.024 | 27.0 | 2025 | 0.6527 | 0.8869 |
| 0.0438 | 28.0 | 2100 | 0.7050 | 0.8819 |
| 0.0088 | 29.0 | 2175 | 0.6605 | 0.8902 |
| 0.0182 | 30.0 | 2250 | 0.6570 | 0.8885 |
| 0.0251 | 31.0 | 2325 | 0.6796 | 0.8819 |
| 0.017 | 32.0 | 2400 | 0.6922 | 0.8852 |
| 0.033 | 33.0 | 2475 | 0.7245 | 0.8719 |
| 0.0216 | 34.0 | 2550 | 0.6972 | 0.8785 |
| 0.0144 | 35.0 | 2625 | 0.7562 | 0.8819 |
| 0.0161 | 36.0 | 2700 | 0.6986 | 0.8835 |
| 0.0005 | 37.0 | 2775 | 0.6981 | 0.8819 |
| 0.0053 | 38.0 | 2850 | 0.7088 | 0.8869 |
| 0.0506 | 39.0 | 2925 | 0.7290 | 0.8869 |
| 0.0237 | 40.0 | 3000 | 0.7146 | 0.8885 |
| 0.0005 | 41.0 | 3075 | 0.7241 | 0.8802 |
| 0.0171 | 42.0 | 3150 | 0.7294 | 0.8819 |
| 0.0152 | 43.0 | 3225 | 0.7178 | 0.8869 |
| 0.0007 | 44.0 | 3300 | 0.7168 | 0.8819 |
| 0.0066 | 45.0 | 3375 | 0.7243 | 0.8819 |
| 0.0023 | 46.0 | 3450 | 0.7324 | 0.8835 |
| 0.053 | 47.0 | 3525 | 0.7341 | 0.8852 |
| 0.0015 | 48.0 | 3600 | 0.7298 | 0.8852 |
| 0.0034 | 49.0 | 3675 | 0.7290 | 0.8852 |
| 0.0068 | 50.0 | 3750 | 0.7289 | 0.8852 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_00001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_00001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5920
- Accuracy: 0.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4085 | 1.0 | 75 | 0.3406 | 0.8733 |
| 0.3125 | 2.0 | 150 | 0.2766 | 0.905 |
| 0.272 | 3.0 | 225 | 0.2526 | 0.9117 |
| 0.2066 | 4.0 | 300 | 0.2426 | 0.9167 |
| 0.1315 | 5.0 | 375 | 0.2415 | 0.9233 |
| 0.1338 | 6.0 | 450 | 0.2667 | 0.9133 |
| 0.095 | 7.0 | 525 | 0.2679 | 0.9183 |
| 0.1144 | 8.0 | 600 | 0.2699 | 0.9267 |
| 0.038 | 9.0 | 675 | 0.2963 | 0.9183 |
| 0.0367 | 10.0 | 750 | 0.3153 | 0.925 |
| 0.0325 | 11.0 | 825 | 0.3378 | 0.92 |
| 0.0172 | 12.0 | 900 | 0.3441 | 0.9183 |
| 0.0285 | 13.0 | 975 | 0.3703 | 0.9217 |
| 0.0132 | 14.0 | 1050 | 0.3979 | 0.9117 |
| 0.0356 | 15.0 | 1125 | 0.3938 | 0.9167 |
| 0.0285 | 16.0 | 1200 | 0.4361 | 0.9117 |
| 0.0435 | 17.0 | 1275 | 0.4564 | 0.905 |
| 0.0412 | 18.0 | 1350 | 0.4606 | 0.905 |
| 0.0106 | 19.0 | 1425 | 0.4449 | 0.9133 |
| 0.0192 | 20.0 | 1500 | 0.4442 | 0.9167 |
| 0.0051 | 21.0 | 1575 | 0.4723 | 0.9117 |
| 0.0266 | 22.0 | 1650 | 0.5052 | 0.9117 |
| 0.0217 | 23.0 | 1725 | 0.4785 | 0.915 |
| 0.0019 | 24.0 | 1800 | 0.5058 | 0.9117 |
| 0.0069 | 25.0 | 1875 | 0.5124 | 0.91 |
| 0.0008 | 26.0 | 1950 | 0.5249 | 0.9117 |
| 0.0081 | 27.0 | 2025 | 0.5029 | 0.91 |
| 0.0213 | 28.0 | 2100 | 0.4919 | 0.9167 |
| 0.0025 | 29.0 | 2175 | 0.5055 | 0.9167 |
| 0.0366 | 30.0 | 2250 | 0.5226 | 0.9117 |
| 0.0192 | 31.0 | 2325 | 0.5652 | 0.91 |
| 0.0012 | 32.0 | 2400 | 0.5128 | 0.92 |
| 0.0191 | 33.0 | 2475 | 0.5580 | 0.9117 |
| 0.0168 | 34.0 | 2550 | 0.5615 | 0.905 |
| 0.0045 | 35.0 | 2625 | 0.5647 | 0.9133 |
| 0.0069 | 36.0 | 2700 | 0.5389 | 0.91 |
| 0.021 | 37.0 | 2775 | 0.5519 | 0.9133 |
| 0.0264 | 38.0 | 2850 | 0.5472 | 0.9117 |
| 0.0403 | 39.0 | 2925 | 0.5693 | 0.91 |
| 0.001 | 40.0 | 3000 | 0.5532 | 0.91 |
| 0.0004 | 41.0 | 3075 | 0.5673 | 0.9117 |
| 0.0344 | 42.0 | 3150 | 0.5624 | 0.9067 |
| 0.0221 | 43.0 | 3225 | 0.5673 | 0.91 |
| 0.0004 | 44.0 | 3300 | 0.5783 | 0.91 |
| 0.0156 | 45.0 | 3375 | 0.5833 | 0.9083 |
| 0.021 | 46.0 | 3450 | 0.5741 | 0.9117 |
| 0.0145 | 47.0 | 3525 | 0.5806 | 0.91 |
| 0.0049 | 48.0 | 3600 | 0.5891 | 0.91 |
| 0.0162 | 49.0 | 3675 | 0.5932 | 0.9083 |
| 0.0336 | 50.0 | 3750 | 0.5920 | 0.91 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_adamax_00001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_00001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9690
- Accuracy: 0.8733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3842 | 1.0 | 75 | 0.3836 | 0.8567 |
| 0.2698 | 2.0 | 150 | 0.3565 | 0.8717 |
| 0.2112 | 3.0 | 225 | 0.3725 | 0.8667 |
| 0.1563 | 4.0 | 300 | 0.3983 | 0.8683 |
| 0.0925 | 5.0 | 375 | 0.3901 | 0.875 |
| 0.1014 | 6.0 | 450 | 0.4180 | 0.8817 |
| 0.0818 | 7.0 | 525 | 0.4236 | 0.8733 |
| 0.0472 | 8.0 | 600 | 0.4670 | 0.87 |
| 0.0417 | 9.0 | 675 | 0.5177 | 0.8767 |
| 0.0198 | 10.0 | 750 | 0.5528 | 0.8683 |
| 0.0232 | 11.0 | 825 | 0.5777 | 0.875 |
| 0.0159 | 12.0 | 900 | 0.6214 | 0.8683 |
| 0.0174 | 13.0 | 975 | 0.6477 | 0.87 |
| 0.0205 | 14.0 | 1050 | 0.7117 | 0.8633 |
| 0.0429 | 15.0 | 1125 | 0.7038 | 0.875 |
| 0.0098 | 16.0 | 1200 | 0.7398 | 0.8733 |
| 0.0056 | 17.0 | 1275 | 0.7568 | 0.8717 |
| 0.016 | 18.0 | 1350 | 0.7774 | 0.8733 |
| 0.0366 | 19.0 | 1425 | 0.7871 | 0.8783 |
| 0.0462 | 20.0 | 1500 | 0.7545 | 0.8867 |
| 0.0036 | 21.0 | 1575 | 0.8298 | 0.8767 |
| 0.013 | 22.0 | 1650 | 0.8793 | 0.875 |
| 0.0139 | 23.0 | 1725 | 0.8645 | 0.88 |
| 0.0044 | 24.0 | 1800 | 0.8813 | 0.8717 |
| 0.0148 | 25.0 | 1875 | 0.8534 | 0.8767 |
| 0.0146 | 26.0 | 1950 | 0.8817 | 0.8767 |
| 0.0054 | 27.0 | 2025 | 0.9081 | 0.87 |
| 0.0007 | 28.0 | 2100 | 0.8989 | 0.8767 |
| 0.0046 | 29.0 | 2175 | 0.8951 | 0.88 |
| 0.0234 | 30.0 | 2250 | 0.9014 | 0.8717 |
| 0.0106 | 31.0 | 2325 | 0.9119 | 0.8667 |
| 0.0085 | 32.0 | 2400 | 0.9313 | 0.8717 |
| 0.0036 | 33.0 | 2475 | 0.9195 | 0.8733 |
| 0.001 | 34.0 | 2550 | 0.9166 | 0.8717 |
| 0.0098 | 35.0 | 2625 | 0.9378 | 0.87 |
| 0.0089 | 36.0 | 2700 | 0.9278 | 0.8717 |
| 0.0099 | 37.0 | 2775 | 0.9534 | 0.8717 |
| 0.0248 | 38.0 | 2850 | 0.9419 | 0.8783 |
| 0.0327 | 39.0 | 2925 | 0.9391 | 0.8733 |
| 0.0223 | 40.0 | 3000 | 0.9364 | 0.875 |
| 0.0147 | 41.0 | 3075 | 0.9305 | 0.8767 |
| 0.0288 | 42.0 | 3150 | 0.9572 | 0.8783 |
| 0.0191 | 43.0 | 3225 | 0.9619 | 0.875 |
| 0.0008 | 44.0 | 3300 | 0.9576 | 0.875 |
| 0.0019 | 45.0 | 3375 | 0.9660 | 0.8733 |
| 0.0022 | 46.0 | 3450 | 0.9692 | 0.875 |
| 0.0015 | 47.0 | 3525 | 0.9668 | 0.875 |
| 0.0054 | 48.0 | 3600 | 0.9744 | 0.8733 |
| 0.0016 | 49.0 | 3675 | 0.9694 | 0.8733 |
| 0.0003 | 50.0 | 3750 | 0.9690 | 0.8733 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
JorgeGIT/finetuned-Leukemia-cell
|
<!-- This model card 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-Leukemia-cell
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1128
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 0.3182 | 2.94 | 100 | 0.2301 | 0.9211 |
| 0.2223 | 5.88 | 200 | 0.3411 | 0.8910 |
| 0.1695 | 8.82 | 300 | 0.1168 | 0.9624 |
| 0.0579 | 11.76 | 400 | 0.1632 | 0.9511 |
| 0.1184 | 14.71 | 500 | 0.4665 | 0.8346 |
| 0.0575 | 17.65 | 600 | 0.1563 | 0.9586 |
| 0.1087 | 20.59 | 700 | 0.2023 | 0.9511 |
| 0.1164 | 23.53 | 800 | 0.2283 | 0.9398 |
| 0.1144 | 26.47 | 900 | 0.1130 | 0.9624 |
| 0.1821 | 29.41 | 1000 | 0.1155 | 0.9737 |
| 0.0882 | 32.35 | 1100 | 0.0760 | 0.9850 |
| 0.1099 | 35.29 | 1200 | 0.0894 | 0.9737 |
| 0.053 | 38.24 | 1300 | 0.1248 | 0.9699 |
| 0.0489 | 41.18 | 1400 | 0.1081 | 0.9774 |
| 0.065 | 44.12 | 1500 | 0.1694 | 0.9549 |
| 0.037 | 47.06 | 1600 | 0.1060 | 0.9699 |
| 0.0281 | 50.0 | 1700 | 0.0892 | 0.9737 |
| 0.0394 | 52.94 | 1800 | 0.1680 | 0.9624 |
| 0.0828 | 55.88 | 1900 | 0.1404 | 0.9774 |
| 0.0663 | 58.82 | 2000 | 0.1683 | 0.9662 |
| 0.0698 | 61.76 | 2100 | 0.1517 | 0.9624 |
| 0.0938 | 64.71 | 2200 | 0.1031 | 0.9737 |
| 0.0324 | 67.65 | 2300 | 0.1251 | 0.9812 |
| 0.0713 | 70.59 | 2400 | 0.1597 | 0.9662 |
| 0.059 | 73.53 | 2500 | 0.1455 | 0.9699 |
| 0.0404 | 76.47 | 2600 | 0.0924 | 0.9624 |
| 0.0526 | 79.41 | 2700 | 0.0853 | 0.9812 |
| 0.0439 | 82.35 | 2800 | 0.0815 | 0.9850 |
| 0.0485 | 85.29 | 2900 | 0.1192 | 0.9774 |
| 0.0498 | 88.24 | 3000 | 0.0958 | 0.9737 |
| 0.0181 | 91.18 | 3100 | 0.1351 | 0.9699 |
| 0.0226 | 94.12 | 3200 | 0.1458 | 0.9774 |
| 0.1115 | 97.06 | 3300 | 0.1453 | 0.9737 |
| 0.0349 | 100.0 | 3400 | 0.1257 | 0.9812 |
| 0.0246 | 102.94 | 3500 | 0.1405 | 0.9662 |
| 0.0084 | 105.88 | 3600 | 0.0666 | 0.9887 |
| 0.0174 | 108.82 | 3700 | 0.1419 | 0.9662 |
| 0.0432 | 111.76 | 3800 | 0.2027 | 0.9662 |
| 0.0164 | 114.71 | 3900 | 0.0671 | 0.9812 |
| 0.0223 | 117.65 | 4000 | 0.0722 | 0.9850 |
| 0.012 | 120.59 | 4100 | 0.1285 | 0.9699 |
| 0.0143 | 123.53 | 4200 | 0.1102 | 0.9812 |
| 0.0254 | 126.47 | 4300 | 0.1139 | 0.9812 |
| 0.018 | 129.41 | 4400 | 0.1056 | 0.9737 |
| 0.0011 | 132.35 | 4500 | 0.1097 | 0.9774 |
| 0.08 | 135.29 | 4600 | 0.1425 | 0.9662 |
| 0.0292 | 138.24 | 4700 | 0.0871 | 0.9812 |
| 0.0248 | 141.18 | 4800 | 0.1082 | 0.9699 |
| 0.0064 | 144.12 | 4900 | 0.0644 | 0.9850 |
| 0.0115 | 147.06 | 5000 | 0.0912 | 0.9812 |
| 0.052 | 150.0 | 5100 | 0.0927 | 0.9850 |
| 0.0103 | 152.94 | 5200 | 0.1129 | 0.9774 |
| 0.0185 | 155.88 | 5300 | 0.1250 | 0.9699 |
| 0.0185 | 158.82 | 5400 | 0.1226 | 0.9737 |
| 0.0002 | 161.76 | 5500 | 0.1146 | 0.9812 |
| 0.0249 | 164.71 | 5600 | 0.1945 | 0.9737 |
| 0.0165 | 167.65 | 5700 | 0.1875 | 0.9586 |
| 0.0028 | 170.59 | 5800 | 0.1045 | 0.9774 |
| 0.0044 | 173.53 | 5900 | 0.1279 | 0.9774 |
| 0.0078 | 176.47 | 6000 | 0.0967 | 0.9774 |
| 0.0093 | 179.41 | 6100 | 0.1450 | 0.9812 |
| 0.0261 | 182.35 | 6200 | 0.0815 | 0.9850 |
| 0.0218 | 185.29 | 6300 | 0.1586 | 0.9699 |
| 0.1184 | 188.24 | 6400 | 0.1481 | 0.9812 |
| 0.0011 | 191.18 | 6500 | 0.1698 | 0.9737 |
| 0.0131 | 194.12 | 6600 | 0.2247 | 0.9662 |
| 0.0156 | 197.06 | 6700 | 0.1205 | 0.9812 |
| 0.007 | 200.0 | 6800 | 0.1864 | 0.9699 |
| 0.015 | 202.94 | 6900 | 0.1684 | 0.9774 |
| 0.0032 | 205.88 | 7000 | 0.0835 | 0.9850 |
| 0.0017 | 208.82 | 7100 | 0.1174 | 0.9812 |
| 0.0397 | 211.76 | 7200 | 0.1926 | 0.9662 |
| 0.0015 | 214.71 | 7300 | 0.1646 | 0.9699 |
| 0.0046 | 217.65 | 7400 | 0.1520 | 0.9774 |
| 0.0193 | 220.59 | 7500 | 0.1436 | 0.9812 |
| 0.0474 | 223.53 | 7600 | 0.1747 | 0.9737 |
| 0.001 | 226.47 | 7700 | 0.1647 | 0.9812 |
| 0.0005 | 229.41 | 7800 | 0.1992 | 0.9699 |
| 0.0119 | 232.35 | 7900 | 0.1545 | 0.9699 |
| 0.0153 | 235.29 | 8000 | 0.2018 | 0.9662 |
| 0.0106 | 238.24 | 8100 | 0.1798 | 0.9774 |
| 0.0012 | 241.18 | 8200 | 0.1896 | 0.9774 |
| 0.0 | 244.12 | 8300 | 0.1500 | 0.9812 |
| 0.0339 | 247.06 | 8400 | 0.1890 | 0.9662 |
| 0.0016 | 250.0 | 8500 | 0.1410 | 0.9812 |
| 0.0003 | 252.94 | 8600 | 0.1341 | 0.9812 |
| 0.001 | 255.88 | 8700 | 0.1209 | 0.9850 |
