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Madhukar7559/vit-fire-detection
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-fire-detection
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0103
- Precision: 0.9987
- Recall: 0.9987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0797 | 1.0 | 190 | 0.0811 | 0.9789 | 0.9775 |
| 0.0536 | 2.0 | 380 | 0.0205 | 0.9947 | 0.9947 |
| 0.0374 | 3.0 | 570 | 0.0283 | 0.9922 | 0.9921 |
| 0.0209 | 4.0 | 760 | 0.0046 | 1.0 | 1.0 |
| 0.0104 | 5.0 | 950 | 0.0128 | 0.9960 | 0.9960 |
| 0.0159 | 6.0 | 1140 | 0.0152 | 0.9947 | 0.9947 |
| 0.0119 | 7.0 | 1330 | 0.0084 | 0.9974 | 0.9974 |
| 0.0044 | 8.0 | 1520 | 0.0111 | 0.9987 | 0.9987 |
| 0.0077 | 9.0 | 1710 | 0.0094 | 0.9987 | 0.9987 |
| 0.0106 | 10.0 | 1900 | 0.0103 | 0.9987 | 0.9987 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.0
|
[
"fire",
"normal",
"smoke"
] |
arieg/bw_spec_cls_4_01_noise_200_confirm
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# arieg/bw_spec_cls_4_01_noise_200_confirm
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: 0.0143
- Train Sparse Categorical Accuracy: 1.0
- Validation Loss: 0.0140
- Validation Sparse Categorical Accuracy: 1.0
- Epoch: 19
## Model description
More information needed
## 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', 'clipnorm': 1.0, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 14400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.6064 | 0.9569 | 0.2224 | 1.0 | 0 |
| 0.1543 | 1.0 | 0.1168 | 1.0 | 1 |
| 0.0979 | 1.0 | 0.0858 | 1.0 | 2 |
| 0.0769 | 1.0 | 0.0709 | 1.0 | 3 |
| 0.0647 | 1.0 | 0.0603 | 1.0 | 4 |
| 0.0558 | 1.0 | 0.0528 | 1.0 | 5 |
| 0.0490 | 1.0 | 0.0465 | 1.0 | 6 |
| 0.0434 | 1.0 | 0.0414 | 1.0 | 7 |
| 0.0387 | 1.0 | 0.0369 | 1.0 | 8 |
| 0.0347 | 1.0 | 0.0332 | 1.0 | 9 |
| 0.0312 | 1.0 | 0.0300 | 1.0 | 10 |
| 0.0282 | 1.0 | 0.0272 | 1.0 | 11 |
| 0.0256 | 1.0 | 0.0248 | 1.0 | 12 |
| 0.0234 | 1.0 | 0.0226 | 1.0 | 13 |
| 0.0214 | 1.0 | 0.0207 | 1.0 | 14 |
| 0.0196 | 1.0 | 0.0190 | 1.0 | 15 |
| 0.0181 | 1.0 | 0.0176 | 1.0 | 16 |
| 0.0167 | 1.0 | 0.0162 | 1.0 | 17 |
| 0.0155 | 1.0 | 0.0150 | 1.0 | 18 |
| 0.0143 | 1.0 | 0.0140 | 1.0 | 19 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"141",
"190",
"193",
"194"
] |
hkivancoral/hushem_conflu_deneme_f1
|
<!-- This model card 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_conflu_deneme_f1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1726
- Accuracy: 0.4222
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4791 | 0.3333 |
| 2.0372 | 2.0 | 12 | 1.3991 | 0.2444 |
| 2.0372 | 3.0 | 18 | 1.9327 | 0.2444 |
| 1.2524 | 4.0 | 24 | 1.4584 | 0.3556 |
| 1.1547 | 5.0 | 30 | 1.3317 | 0.3556 |
| 1.1547 | 6.0 | 36 | 1.9319 | 0.3333 |
| 0.8748 | 7.0 | 42 | 1.3603 | 0.4222 |
| 0.8748 | 8.0 | 48 | 1.0979 | 0.5333 |
| 0.8902 | 9.0 | 54 | 1.9103 | 0.4222 |
| 0.6653 | 10.0 | 60 | 2.0004 | 0.3778 |
| 0.6653 | 11.0 | 66 | 2.0962 | 0.4 |
| 0.5253 | 12.0 | 72 | 1.2246 | 0.5111 |
| 0.5253 | 13.0 | 78 | 1.6731 | 0.4889 |
| 0.5223 | 14.0 | 84 | 2.1516 | 0.4 |
| 0.2968 | 15.0 | 90 | 2.5065 | 0.4 |
| 0.2968 | 16.0 | 96 | 2.0657 | 0.4444 |
| 0.4394 | 17.0 | 102 | 1.5876 | 0.4667 |
| 0.4394 | 18.0 | 108 | 2.1433 | 0.4 |
| 0.2725 | 19.0 | 114 | 1.4220 | 0.5556 |
| 0.1718 | 20.0 | 120 | 1.7558 | 0.4667 |
| 0.1718 | 21.0 | 126 | 2.3734 | 0.4667 |
| 0.0642 | 22.0 | 132 | 2.9683 | 0.4667 |
| 0.0642 | 23.0 | 138 | 2.9217 | 0.4889 |
| 0.0435 | 24.0 | 144 | 3.4732 | 0.4667 |
| 0.0409 | 25.0 | 150 | 3.8797 | 0.4667 |
| 0.0409 | 26.0 | 156 | 4.3387 | 0.4444 |
| 0.0418 | 27.0 | 162 | 3.9839 | 0.4444 |
| 0.0418 | 28.0 | 168 | 4.5122 | 0.4444 |
| 0.0035 | 29.0 | 174 | 4.2517 | 0.4444 |
| 0.0006 | 30.0 | 180 | 3.9958 | 0.4444 |
| 0.0006 | 31.0 | 186 | 3.9647 | 0.4444 |
| 0.0004 | 32.0 | 192 | 3.9928 | 0.4444 |
| 0.0004 | 33.0 | 198 | 4.0376 | 0.4222 |
| 0.0003 | 34.0 | 204 | 4.0736 | 0.4222 |
| 0.0002 | 35.0 | 210 | 4.1046 | 0.4222 |
| 0.0002 | 36.0 | 216 | 4.1284 | 0.4222 |
| 0.0002 | 37.0 | 222 | 4.1466 | 0.4222 |
| 0.0002 | 38.0 | 228 | 4.1585 | 0.4222 |
| 0.0002 | 39.0 | 234 | 4.1664 | 0.4222 |
| 0.0002 | 40.0 | 240 | 4.1704 | 0.4222 |
| 0.0002 | 41.0 | 246 | 4.1721 | 0.4222 |
| 0.0002 | 42.0 | 252 | 4.1726 | 0.4222 |
| 0.0002 | 43.0 | 258 | 4.1726 | 0.4222 |
| 0.0002 | 44.0 | 264 | 4.1726 | 0.4222 |
| 0.0002 | 45.0 | 270 | 4.1726 | 0.4222 |
| 0.0002 | 46.0 | 276 | 4.1726 | 0.4222 |
| 0.0002 | 47.0 | 282 | 4.1726 | 0.4222 |
| 0.0002 | 48.0 | 288 | 4.1726 | 0.4222 |
| 0.0002 | 49.0 | 294 | 4.1726 | 0.4222 |
| 0.0002 | 50.0 | 300 | 4.1726 | 0.4222 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
dwiedarioo/vit-base-patch16-224-in21k-datascience2
|
<!-- 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. -->
# dwiedarioo/vit-base-patch16-224-in21k-datascience2
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: 0.0109
- Train Accuracy: 0.9997
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.0242
- Validation Accuracy: 0.9948
- Validation Top-3-accuracy: 1.0
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2880, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.3365 | 0.9206 | 0.9902 | 0.1057 | 0.9809 | 1.0 | 0 |
| 0.0657 | 0.9891 | 0.9999 | 0.0509 | 0.9902 | 1.0 | 1 |
| 0.0252 | 0.9980 | 1.0 | 0.0314 | 0.9945 | 1.0 | 2 |
| 0.0146 | 0.9992 | 1.0 | 0.0260 | 0.9948 | 1.0 | 3 |
| 0.0109 | 0.9997 | 1.0 | 0.0242 | 0.9948 | 1.0 | 4 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"meningioma_tumor",
".ipynb_checkpoints",
"glioma_tumor",
"pituitary_tumor",
"normal"
] |
hkivancoral/hushem_conflu_deneme_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_conflu_deneme_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8961
- 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: 0.001
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4190 | 0.2444 |
| 1.9213 | 2.0 | 12 | 1.3227 | 0.3111 |
| 1.9213 | 3.0 | 18 | 2.3526 | 0.2444 |
| 1.2734 | 4.0 | 24 | 1.7104 | 0.3778 |
| 1.0407 | 5.0 | 30 | 1.6039 | 0.3556 |
| 1.0407 | 6.0 | 36 | 1.2459 | 0.4667 |
| 0.733 | 7.0 | 42 | 1.3344 | 0.4667 |
| 0.733 | 8.0 | 48 | 1.5744 | 0.5556 |
| 0.448 | 9.0 | 54 | 1.2479 | 0.5556 |
| 0.3254 | 10.0 | 60 | 2.2545 | 0.5333 |
| 0.3254 | 11.0 | 66 | 1.7472 | 0.5333 |
| 0.2088 | 12.0 | 72 | 2.0350 | 0.5778 |
| 0.2088 | 13.0 | 78 | 3.0002 | 0.4889 |
| 0.1216 | 14.0 | 84 | 2.1774 | 0.5556 |
| 0.0746 | 15.0 | 90 | 2.5953 | 0.5333 |
| 0.0746 | 16.0 | 96 | 2.8934 | 0.5111 |
| 0.0176 | 17.0 | 102 | 2.8961 | 0.5111 |
| 0.0176 | 18.0 | 108 | 2.8961 | 0.5111 |
| 0.0201 | 19.0 | 114 | 2.8961 | 0.5111 |
| 0.0136 | 20.0 | 120 | 2.8961 | 0.5111 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_conflu_deneme_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_conflu_deneme_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9900
- Accuracy: 0.5333
## Model description
More information needed
## Intended uses & 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5124 | 0.2444 |
| 2.1014 | 2.0 | 12 | 1.4172 | 0.2667 |
| 2.1014 | 3.0 | 18 | 1.3682 | 0.2667 |
| 1.3494 | 4.0 | 24 | 1.5568 | 0.3333 |
| 1.1794 | 5.0 | 30 | 1.1703 | 0.3778 |
| 1.1794 | 6.0 | 36 | 1.1853 | 0.5333 |
| 0.9962 | 7.0 | 42 | 0.9960 | 0.5778 |
| 0.9962 | 8.0 | 48 | 0.9911 | 0.5778 |
| 0.7941 | 9.0 | 54 | 1.7710 | 0.4444 |
| 0.6504 | 10.0 | 60 | 1.0188 | 0.5111 |
| 0.6504 | 11.0 | 66 | 1.3899 | 0.4889 |
| 0.3424 | 12.0 | 72 | 1.3633 | 0.5333 |
| 0.3424 | 13.0 | 78 | 1.6911 | 0.4667 |