| 0.0071 | 258.82 | 8800 | 0.1191 | 0.9812 |
| 0.0 | 261.76 | 8900 | 0.0960 | 0.9887 |
| 0.0016 | 264.71 | 9000 | 0.1063 | 0.9850 |
| 0.0048 | 267.65 | 9100 | 0.1583 | 0.9737 |
| 0.0026 | 270.59 | 9200 | 0.1473 | 0.9774 |
| 0.0006 | 273.53 | 9300 | 0.1325 | 0.9812 |
| 0.0226 | 276.47 | 9400 | 0.1214 | 0.9812 |
| 0.0075 | 279.41 | 9500 | 0.1399 | 0.9812 |
| 0.0047 | 282.35 | 9600 | 0.1291 | 0.9850 |
| 0.0 | 285.29 | 9700 | 0.1117 | 0.9812 |
| 0.0001 | 288.24 | 9800 | 0.1137 | 0.9850 |
| 0.0001 | 291.18 | 9900 | 0.1117 | 0.9850 |
| 0.0 | 294.12 | 10000 | 0.1061 | 0.9850 |
| 0.0 | 297.06 | 10100 | 0.1129 | 0.9850 |
| 0.0057 | 300.0 | 10200 | 0.1128 | 0.9850 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"lla",
"folicular",
"linfos normales",
"llc",
"marginal",
"mononucleosis",
"trico"
] |
hkivancoral/smids_1x_beit_base_adamax_00001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_adamax_00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6882
- Accuracy: 0.89
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3992 | 1.0 | 75 | 0.3544 | 0.845 |
| 0.2938 | 2.0 | 150 | 0.2944 | 0.88 |
| 0.2043 | 3.0 | 225 | 0.2889 | 0.8733 |
| 0.1457 | 4.0 | 300 | 0.2668 | 0.8917 |
| 0.1371 | 5.0 | 375 | 0.2691 | 0.8833 |
| 0.1186 | 6.0 | 450 | 0.2876 | 0.8733 |
| 0.0675 | 7.0 | 525 | 0.2905 | 0.895 |
| 0.0675 | 8.0 | 600 | 0.3070 | 0.8983 |
| 0.0951 | 9.0 | 675 | 0.3449 | 0.8917 |
| 0.0427 | 10.0 | 750 | 0.3642 | 0.885 |
| 0.0217 | 11.0 | 825 | 0.3880 | 0.8817 |
| 0.0513 | 12.0 | 900 | 0.3991 | 0.9 |
| 0.0247 | 13.0 | 975 | 0.4163 | 0.8983 |
| 0.018 | 14.0 | 1050 | 0.4538 | 0.8883 |
| 0.0291 | 15.0 | 1125 | 0.4599 | 0.8917 |
| 0.0096 | 16.0 | 1200 | 0.5126 | 0.89 |
| 0.0106 | 17.0 | 1275 | 0.5125 | 0.8867 |
| 0.0447 | 18.0 | 1350 | 0.5410 | 0.8883 |
| 0.016 | 19.0 | 1425 | 0.5359 | 0.8883 |
| 0.0033 | 20.0 | 1500 | 0.5522 | 0.8867 |
| 0.0086 | 21.0 | 1575 | 0.5579 | 0.8883 |
| 0.0299 | 22.0 | 1650 | 0.5864 | 0.8833 |
| 0.0058 | 23.0 | 1725 | 0.5904 | 0.8867 |
| 0.0156 | 24.0 | 1800 | 0.6102 | 0.89 |
| 0.0161 | 25.0 | 1875 | 0.6210 | 0.8883 |
| 0.0066 | 26.0 | 1950 | 0.6149 | 0.8883 |
| 0.0424 | 27.0 | 2025 | 0.6199 | 0.8867 |
| 0.011 | 28.0 | 2100 | 0.6388 | 0.8867 |
| 0.0021 | 29.0 | 2175 | 0.6358 | 0.8917 |
| 0.0014 | 30.0 | 2250 | 0.6319 | 0.8883 |
| 0.0203 | 31.0 | 2325 | 0.6459 | 0.89 |
| 0.0221 | 32.0 | 2400 | 0.6739 | 0.8883 |
| 0.0066 | 33.0 | 2475 | 0.6562 | 0.89 |
| 0.0119 | 34.0 | 2550 | 0.6704 | 0.885 |
| 0.0088 | 35.0 | 2625 | 0.6526 | 0.89 |
| 0.0115 | 36.0 | 2700 | 0.6534 | 0.8867 |
| 0.0355 | 37.0 | 2775 | 0.6663 | 0.8883 |
| 0.0376 | 38.0 | 2850 | 0.6538 | 0.89 |
| 0.0299 | 39.0 | 2925 | 0.6757 | 0.8867 |
| 0.0019 | 40.0 | 3000 | 0.6764 | 0.8883 |
| 0.0235 | 41.0 | 3075 | 0.6776 | 0.89 |
| 0.0081 | 42.0 | 3150 | 0.6798 | 0.8883 |
| 0.0053 | 43.0 | 3225 | 0.6758 | 0.8883 |
| 0.0234 | 44.0 | 3300 | 0.6788 | 0.8933 |
| 0.0053 | 45.0 | 3375 | 0.6853 | 0.8883 |
| 0.0121 | 46.0 | 3450 | 0.6875 | 0.8867 |
| 0.001 | 47.0 | 3525 | 0.6878 | 0.8883 |
| 0.0104 | 48.0 | 3600 | 0.6872 | 0.89 |
| 0.0042 | 49.0 | 3675 | 0.6870 | 0.8883 |
| 0.0115 | 50.0 | 3750 | 0.6882 | 0.89 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
barghavani/Cheese_xray
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Cheese_xray
This model is a fine-tuned version of [barghavani/Cheese_xray](https://huggingface.co/barghavani/Cheese_xray) on the chest-xray-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2827
- Accuracy: 0.8883
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3993 | 0.99 | 63 | 0.4364 | 0.7165 |
| 0.3454 | 1.99 | 127 | 0.3947 | 0.7680 |
| 0.3327 | 3.0 | 191 | 0.3582 | 0.8591 |
| 0.3329 | 4.0 | 255 | 0.3371 | 0.8746 |
| 0.2992 | 4.99 | 318 | 0.3449 | 0.8643 |
| 0.3289 | 5.99 | 382 | 0.3172 | 0.8832 |
| 0.3309 | 7.0 | 446 | 0.2956 | 0.8935 |
| 0.2875 | 8.0 | 510 | 0.2911 | 0.8883 |
| 0.2764 | 8.99 | 573 | 0.2884 | 0.9124 |
| 0.265 | 9.88 | 630 | 0.2827 | 0.8883 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"normal",
"pneumonia"
] |
canadianjosieharrison/swinv2-large-patch4-window12-192-22k-baseline
|
<!-- This model card 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-large-patch4-window12-192-22k-baseline
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3489
- Accuracy: 0.8765
## Model description
More information needed
## Intended uses & 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: 18
- eval_batch_size: 18
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 36
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1721 | 1.0 | 20 | 0.8152 | 0.7407 |
| 0.5878 | 2.0 | 40 | 0.4285 | 0.8395 |
| 0.5201 | 3.0 | 60 | 0.5102 | 0.8148 |
| 0.3366 | 4.0 | 80 | 0.3463 | 0.8519 |
| 0.2792 | 5.0 | 100 | 0.4444 | 0.8272 |
| 0.2807 | 6.0 | 120 | 0.3282 | 0.8765 |
| 0.1978 | 7.0 | 140 | 0.3047 | 0.8642 |
| 0.2262 | 8.0 | 160 | 0.4534 | 0.8765 |
| 0.176 | 9.0 | 180 | 0.3605 | 0.8148 |
| 0.17 | 10.0 | 200 | 0.4222 | 0.8642 |
| 0.1445 | 11.0 | 220 | 0.3569 | 0.9012 |
| 0.128 | 12.0 | 240 | 0.4649 | 0.8642 |
| 0.1316 | 13.0 | 260 | 0.3848 | 0.8765 |
| 0.1772 | 14.0 | 280 | 0.4242 | 0.8395 |
| 0.1087 | 15.0 | 300 | 0.3756 | 0.8889 |
| 0.0858 | 16.0 | 320 | 0.4190 | 0.8519 |
| 0.1136 | 17.0 | 340 | 0.4902 | 0.8765 |
| 0.0425 | 18.0 | 360 | 0.3041 | 0.9012 |
| 0.07 | 19.0 | 380 | 0.3456 | 0.8889 |
| 0.0595 | 20.0 | 400 | 0.3489 | 0.8765 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"brick",
"metal",
"null",
"other",
"rustication",
"siding",
"stucco",
"wood"
] |
hkivancoral/smids_1x_beit_base_rms_00001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7081
- Accuracy: 0.8965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3415 | 1.0 | 76 | 0.3600 | 0.8531 |
| 0.1821 | 2.0 | 152 | 0.2813 | 0.8865 |
| 0.1106 | 3.0 | 228 | 0.2915 | 0.8965 |
| 0.0837 | 4.0 | 304 | 0.4355 | 0.8748 |
| 0.0461 | 5.0 | 380 | 0.3524 | 0.8831 |
| 0.0314 | 6.0 | 456 | 0.3471 | 0.9065 |
| 0.052 | 7.0 | 532 | 0.3906 | 0.9032 |
| 0.0094 | 8.0 | 608 | 0.4902 | 0.8998 |
| 0.0397 | 9.0 | 684 | 0.5074 | 0.8848 |
| 0.0068 | 10.0 | 760 | 0.5396 | 0.8965 |
| 0.0009 | 11.0 | 836 | 0.4910 | 0.9032 |
| 0.0007 | 12.0 | 912 | 0.5441 | 0.8982 |
| 0.0176 | 13.0 | 988 | 0.5729 | 0.8965 |
| 0.008 | 14.0 | 1064 | 0.5831 | 0.8965 |
| 0.0023 | 15.0 | 1140 | 0.6581 | 0.8982 |
| 0.0112 | 16.0 | 1216 | 0.6373 | 0.9048 |
| 0.0122 | 17.0 | 1292 | 0.6091 | 0.8982 |
| 0.0218 | 18.0 | 1368 | 0.7005 | 0.8965 |
| 0.0052 | 19.0 | 1444 | 0.6533 | 0.8998 |
| 0.0143 | 20.0 | 1520 | 0.5987 | 0.9048 |
| 0.0047 | 21.0 | 1596 | 0.6407 | 0.8982 |
| 0.005 | 22.0 | 1672 | 0.7577 | 0.8898 |
| 0.0133 | 23.0 | 1748 | 0.7568 | 0.8848 |
| 0.0064 | 24.0 | 1824 | 0.6963 | 0.8915 |
| 0.0056 | 25.0 | 1900 | 0.6832 | 0.8982 |
| 0.0033 | 26.0 | 1976 | 0.6578 | 0.8982 |
| 0.0048 | 27.0 | 2052 | 0.6821 | 0.9032 |
| 0.0003 | 28.0 | 2128 | 0.6751 | 0.8998 |
| 0.0002 | 29.0 | 2204 | 0.6826 | 0.8998 |
| 0.0054 | 30.0 | 2280 | 0.7208 | 0.8965 |
| 0.0234 | 31.0 | 2356 | 0.7169 | 0.8915 |
| 0.0066 | 32.0 | 2432 | 0.7161 | 0.8982 |
| 0.0078 | 33.0 | 2508 | 0.6895 | 0.8982 |
| 0.004 | 34.0 | 2584 | 0.7616 | 0.8982 |
| 0.0117 | 35.0 | 2660 | 0.7211 | 0.9032 |
| 0.0 | 36.0 | 2736 | 0.6772 | 0.8982 |
| 0.0027 | 37.0 | 2812 | 0.6751 | 0.8998 |
| 0.0023 | 38.0 | 2888 | 0.7465 | 0.9082 |
| 0.0025 | 39.0 | 2964 | 0.6434 | 0.9132 |
| 0.0043 | 40.0 | 3040 | 0.6803 | 0.9032 |
| 0.005 | 41.0 | 3116 | 0.6970 | 0.8982 |
| 0.0 | 42.0 | 3192 | 0.6953 | 0.8998 |
| 0.0002 | 43.0 | 3268 | 0.6864 | 0.8982 |
| 0.0001 | 44.0 | 3344 | 0.6955 | 0.9015 |
| 0.0058 | 45.0 | 3420 | 0.7259 | 0.8948 |
| 0.0 | 46.0 | 3496 | 0.7126 | 0.9032 |
| 0.0044 | 47.0 | 3572 | 0.7081 | 0.8965 |
| 0.0032 | 48.0 | 3648 | 0.7104 | 0.8965 |
| 0.0023 | 49.0 | 3724 | 0.7077 | 0.8965 |
| 0.0057 | 50.0 | 3800 | 0.7081 | 0.8965 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
Natalia2314/vit-base-catsVSdogs-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-catsVSdogs-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 cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0523
- Accuracy: 0.98
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0337 | 2.0 | 100 | 0.0523 | 0.98 |
| 0.0038 | 4.0 | 200 | 0.0591 | 0.985 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"cat",
"dog"
] |
Camilosan/Modelo-catsVSdogs
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Modelo-catsVSdogs
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 cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0129
- Accuracy: 0.995
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0333 | 2.0 | 100 | 0.0633 | 0.985 |
| 0.0039 | 4.0 | 200 | 0.0129 | 0.995 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"cat",
"dog"
] |
hkivancoral/smids_1x_beit_base_rms_00001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_00001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7810
- Accuracy: 0.8885
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3067 | 1.0 | 75 | 0.2670 | 0.9018 |
| 0.1737 | 2.0 | 150 | 0.2937 | 0.8918 |
| 0.1236 | 3.0 | 225 | 0.2592 | 0.8968 |
| 0.093 | 4.0 | 300 | 0.2806 | 0.9085 |
| 0.0342 | 5.0 | 375 | 0.3377 | 0.9035 |
| 0.0514 | 6.0 | 450 | 0.4662 | 0.8769 |
| 0.0285 | 7.0 | 525 | 0.4751 | 0.8902 |
| 0.0245 | 8.0 | 600 | 0.4931 | 0.8968 |
| 0.0336 | 9.0 | 675 | 0.4686 | 0.9035 |
| 0.0057 | 10.0 | 750 | 0.6619 | 0.8852 |
| 0.0005 | 11.0 | 825 | 0.5601 | 0.9018 |
| 0.045 | 12.0 | 900 | 0.6300 | 0.8869 |
| 0.0006 | 13.0 | 975 | 0.6005 | 0.8968 |
| 0.0104 | 14.0 | 1050 | 0.6903 | 0.8769 |
| 0.0013 | 15.0 | 1125 | 0.6574 | 0.8968 |
| 0.0022 | 16.0 | 1200 | 0.6330 | 0.8952 |
| 0.0138 | 17.0 | 1275 | 0.6340 | 0.9018 |
| 0.0109 | 18.0 | 1350 | 0.7199 | 0.8902 |
| 0.007 | 19.0 | 1425 | 0.7166 | 0.8968 |
| 0.009 | 20.0 | 1500 | 0.8141 | 0.8802 |
| 0.0077 | 21.0 | 1575 | 0.8216 | 0.8935 |
| 0.0199 | 22.0 | 1650 | 0.8347 | 0.8802 |
| 0.0056 | 23.0 | 1725 | 0.7454 | 0.8869 |
| 0.0076 | 24.0 | 1800 | 0.6539 | 0.8968 |
| 0.0001 | 25.0 | 1875 | 0.7625 | 0.8819 |
| 0.0105 | 26.0 | 1950 | 0.7771 | 0.8918 |
| 0.0191 | 27.0 | 2025 | 0.7871 | 0.8902 |
| 0.0146 | 28.0 | 2100 | 0.7395 | 0.8968 |
| 0.0063 | 29.0 | 2175 | 0.7297 | 0.8968 |
| 0.0064 | 30.0 | 2250 | 0.6880 | 0.8952 |
| 0.0044 | 31.0 | 2325 | 0.7924 | 0.8918 |
| 0.0115 | 32.0 | 2400 | 0.7709 | 0.8918 |
| 0.0213 | 33.0 | 2475 | 0.6964 | 0.8918 |
| 0.0034 | 34.0 | 2550 | 0.7612 | 0.8918 |
| 0.0052 | 35.0 | 2625 | 0.7685 | 0.8968 |
| 0.0043 | 36.0 | 2700 | 0.8478 | 0.8869 |
| 0.0001 | 37.0 | 2775 | 0.7661 | 0.8902 |
| 0.0031 | 38.0 | 2850 | 0.7393 | 0.8968 |
| 0.0464 | 39.0 | 2925 | 0.7536 | 0.8918 |
| 0.0026 | 40.0 | 3000 | 0.7768 | 0.8852 |
| 0.0001 | 41.0 | 3075 | 0.8423 | 0.8835 |
| 0.0041 | 42.0 | 3150 | 0.7762 | 0.8918 |
| 0.0072 | 43.0 | 3225 | 0.7847 | 0.8902 |
| 0.0 | 44.0 | 3300 | 0.7699 | 0.8835 |
| 0.0049 | 45.0 | 3375 | 0.7675 | 0.8852 |
| 0.0001 | 46.0 | 3450 | 0.7767 | 0.8885 |
| 0.0029 | 47.0 | 3525 | 0.7697 | 0.8885 |
| 0.0 | 48.0 | 3600 | 0.7734 | 0.8885 |
| 0.0 | 49.0 | 3675 | 0.7813 | 0.8885 |
| 0.0231 | 50.0 | 3750 | 0.7810 | 0.8885 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_rms_00001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_00001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6179
- Accuracy: 0.92
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.378 | 1.0 | 75 | 0.2655 | 0.905 |
| 0.1995 | 2.0 | 150 | 0.2472 | 0.9067 |
| 0.1269 | 3.0 | 225 | 0.2601 | 0.9133 |
| 0.0982 | 4.0 | 300 | 0.2718 | 0.9183 |
| 0.0334 | 5.0 | 375 | 0.3064 | 0.9183 |
| 0.0325 | 6.0 | 450 | 0.3593 | 0.9017 |
| 0.0122 | 7.0 | 525 | 0.4158 | 0.9133 |
| 0.0276 | 8.0 | 600 | 0.3999 | 0.915 |
| 0.0023 | 9.0 | 675 | 0.4376 | 0.91 |
| 0.0029 | 10.0 | 750 | 0.4955 | 0.91 |
| 0.0282 | 11.0 | 825 | 0.4886 | 0.9133 |
| 0.0074 | 12.0 | 900 | 0.4903 | 0.9083 |
| 0.0119 | 13.0 | 975 | 0.4968 | 0.9183 |
| 0.0151 | 14.0 | 1050 | 0.4966 | 0.9067 |
| 0.0139 | 15.0 | 1125 | 0.4573 | 0.9267 |
| 0.0049 | 16.0 | 1200 | 0.4797 | 0.9267 |
| 0.0357 | 17.0 | 1275 | 0.4808 | 0.9317 |
| 0.0195 | 18.0 | 1350 | 0.5297 | 0.9133 |
| 0.0164 | 19.0 | 1425 | 0.5446 | 0.9233 |
| 0.0136 | 20.0 | 1500 | 0.5630 | 0.915 |
| 0.0002 | 21.0 | 1575 | 0.6196 | 0.9083 |
| 0.0053 | 22.0 | 1650 | 0.5529 | 0.915 |
| 0.002 | 23.0 | 1725 | 0.5621 | 0.9183 |
| 0.0001 | 24.0 | 1800 | 0.5333 | 0.9233 |
| 0.0008 | 25.0 | 1875 | 0.5371 | 0.9217 |
| 0.0014 | 26.0 | 1950 | 0.5172 | 0.93 |
| 0.0001 | 27.0 | 2025 | 0.5437 | 0.9233 |
| 0.0001 | 28.0 | 2100 | 0.5344 | 0.9283 |
| 0.0001 | 29.0 | 2175 | 0.5536 | 0.9183 |
| 0.0075 | 30.0 | 2250 | 0.6086 | 0.9083 |
| 0.0046 | 31.0 | 2325 | 0.5570 | 0.9133 |
| 0.0077 | 32.0 | 2400 | 0.6038 | 0.915 |
| 0.0016 | 33.0 | 2475 | 0.6324 | 0.9133 |
| 0.0004 | 34.0 | 2550 | 0.5847 | 0.9217 |
| 0.0039 | 35.0 | 2625 | 0.6482 | 0.9183 |
| 0.0029 | 36.0 | 2700 | 0.6146 | 0.9267 |
| 0.0076 | 37.0 | 2775 | 0.5750 | 0.9217 |
| 0.0017 | 38.0 | 2850 | 0.5846 | 0.9233 |
| 0.0 | 39.0 | 2925 | 0.5952 | 0.9233 |
| 0.0018 | 40.0 | 3000 | 0.6016 | 0.9217 |
| 0.0 | 41.0 | 3075 | 0.6081 | 0.9267 |
| 0.0026 | 42.0 | 3150 | 0.6036 | 0.9233 |
| 0.0001 | 43.0 | 3225 | 0.6419 | 0.915 |
| 0.0 | 44.0 | 3300 | 0.6346 | 0.915 |
| 0.0 | 45.0 | 3375 | 0.6400 | 0.915 |
| 0.0001 | 46.0 | 3450 | 0.6220 | 0.9233 |
| 0.0039 | 47.0 | 3525 | 0.6179 | 0.9233 |
| 0.0001 | 48.0 | 3600 | 0.6159 | 0.9183 |
| 0.0 | 49.0 | 3675 | 0.6170 | 0.92 |
| 0.0 | 50.0 | 3750 | 0.6179 | 0.92 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_rms_00001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_00001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2766
- Accuracy: 0.8683
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.295 | 1.0 | 75 | 0.3791 | 0.8483 |
| 0.2062 | 2.0 | 150 | 0.3581 | 0.8683 |
| 0.101 | 3.0 | 225 | 0.4288 | 0.8783 |
| 0.0893 | 4.0 | 300 | 0.4864 | 0.8633 |
| 0.005 | 5.0 | 375 | 0.6074 | 0.8617 |
| 0.0333 | 6.0 | 450 | 0.7247 | 0.855 |
| 0.0079 | 7.0 | 525 | 0.7367 | 0.8667 |
| 0.0011 | 8.0 | 600 | 0.7491 | 0.8767 |
| 0.0337 | 9.0 | 675 | 0.8841 | 0.8667 |
| 0.023 | 10.0 | 750 | 0.9423 | 0.8617 |
| 0.0015 | 11.0 | 825 | 0.9063 | 0.87 |
| 0.0273 | 12.0 | 900 | 0.8724 | 0.8717 |
| 0.0004 | 13.0 | 975 | 0.8534 | 0.875 |
| 0.0254 | 14.0 | 1050 | 1.0178 | 0.8667 |
| 0.0142 | 15.0 | 1125 | 1.0491 | 0.86 |
| 0.0062 | 16.0 | 1200 | 1.0376 | 0.8733 |
| 0.0472 | 17.0 | 1275 | 1.0729 | 0.8683 |
| 0.0106 | 18.0 | 1350 | 1.0840 | 0.875 |
| 0.0563 | 19.0 | 1425 | 1.0588 | 0.8733 |
| 0.0079 | 20.0 | 1500 | 1.0867 | 0.87 |
| 0.0097 | 21.0 | 1575 | 1.1355 | 0.8567 |
| 0.0002 | 22.0 | 1650 | 1.1387 | 0.8633 |
| 0.0053 | 23.0 | 1725 | 1.0714 | 0.8633 |
| 0.0003 | 24.0 | 1800 | 1.0507 | 0.8717 |
| 0.006 | 25.0 | 1875 | 1.0737 | 0.87 |
| 0.0012 | 26.0 | 1950 | 1.0580 | 0.8817 |
| 0.0001 | 27.0 | 2025 | 1.0351 | 0.8733 |
| 0.0 | 28.0 | 2100 | 1.0876 | 0.8633 |
| 0.0003 | 29.0 | 2175 | 1.1172 | 0.865 |
| 0.0 | 30.0 | 2250 | 1.1601 | 0.8567 |
| 0.0175 | 31.0 | 2325 | 1.2685 | 0.8683 |
| 0.0003 | 32.0 | 2400 | 1.2370 | 0.8617 |
| 0.0 | 33.0 | 2475 | 1.2456 | 0.865 |
| 0.0 | 34.0 | 2550 | 1.2360 | 0.865 |
| 0.0047 | 35.0 | 2625 | 1.3021 | 0.8567 |
| 0.0001 | 36.0 | 2700 | 1.2287 | 0.8583 |
| 0.0 | 37.0 | 2775 | 1.2544 | 0.8667 |
| 0.0032 | 38.0 | 2850 | 1.2432 | 0.8683 |
| 0.0007 | 39.0 | 2925 | 1.3277 | 0.8633 |
| 0.0 | 40.0 | 3000 | 1.2887 | 0.86 |
| 0.0034 | 41.0 | 3075 | 1.2930 | 0.86 |
| 0.0066 | 42.0 | 3150 | 1.2756 | 0.855 |
| 0.0 | 43.0 | 3225 | 1.2450 | 0.8583 |
| 0.0 | 44.0 | 3300 | 1.2340 | 0.8633 |
| 0.0001 | 45.0 | 3375 | 1.2507 | 0.8667 |
| 0.0 | 46.0 | 3450 | 1.2915 | 0.8633 |
| 0.0 | 47.0 | 3525 | 1.2863 | 0.8683 |
| 0.0 | 48.0 | 3600 | 1.2824 | 0.8667 |
| 0.0022 | 49.0 | 3675 | 1.2757 | 0.8683 |
| 0.0021 | 50.0 | 3750 | 1.2766 | 0.8683 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_rms_00001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9075
- Accuracy: 0.895
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3429 | 1.0 | 75 | 0.3196 | 0.8817 |
| 0.2319 | 2.0 | 150 | 0.2825 | 0.8883 |
| 0.1642 | 3.0 | 225 | 0.2956 | 0.8883 |
| 0.0613 | 4.0 | 300 | 0.2991 | 0.905 |
| 0.0375 | 5.0 | 375 | 0.4173 | 0.89 |
| 0.0392 | 6.0 | 450 | 0.4376 | 0.895 |
| 0.0266 | 7.0 | 525 | 0.5591 | 0.8933 |
| 0.0211 | 8.0 | 600 | 0.6357 | 0.8883 |
| 0.0129 | 9.0 | 675 | 0.5589 | 0.8967 |
| 0.039 | 10.0 | 750 | 0.6087 | 0.8933 |
| 0.0196 | 11.0 | 825 | 0.6853 | 0.8967 |
| 0.0875 | 12.0 | 900 | 0.6905 | 0.8833 |
| 0.0161 | 13.0 | 975 | 0.7505 | 0.8867 |
| 0.0005 | 14.0 | 1050 | 0.7592 | 0.875 |
| 0.0258 | 15.0 | 1125 | 0.7859 | 0.8783 |
| 0.0008 | 16.0 | 1200 | 0.7624 | 0.8783 |
| 0.0078 | 17.0 | 1275 | 0.7129 | 0.8917 |
| 0.0151 | 18.0 | 1350 | 0.7730 | 0.885 |
| 0.015 | 19.0 | 1425 | 0.7612 | 0.88 |
| 0.0036 | 20.0 | 1500 | 0.7765 | 0.89 |
| 0.0036 | 21.0 | 1575 | 0.7746 | 0.89 |
| 0.0163 | 22.0 | 1650 | 0.7920 | 0.88 |
| 0.0002 | 23.0 | 1725 | 0.7971 | 0.8867 |
| 0.0013 | 24.0 | 1800 | 0.8091 | 0.8833 |
| 0.0084 | 25.0 | 1875 | 0.8422 | 0.8817 |
| 0.0077 | 26.0 | 1950 | 0.8718 | 0.89 |
| 0.0059 | 27.0 | 2025 | 0.8359 | 0.89 |
| 0.0135 | 28.0 | 2100 | 0.8777 | 0.8833 |
| 0.0007 | 29.0 | 2175 | 0.8422 | 0.895 |
| 0.0059 | 30.0 | 2250 | 0.8920 | 0.8933 |
| 0.0039 | 31.0 | 2325 | 0.9311 | 0.875 |
| 0.0027 | 32.0 | 2400 | 0.8796 | 0.89 |
| 0.0001 | 33.0 | 2475 | 0.9632 | 0.88 |
| 0.0031 | 34.0 | 2550 | 0.8453 | 0.89 |
| 0.0036 | 35.0 | 2625 | 0.8275 | 0.895 |
| 0.003 | 36.0 | 2700 | 0.8573 | 0.8883 |
| 0.0273 | 37.0 | 2775 | 0.8009 | 0.8967 |
| 0.0042 | 38.0 | 2850 | 0.8716 | 0.8917 |
| 0.0032 | 39.0 | 2925 | 0.9439 | 0.88 |
| 0.0005 | 40.0 | 3000 | 0.8577 | 0.8917 |
| 0.0023 | 41.0 | 3075 | 0.8426 | 0.8867 |
| 0.0083 | 42.0 | 3150 | 0.8441 | 0.895 |
| 0.0 | 43.0 | 3225 | 0.8722 | 0.8883 |
| 0.0036 | 44.0 | 3300 | 0.8679 | 0.8883 |
| 0.0009 | 45.0 | 3375 | 0.9113 | 0.8917 |
| 0.0131 | 46.0 | 3450 | 0.8965 | 0.89 |
| 0.0 | 47.0 | 3525 | 0.8892 | 0.8933 |
| 0.0001 | 48.0 | 3600 | 0.9072 | 0.8933 |
| 0.0024 | 49.0 | 3675 | 0.9074 | 0.8933 |
| 0.0054 | 50.0 | 3750 | 0.9075 | 0.895 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
jcollado/swin-tiny-patch4-window7-224-finetuned-cifar10
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-cifar10
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0948
- Accuracy: 0.9698
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5048 | 1.0 | 351 | 0.1324 | 0.9592 |
| 0.4048 | 2.0 | 703 | 0.1134 | 0.9628 |
| 0.3391 | 2.99 | 1053 | 0.0948 | 0.9698 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
laiagdla/cancer-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. -->
# 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1898
- Accuracy: 0.9243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2732 | 0.1 | 100 | 0.3969 | 0.8461 |
| 0.2784 | 0.21 | 200 | 0.3714 | 0.8579 |
| 0.301 | 0.31 | 300 | 0.3504 | 0.8376 |
| 0.2372 | 0.42 | 400 | 0.3391 | 0.8812 |
| 0.3136 | 0.52 | 500 | 0.2559 | 0.8967 |
| 0.3517 | 0.62 | 600 | 0.4141 | 0.8397 |
| 0.3312 | 0.73 | 700 | 0.3043 | 0.8841 |
| 0.2515 | 0.83 | 800 | 0.2541 | 0.9062 |
| 0.2854 | 0.93 | 900 | 0.2561 | 0.9006 |
| 0.2594 | 1.04 | 1000 | 0.2681 | 0.9020 |
| 0.177 | 1.14 | 1100 | 0.3406 | 0.8773 |
| 0.2717 | 1.25 | 1200 | 0.2266 | 0.9171 |
| 0.2197 | 1.35 | 1300 | 0.2080 | 0.9236 |
| 0.155 | 1.45 | 1400 | 0.2048 | 0.9236 |
| 0.2657 | 1.56 | 1500 | 0.2037 | 0.9256 |
| 0.118 | 1.66 | 1600 | 0.2616 | 0.9096 |
| 0.1823 | 1.77 | 1700 | 0.2158 | 0.9241 |
| 0.2175 | 1.87 | 1800 | 0.2159 | 0.9182 |
| 0.143 | 1.97 | 1900 | 0.1898 | 0.9243 |
| 0.1051 | 2.08 | 2000 | 0.2308 | 0.9226 |
| 0.1963 | 2.18 | 2100 | 0.2354 | 0.9205 |
| 0.0524 | 2.28 | 2200 | 0.2298 | 0.9282 |
| 0.097 | 2.39 | 2300 | 0.2495 | 0.9241 |
| 0.0744 | 2.49 | 2400 | 0.2493 | 0.9194 |
| 0.0744 | 2.6 | 2500 | 0.2429 | 0.9323 |
| 0.0345 | 2.7 | 2600 | 0.2587 | 0.9252 |
| 0.0097 | 2.8 | 2700 | 0.2284 | 0.9265 |