| 0.1576 | 14.0 | 84 | 1.8405 | 0.5556 |
| 0.0563 | 15.0 | 90 | 1.8925 | 0.5333 |
| 0.0563 | 16.0 | 96 | 2.0167 | 0.5333 |
| 0.0162 | 17.0 | 102 | 1.9900 | 0.5333 |
| 0.0162 | 18.0 | 108 | 1.9900 | 0.5333 |
| 0.009 | 19.0 | 114 | 1.9900 | 0.5333 |
| 0.0088 | 20.0 | 120 | 1.9900 | 0.5333 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_conflu_deneme_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_conflu_deneme_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9617
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6871 | 0.2558 |
| 1.9835 | 2.0 | 12 | 1.3632 | 0.2326 |
| 1.9835 | 3.0 | 18 | 1.4109 | 0.3256 |
| 1.294 | 4.0 | 24 | 1.3794 | 0.4186 |
| 1.2341 | 5.0 | 30 | 1.2119 | 0.4651 |
| 1.2341 | 6.0 | 36 | 1.4964 | 0.4419 |
| 1.0897 | 7.0 | 42 | 1.2398 | 0.4651 |
| 1.0897 | 8.0 | 48 | 1.0532 | 0.5349 |
| 0.9835 | 9.0 | 54 | 1.1022 | 0.5116 |
| 0.9034 | 10.0 | 60 | 0.9784 | 0.6279 |
| 0.9034 | 11.0 | 66 | 1.5952 | 0.5116 |
| 0.8061 | 12.0 | 72 | 0.9828 | 0.5581 |
| 0.8061 | 13.0 | 78 | 0.9199 | 0.7209 |
| 0.765 | 14.0 | 84 | 1.0672 | 0.5581 |
| 0.6513 | 15.0 | 90 | 1.0129 | 0.6744 |
| 0.6513 | 16.0 | 96 | 0.9247 | 0.6977 |
| 0.4919 | 17.0 | 102 | 0.9617 | 0.6279 |
| 0.4919 | 18.0 | 108 | 0.9617 | 0.6279 |
| 0.4742 | 19.0 | 114 | 0.9617 | 0.6279 |
| 0.4695 | 20.0 | 120 | 0.9617 | 0.6279 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_conflu_deneme_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_conflu_deneme_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8165
- Accuracy: 0.7381
## Model description
More information needed
## Intended uses & 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.7088 | 0.2381 |
| 1.9076 | 2.0 | 12 | 1.4617 | 0.2381 |
| 1.9076 | 3.0 | 18 | 1.4512 | 0.2619 |
| 1.4689 | 4.0 | 24 | 1.3283 | 0.2381 |
| 1.3599 | 5.0 | 30 | 1.0112 | 0.6667 |
| 1.3599 | 6.0 | 36 | 1.1598 | 0.3810 |
| 1.2233 | 7.0 | 42 | 1.4323 | 0.4524 |
| 1.2233 | 8.0 | 48 | 0.9658 | 0.6667 |
| 1.0502 | 9.0 | 54 | 0.9166 | 0.6429 |
| 0.8636 | 10.0 | 60 | 0.8181 | 0.6190 |
| 0.8636 | 11.0 | 66 | 1.2729 | 0.5238 |
| 0.8856 | 12.0 | 72 | 0.7434 | 0.7381 |
| 0.8856 | 13.0 | 78 | 0.6840 | 0.7143 |
| 0.6672 | 14.0 | 84 | 0.9596 | 0.5238 |
| 0.5861 | 15.0 | 90 | 0.7243 | 0.7381 |
| 0.5861 | 16.0 | 96 | 0.8378 | 0.7143 |
| 0.4357 | 17.0 | 102 | 0.8165 | 0.7381 |
| 0.4357 | 18.0 | 108 | 0.8165 | 0.7381 |
| 0.4614 | 19.0 | 114 | 0.8165 | 0.7381 |
| 0.431 | 20.0 | 120 | 0.8165 | 0.7381 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_conflu_deneme_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_conflu_deneme_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9630
- Accuracy: 0.6341
## Model description
More information needed
## Intended uses & 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4708 | 0.2439 |
| 1.7951 | 2.0 | 12 | 1.3099 | 0.2439 |
| 1.7951 | 3.0 | 18 | 1.1130 | 0.4146 |
| 1.2772 | 4.0 | 24 | 1.0471 | 0.7073 |
| 1.1124 | 5.0 | 30 | 1.2680 | 0.5366 |
| 1.1124 | 6.0 | 36 | 1.0908 | 0.5122 |
| 0.9481 | 7.0 | 42 | 1.5674 | 0.3902 |
| 0.9481 | 8.0 | 48 | 0.8947 | 0.6098 |
| 0.9653 | 9.0 | 54 | 1.1885 | 0.6098 |
| 0.639 | 10.0 | 60 | 0.9898 | 0.6585 |
| 0.639 | 11.0 | 66 | 1.7943 | 0.4634 |
| 0.5108 | 12.0 | 72 | 1.7088 | 0.5366 |
| 0.5108 | 13.0 | 78 | 1.6432 | 0.5610 |
| 0.1679 | 14.0 | 84 | 1.5598 | 0.5854 |
| 0.1286 | 15.0 | 90 | 2.1600 | 0.5854 |
| 0.1286 | 16.0 | 96 | 1.9849 | 0.5854 |
| 0.0501 | 17.0 | 102 | 1.9630 | 0.6341 |
| 0.0501 | 18.0 | 108 | 1.9630 | 0.6341 |
| 0.0271 | 19.0 | 114 | 1.9630 | 0.6341 |
| 0.0437 | 20.0 | 120 | 1.9630 | 0.6341 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
arieg/bw_spec_cls_4_01_s_200
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# arieg/bw_spec_cls_4_01_s_200
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: 0.0046
- Train Sparse Categorical Accuracy: 1.0
- Validation Loss: 0.0045
- Validation Sparse Categorical Accuracy: 1.0
- Epoch: 39
## Model description
More information needed
## 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', 'clipnorm': 1.0, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 28800, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.7335 | 0.9306 | 0.3009 | 1.0 | 0 |
| 0.1862 | 1.0 | 0.1287 | 1.0 | 1 |
| 0.1060 | 1.0 | 0.0894 | 1.0 | 2 |
| 0.0803 | 1.0 | 0.0719 | 1.0 | 3 |
| 0.0664 | 1.0 | 0.0611 | 1.0 | 4 |
| 0.0570 | 1.0 | 0.0530 | 1.0 | 5 |
| 0.0498 | 1.0 | 0.0468 | 1.0 | 6 |
| 0.0440 | 1.0 | 0.0415 | 1.0 | 7 |
| 0.0392 | 1.0 | 0.0372 | 1.0 | 8 |
| 0.0352 | 1.0 | 0.0334 | 1.0 | 9 |
| 0.0317 | 1.0 | 0.0302 | 1.0 | 10 |
| 0.0287 | 1.0 | 0.0274 | 1.0 | 11 |
| 0.0261 | 1.0 | 0.0250 | 1.0 | 12 |
| 0.0238 | 1.0 | 0.0228 | 1.0 | 13 |
| 0.0218 | 1.0 | 0.0209 | 1.0 | 14 |
| 0.0200 | 1.0 | 0.0193 | 1.0 | 15 |
| 0.0184 | 1.0 | 0.0178 | 1.0 | 16 |
| 0.0170 | 1.0 | 0.0164 | 1.0 | 17 |
| 0.0157 | 1.0 | 0.0152 | 1.0 | 18 |
| 0.0146 | 1.0 | 0.0141 | 1.0 | 19 |
| 0.0136 | 1.0 | 0.0132 | 1.0 | 20 |
| 0.0126 | 1.0 | 0.0123 | 1.0 | 21 |
| 0.0118 | 1.0 | 0.0115 | 1.0 | 22 |
| 0.0111 | 1.0 | 0.0108 | 1.0 | 23 |
| 0.0104 | 1.0 | 0.0101 | 1.0 | 24 |
| 0.0097 | 1.0 | 0.0095 | 1.0 | 25 |
| 0.0091 | 1.0 | 0.0089 | 1.0 | 26 |
| 0.0086 | 1.0 | 0.0084 | 1.0 | 27 |
| 0.0081 | 1.0 | 0.0079 | 1.0 | 28 |
| 0.0077 | 1.0 | 0.0075 | 1.0 | 29 |
| 0.0072 | 1.0 | 0.0071 | 1.0 | 30 |
| 0.0069 | 1.0 | 0.0067 | 1.0 | 31 |
| 0.0065 | 1.0 | 0.0064 | 1.0 | 32 |
| 0.0062 | 1.0 | 0.0060 | 1.0 | 33 |
| 0.0058 | 1.0 | 0.0057 | 1.0 | 34 |
| 0.0056 | 1.0 | 0.0055 | 1.0 | 35 |
| 0.0053 | 1.0 | 0.0052 | 1.0 | 36 |
| 0.0050 | 1.0 | 0.0049 | 1.0 | 37 |
| 0.0048 | 1.0 | 0.0047 | 1.0 | 38 |
| 0.0046 | 1.0 | 0.0045 | 1.0 | 39 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"141",
"190",
"193",
"194"
] |
thomastess/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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Framework versions
- Transformers 4.35.0
- Pytorch 1.10.2
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
deathperminutV2/hojas
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hojas
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: 0.0340
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1438 | 3.85 | 500 | 0.0340 | 0.9850 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Noobjing/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Noobjing/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2571
- Validation Loss: 1.1757
- Train Accuracy: 1.0
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 3.6012 | 2.6090 | 1.0 | 0 |
| 2.1348 | 1.8255 | 1.0 | 1 |
| 1.6677 | 1.5386 | 1.0 | 2 |
| 1.4364 | 1.3427 | 1.0 | 3 |
| 1.2571 | 1.1757 | 1.0 | 4 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
Nititorn/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Nititorn/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8401
- Validation Loss: 1.6982
- Train Accuracy: 0.805
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8401 | 1.6982 | 0.805 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
Artemiy27/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0136
- Accuracy: 0.9938
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0694 | 1.0 | 56 | 0.0158 | 0.995 |
| 0.0495 | 1.99 | 112 | 0.0207 | 0.9925 |
| 0.0402 | 2.99 | 168 | 0.0136 | 0.9938 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"cat",
"dog"
] |
dima806/celebs_face_image_detection
|
Returns celebrity name given a facial image with about 93% accuracy.