| 0.0775 | 2.91 | 2800 | 0.2242 | 0.9321 |
| 0.0634 | 3.01 | 2900 | 0.2314 | 0.9286 |
| 0.0109 | 3.12 | 3000 | 0.2203 | 0.9338 |
| 0.0039 | 3.22 | 3100 | 0.2575 | 0.9358 |
| 0.0139 | 3.32 | 3200 | 0.2570 | 0.9356 |
| 0.0358 | 3.43 | 3300 | 0.2630 | 0.9335 |
| 0.0347 | 3.53 | 3400 | 0.2633 | 0.9358 |
| 0.0408 | 3.63 | 3500 | 0.2591 | 0.9335 |
| 0.041 | 3.74 | 3600 | 0.2613 | 0.9367 |
| 0.004 | 3.84 | 3700 | 0.2587 | 0.9370 |
| 0.0389 | 3.95 | 3800 | 0.2535 | 0.9373 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
[
"0",
"1"
] |
3una/finetuned-FER2013
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-FER2013
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8366
- Accuracy: 0.7081
## Model description
More information needed
## Intended uses & 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-06
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.8119 | 1.0 | 202 | 1.7993 | 0.3079 |
| 1.6155 | 2.0 | 404 | 1.5446 | 0.4302 |
| 1.4279 | 3.0 | 606 | 1.3084 | 0.5301 |
| 1.3222 | 4.0 | 808 | 1.1817 | 0.5590 |
| 1.2532 | 5.0 | 1010 | 1.1026 | 0.5789 |
| 1.2019 | 6.0 | 1212 | 1.0432 | 0.5998 |
| 1.2037 | 7.0 | 1414 | 1.0030 | 0.6137 |
| 1.1757 | 8.0 | 1616 | 0.9873 | 0.6235 |
| 1.1359 | 9.0 | 1818 | 0.9377 | 0.6423 |
| 1.1282 | 10.0 | 2020 | 0.9231 | 0.6486 |
| 1.1019 | 11.0 | 2222 | 0.9011 | 0.6562 |
| 1.0494 | 12.0 | 2424 | 0.8968 | 0.6545 |
| 0.9951 | 13.0 | 2626 | 0.8876 | 0.6607 |
| 1.0121 | 14.0 | 2828 | 0.8720 | 0.6695 |
| 1.0571 | 15.0 | 3030 | 0.8776 | 0.6691 |
| 1.0049 | 16.0 | 3232 | 0.8627 | 0.6733 |
| 0.988 | 17.0 | 3434 | 0.8639 | 0.6719 |
| 0.9955 | 18.0 | 3636 | 0.8397 | 0.6806 |
| 0.9381 | 19.0 | 3838 | 0.8430 | 0.6820 |
| 0.9911 | 20.0 | 4040 | 0.8370 | 0.6837 |
| 0.9305 | 21.0 | 4242 | 0.8373 | 0.6837 |
| 0.9653 | 22.0 | 4444 | 0.8283 | 0.6883 |
| 0.9134 | 23.0 | 4646 | 0.8289 | 0.6879 |
| 0.9098 | 24.0 | 4848 | 0.8365 | 0.6837 |
| 0.8761 | 25.0 | 5050 | 0.8190 | 0.6869 |
| 0.9067 | 26.0 | 5252 | 0.8303 | 0.6876 |
| 0.8765 | 27.0 | 5454 | 0.8188 | 0.6942 |
| 0.8486 | 28.0 | 5656 | 0.8142 | 0.6959 |
| 0.9357 | 29.0 | 5858 | 0.8114 | 0.6984 |
| 0.9037 | 30.0 | 6060 | 0.8150 | 0.6917 |
| 0.8758 | 31.0 | 6262 | 0.8165 | 0.6931 |
| 0.8688 | 32.0 | 6464 | 0.8061 | 0.6994 |
| 0.8736 | 33.0 | 6666 | 0.8056 | 0.6994 |
| 0.8785 | 34.0 | 6868 | 0.8045 | 0.6991 |
| 0.8292 | 35.0 | 7070 | 0.8095 | 0.6987 |
| 0.8407 | 36.0 | 7272 | 0.8096 | 0.6956 |
| 0.8609 | 37.0 | 7474 | 0.8137 | 0.6984 |
| 0.9055 | 38.0 | 7676 | 0.8054 | 0.7018 |
| 0.8355 | 39.0 | 7878 | 0.8080 | 0.6980 |
| 0.8391 | 40.0 | 8080 | 0.8087 | 0.6966 |
| 0.7987 | 41.0 | 8282 | 0.8041 | 0.6998 |
| 0.818 | 42.0 | 8484 | 0.8070 | 0.7039 |
| 0.7836 | 43.0 | 8686 | 0.8091 | 0.7025 |
| 0.8348 | 44.0 | 8888 | 0.8047 | 0.7025 |
| 0.8205 | 45.0 | 9090 | 0.8076 | 0.7025 |
| 0.8023 | 46.0 | 9292 | 0.8056 | 0.7053 |
| 0.8241 | 47.0 | 9494 | 0.8022 | 0.7039 |
| 0.763 | 48.0 | 9696 | 0.8079 | 0.6994 |
| 0.7422 | 49.0 | 9898 | 0.8062 | 0.7039 |
| 0.7762 | 50.0 | 10100 | 0.8090 | 0.6998 |
| 0.7786 | 51.0 | 10302 | 0.8122 | 0.6994 |
| 0.8027 | 52.0 | 10504 | 0.8129 | 0.7043 |
| 0.7966 | 53.0 | 10706 | 0.8094 | 0.7039 |
| 0.8103 | 54.0 | 10908 | 0.8107 | 0.7039 |
| 0.7827 | 55.0 | 11110 | 0.8126 | 0.7057 |
| 0.7949 | 56.0 | 11312 | 0.8104 | 0.7119 |
| 0.7511 | 57.0 | 11514 | 0.8122 | 0.7050 |
| 0.7727 | 58.0 | 11716 | 0.8123 | 0.7078 |
| 0.7723 | 59.0 | 11918 | 0.8194 | 0.7015 |
| 0.7796 | 60.0 | 12120 | 0.8193 | 0.7053 |
| 0.7768 | 61.0 | 12322 | 0.8159 | 0.7029 |
| 0.7604 | 62.0 | 12524 | 0.8081 | 0.7085 |
| 0.7784 | 63.0 | 12726 | 0.8169 | 0.7106 |
| 0.7235 | 64.0 | 12928 | 0.8131 | 0.7015 |
| 0.7384 | 65.0 | 13130 | 0.8149 | 0.7085 |
| 0.6638 | 66.0 | 13332 | 0.8192 | 0.7078 |
| 0.6998 | 67.0 | 13534 | 0.8243 | 0.7113 |
| 0.7249 | 68.0 | 13736 | 0.8200 | 0.7015 |
| 0.6809 | 69.0 | 13938 | 0.8140 | 0.7081 |
| 0.701 | 70.0 | 14140 | 0.8177 | 0.7095 |
| 0.7122 | 71.0 | 14342 | 0.8245 | 0.7053 |
| 0.7269 | 72.0 | 14544 | 0.8245 | 0.7050 |
| 0.6973 | 73.0 | 14746 | 0.8207 | 0.7095 |
| 0.7241 | 74.0 | 14948 | 0.8210 | 0.7057 |
| 0.7397 | 75.0 | 15150 | 0.8230 | 0.7060 |
| 0.6832 | 76.0 | 15352 | 0.8308 | 0.7057 |
| 0.7213 | 77.0 | 15554 | 0.8256 | 0.7025 |
| 0.7115 | 78.0 | 15756 | 0.8291 | 0.7057 |
| 0.688 | 79.0 | 15958 | 0.8337 | 0.7088 |
| 0.6997 | 80.0 | 16160 | 0.8312 | 0.7060 |
| 0.6924 | 81.0 | 16362 | 0.8321 | 0.7053 |
| 0.7382 | 82.0 | 16564 | 0.8340 | 0.7050 |
| 0.7513 | 83.0 | 16766 | 0.8320 | 0.7015 |
| 0.656 | 84.0 | 16968 | 0.8389 | 0.7053 |
| 0.6503 | 85.0 | 17170 | 0.8321 | 0.7085 |
| 0.6661 | 86.0 | 17372 | 0.8355 | 0.7092 |
| 0.7026 | 87.0 | 17574 | 0.8339 | 0.7088 |
| 0.76 | 88.0 | 17776 | 0.8361 | 0.7092 |
| 0.696 | 89.0 | 17978 | 0.8343 | 0.7106 |
| 0.6713 | 90.0 | 18180 | 0.8337 | 0.7106 |
| 0.6621 | 91.0 | 18382 | 0.8349 | 0.7057 |
| 0.7042 | 92.0 | 18584 | 0.8360 | 0.7085 |
| 0.7087 | 93.0 | 18786 | 0.8353 | 0.7085 |
| 0.64 | 94.0 | 18988 | 0.8371 | 0.7088 |
| 0.659 | 95.0 | 19190 | 0.8376 | 0.7071 |
| 0.6246 | 96.0 | 19392 | 0.8376 | 0.7088 |
| 0.6797 | 97.0 | 19594 | 0.8368 | 0.7092 |
| 0.6652 | 98.0 | 19796 | 0.8376 | 0.7092 |
| 0.629 | 99.0 | 19998 | 0.8370 | 0.7088 |
| 0.6762 | 100.0 | 20200 | 0.8366 | 0.7081 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"angry",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
akashmaggon/vit-base-crack-classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-crack-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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0225
- Accuracy: 0.9972
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0086 | 1.0 | 203 | 0.0221 | 0.9958 |
| 0.0066 | 2.0 | 406 | 0.0216 | 0.9972 |
| 0.0064 | 3.0 | 609 | 0.0225 | 0.9972 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
akashmaggon/vit-base-crack-classification-2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-crack-classification-2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0212
- 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.222 | 1.0 | 203 | 0.2224 | 0.9097 |
| 0.0911 | 2.0 | 406 | 0.0806 | 0.9653 |
| 0.0163 | 3.0 | 609 | 0.0560 | 0.9681 |
| 0.0126 | 4.0 | 812 | 0.0554 | 0.9792 |
| 0.0233 | 5.0 | 1015 | 0.0347 | 0.9806 |
| 0.0096 | 6.0 | 1218 | 0.0949 | 0.9792 |
| 0.0013 | 7.0 | 1421 | 0.0440 | 0.9917 |
| 0.0011 | 8.0 | 1624 | 0.0222 | 0.9917 |
| 0.0009 | 9.0 | 1827 | 0.0213 | 0.9917 |
| 0.0009 | 10.0 | 2030 | 0.0212 | 0.9917 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
akashmaggon/vit-base-crack-classification-5
|
<!-- This model card 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-crack-classification-5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
akashmaggon/vit-base-crack-classification-129
|
<!-- This model card 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-crack-classification-129
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4641
- Accuracy: 0.8889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3061 | 1.0 | 212 | 1.1094 | 0.6759 |
| 0.844 | 2.0 | 424 | 0.7624 | 0.7940 |
| 0.5972 | 3.0 | 636 | 0.5760 | 0.8472 |
| 0.4424 | 4.0 | 848 | 0.4922 | 0.875 |
| 0.3815 | 5.0 | 1060 | 0.4641 | 0.8889 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
abhijitgayen/vit-large-0.0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-lage-beans-demo-v5
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1027
- Accuracy: 0.9792
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7861 | 0.28 | 100 | 0.7609 | 0.725 |
| 0.6276 | 0.56 | 200 | 0.6835 | 0.7764 |
| 0.6089 | 0.83 | 300 | 0.5568 | 0.7931 |
| 0.302 | 1.11 | 400 | 0.5887 | 0.8194 |
| 0.1723 | 1.39 | 500 | 0.2988 | 0.8903 |
| 0.2522 | 1.67 | 600 | 0.2564 | 0.9167 |
| 0.1115 | 1.94 | 700 | 0.1680 | 0.9472 |
| 0.1445 | 2.22 | 800 | 0.2065 | 0.9403 |
| 0.0648 | 2.5 | 900 | 0.1673 | 0.9569 |
| 0.0209 | 2.78 | 1000 | 0.1636 | 0.9569 |
| 0.0003 | 3.06 | 1100 | 0.1293 | 0.9694 |
| 0.0034 | 3.33 | 1200 | 0.0817 | 0.9792 |
| 0.0006 | 3.61 | 1300 | 0.0874 | 0.9833 |
| 0.0023 | 3.89 | 1400 | 0.1076 | 0.9778 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
abhijitgayen/super-cool-model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-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).
It achieves the following results on the evaluation set:
- Loss: 0.0816
- Accuracy: 0.9819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5092 | 0.28 | 100 | 0.6420 | 0.7681 |
| 0.5076 | 0.56 | 200 | 0.4069 | 0.8722 |
| 0.3291 | 0.83 | 300 | 0.4342 | 0.8569 |
| 0.108 | 1.11 | 400 | 0.2410 | 0.9292 |
| 0.0378 | 1.39 | 500 | 0.3107 | 0.9139 |
| 0.1488 | 1.67 | 600 | 0.1984 | 0.9389 |
| 0.0532 | 1.94 | 700 | 0.1714 | 0.9514 |
| 0.0122 | 2.22 | 800 | 0.1334 | 0.9611 |
| 0.0529 | 2.5 | 900 | 0.1139 | 0.9653 |
| 0.0221 | 2.78 | 1000 | 0.0875 | 0.9736 |
| 0.0052 | 3.06 | 1100 | 0.0816 | 0.9819 |
| 0.0045 | 3.33 | 1200 | 0.0873 | 0.9792 |
| 0.0113 | 3.61 | 1300 | 0.0882 | 0.9833 |
| 0.0043 | 3.89 | 1400 | 0.0865 | 0.9806 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
akashmaggon/vit-base-crack-classification-aug
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-crack-classification-aug
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0165
- Accuracy: 0.9907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4964 | 1.0 | 212 | 0.3400 | 0.8796 |
| 0.249 | 2.0 | 424 | 0.1651 | 0.9236 |
| 0.1216 | 3.0 | 636 | 0.0585 | 0.9676 |
| 0.0488 | 4.0 | 848 | 0.0382 | 0.9769 |
| 0.0304 | 5.0 | 1060 | 0.0302 | 0.9907 |
| 0.0107 | 6.0 | 1272 | 0.0294 | 0.9838 |
| 0.0093 | 7.0 | 1484 | 0.0165 | 0.9907 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
Luwayy/disaster_images_model
|
# 🌍 Disaster Image Classification using Vision Transformer
This project uses a fine-tuned Vision Transformer (ViT) model to classify disaster-related images into various categories such as **Water Disaster**, **Fire Disaster**, **Human Damage**, etc.