See https://www.kaggle.com/code/dima806/celebs-face-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
Adriana Lima 0.9462 0.9362 0.9412 94
Alex Lawther 0.9490 0.9789 0.9637 95
Alexandra Daddario 0.9485 0.9684 0.9583 95
Alvaro Morte 0.9794 1.0000 0.9896 95
Alycia Dabnem Carey 0.9620 0.8000 0.8736 95
Amanda Crew 0.9286 0.9579 0.9430 95
Amber Heard 0.8652 0.8105 0.8370 95
Andy Samberg 0.9785 0.9681 0.9733 94
Anne Hathaway 0.9109 0.9684 0.9388 95
Anthony Mackie 1.0000 1.0000 1.0000 95
Avril Lavigne 0.9135 1.0000 0.9548 95
Barack Obama 1.0000 1.0000 1.0000 95
Barbara Palvin 0.9175 0.9368 0.9271 95
Ben Affleck 0.9474 0.9474 0.9474 95
Bill Gates 1.0000 1.0000 1.0000 95
Bobby Morley 0.9400 0.9895 0.9641 95
Brenton Thwaites 0.9474 0.9574 0.9524 94
Brian J. Smith 0.8559 1.0000 0.9223 95
Brie Larson 0.8558 0.9368 0.8945 95
Camila Mendes 0.9495 0.9895 0.9691 95
Chris Evans 0.9247 0.9053 0.9149 95
Chris Hemsworth 0.9565 0.9263 0.9412 95
Chris Pratt 0.9691 0.9895 0.9792 95
Christian Bale 0.9783 0.9574 0.9677 94
Cristiano Ronaldo 1.0000 1.0000 1.0000 94
Danielle Panabaker 0.9859 0.7368 0.8434 95
Dominic Purcell 0.9792 0.9895 0.9843 95
Dwayne Johnson 0.9895 1.0000 0.9947 94
Eliza Taylor 0.9750 0.8211 0.8914 95
Elizabeth Lail 0.9670 0.9263 0.9462 95
Elizabeth Olsen 0.8411 0.9474 0.8911 95
Ellen Page 0.8687 0.9053 0.8866 95
Elon Musk 0.9583 0.9684 0.9634 95
Emilia Clarke 0.9206 0.6105 0.7342 95
Emma Stone 0.9500 0.8000 0.8686 95
Emma Watson 0.9615 0.5263 0.6803 95
Gal Gadot 0.9296 0.6947 0.7952 95
Grant Gustin 0.9468 0.9368 0.9418 95
Gwyneth Paltrow 0.8796 1.0000 0.9360 95
Henry Cavil 0.9487 0.7789 0.8555 95
Hugh Jackman 0.9570 0.9368 0.9468 95
Inbar Lavi 0.9570 0.9368 0.9468 95
Irina Shayk 0.9592 0.9895 0.9741 95
Jake Mcdorman 1.0000 0.9789 0.9894 95
Jason Momoa 0.9894 0.9789 0.9841 95
Jeff Bezos 0.9896 1.0000 0.9948 95
Jennifer Lawrence 0.8876 0.8404 0.8634 94
Jeremy Renner 0.9691 0.9895 0.9792 95
Jessica Barden 0.8624 1.0000 0.9261 94
Jimmy Fallon 0.9792 0.9895 0.9843 95
Johnny Depp 0.9140 0.8947 0.9043 95
Josh Radnor 0.9792 0.9895 0.9843 95
Katharine Mcphee 0.9333 0.8842 0.9081 95
Katherine Langford 0.7851 1.0000 0.8796 95
Keanu Reeves 0.9785 0.9579 0.9681 95
Kiernen Shipka 0.6078 0.9789 0.7500 95
Krysten Ritter 0.9118 0.9894 0.9490 94
Leonardo Dicaprio 0.9588 0.9789 0.9688 95
Lili Reinhart 0.8144 0.8404 0.8272 94
Lindsey Morgan 0.8571 0.9474 0.9000 95
Lionel Messi 0.9890 0.9474 0.9677 95
Logan Lerman 0.9583 0.9684 0.9634 95
Madelaine Petsch 0.9072 0.9362 0.9215 94
Maisie Williams 0.8713 0.9362 0.9026 94
Margot Robbie 0.7634 0.7474 0.7553 95
Maria Pedraza 0.9310 0.8617 0.8950 94
Marie Avgeropoulos 0.9118 0.9789 0.9442 95
Mark Ruffalo 1.0000 0.8632 0.9266 95
Mark Zuckerberg 0.9896 1.0000 0.9948 95
Megan Fox 1.0000 0.9362 0.9670 94
Melissa Fumero 0.9400 0.9895 0.9641 95
Miley Cyrus 1.0000 0.7053 0.8272 95
Millie Bobby Brown 0.9192 0.9579 0.9381 95
Morena Baccarin 0.9789 0.9789 0.9789 95
Morgan Freeman 1.0000 1.0000 1.0000 94
Nadia Hilker 0.9892 0.9787 0.9840 94