---
## 🚀 Installation
Install the required Python packages:
```bash
pip install transformers torch torchvision pillow requests
```
---
## 🔍 Quick Start
Use the pipeline to classify an image directly from a URL:
```python
from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO
# Load the image classification pipeline
pipe = pipeline("image-classification", model="Luwayy/disaster_images_model")
# Load an image from a URL
url = 'https://www.spml.co.in/Images/blog/wdt&c-152776632.jpg'
response = requests.get(url)
image = Image.open(BytesIO(response.content))
# Classify the image
results = pipe(image)
# Print results
print(results)
```
**Example Output:**
```json
[
{"label": "Water_Disaster", "score": 0.9184},
{"label": "Land_Disaster", "score": 0.0200},
{"label": "Non_Damage", "score": 0.0169},
{"label": "Human_Damage", "score": 0.0164},
{"label": "Fire_Disaster", "score": 0.0143}
]
```
---
## 🧠 Model Details
- **Base Model:** `google/vit-base-patch16-224-in21k`
- **Architecture:** Vision Transformer (`ViTForImageClassification`)
- **Image Size:** 224x224
- **Classes:**
- `Damaged_Infrastructure`
- `Fire_Disaster`
- `Human_Damage`
- `Land_Disaster`
- `Non_Damage`
- `Water_Disaster`
---
## ⚙️ Training Configuration
- **Image Normalization:** Mean `[0.5, 0.5, 0.5]`, Std `[0.5, 0.5, 0.5]`
- **Resize Method:** Bilinear to `224x224`
- **Augmentations:** Resize, Normalize, Convert to Tensor
- **Batch Size:** 16
- **Epochs:** 3
- **Learning Rate:** `3e-5`
- **Weight Decay:** `0.01`
- **Evaluation Strategy:** Per epoch
|
[
"damaged_infrastructure",
"fire_disaster",
"human_damage",
"land_disaster",
"non_damage",
"water_disaster"
] |
hkivancoral/smids_1x_beit_base_rms_0001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_0001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7464
- Accuracy: 0.6978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1002 | 1.0 | 76 | 0.9320 | 0.5459 |
| 0.9176 | 2.0 | 152 | 0.9156 | 0.4975 |
| 0.8828 | 3.0 | 228 | 1.4808 | 0.3239 |
| 0.9116 | 4.0 | 304 | 0.9182 | 0.5058 |
| 0.9681 | 5.0 | 380 | 0.8261 | 0.5726 |
| 0.8914 | 6.0 | 456 | 0.8412 | 0.5442 |
| 0.8118 | 7.0 | 532 | 0.8070 | 0.5843 |
| 0.7886 | 8.0 | 608 | 0.7873 | 0.6144 |
| 0.8228 | 9.0 | 684 | 0.8018 | 0.5593 |
| 0.7855 | 10.0 | 760 | 0.8650 | 0.5659 |
| 0.7506 | 11.0 | 836 | 0.8105 | 0.5726 |
| 0.8105 | 12.0 | 912 | 0.7718 | 0.5760 |
| 0.7542 | 13.0 | 988 | 0.7814 | 0.6027 |
| 0.8063 | 14.0 | 1064 | 0.7598 | 0.6244 |
| 0.6853 | 15.0 | 1140 | 0.9554 | 0.5526 |
| 0.6995 | 16.0 | 1216 | 0.7869 | 0.6277 |
| 0.7413 | 17.0 | 1292 | 0.7345 | 0.6561 |
| 0.6942 | 18.0 | 1368 | 0.7274 | 0.6511 |
| 0.7698 | 19.0 | 1444 | 0.7431 | 0.6711 |
| 0.7328 | 20.0 | 1520 | 0.7361 | 0.6327 |
| 0.7002 | 21.0 | 1596 | 0.7435 | 0.6427 |
| 0.6967 | 22.0 | 1672 | 0.8269 | 0.6010 |
| 0.651 | 23.0 | 1748 | 0.7688 | 0.6528 |
| 0.6937 | 24.0 | 1824 | 0.7386 | 0.6578 |
| 0.5694 | 25.0 | 1900 | 0.7657 | 0.6277 |
| 0.6705 | 26.0 | 1976 | 0.7210 | 0.6811 |
| 0.5989 | 27.0 | 2052 | 0.7453 | 0.6561 |
| 0.6274 | 28.0 | 2128 | 0.7780 | 0.6578 |
| 0.5748 | 29.0 | 2204 | 0.7338 | 0.6845 |
| 0.6764 | 30.0 | 2280 | 0.7373 | 0.6394 |
| 0.6934 | 31.0 | 2356 | 0.7055 | 0.6845 |
| 0.6007 | 32.0 | 2432 | 0.7394 | 0.6511 |
| 0.5933 | 33.0 | 2508 | 0.7124 | 0.6795 |
| 0.5894 | 34.0 | 2584 | 0.7760 | 0.6711 |
| 0.6837 | 35.0 | 2660 | 0.7002 | 0.6628 |
| 0.5776 | 36.0 | 2736 | 0.7352 | 0.6694 |
| 0.6485 | 37.0 | 2812 | 0.7046 | 0.6878 |
| 0.5352 | 38.0 | 2888 | 0.7058 | 0.6861 |
| 0.577 | 39.0 | 2964 | 0.6974 | 0.7028 |
| 0.5712 | 40.0 | 3040 | 0.7122 | 0.6811 |
| 0.5117 | 41.0 | 3116 | 0.7026 | 0.6845 |
| 0.4908 | 42.0 | 3192 | 0.7187 | 0.7045 |
| 0.4784 | 43.0 | 3268 | 0.7103 | 0.7028 |
| 0.4739 | 44.0 | 3344 | 0.7027 | 0.7162 |
| 0.5942 | 45.0 | 3420 | 0.7242 | 0.6962 |
| 0.4258 | 46.0 | 3496 | 0.7593 | 0.6912 |
| 0.4726 | 47.0 | 3572 | 0.7433 | 0.6895 |
| 0.4422 | 48.0 | 3648 | 0.7412 | 0.6928 |
| 0.4049 | 49.0 | 3724 | 0.7425 | 0.6995 |
| 0.5059 | 50.0 | 3800 | 0.7464 | 0.6978 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
Raihan004/Hierarchical_Agent_Action
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Hierarchical_Agent_Action
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 agent_action_class dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5942
- Accuracy: 0.8403
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4407 | 0.81 | 100 | 2.2716 | 0.6058 |
| 1.7756 | 1.61 | 200 | 1.6162 | 0.7065 |
| 1.3948 | 2.42 | 300 | 1.2200 | 0.7698 |
| 1.131 | 3.23 | 400 | 1.0012 | 0.7856 |
| 0.9239 | 4.03 | 500 | 0.9055 | 0.7827 |
| 0.8699 | 4.84 | 600 | 0.8103 | 0.7827 |
| 0.6707 | 5.65 | 700 | 0.7610 | 0.7842 |
| 0.6206 | 6.45 | 800 | 0.7312 | 0.7885 |
| 0.5795 | 7.26 | 900 | 0.6989 | 0.8101 |
| 0.4914 | 8.06 | 1000 | 0.7066 | 0.7813 |
| 0.5087 | 8.87 | 1100 | 0.6398 | 0.8187 |
| 0.4373 | 9.68 | 1200 | 0.6293 | 0.8043 |
| 0.4365 | 10.48 | 1300 | 0.6726 | 0.7971 |
| 0.4517 | 11.29 | 1400 | 0.6047 | 0.8245 |
| 0.4114 | 12.1 | 1500 | 0.6088 | 0.8230 |
| 0.426 | 12.9 | 1600 | 0.6165 | 0.8201 |
| 0.3456 | 13.71 | 1700 | 0.6133 | 0.8259 |
| 0.332 | 14.52 | 1800 | 0.6736 | 0.8201 |
| 0.3646 | 15.32 | 1900 | 0.6406 | 0.8173 |
| 0.3287 | 16.13 | 2000 | 0.6978 | 0.7971 |
| 0.2793 | 16.94 | 2100 | 0.6433 | 0.8173 |
| 0.2924 | 17.74 | 2200 | 0.6474 | 0.8144 |
| 0.2605 | 18.55 | 2300 | 0.6279 | 0.8288 |
| 0.2016 | 19.35 | 2400 | 0.6361 | 0.8216 |
| 0.2524 | 20.16 | 2500 | 0.6394 | 0.8259 |
| 0.2017 | 20.97 | 2600 | 0.6683 | 0.8158 |
| 0.2082 | 21.77 | 2700 | 0.6389 | 0.8345 |
| 0.2751 | 22.58 | 2800 | 0.6141 | 0.8374 |
| 0.207 | 23.39 | 2900 | 0.6052 | 0.8259 |
| 0.1791 | 24.19 | 3000 | 0.6332 | 0.8230 |
| 0.1719 | 25.0 | 3100 | 0.5942 | 0.8403 |
| 0.1685 | 25.81 | 3200 | 0.6121 | 0.8360 |
| 0.1557 | 26.61 | 3300 | 0.6237 | 0.8345 |
| 0.1694 | 27.42 | 3400 | 0.6372 | 0.8317 |
| 0.1927 | 28.23 | 3500 | 0.6378 | 0.8273 |
| 0.1375 | 29.03 | 3600 | 0.6258 | 0.8331 |
| 0.1653 | 29.84 | 3700 | 0.6262 | 0.8331 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
[
"কুকুর_কম্পিউটার_ব্যবহার_করা",
"কুকুর_খাওয়া",
"ছেলে_খেলা_করা",
"ছেলে_ঘুমানো",
"ছেলে_পান_করা",
"ছেলে_পড়া",
"ছেলে_রান্না_করা",
"ছেলে_লেখা",
"ছেলে_হাঁটা",
"বিড়াল_কম্পিউটার_ব্যবহার_করা",
"বিড়াল_খাওয়া",
"বিড়াল_খেলা_করা",
"কুকুর_খেলা_করা",
"বিড়াল_ঘুমানো",
"বিড়াল_পান_করা",
"বিড়াল_পড়া",
"বিড়াল_হাঁটা",
"মেয়ে_কথা_বলা",
"মেয়ে_কম্পিউটার_ব্যবহার_করা",
"মেয়ে_খাওয়া",
"মেয়ে_খেলা_করা",
"মেয়ে_ঘুমানো",
"মেয়ে_পান_করা",
"কুকুর_ঘুমানো",
"মেয়ে_পড়া",
"মেয়ে_রান্না_করা",
"মেয়ে_লেখা",
"মেয়ে_হাঁটা",
"কুকুর_পান_করা",
"কুকুর_পড়া",
"কুকুর_হাঁটা",
"ছেলে_কথা_বলা",
"ছেলে_কম্পিউটার_ব্যবহার_করা",
"ছেলে_খাওয়া"
] |
hkivancoral/smids_1x_beit_base_rms_0001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_0001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9358
- Accuracy: 0.7404
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0437 | 1.0 | 75 | 0.9679 | 0.5042 |
| 0.9234 | 2.0 | 150 | 0.8669 | 0.5208 |
| 1.0795 | 3.0 | 225 | 0.7926 | 0.5874 |
| 0.9543 | 4.0 | 300 | 0.8244 | 0.5507 |
| 0.8239 | 5.0 | 375 | 0.7959 | 0.5857 |
| 0.7924 | 6.0 | 450 | 0.7928 | 0.5890 |
| 0.8468 | 7.0 | 525 | 0.7806 | 0.6256 |
| 0.8608 | 8.0 | 600 | 0.9027 | 0.5408 |
| 0.7878 | 9.0 | 675 | 0.7544 | 0.6373 |
| 0.9079 | 10.0 | 750 | 0.7732 | 0.6190 |
| 0.7705 | 11.0 | 825 | 0.7349 | 0.6290 |
| 0.7586 | 12.0 | 900 | 0.7322 | 0.6306 |
| 0.7794 | 13.0 | 975 | 0.7224 | 0.6323 |
| 0.7123 | 14.0 | 1050 | 0.7252 | 0.6572 |
| 0.744 | 15.0 | 1125 | 0.7450 | 0.5990 |
| 0.7086 | 16.0 | 1200 | 0.6962 | 0.6639 |
| 0.7295 | 17.0 | 1275 | 0.7508 | 0.6489 |
| 0.7289 | 18.0 | 1350 | 0.6978 | 0.6722 |
| 0.6947 | 19.0 | 1425 | 0.7112 | 0.6739 |
| 0.6923 | 20.0 | 1500 | 0.7131 | 0.6805 |
| 0.7545 | 21.0 | 1575 | 0.7480 | 0.6223 |
| 0.68 | 22.0 | 1650 | 0.6683 | 0.6839 |
| 0.7107 | 23.0 | 1725 | 0.6889 | 0.6772 |
| 0.6933 | 24.0 | 1800 | 0.6566 | 0.6822 |
| 0.6429 | 25.0 | 1875 | 0.6381 | 0.7005 |
| 0.6742 | 26.0 | 1950 | 0.6536 | 0.6822 |
| 0.6753 | 27.0 | 2025 | 0.6462 | 0.6889 |
| 0.6228 | 28.0 | 2100 | 0.6368 | 0.7022 |
| 0.6193 | 29.0 | 2175 | 0.6115 | 0.7171 |
| 0.5568 | 30.0 | 2250 | 0.6625 | 0.7188 |
| 0.584 | 31.0 | 2325 | 0.6680 | 0.6922 |
| 0.581 | 32.0 | 2400 | 0.5723 | 0.7654 |
| 0.5698 | 33.0 | 2475 | 0.6173 | 0.7205 |
| 0.5032 | 34.0 | 2550 | 0.6176 | 0.7338 |
| 0.5019 | 35.0 | 2625 | 0.6137 | 0.7438 |
| 0.4921 | 36.0 | 2700 | 0.5855 | 0.7571 |
| 0.453 | 37.0 | 2775 | 0.6724 | 0.7271 |
| 0.4913 | 38.0 | 2850 | 0.6043 | 0.7720 |
| 0.3871 | 39.0 | 2925 | 0.6124 | 0.7704 |
| 0.4014 | 40.0 | 3000 | 0.6591 | 0.7521 |
| 0.4698 | 41.0 | 3075 | 0.6575 | 0.7604 |
| 0.375 | 42.0 | 3150 | 0.6735 | 0.7471 |
| 0.317 | 43.0 | 3225 | 0.7867 | 0.7504 |
| 0.2968 | 44.0 | 3300 | 0.7423 | 0.7521 |
| 0.2919 | 45.0 | 3375 | 0.8253 | 0.7504 |
| 0.2598 | 46.0 | 3450 | 0.8629 | 0.7421 |
| 0.1951 | 47.0 | 3525 | 0.8586 | 0.7704 |
| 0.1905 | 48.0 | 3600 | 0.9010 | 0.7438 |
| 0.1278 | 49.0 | 3675 | 0.9354 | 0.7454 |
| 0.2294 | 50.0 | 3750 | 0.9358 | 0.7404 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_rms_0001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_0001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7846
- Accuracy: 0.7133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1199 | 1.0 | 75 | 1.1044 | 0.325 |
| 1.1759 | 2.0 | 150 | 1.1239 | 0.47 |
| 1.1465 | 3.0 | 225 | 0.9168 | 0.5 |
| 0.8955 | 4.0 | 300 | 0.8917 | 0.5017 |
| 0.8948 | 5.0 | 375 | 0.8301 | 0.5533 |
| 0.9774 | 6.0 | 450 | 0.8272 | 0.5467 |
| 0.8001 | 7.0 | 525 | 0.8058 | 0.5567 |
| 0.7633 | 8.0 | 600 | 0.8140 | 0.545 |
| 0.7814 | 9.0 | 675 | 0.7815 | 0.5733 |
| 0.8175 | 10.0 | 750 | 0.7839 | 0.5633 |
| 0.7605 | 11.0 | 825 | 0.7664 | 0.615 |
| 0.762 | 12.0 | 900 | 0.7781 | 0.59 |
| 0.6797 | 13.0 | 975 | 0.7875 | 0.575 |
| 0.7699 | 14.0 | 1050 | 0.7772 | 0.6117 |
| 0.6167 | 15.0 | 1125 | 0.8129 | 0.585 |
| 0.7106 | 16.0 | 1200 | 0.7392 | 0.6633 |
| 0.7174 | 17.0 | 1275 | 0.7176 | 0.6717 |
| 0.704 | 18.0 | 1350 | 0.7772 | 0.63 |
| 0.6617 | 19.0 | 1425 | 0.7359 | 0.65 |
| 0.6722 | 20.0 | 1500 | 0.7009 | 0.6783 |
| 0.676 | 21.0 | 1575 | 0.6946 | 0.6667 |
| 0.6441 | 22.0 | 1650 | 0.7089 | 0.6917 |
| 0.6565 | 23.0 | 1725 | 0.7160 | 0.665 |
| 0.6009 | 24.0 | 1800 | 0.6902 | 0.6783 |
| 0.6592 | 25.0 | 1875 | 0.7159 | 0.665 |
| 0.6628 | 26.0 | 1950 | 0.7741 | 0.6233 |
| 0.6044 | 27.0 | 2025 | 0.7147 | 0.66 |
| 0.585 | 28.0 | 2100 | 0.6827 | 0.69 |
| 0.5831 | 29.0 | 2175 | 0.6975 | 0.6833 |
| 0.6301 | 30.0 | 2250 | 0.6815 | 0.6633 |
| 0.6457 | 31.0 | 2325 | 0.6813 | 0.6817 |
| 0.6492 | 32.0 | 2400 | 0.6894 | 0.6783 |
| 0.5418 | 33.0 | 2475 | 0.7461 | 0.6783 |
| 0.5925 | 34.0 | 2550 | 0.6773 | 0.6933 |
| 0.5913 | 35.0 | 2625 | 0.6656 | 0.7083 |
| 0.5761 | 36.0 | 2700 | 0.6491 | 0.7133 |
| 0.528 | 37.0 | 2775 | 0.6784 | 0.7 |
| 0.5718 | 38.0 | 2850 | 0.7007 | 0.6783 |
| 0.5083 | 39.0 | 2925 | 0.6815 | 0.7 |
| 0.5069 | 40.0 | 3000 | 0.6638 | 0.71 |
| 0.4838 | 41.0 | 3075 | 0.6813 | 0.7167 |
| 0.5071 | 42.0 | 3150 | 0.6709 | 0.7183 |
| 0.5091 | 43.0 | 3225 | 0.6746 | 0.7167 |
| 0.4355 | 44.0 | 3300 | 0.7138 | 0.71 |
| 0.4287 | 45.0 | 3375 | 0.7080 | 0.7133 |
| 0.3954 | 46.0 | 3450 | 0.7468 | 0.7 |
| 0.3389 | 47.0 | 3525 | 0.7428 | 0.7183 |
| 0.3613 | 48.0 | 3600 | 0.7469 | 0.725 |
| 0.388 | 49.0 | 3675 | 0.7685 | 0.7167 |
| 0.2972 | 50.0 | 3750 | 0.7846 | 0.7133 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
dima806/animal_151_types_image_detection
|
Returns animal type given image with about 99% accuracy.
See https://www.kaggle.com/code/dima806/animal-151-types-detection-vit for more details.
```
Classification report:
precision recall f1-score support
acinonyx-jubatus 1.0000 1.0000 1.0000 12
aethia-cristatella 1.0000 0.9167 0.9565 12
agalychnis-callidryas 1.0000 1.0000 1.0000 12
agkistrodon-contortrix 1.0000 1.0000 1.0000 12
ailuropoda-melanoleuca 1.0000 1.0000 1.0000 12
ailurus-fulgens 1.0000 1.0000 1.0000 12
alces-alces 1.0000 1.0000 1.0000 12
anas-platyrhynchos 1.0000 1.0000 1.0000 12
ankylosaurus-magniventris 0.9167 0.9167 0.9167 12
apis-mellifera 1.0000 1.0000 1.0000 12
aptenodytes-forsteri 1.0000 1.0000 1.0000 12
aquila-chrysaetos 1.0000 1.0000 1.0000 12
ara-macao 1.0000 1.0000 1.0000 12
architeuthis-dux 0.9231 1.0000 0.9600 12
ardea-herodias 1.0000 1.0000 1.0000 12
balaenoptera-musculus 1.0000 1.0000 1.0000 12
betta-splendens 1.0000 1.0000 1.0000 12
bison-bison 1.0000 1.0000 1.0000 12
bos-gaurus 1.0000 1.0000 1.0000 12
bos-taurus 1.0000 1.0000 1.0000 12
bradypus-variegatus 1.0000 1.0000 1.0000 12
branta-canadensis 1.0000 1.0000 1.0000 12
canis-lupus 1.0000 1.0000 1.0000 12
canis-lupus-familiaris 1.0000 1.0000 1.0000 12
carcharodon-carcharias 1.0000 1.0000 1.0000 12
cardinalis-cardinalis 1.0000 1.0000 1.0000 12
cathartes-aura 1.0000 1.0000 1.0000 12
centrochelys-sulcata 1.0000 1.0000 1.0000 12
centruroides-vittatus 1.0000 1.0000 1.0000 12
ceratitis-capitata 1.0000 0.9167 0.9565 12
ceratotherium-simum 1.0000 1.0000 1.0000 12
chelonia-mydas 1.0000 1.0000 1.0000 12
chrysemys-picta 1.0000 1.0000 1.0000 12
circus-hudsonius 1.0000 1.0000 1.0000 12
codium-fragile 1.0000 1.0000 1.0000 12
coelacanthiformes 0.9231 1.0000 0.9600 12
colaptes-auratus 1.0000 1.0000 1.0000 12
connochaetes-gnou 1.0000 1.0000 1.0000 12
correlophus-ciliatus 1.0000 1.0000 1.0000 12
crocodylus-niloticus 1.0000 1.0000 1.0000 12
crotalus-atrox 1.0000 1.0000 1.0000 12
crotophaga-sulcirostris 1.0000 1.0000 1.0000 12
cryptoprocta-ferox 1.0000 1.0000 1.0000 12
cyanocitta-cristata 1.0000 1.0000 1.0000 12
danaus-plexippus 1.0000 1.0000 1.0000 12
dasypus-novemcinctus 1.0000 0.9167 0.9565 12
delphinapterus-leucas 1.0000 1.0000 1.0000 12
dendrobatidae 1.0000 1.0000 1.0000 12
dermochelys-coriacea 0.9231 1.0000 0.9600 12
desmodus-rotundus 1.0000 0.9167 0.9565 12
diplodocus 1.0000 1.0000 1.0000 12
dugong-dugon 1.0000 1.0000 1.0000 12
eidolon-helvum 1.0000 1.0000 1.0000 12
enhydra-lutris 1.0000 1.0000 1.0000 12
enteroctopus-dofleini 1.0000 1.0000 1.0000 12
equus-caballus 0.9231 1.0000 0.9600 12
equus-quagga 1.0000 1.0000 1.0000 12
eudocimus-albus 1.0000 1.0000 1.0000 12
eunectes-murinus 1.0000 1.0000 1.0000 12
falco-peregrinus 1.0000 1.0000 1.0000 12
felis-catus 1.0000 1.0000 1.0000 12
formicidae 1.0000 1.0000 1.0000 12
gallus-gallus-domesticus 1.0000 1.0000 1.0000 12
gavialis-gangeticus 1.0000 1.0000 1.0000 12
geococcyx-californianus 1.0000 1.0000 1.0000 12
giraffa-camelopardalis 1.0000 1.0000 1.0000 12
gorilla-gorilla 1.0000 1.0000 1.0000 12
haliaeetus-leucocephalus 1.0000 1.0000 1.0000 12
hapalochlaena-maculosa 1.0000 1.0000 1.0000 12
heloderma-suspectum 1.0000 1.0000 1.0000 12
heterocera 0.9231 1.0000 0.9600 12
hippopotamus-amphibius 1.0000 1.0000 1.0000 12
homo-sapiens 0.9231 1.0000 0.9600 12
hydrurga-leptonyx 0.9231 1.0000 0.9600 12
icterus-galbula 1.0000 1.0000 1.0000 12
icterus-gularis 1.0000 1.0000 1.0000 12
icterus-spurius 1.0000 1.0000 1.0000 12
iguana-iguana 1.0000 1.0000 1.0000 12
iguanodon-bernissartensis 1.0000 1.0000 1.0000 12
inia-geoffrensis 1.0000 1.0000 1.0000 12
lampropeltis-triangulum 1.0000 1.0000 1.0000 12
lemur-catta 1.0000 1.0000 1.0000 12
lepus-americanus 1.0000 1.0000 1.0000 12
loxodonta-africana 1.0000 1.0000 1.0000 12
macropus-giganteus 1.0000 1.0000 1.0000 12
malayopython-reticulatus 1.0000 1.0000 1.0000 12
mammuthus-primigeniu 1.0000 1.0000 1.0000 12
martes-americana 1.0000 1.0000 1.0000 12
megaptera-novaeangliae 1.0000 1.0000 1.0000 12
melanerpes-carolinus 1.0000 1.0000 1.0000 12
mellisuga-helenae 1.0000 1.0000 1.0000 12
mergus-serrator 1.0000 1.0000 1.0000 12
mimus-polyglottos 1.0000 1.0000 1.0000 12
monodon-monoceros 0.9231 1.0000 0.9600 12
musca-domestica 1.0000 1.0000 1.0000 12
odobenus-rosmarus 1.0000 1.0000 1.0000 12
okapia-johnstoni 1.0000 1.0000 1.0000 12
ophiophagus-hannah 1.0000 1.0000 1.0000 12
orcinus-orca 1.0000 1.0000 1.0000 12
ornithorhynchus-anatinus 1.0000 1.0000 1.0000 12
ovis-aries 1.0000 1.0000 1.0000 12
ovis-canadensis 1.0000 1.0000 1.0000 12
panthera-leo 1.0000 0.9167 0.9565 12
panthera-onca 0.8571 1.0000 0.9231 12
panthera-pardus 1.0000 0.8333 0.9091 12
panthera-tigris 1.0000 1.0000 1.0000 12
pantherophis-alleghaniensis 1.0000 1.0000 1.0000 12
pantherophis-guttatus 1.0000 1.0000 1.0000 12
papilio-glaucus 1.0000 0.9167 0.9565 12
passerina-ciris 1.0000 1.0000 1.0000 12
pavo-cristatus 1.0000 1.0000 1.0000 12
periplaneta-americana 1.0000 1.0000 1.0000 12
phascolarctos-cinereus 1.0000 1.0000 1.0000 12
phoebetria-fusca 1.0000 1.0000 1.0000 12
phoenicopterus-ruber 1.0000 1.0000 1.0000 12
phyllobates-terribilis 1.0000 1.0000 1.0000 12
physalia-physalis 1.0000 1.0000 1.0000 12
physeter-macrocephalus 0.9231 1.0000 0.9600 12
poecile-atricapillus 1.0000 1.0000 1.0000 12
pongo-abelii 1.0000 1.0000 1.0000 12
procyon-lotor 1.0000 1.0000 1.0000 12
pteranodon-longiceps 1.0000 1.0000 1.0000 12
pterois-mombasae 1.0000 0.8333 0.9091 12
pterois-volitans 0.8571 1.0000 0.9231 12
puma-concolor 1.0000 0.9167 0.9565 12
rattus-rattus 1.0000 1.0000 1.0000 12
rusa-unicolor 1.0000 1.0000 1.0000 12
salmo-salar 1.0000 1.0000 1.0000 12
sciurus-carolinensis 1.0000 1.0000 1.0000 12
smilodon-populator 1.0000 1.0000 1.0000 12
spheniscus-demersus 1.0000 1.0000 1.0000 12
sphyrna-mokarran 1.0000 1.0000 1.0000 12
spinosaurus-aegyptiacus 1.0000 1.0000 1.0000 12
stegosaurus-stenops 1.0000 1.0000 1.0000 12
struthio-camelus 1.0000 1.0000 1.0000 12
tapirus 1.0000 1.0000 1.0000 12
tarsius-pumilus 1.0000 1.0000 1.0000 12
taurotragus-oryx 1.0000 1.0000 1.0000 12
telmatobufo-bullocki 1.0000 1.0000 1.0000 12
thryothorus-ludovicianus 1.0000 1.0000 1.0000 12
triceratops-horridus 1.0000 0.9167 0.9565 12
trilobita 1.0000 0.9167 0.9565 12
turdus-migratorius 1.0000 1.0000 1.0000 12
tursiops-truncatus 1.0000 1.0000 1.0000 12
tyrannosaurus-rex 1.0000 1.0000 1.0000 12
tyrannus-tyrannus 1.0000 1.0000 1.0000 12
ursus-arctos-horribilis 1.0000 1.0000 1.0000 12
ursus-maritimus 1.0000 1.0000 1.0000 12
varanus-komodoensis 1.0000 1.0000 1.0000 12
vulpes-vulpes 1.0000 1.0000 1.0000 12
vultur-gryphus 1.0000 1.0000 1.0000 12
accuracy 0.9923 1812
macro avg 0.9930 0.9923 0.9922 1812
weighted avg 0.9930 0.9923 0.9922 1812
```
|
[
"acinonyx-jubatus",
"aethia-cristatella",
"agalychnis-callidryas",
"agkistrodon-contortrix",
"ailuropoda-melanoleuca",
"ailurus-fulgens",
"alces-alces",
"anas-platyrhynchos",
"ankylosaurus-magniventris",
"apis-mellifera",
"aptenodytes-forsteri",
"aquila-chrysaetos",
"ara-macao",
"architeuthis-dux",
"ardea-herodias",
"balaenoptera-musculus",
"betta-splendens",
"bison-bison",
"bos-gaurus",
"bos-taurus",
"bradypus-variegatus",
"branta-canadensis",
"canis-lupus",
"canis-lupus-familiaris",
"carcharodon-carcharias",
"cardinalis-cardinalis",
"cathartes-aura",
"centrochelys-sulcata",
"centruroides-vittatus",
"ceratitis-capitata",
"ceratotherium-simum",
"chelonia-mydas",
"chrysemys-picta",
"circus-hudsonius",
"codium-fragile",
"coelacanthiformes",
"colaptes-auratus",
"connochaetes-gnou",
"correlophus-ciliatus",
"crocodylus-niloticus",
"crotalus-atrox",
"crotophaga-sulcirostris",
"cryptoprocta-ferox",
"cyanocitta-cristata",
"danaus-plexippus",
"dasypus-novemcinctus",
"delphinapterus-leucas",
"dendrobatidae",
"dermochelys-coriacea",
"desmodus-rotundus",
"diplodocus",
"dugong-dugon",
"eidolon-helvum",
"enhydra-lutris",
"enteroctopus-dofleini",
"equus-caballus",
"equus-quagga",
"eudocimus-albus",
"eunectes-murinus",
"falco-peregrinus",
"felis-catus",
"formicidae",
"gallus-gallus-domesticus",
"gavialis-gangeticus",
"geococcyx-californianus",
"giraffa-camelopardalis",
"gorilla-gorilla",
"haliaeetus-leucocephalus",
"hapalochlaena-maculosa",
"heloderma-suspectum",
"heterocera",
"hippopotamus-amphibius",
"homo-sapiens",
"hydrurga-leptonyx",
"icterus-galbula",
"icterus-gularis",
"icterus-spurius",
"iguana-iguana",
"iguanodon-bernissartensis",
"inia-geoffrensis",
"lampropeltis-triangulum",
"lemur-catta",
"lepus-americanus",
"loxodonta-africana",
"macropus-giganteus",
"malayopython-reticulatus",
"mammuthus-primigeniu",
"martes-americana",
"megaptera-novaeangliae",
"melanerpes-carolinus",
"mellisuga-helenae",
"mergus-serrator",
"mimus-polyglottos",
"monodon-monoceros",
"musca-domestica",
"odobenus-rosmarus",
"okapia-johnstoni",
"ophiophagus-hannah",
"orcinus-orca",
"ornithorhynchus-anatinus",
"ovis-aries",
"ovis-canadensis",
"panthera-leo",
"panthera-onca",
"panthera-pardus",
"panthera-tigris",
"pantherophis-alleghaniensis",
"pantherophis-guttatus",
"papilio-glaucus",
"passerina-ciris",
"pavo-cristatus",
"periplaneta-americana",
"phascolarctos-cinereus",
"phoebetria-fusca",
"phoenicopterus-ruber",
"phyllobates-terribilis",
"physalia-physalis",
"physeter-macrocephalus",
"poecile-atricapillus",
"pongo-abelii",
"procyon-lotor",
"pteranodon-longiceps",
"pterois-mombasae",
"pterois-volitans",
"puma-concolor",
"rattus-rattus",
"rusa-unicolor",
"salmo-salar",
"sciurus-carolinensis",
"smilodon-populator",
"spheniscus-demersus",
"sphyrna-mokarran",
"spinosaurus-aegyptiacus",
"stegosaurus-stenops",
"struthio-camelus",
"tapirus",
"tarsius-pumilus",
"taurotragus-oryx",
"telmatobufo-bullocki",
"thryothorus-ludovicianus",
"triceratops-horridus",
"trilobita",
"turdus-migratorius",
"tursiops-truncatus",
"tyrannosaurus-rex",
"tyrannus-tyrannus",
"ursus-arctos-horribilis",
"ursus-maritimus",
"varanus-komodoensis",
"vulpes-vulpes",
"vultur-gryphus"
] |
p1atdev/pvc-quality-swinv2-base
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pvc-quality-swinv2-base
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the [pvc figure images dataset](https://huggingface.co/datasets/p1atdev/pvc).
It achieves the following results on the evaluation set:
- Loss: 1.2396
- Accuracy: 0.5317
## Model description
More information needed
## Intended uses & 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7254 | 0.98 | 39 | 1.4826 | 0.4109 |
| 1.3316 | 1.99 | 79 | 1.2177 | 0.5136 |
| 1.0864 | 2.99 | 119 | 1.3006 | 0.4653 |
| 0.8572 | 4.0 | 159 | 1.2090 | 0.5015 |
| 0.7466 | 4.98 | 198 | 1.2150 | 0.5378 |
| 0.5986 | 5.99 | 238 | 1.4600 | 0.4955 |
| 0.4784 | 6.99 | 278 | 1.4131 | 0.5196 |
| 0.3525 | 8.0 | 318 | 1.5256 | 0.4985 |
| 0.3472 | 8.98 | 357 | 1.3883 | 0.5166 |
| 0.3281 | 9.81 | 390 | 1.5012 | 0.4955 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
[
"best quality",
"high quality",
"medium quality",
"low quality",
"worst quality",
"parts",
"other"
] |
SuperMaker/vit-base-patch16-224-in21k-leukemia
|
<!-- 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. -->
# vit-base-patch16-224-in21k-leukemia
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 Leukemia Dataset hosted on kaggle https://www.kaggle.com/datasets/andrewmvd/leukemia-classification.
It achieves the following results on the evaluation set:
- Train Loss: 0.3256
- Train Accuracy: 0.8795
- Validation Loss: 0.6907
- Validation Accuracy: 0.6848
- Epoch: 13
## Model description
Google Vision Transormer (ViT). fine-tuned on the white blood cancer - Leukemia - dataset
## Intended uses & limitations
This model was fine-tuned as a part of my project `LeukemiaAI`, a fully integrated pipeline
to detect Leukemia.
**Github Repo**:
https://github.com/MohammedSaLah-Eldeen/LeukemiaAI
### Training hyperparameters
- training_precision: mixed_float16
- optimizer: {
'inner_optimizer': {
'module': 'keras.optimizers.experimental',
'class_name': 'SGD',
'config': {
'name': 'SGD',
'weight_decay': None,
'clipnorm': None,
'global_clipnorm': 1,
'clipvalue': None,
'use_ema': False,
'ema_momentum': 0.99,
'ema_overwrite_frequency': None,
'jit_compile': True,
'is_legacy_optimizer': False,
'learning_rate': {
'module': 'keras.optimizers.schedules',
'class_name': 'CosineDecay',
'config': {
'initial_learning_rate': 0.001,
'decay_steps': 896,
'alpha': 0.0,
'name': None,
'warmup_target': None,
'warmup_steps': 0
},
'registered_name': None
},
'momentum': 0.9,
'nesterov': False
},
'registered_name': None
},
'dynamic': True,
'initial_scale': 32768.0,
'dynamic_growth_steps': 2000
}
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5007 | 0.7629 | 0.7206 | 0.6643 | 0 |
| 0.3958 | 0.8418 | 0.7137 | 0.6686 | 1 |
| 0.3578 | 0.8632 | 0.6998 | 0.6789 | 2 |
| 0.3377 | 0.8713 | 0.6899 | 0.6843 | 3 |
| 0.3274 | 0.8778 | 0.6869 | 0.6832 | 4 |
| 0.3261 | 0.8792 | 0.6880 | 0.6859 | 5 |
| 0.3257 | 0.8797 | 0.6906 | 0.6848 | 6 |
| 0.3255 | 0.8796 | 0.6896 | 0.6859 | 7 |
| 0.3256 | 0.8794 | 0.6901 | 0.6848 | 8 |
| 0.3258 | 0.8795 | 0.6867 | 0.6864 | 9 |
| 0.3258 | 0.8793 | 0.6896 | 0.6859 | 10 |
| 0.3256 | 0.8796 | 0.6871 | 0.6864 | 11 |
| 0.3255 | 0.8795 | 0.6897 | 0.6853 | 12 |
| 0.3256 | 0.8795 | 0.6907 | 0.6848 | 13 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.13.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
[
"hem",
"all"
] |
dima806/vegetable_15_types_image_detection
|
Returns vegetable type based on image.