Natalie Dormer 0.7417 0.9368 0.8279 95
Natalie Portman 0.8804 0.8526 0.8663 95
Neil Patrick Harris 1.0000 0.9789 0.9894 95
Pedro Alonso 0.9579 0.9579 0.9579 95
Penn Badgley 0.9583 0.9787 0.9684 94
Rami Malek 0.9792 0.9895 0.9843 95
Rebecca Ferguson 0.8304 0.9789 0.8986 95
Richard Harmon 0.9381 0.9579 0.9479 95
Rihanna 0.9485 0.9787 0.9634 94
Robert De Niro 0.8687 0.9053 0.8866 95
Robert Downey Jr 0.9765 0.8830 0.9274 94
Sarah Wayne Callies 0.8476 0.9368 0.8900 95
Scarlett Johansson 0.9302 0.4211 0.5797 95
Selena Gomez 0.9359 0.7684 0.8439 95
Shakira Isabel Mebarak 0.9368 0.9368 0.9368 95
Sophie Turner 0.8969 0.9158 0.9062 95
Stephen Amell 0.9500 1.0000 0.9744 95
Taylor Swift 0.9300 0.9789 0.9538 95
Tom Cruise 0.9688 0.9789 0.9738 95
Tom Ellis 0.9208 0.9894 0.9538 94
Tom Hardy 0.9765 0.8737 0.9222 95
Tom Hiddleston 0.9451 0.9053 0.9247 95
Tom Holland 0.9300 0.9789 0.9538 95
Tuppence Middleton 0.8304 0.9789 0.8986 95
Ursula Corbero 0.9278 0.9474 0.9375 95
Wentworth Miller 0.9694 1.0000 0.9845 95
Zac Efron 0.9192 0.9579 0.9381 95
Zendaya 0.8468 0.9895 0.9126 95
Zoe Saldana 1.0000 1.0000 1.0000 94
accuracy 0.9277 9954
macro avg 0.9324 0.9277 0.9260 9954
weighted avg 0.9324 0.9277 0.9259 9954
```
|
[
"adriana lima",
"alex lawther",
"alexandra daddario",
"alvaro morte",
"alycia dabnem carey",
"amanda crew",
"amber heard",
"andy samberg",
"anne hathaway",
"anthony mackie",
"avril lavigne",
"barack obama",
"barbara palvin",
"ben affleck",
"bill gates",
"bobby morley",
"brenton thwaites",
"brian j. smith",
"brie larson",
"camila mendes",
"chris evans",
"chris hemsworth",
"chris pratt",
"christian bale",
"cristiano ronaldo",
"danielle panabaker",
"dominic purcell",
"dwayne johnson",
"eliza taylor",
"elizabeth lail",
"elizabeth olsen",
"ellen page",
"elon musk",
"emilia clarke",
"emma stone",
"emma watson",
"gal gadot",
"grant gustin",
"gwyneth paltrow",
"henry cavil",
"hugh jackman",
"inbar lavi",
"irina shayk",
"jake mcdorman",
"jason momoa",
"jeff bezos",
"jennifer lawrence",
"jeremy renner",
"jessica barden",
"jimmy fallon",
"johnny depp",
"josh radnor",
"katharine mcphee",
"katherine langford",
"keanu reeves",
"kiernen shipka",
"krysten ritter",
"leonardo dicaprio",
"lili reinhart",
"lindsey morgan",
"lionel messi",
"logan lerman",
"madelaine petsch",
"maisie williams",
"margot robbie",
"maria pedraza",
"marie avgeropoulos",
"mark ruffalo",
"mark zuckerberg",
"megan fox",
"melissa fumero",
"miley cyrus",
"millie bobby brown",
"morena baccarin",
"morgan freeman",
"nadia hilker",
"natalie dormer",
"natalie portman",
"neil patrick harris",
"pedro alonso",
"penn badgley",
"rami malek",
"rebecca ferguson",
"richard harmon",
"rihanna",
"robert de niro",
"robert downey jr",
"sarah wayne callies",
"scarlett johansson",
"selena gomez",
"shakira isabel mebarak",
"sophie turner",
"stephen amell",
"taylor swift",
"tom cruise",
"tom ellis",
"tom hardy",
"tom hiddleston",
"tom holland",
"tuppence middleton",
"ursula corbero",
"wentworth miller",
"zac efron",
"zendaya",