See https://www.kaggle.com/code/dima806/vegetable-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
Bean 1.0000 1.0000 1.0000 280
Bitter_Gourd 1.0000 1.0000 1.0000 280
Bottle_Gourd 1.0000 1.0000 1.0000 280
Brinjal 1.0000 1.0000 1.0000 280
Broccoli 1.0000 1.0000 1.0000 280
Cabbage 1.0000 0.9964 0.9982 280
Capsicum 1.0000 1.0000 1.0000 280
Carrot 1.0000 1.0000 1.0000 280
Cauliflower 0.9964 1.0000 0.9982 280
Cucumber 1.0000 1.0000 1.0000 280
Papaya 1.0000 1.0000 1.0000 280
Potato 1.0000 1.0000 1.0000 280
Pumpkin 1.0000 1.0000 1.0000 280
Radish 1.0000 1.0000 1.0000 280
Tomato 1.0000 1.0000 1.0000 280
accuracy 0.9998 4200
macro avg 0.9998 0.9998 0.9998 4200
weighted avg 0.9998 0.9998 0.9998 4200
```
|
[
"bean",
"bitter_gourd",
"bottle_gourd",
"brinjal",
"broccoli",
"cabbage",
"capsicum",
"carrot",
"cauliflower",
"cucumber",
"papaya",
"potato",
"pumpkin",
"radish",
"tomato"
] |
kiranlagad/vit-base-patch16-224-finetuned-flower
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
hkivancoral/smids_1x_beit_base_rms_0001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_0001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6670
- Accuracy: 0.7333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1121 | 1.0 | 75 | 1.0797 | 0.495 |
| 1.1167 | 2.0 | 150 | 1.0990 | 0.3383 |
| 1.1124 | 3.0 | 225 | 1.0945 | 0.3583 |
| 1.0914 | 4.0 | 300 | 1.0750 | 0.35 |
| 1.0647 | 5.0 | 375 | 0.8667 | 0.5733 |
| 0.9583 | 6.0 | 450 | 0.8905 | 0.51 |
| 0.8629 | 7.0 | 525 | 0.7806 | 0.5767 |
| 0.8438 | 8.0 | 600 | 0.7603 | 0.5833 |
| 0.812 | 9.0 | 675 | 0.7613 | 0.595 |
| 0.7427 | 10.0 | 750 | 0.8115 | 0.5917 |
| 0.8147 | 11.0 | 825 | 0.7428 | 0.63 |
| 0.7859 | 12.0 | 900 | 0.7365 | 0.635 |
| 0.8142 | 13.0 | 975 | 0.7468 | 0.6033 |
| 0.7961 | 14.0 | 1050 | 0.7567 | 0.5983 |
| 0.6725 | 15.0 | 1125 | 0.7876 | 0.6067 |
| 0.7608 | 16.0 | 1200 | 0.7339 | 0.635 |
| 0.7146 | 17.0 | 1275 | 0.7178 | 0.645 |
| 0.6646 | 18.0 | 1350 | 0.7089 | 0.67 |
| 0.7767 | 19.0 | 1425 | 0.7436 | 0.6433 |
| 0.7149 | 20.0 | 1500 | 0.7664 | 0.655 |
| 0.7622 | 21.0 | 1575 | 0.7227 | 0.6617 |
| 0.6643 | 22.0 | 1650 | 0.7547 | 0.64 |
| 0.7546 | 23.0 | 1725 | 0.7439 | 0.6483 |
| 0.727 | 24.0 | 1800 | 0.7101 | 0.6633 |
| 0.7334 | 25.0 | 1875 | 0.7022 | 0.6583 |
| 0.6824 | 26.0 | 1950 | 0.7040 | 0.6767 |
| 0.7383 | 27.0 | 2025 | 0.6953 | 0.6733 |
| 0.6459 | 28.0 | 2100 | 0.6860 | 0.6883 |
| 0.7094 | 29.0 | 2175 | 0.6882 | 0.695 |
| 0.7817 | 30.0 | 2250 | 0.6855 | 0.6883 |
| 0.6417 | 31.0 | 2325 | 0.6762 | 0.705 |
| 0.7236 | 32.0 | 2400 | 0.6870 | 0.6917 |
| 0.6676 | 33.0 | 2475 | 0.7290 | 0.685 |
| 0.5839 | 34.0 | 2550 | 0.6648 | 0.7117 |
| 0.6323 | 35.0 | 2625 | 0.6543 | 0.7017 |
| 0.6129 | 36.0 | 2700 | 0.6910 | 0.6883 |
| 0.5785 | 37.0 | 2775 | 0.6666 | 0.7217 |
| 0.6055 | 38.0 | 2850 | 0.6452 | 0.7233 |
| 0.5778 | 39.0 | 2925 | 0.6586 | 0.7217 |
| 0.5892 | 40.0 | 3000 | 0.6725 | 0.7233 |
| 0.6346 | 41.0 | 3075 | 0.6632 | 0.715 |
| 0.5806 | 42.0 | 3150 | 0.6697 | 0.7217 |
| 0.6328 | 43.0 | 3225 | 0.6659 | 0.7117 |
| 0.5711 | 44.0 | 3300 | 0.6651 | 0.71 |
| 0.5685 | 45.0 | 3375 | 0.6727 | 0.7283 |
| 0.4903 | 46.0 | 3450 | 0.6607 | 0.7383 |
| 0.5197 | 47.0 | 3525 | 0.6770 | 0.7283 |
| 0.5572 | 48.0 | 3600 | 0.6616 | 0.7183 |
| 0.5197 | 49.0 | 3675 | 0.6636 | 0.73 |
| 0.489 | 50.0 | 3750 | 0.6670 | 0.7333 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_beit_base_rms_0001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_beit_base_rms_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6915
- Accuracy: 0.72
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2962 | 1.0 | 75 | 0.9009 | 0.4967 |
| 0.8616 | 2.0 | 150 | 0.8829 | 0.5333 |
| 0.8905 | 3.0 | 225 | 0.8472 | 0.5367 |
| 0.8302 | 4.0 | 300 | 0.9953 | 0.5067 |
| 0.8678 | 5.0 | 375 | 0.8690 | 0.525 |
| 0.8529 | 6.0 | 450 | 0.8769 | 0.5283 |
| 0.8841 | 7.0 | 525 | 0.8786 | 0.53 |
| 0.8327 | 8.0 | 600 | 0.8584 | 0.5367 |
| 0.8106 | 9.0 | 675 | 0.8478 | 0.5817 |
| 0.8163 | 10.0 | 750 | 0.8420 | 0.54 |
| 0.8203 | 11.0 | 825 | 0.8233 | 0.615 |
| 0.849 | 12.0 | 900 | 0.8207 | 0.56 |
| 0.7448 | 13.0 | 975 | 0.9969 | 0.48 |
| 0.8104 | 14.0 | 1050 | 0.8107 | 0.5717 |
| 0.8455 | 15.0 | 1125 | 0.8387 | 0.56 |
| 0.7497 | 16.0 | 1200 | 0.7795 | 0.5983 |
| 0.7595 | 17.0 | 1275 | 0.7579 | 0.63 |
| 0.7118 | 18.0 | 1350 | 0.7723 | 0.63 |
| 0.7898 | 19.0 | 1425 | 0.7567 | 0.635 |
| 0.7627 | 20.0 | 1500 | 0.7797 | 0.6367 |
| 0.8345 | 21.0 | 1575 | 0.7467 | 0.6217 |
| 0.745 | 22.0 | 1650 | 0.7264 | 0.655 |
| 0.7402 | 23.0 | 1725 | 0.7241 | 0.6633 |
| 0.6239 | 24.0 | 1800 | 0.7183 | 0.665 |
| 0.6855 | 25.0 | 1875 | 0.7858 | 0.6333 |
| 0.7229 | 26.0 | 1950 | 0.7404 | 0.6333 |
| 0.7229 | 27.0 | 2025 | 0.7258 | 0.68 |
| 0.7197 | 28.0 | 2100 | 0.6990 | 0.6917 |
| 0.7057 | 29.0 | 2175 | 0.7035 | 0.68 |
| 0.7315 | 30.0 | 2250 | 0.7188 | 0.6683 |
| 0.6562 | 31.0 | 2325 | 0.7484 | 0.6283 |
| 0.6918 | 32.0 | 2400 | 0.6817 | 0.6917 |
| 0.6871 | 33.0 | 2475 | 0.7362 | 0.6717 |
| 0.6724 | 34.0 | 2550 | 0.6752 | 0.7 |
| 0.6677 | 35.0 | 2625 | 0.6742 | 0.6933 |
| 0.6138 | 36.0 | 2700 | 0.6850 | 0.6867 |
| 0.582 | 37.0 | 2775 | 0.6804 | 0.6817 |
| 0.6731 | 38.0 | 2850 | 0.6827 | 0.6917 |
| 0.5577 | 39.0 | 2925 | 0.7025 | 0.6833 |
| 0.5702 | 40.0 | 3000 | 0.6473 | 0.7117 |
| 0.578 | 41.0 | 3075 | 0.6455 | 0.72 |
| 0.6074 | 42.0 | 3150 | 0.6478 | 0.715 |
| 0.6019 | 43.0 | 3225 | 0.6442 | 0.7 |
| 0.5836 | 44.0 | 3300 | 0.6632 | 0.6983 |
| 0.5466 | 45.0 | 3375 | 0.6605 | 0.68 |
| 0.4891 | 46.0 | 3450 | 0.6690 | 0.715 |
| 0.5107 | 47.0 | 3525 | 0.6729 | 0.7167 |
| 0.3981 | 48.0 | 3600 | 0.7111 | 0.7017 |
| 0.434 | 49.0 | 3675 | 0.6850 | 0.7183 |
| 0.3741 | 50.0 | 3750 | 0.6915 | 0.72 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
dima806/vessel_ship_types_image_detection
|
Returns vessel/ship type based on image with about 99% accuracy.
See https://www.kaggle.com/code/dima806/vessel-ship-type-detection for more details.
```
Classification report:
precision recall f1-score support
Cargo 0.9927 0.9623 0.9772 424
Carrier 0.9976 1.0000 0.9988 424
Cruise 1.0000 1.0000 1.0000 424
Military 0.9976 0.9976 0.9976 424
Tankers 0.9679 0.9953 0.9814 424
accuracy 0.9910 2120
macro avg 0.9912 0.9910 0.9910 2120
weighted avg 0.9912 0.9910 0.9910 2120
```
|
[
"cargo",
"carrier",
"cruise",
"military",
"tankers"
] |
krich97/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: 0.4435
- Accuracy: 0.8111
## Model description
More information needed
## Intended uses & 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5077 | 0.98 | 41 | 0.6378 | 0.6796 |
| 0.5111 | 1.99 | 83 | 0.7097 | 0.6577 |
| 0.5395 | 2.99 | 125 | 0.5374 | 0.7470 |
| 0.5498 | 4.0 | 167 | 0.5524 | 0.7420 |
| 0.4754 | 4.98 | 208 | 0.5324 | 0.7639 |
| 0.4662 | 5.99 | 250 | 0.4962 | 0.7639 |
| 0.4677 | 6.99 | 292 | 0.5070 | 0.7774 |
| 0.4525 | 8.0 | 334 | 0.5144 | 0.7673 |
| 0.4635 | 8.98 | 375 | 0.4978 | 0.7757 |
| 0.4309 | 9.99 | 417 | 0.5388 | 0.7774 |
| 0.4292 | 10.99 | 459 | 0.4937 | 0.7825 |
| 0.4182 | 12.0 | 501 | 0.5234 | 0.7808 |
| 0.4242 | 12.98 | 542 | 0.4539 | 0.7960 |
| 0.4053 | 13.99 | 584 | 0.5089 | 0.7858 |
| 0.4135 | 14.99 | 626 | 0.4655 | 0.8044 |
| 0.3888 | 16.0 | 668 | 0.4398 | 0.8212 |
| 0.3701 | 16.98 | 709 | 0.4258 | 0.8145 |
| 0.3641 | 17.99 | 751 | 0.4339 | 0.8196 |
| 0.3547 | 18.99 | 793 | 0.4556 | 0.7993 |
| 0.3623 | 19.64 | 820 | 0.4435 | 0.8111 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"mc",
"other"
] |
Vero1nika3q/vit-base-patch16-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-eurosat
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9273
- Accuracy: 0.7992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0627 | 1.0 | 532 | 0.9273 | 0.7992 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
|
[
"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",
"cheese_plate",
"cheesecake",
"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"
] |
rochtar/brain_tumors_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. -->
# brain_tumors_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 brain-tumor-collection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4077
- Accuracy: 0.8975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.961 | 1.0 | 25 | 0.7429 | 0.6825 |
| 0.5196 | 2.0 | 50 | 0.4773 | 0.8725 |
| 0.4218 | 3.0 | 75 | 0.4077 | 0.8975 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"glioma tumor",
"meningioma tumor",
"pituitary tumor",
"no tumor"
] |
Svetcher/vit-base-patch16-224-in21k-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-finetuned-eurosat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3774
- Accuracy: 0.7611
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4111 | 1.0 | 710 | 2.3774 | 0.7611 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito",
"bruschetta",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheese_plate",
"cheesecake",
"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"
] |
Jacques7103/Food-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. -->
# food-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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2610
- Accuracy: 0.9324
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5974 | 0.21 | 100 | 0.6096 | 0.8292 |
| 0.5911 | 0.43 | 200 | 0.5204 | 0.8476 |
| 0.7085 | 0.64 | 300 | 0.4329 | 0.8708 |
| 0.5302 | 0.85 | 400 | 0.4843 | 0.8428 |
| 0.2436 | 1.07 | 500 | 0.3767 | 0.886 |
| 0.2355 | 1.28 | 600 | 0.3344 | 0.8956 |
| 0.1497 | 1.49 | 700 | 0.3447 | 0.8932 |
| 0.2213 | 1.71 | 800 | 0.3082 | 0.9072 |
| 0.2197 | 1.92 | 900 | 0.3169 | 0.902 |
| 0.0719 | 2.13 | 1000 | 0.2977 | 0.9136 |
| 0.0526 | 2.35 | 1100 | 0.3455 | 0.9084 |
| 0.0926 | 2.56 | 1200 | 0.3140 | 0.9208 |
| 0.0427 | 2.77 | 1300 | 0.3307 | 0.9128 |
| 0.0716 | 2.99 | 1400 | 0.3007 | 0.9204 |
| 0.0151 | 3.2 | 1500 | 0.2791 | 0.9292 |
| 0.032 | 3.41 | 1600 | 0.2737 | 0.9296 |
| 0.0611 | 3.62 | 1700 | 0.2620 | 0.9336 |
| 0.0175 | 3.84 | 1800 | 0.2610 | 0.9324 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"apple_pie",
"baby_back_ribs",
"baklava",
"beef_carpaccio",
"beef_tartare",
"beet_salad",
"beignets",
"bibimbap",
"bread_pudding",
"breakfast_burrito"
] |
TechRoC123/carmodel
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# carmodel
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0675
- F1: 0.9931
## Model description
More information needed
## Intended uses & 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: 4
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1732 | 0.31 | 500 | 0.6651 | 0.8403 |
| 0.3989 | 0.62 | 1000 | 0.2942 | 0.9167 |
| 0.2136 | 0.93 | 1500 | 0.1782 | 0.9542 |
| 0.0549 | 1.23 | 2000 | 0.2001 | 0.9639 |
| 0.0287 | 1.54 | 2500 | 0.1304 | 0.9819 |
| 0.0091 | 1.85 | 3000 | 0.1112 | 0.9819 |
| 0.0039 | 2.16 | 3500 | 0.0667 | 0.9917 |
| 0.0023 | 2.47 | 4000 | 0.0708 | 0.9903 |
| 0.0002 | 2.78 | 4500 | 0.0635 | 0.9931 |
| 0.0002 | 3.09 | 5000 | 0.0619 | 0.9931 |
| 0.0002 | 3.4 | 5500 | 0.0730 | 0.9917 |
| 0.0 | 3.7 | 6000 | 0.0684 | 0.9917 |
| 0.0009 | 4.01 | 6500 | 0.0696 | 0.9917 |
| 0.0 | 4.32 | 7000 | 0.0693 | 0.9917 |
| 0.0 | 4.63 | 7500 | 0.0686 | 0.9931 |
| 0.0004 | 4.94 | 8000 | 0.0675 | 0.9931 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"none",
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
DownwardSpiral33/hands_palms_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. -->
# DownwardSpiral33/hands_palms_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: 0.4367
- Validation Loss: 0.7459
- Train Accuracy: 0.5806
- Epoch: 38
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 17400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6873 | 0.6761 | 0.6129 | 0 |
| 0.6720 | 0.6625 | 0.6452 | 1 |
| 0.6638 | 0.6577 | 0.6452 | 2 |
| 0.6634 | 0.6547 | 0.6774 | 3 |
| 0.6547 | 0.6507 | 0.6774 | 4 |
| 0.6556 | 0.6423 | 0.6774 | 5 |
| 0.6433 | 0.6346 | 0.6774 | 6 |
| 0.6394 | 0.6293 | 0.7097 | 7 |
| 0.6344 | 0.6239 | 0.7419 | 8 |
| 0.6205 | 0.6206 | 0.7742 | 9 |
| 0.6047 | 0.6115 | 0.7097 | 10 |
| 0.6163 | 0.5970 | 0.7419 | 11 |
| 0.6022 | 0.6069 | 0.7097 | 12 |
| 0.5958 | 0.6009 | 0.7419 | 13 |
| 0.5789 | 0.5971 | 0.6774 | 14 |
| 0.5758 | 0.5962 | 0.6774 | 15 |
| 0.5662 | 0.5976 | 0.6774 | 16 |
| 0.5579 | 0.5926 | 0.6774 | 17 |
| 0.5577 | 0.5811 | 0.6452 | 18 |
| 0.5474 | 0.5880 | 0.6452 | 19 |
| 0.5249 | 0.5921 | 0.6774 | 20 |
| 0.5412 | 0.6075 | 0.6774 | 21 |
| 0.5154 | 0.6266 | 0.7097 | 22 |
| 0.5199 | 0.6063 | 0.6129 | 23 |
| 0.5150 | 0.6054 | 0.5806 | 24 |
| 0.5199 | 0.6107 | 0.6774 | 25 |
| 0.4823 | 0.5959 | 0.6129 | 26 |
| 0.4800 | 0.6581 | 0.6452 | 27 |
| 0.4732 | 0.6620 | 0.6129 | 28 |
| 0.4766 | 0.6284 | 0.6129 | 29 |
| 0.4889 | 0.6978 | 0.5806 | 30 |
| 0.4530 | 0.6636 | 0.5806 | 31 |
| 0.4320 | 0.6348 | 0.6129 | 32 |
| 0.4704 | 0.6326 | 0.6774 | 33 |
| 0.4487 | 0.6937 | 0.6774 | 34 |
| 0.4382 | 0.6423 | 0.5806 | 35 |
| 0.4035 | 0.6926 | 0.5806 | 36 |
| 0.4330 | 0.7225 | 0.5484 | 37 |
| 0.4367 | 0.7459 | 0.5806 | 38 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"badhand",
"goodhand"
] |
akashmaggon/vit-base-crack-classification-aug-last
|
<!-- This model card 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-crack-classification-aug-last
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0124
- F1: 0.9943
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4012 | 1.0 | 212 | 0.3809 | 0.8400 |
| 0.1153 | 2.0 | 424 | 0.1429 | 0.9465 |
| 0.0467 | 3.0 | 636 | 0.0742 | 0.9628 |
| 0.0097 | 4.0 | 848 | 0.0194 | 0.9907 |
| 0.0062 | 5.0 | 1060 | 0.0163 | 0.9943 |
| 0.0039 | 6.0 | 1272 | 0.0124 | 0.9943 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
Miotvinnik00/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8575
- Accuracy: 0.918
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1974 | 0.99 | 62 | 1.1935 | 0.901 |
| 0.8604 | 2.0 | 125 | 0.9183 | 0.914 |
| 0.7686 | 2.98 | 186 | 0.8575 | 0.918 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
beingamit99/car_damage_detection
|
# 🚗 Car Damage Prediction Model 🛠️
Predict car damage with confidence using the **llm VIT bEIT** model! This model is trained to classify car damage into six distinct classes:
- **"0"**: *Crack*
- **"1"**: *Scratch*
- **"2"**: *Tire Flat*
- **"3"**: *Dent*
- **"4"**: *Glass Shatter*
- **"5"**: *Lamp Broken*
## Key Features 🔍
- Accurate classification into six car damage categories.