"zoe saldana"
] |
jerryteps/swin-tiny-patch4-window7-224
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8630
- Accuracy: 0.6846
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3586 | 1.0 | 252 | 1.2051 | 0.5403 |
| 1.2281 | 2.0 | 505 | 1.0535 | 0.6108 |
| 1.148 | 3.0 | 757 | 0.9985 | 0.6194 |
| 1.087 | 4.0 | 1010 | 0.9658 | 0.6361 |
| 1.1121 | 5.0 | 1262 | 0.9203 | 0.6539 |
| 1.0127 | 6.0 | 1515 | 0.9245 | 0.6567 |
| 0.9858 | 7.0 | 1767 | 0.8846 | 0.6757 |
| 0.9948 | 8.0 | 2020 | 0.8793 | 0.6748 |
| 0.9398 | 9.0 | 2272 | 0.8671 | 0.6765 |
| 0.9904 | 9.98 | 2520 | 0.8630 | 0.6846 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"angry",
"disgusted",
"fearful",
"happy",
"neutral",
"sad",
"surprised"
] |
dwiedarioo/vit-base-patch16-224-in21k-datascience4
|
<!-- 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. -->
# dwiedarioo/vit-base-patch16-224-in21k-datascience4
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: 0.0225
- Train Accuracy: 0.9974
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.0312
- Validation Accuracy: 0.9945
- Validation Top-3-accuracy: 1.0
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2880, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.3153 | 0.9166 | 0.9916 | 0.1097 | 0.9757 | 1.0 | 0 |
| 0.0583 | 0.9898 | 1.0 | 0.0558 | 0.9877 | 1.0 | 1 |
| 0.0225 | 0.9974 | 1.0 | 0.0312 | 0.9945 | 1.0 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"glioma_tumor",
"normal",
"pituitary_tumor",
"meningioma_tumor"
] |
JLB-JLB/seizure_vit_jlb_231112_fft_raw_combo
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# seizure_vit_jlb_231112_fft_raw_combo
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the JLB-JLB/seizure_detection_224x224_raw_frequency dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4822
- Roc Auc: 0.7667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4777 | 0.17 | 500 | 0.5237 | 0.7455 |
| 0.4469 | 0.34 | 1000 | 0.5114 | 0.7542 |
| 0.4122 | 0.52 | 1500 | 0.5084 | 0.7567 |
| 0.3904 | 0.69 | 2000 | 0.5043 | 0.7611 |
| 0.3619 | 0.86 | 2500 | 0.5283 | 0.7609 |
| 0.3528 | 1.03 | 3000 | 0.5352 | 0.7517 |
| 0.3445 | 1.2 | 3500 | 0.5338 | 0.7572 |
| 0.3221 | 1.37 | 4000 | 0.5388 | 0.7509 |
| 0.3109 | 1.55 | 4500 | 0.5641 | 0.7458 |
| 0.3203 | 1.72 | 5000 | 0.5404 | 0.7574 |
| 0.294 | 1.89 | 5500 | 0.5421 | 0.7564 |
| 0.2964 | 2.06 | 6000 | 0.5582 | 0.7493 |
| 0.292 | 2.23 | 6500 | 0.5513 | 0.7561 |
| 0.2838 | 2.4 | 7000 | 0.5557 | 0.7598 |
| 0.2736 | 2.58 | 7500 | 0.5514 | 0.7606 |
| 0.2922 | 2.75 | 8000 | 0.5503 | 0.7538 |
| 0.2699 | 2.92 | 8500 | 0.5535 | 0.7578 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"bckg",
"seiz"
] |
EstherSan/car_identified_model_7
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# car_identified_model_7
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5755
- F1: 0.3629
- Roc Auc: 0.6990
- Accuracy: 0.0714
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6919 | 0.73 | 1 | 0.6887 | 0.1786 | 0.5738 | 0.0 |
| 0.6919 | 1.45 | 2 | 0.6856 | 0.1818 | 0.5761 | 0.0 |