- Seamless integration into various applications.
- Streamlined image processing with transformer-based architecture.
## Applications 🌐
This powerful car damage prediction model can be seamlessly integrated into various applications, such as:
- **Auto Insurance Claim Processing:** Streamline the assessment of car damage for faster claim processing.
- **Vehicle Inspection Services:** Enhance efficiency in vehicle inspection services by automating damage detection.
- **Used Car Marketplaces:** Provide detailed insights into the condition of used cars through automated damage analysis.
Feel free to explore and integrate this model into your applications for accurate car damage predictions! 🌟
## How to Use This Model 🤖
### Approach
### First Approach
```python
import numpy as np
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
# Load the model and image processor
processor = AutoImageProcessor.from_pretrained("beingamit99/car_damage_detection")
model = AutoModelForImageClassification.from_pretrained("beingamit99/car_damage_detection")
# Load and process the image
image = Image.open(IMAGE)
inputs = processor(images=image, return_tensors="pt")
# Make predictions
outputs = model(**inputs)
logits = outputs.logits.detach().cpu().numpy()
predicted_class_id = np.argmax(logits)
predicted_proba = np.max(logits)
label_map = model.config.id2label
predicted_class_name = label_map[predicted_class_id]
# Print the results
print(f"Predicted class: {predicted_class_name} (probability: {predicted_proba:.4f}")
```
### Second Approach
```python
from transformers import pipeline
#Create a classification pipeline
pipe = pipeline("image-classification", model="beingamit99/car_damage_detection")
pipe(IMAGE)
```
|
[
"crack",
"scratch",
"tire flat",
"dent",
"glass shatter",
"lamp broken"
] |
platzi/platzi-vit-model-aleckeith
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-aleckeith
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: 0.0621
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1238 | 3.85 | 500 | 0.0621 | 0.9774 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
hkivancoral/smids_1x_deit_small_rms_00001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_deit_small_rms_00001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7203
- Accuracy: 0.8848
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4024 | 1.0 | 76 | 0.3457 | 0.8598 |
| 0.2939 | 2.0 | 152 | 0.3056 | 0.8765 |
| 0.1494 | 3.0 | 228 | 0.3010 | 0.8815 |
| 0.1219 | 4.0 | 304 | 0.3026 | 0.8848 |
| 0.0709 | 5.0 | 380 | 0.3230 | 0.8881 |
| 0.0265 | 6.0 | 456 | 0.3473 | 0.8915 |
| 0.0053 | 7.0 | 532 | 0.4250 | 0.8815 |
| 0.0086 | 8.0 | 608 | 0.4355 | 0.8848 |
| 0.0119 | 9.0 | 684 | 0.4635 | 0.8865 |
| 0.0011 | 10.0 | 760 | 0.4824 | 0.8932 |
| 0.0255 | 11.0 | 836 | 0.5139 | 0.8831 |
| 0.0006 | 12.0 | 912 | 0.5793 | 0.8815 |
| 0.0183 | 13.0 | 988 | 0.5403 | 0.8848 |
| 0.0037 | 14.0 | 1064 | 0.5951 | 0.8848 |
| 0.024 | 15.0 | 1140 | 0.5951 | 0.8815 |
| 0.0002 | 16.0 | 1216 | 0.6061 | 0.8798 |
| 0.0001 | 17.0 | 1292 | 0.5992 | 0.8948 |
| 0.0157 | 18.0 | 1368 | 0.6206 | 0.8848 |
| 0.0002 | 19.0 | 1444 | 0.6514 | 0.8881 |
| 0.0058 | 20.0 | 1520 | 0.6656 | 0.8798 |
| 0.0096 | 21.0 | 1596 | 0.6589 | 0.8915 |
| 0.0045 | 22.0 | 1672 | 0.6509 | 0.8848 |
| 0.0001 | 23.0 | 1748 | 0.6180 | 0.8881 |
| 0.0001 | 24.0 | 1824 | 0.6676 | 0.8765 |
| 0.0077 | 25.0 | 1900 | 0.6271 | 0.8831 |
| 0.0032 | 26.0 | 1976 | 0.7135 | 0.8848 |
| 0.0043 | 27.0 | 2052 | 0.7062 | 0.8765 |
| 0.0034 | 28.0 | 2128 | 0.7064 | 0.8781 |
| 0.0062 | 29.0 | 2204 | 0.6764 | 0.8781 |
| 0.0001 | 30.0 | 2280 | 0.6847 | 0.8831 |
| 0.006 | 31.0 | 2356 | 0.6868 | 0.8865 |
| 0.009 | 32.0 | 2432 | 0.7122 | 0.8881 |
| 0.0 | 33.0 | 2508 | 0.7011 | 0.8865 |
| 0.0 | 34.0 | 2584 | 0.7102 | 0.8881 |
| 0.0121 | 35.0 | 2660 | 0.7023 | 0.8881 |
| 0.0034 | 36.0 | 2736 | 0.7188 | 0.8765 |
| 0.0064 | 37.0 | 2812 | 0.7029 | 0.8848 |
| 0.0001 | 38.0 | 2888 | 0.7098 | 0.8798 |
| 0.0031 | 39.0 | 2964 | 0.7171 | 0.8815 |
| 0.0 | 40.0 | 3040 | 0.7137 | 0.8815 |
| 0.0029 | 41.0 | 3116 | 0.7143 | 0.8815 |
| 0.0 | 42.0 | 3192 | 0.7224 | 0.8815 |
| 0.0048 | 43.0 | 3268 | 0.7157 | 0.8831 |
| 0.0 | 44.0 | 3344 | 0.7190 | 0.8848 |
| 0.0 | 45.0 | 3420 | 0.7200 | 0.8848 |
| 0.0 | 46.0 | 3496 | 0.7204 | 0.8848 |
| 0.0 | 47.0 | 3572 | 0.7209 | 0.8848 |
| 0.0024 | 48.0 | 3648 | 0.7205 | 0.8848 |
| 0.0 | 49.0 | 3724 | 0.7204 | 0.8848 |
| 0.0 | 50.0 | 3800 | 0.7203 | 0.8848 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_deit_small_rms_00001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_deit_small_rms_00001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8494
- Accuracy: 0.8702
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.391 | 1.0 | 75 | 0.3306 | 0.8569 |
| 0.2024 | 2.0 | 150 | 0.3078 | 0.8719 |
| 0.1659 | 3.0 | 225 | 0.3046 | 0.8636 |
| 0.1089 | 4.0 | 300 | 0.3233 | 0.8702 |
| 0.0832 | 5.0 | 375 | 0.4345 | 0.8552 |
| 0.0315 | 6.0 | 450 | 0.4227 | 0.8686 |
| 0.0247 | 7.0 | 525 | 0.5432 | 0.8652 |
| 0.0031 | 8.0 | 600 | 0.5857 | 0.8769 |
| 0.0058 | 9.0 | 675 | 0.5689 | 0.8619 |
| 0.0354 | 10.0 | 750 | 0.6368 | 0.8619 |
| 0.0193 | 11.0 | 825 | 0.5921 | 0.8752 |
| 0.0019 | 12.0 | 900 | 0.6514 | 0.8785 |
| 0.0447 | 13.0 | 975 | 0.6838 | 0.8686 |
| 0.0527 | 14.0 | 1050 | 0.6693 | 0.8735 |
| 0.0047 | 15.0 | 1125 | 0.6444 | 0.8735 |
| 0.0064 | 16.0 | 1200 | 0.7052 | 0.8719 |
| 0.0002 | 17.0 | 1275 | 0.7289 | 0.8636 |
| 0.0092 | 18.0 | 1350 | 0.7405 | 0.8669 |
| 0.0001 | 19.0 | 1425 | 0.7743 | 0.8619 |
| 0.0038 | 20.0 | 1500 | 0.7512 | 0.8686 |
| 0.0001 | 21.0 | 1575 | 0.8249 | 0.8602 |
| 0.0001 | 22.0 | 1650 | 0.7832 | 0.8686 |
| 0.0001 | 23.0 | 1725 | 0.8312 | 0.8636 |
| 0.0 | 24.0 | 1800 | 0.7877 | 0.8669 |
| 0.0 | 25.0 | 1875 | 0.7958 | 0.8719 |
| 0.0001 | 26.0 | 1950 | 0.7718 | 0.8752 |
| 0.0055 | 27.0 | 2025 | 0.7918 | 0.8686 |
| 0.0032 | 28.0 | 2100 | 0.8022 | 0.8735 |
| 0.0023 | 29.0 | 2175 | 0.8185 | 0.8735 |
| 0.0031 | 30.0 | 2250 | 0.8365 | 0.8735 |
| 0.0028 | 31.0 | 2325 | 0.7946 | 0.8686 |
| 0.0 | 32.0 | 2400 | 0.8222 | 0.8752 |
| 0.0 | 33.0 | 2475 | 0.7981 | 0.8719 |
| 0.0 | 34.0 | 2550 | 0.8313 | 0.8752 |
| 0.0084 | 35.0 | 2625 | 0.8895 | 0.8702 |
| 0.0 | 36.0 | 2700 | 0.8170 | 0.8686 |
| 0.0 | 37.0 | 2775 | 0.8344 | 0.8752 |
| 0.0 | 38.0 | 2850 | 0.8561 | 0.8735 |
| 0.0022 | 39.0 | 2925 | 0.8329 | 0.8702 |
| 0.0 | 40.0 | 3000 | 0.8473 | 0.8719 |
| 0.0026 | 41.0 | 3075 | 0.8354 | 0.8686 |
| 0.0 | 42.0 | 3150 | 0.8451 | 0.8735 |
| 0.0025 | 43.0 | 3225 | 0.8430 | 0.8735 |
| 0.0025 | 44.0 | 3300 | 0.8484 | 0.8719 |
| 0.0 | 45.0 | 3375 | 0.8461 | 0.8702 |
| 0.0 | 46.0 | 3450 | 0.8473 | 0.8735 |
| 0.0023 | 47.0 | 3525 | 0.8487 | 0.8719 |
| 0.0 | 48.0 | 3600 | 0.8492 | 0.8702 |
| 0.0022 | 49.0 | 3675 | 0.8491 | 0.8686 |
| 0.0022 | 50.0 | 3750 | 0.8494 | 0.8702 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_1x_deit_small_rms_00001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_1x_deit_small_rms_00001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7182
- Accuracy: 0.905
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3259 | 1.0 | 75 | 0.3001 | 0.89 |
| 0.2426 | 2.0 | 150 | 0.3217 | 0.8717 |
| 0.1676 | 3.0 | 225 | 0.2596 | 0.9083 |
| 0.1287 | 4.0 | 300 | 0.2827 | 0.895 |
| 0.0316 | 5.0 | 375 | 0.3452 | 0.885 |
| 0.0237 | 6.0 | 450 | 0.3793 | 0.9017 |
| 0.0244 | 7.0 | 525 | 0.4128 | 0.8967 |
| 0.0233 | 8.0 | 600 | 0.4590 | 0.8883 |
| 0.0286 | 9.0 | 675 | 0.4790 | 0.8983 |
| 0.0295 | 10.0 | 750 | 0.4835 | 0.8917 |
| 0.0562 | 11.0 | 825 | 0.4705 | 0.9067 |
| 0.0087 | 12.0 | 900 | 0.5035 | 0.9033 |
| 0.0083 | 13.0 | 975 | 0.5418 | 0.9017 |
| 0.0001 | 14.0 | 1050 | 0.5563 | 0.9 |
| 0.0012 | 15.0 | 1125 | 0.5874 | 0.8983 |
| 0.0001 | 16.0 | 1200 | 0.5698 | 0.8967 |
| 0.0001 | 17.0 | 1275 | 0.5930 | 0.9033 |
| 0.0062 | 18.0 | 1350 | 0.5972 | 0.9017 |
| 0.0048 | 19.0 | 1425 | 0.5918 | 0.9033 |
| 0.0089 | 20.0 | 1500 | 0.6518 | 0.9017 |
| 0.0001 | 21.0 | 1575 | 0.7835 | 0.885 |
| 0.0001 | 22.0 | 1650 | 0.6700 | 0.9 |
| 0.0031 | 23.0 | 1725 | 0.6679 | 0.8983 |
| 0.0 | 24.0 | 1800 | 0.6364 | 0.9033 |
| 0.0001 | 25.0 | 1875 | 0.6464 | 0.8983 |
| 0.003 | 26.0 | 1950 | 0.6535 | 0.8967 |
| 0.0 | 27.0 | 2025 | 0.6525 | 0.8983 |
| 0.0 | 28.0 | 2100 | 0.6526 | 0.8983 |
| 0.0 | 29.0 | 2175 | 0.6663 | 0.895 |
| 0.0 | 30.0 | 2250 | 0.6645 | 0.8983 |
| 0.0 | 31.0 | 2325 | 0.6717 | 0.9 |
| 0.0 | 32.0 | 2400 | 0.6659 | 0.8983 |
| 0.0 | 33.0 | 2475 | 0.6774 | 0.9017 |
| 0.0051 | 34.0 | 2550 | 0.6726 | 0.905 |
| 0.0059 | 35.0 | 2625 | 0.7209 | 0.8933 |
| 0.0031 | 36.0 | 2700 | 0.6818 | 0.9067 |
| 0.0022 | 37.0 | 2775 | 0.6938 | 0.8967 |
| 0.0 | 38.0 | 2850 | 0.6968 | 0.8967 |
| 0.0 | 39.0 | 2925 | 0.7122 | 0.8983 |
| 0.0 | 40.0 | 3000 | 0.7008 | 0.8983 |
| 0.0 | 41.0 | 3075 | 0.7070 | 0.8983 |
| 0.0026 | 42.0 | 3150 | 0.7002 | 0.9 |
| 0.0025 | 43.0 | 3225 | 0.7107 | 0.9 |
| 0.0 | 44.0 | 3300 | 0.7106 | 0.9033 |
| 0.0025 | 45.0 | 3375 | 0.7116 | 0.905 |
| 0.0025 | 46.0 | 3450 | 0.7142 | 0.905 |
| 0.0047 | 47.0 | 3525 | 0.7163 | 0.9033 |
| 0.0 | 48.0 | 3600 | 0.7169 | 0.9033 |
| 0.0 | 49.0 | 3675 | 0.7178 | 0.9033 |
| 0.0045 | 50.0 | 3750 | 0.7182 | 0.905 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
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