| 0.6919 | 2.91 | 4 | 0.6802 | 0.2116 | 0.6066 | 0.0 |
| 0.6919 | 3.64 | 5 | 0.6800 | 0.1861 | 0.5826 | 0.0 |
| 0.6919 | 4.36 | 6 | 0.6858 | 0.1905 | 0.5973 | 0.0 |
| 0.6919 | 5.82 | 8 | 0.6938 | 0.1549 | 0.5342 | 0.0 |
| 0.6919 | 6.55 | 9 | 0.6917 | 0.1805 | 0.5802 | 0.0 |
| 0.6919 | 8.0 | 11 | 0.6735 | 0.1905 | 0.5932 | 0.0 |
| 0.6919 | 8.73 | 12 | 0.6727 | 0.1952 | 0.6007 | 0.0 |
| 0.6919 | 9.45 | 13 | 0.6698 | 0.2061 | 0.6172 | 0.0 |
| 0.6919 | 10.91 | 15 | 0.6672 | 0.2008 | 0.6092 | 0.0 |
| 0.6919 | 11.64 | 16 | 0.6645 | 0.2092 | 0.6196 | 0.0 |
| 0.6919 | 12.36 | 17 | 0.6646 | 0.2049 | 0.6144 | 0.0 |
| 0.6919 | 13.82 | 19 | 0.6623 | 0.2081 | 0.6167 | 0.0 |
| 0.6919 | 14.55 | 20 | 0.6607 | 0.2078 | 0.6149 | 0.0 |
| 0.6919 | 16.0 | 22 | 0.6585 | 0.2203 | 0.6320 | 0.0 |
| 0.6919 | 16.73 | 23 | 0.6562 | 0.2156 | 0.6219 | 0.0 |
| 0.6919 | 17.45 | 24 | 0.6555 | 0.2182 | 0.6263 | 0.0 |
| 0.6919 | 18.91 | 26 | 0.6522 | 0.2185 | 0.6232 | 0.0 |
| 0.6919 | 19.64 | 27 | 0.6512 | 0.2228 | 0.6273 | 0.0 |
| 0.6919 | 20.36 | 28 | 0.6501 | 0.2356 | 0.6410 | 0.0 |
| 0.6919 | 21.82 | 30 | 0.6477 | 0.2280 | 0.6284 | 0.0 |
| 0.6919 | 22.55 | 31 | 0.6476 | 0.2326 | 0.6343 | 0.0 |
| 0.6919 | 24.0 | 33 | 0.6469 | 0.2408 | 0.6434 | 0.0 |
| 0.6919 | 24.73 | 34 | 0.6432 | 0.2409 | 0.6369 | 0.0 |
| 0.6919 | 25.45 | 35 | 0.6432 | 0.2431 | 0.6408 | 0.0 |
| 0.6919 | 26.91 | 37 | 0.6402 | 0.2486 | 0.6449 | 0.0 |
| 0.6919 | 27.64 | 38 | 0.6386 | 0.2686 | 0.6664 | 0.0 |
| 0.6919 | 28.36 | 39 | 0.6376 | 0.2762 | 0.6796 | 0.0 |
| 0.6919 | 29.82 | 41 | 0.6347 | 0.2692 | 0.6721 | 0.0 |
| 0.6919 | 30.55 | 42 | 0.6339 | 0.2655 | 0.6643 | 0.0 |
| 0.6919 | 32.0 | 44 | 0.6310 | 0.2674 | 0.6630 | 0.0 |
| 0.6919 | 32.73 | 45 | 0.6307 | 0.2789 | 0.6731 | 0.0 |
| 0.6919 | 33.45 | 46 | 0.6291 | 0.2714 | 0.6656 | 0.0 |
| 0.6919 | 34.91 | 48 | 0.6271 | 0.2761 | 0.6659 | 0.0 |
| 0.6919 | 35.64 | 49 | 0.6271 | 0.2687 | 0.6612 | 0.0 |
| 0.6919 | 36.36 | 50 | 0.6277 | 0.2606 | 0.6509 | 0.0 |
| 0.6919 | 37.82 | 52 | 0.6257 | 0.2741 | 0.6620 | 0.0 |
| 0.6919 | 38.55 | 53 | 0.6244 | 0.2892 | 0.6793 | 0.0 |
| 0.6919 | 40.0 | 55 | 0.6203 | 0.2968 | 0.6806 | 0.0 |
| 0.6919 | 40.73 | 56 | 0.6198 | 0.2902 | 0.6770 | 0.0 |
| 0.6919 | 41.45 | 57 | 0.6184 | 0.3023 | 0.6866 | 0.0 |
| 0.6919 | 42.91 | 59 | 0.6163 | 0.2977 | 0.6812 | 0.0 |
| 0.6919 | 43.64 | 60 | 0.6147 | 0.3322 | 0.7112 | 0.0 |
| 0.6919 | 44.36 | 61 | 0.6154 | 0.3197 | 0.6954 | 0.0 |
| 0.6919 | 45.82 | 63 | 0.6129 | 0.3016 | 0.6832 | 0.0 |
| 0.6919 | 46.55 | 64 | 0.6112 | 0.3020 | 0.6804 | 0.0 |
| 0.6919 | 48.0 | 66 | 0.6095 | 0.2961 | 0.6773 | 0.0 |
| 0.6919 | 48.73 | 67 | 0.6091 | 0.3133 | 0.6923 | 0.0 |
| 0.6919 | 49.45 | 68 | 0.6090 | 0.3265 | 0.7019 | 0.0 |
| 0.6919 | 50.91 | 70 | 0.6077 | 0.3093 | 0.6840 | 0.0 |
| 0.6919 | 51.64 | 71 | 0.6065 | 0.3239 | 0.6941 | 0.0 |
| 0.6919 | 52.36 | 72 | 0.6058 | 0.3237 | 0.6907 | 0.0 |
| 0.6919 | 53.82 | 74 | 0.6028 | 0.3285 | 0.6928 | 0.0 |
| 0.6919 | 54.55 | 75 | 0.6038 | 0.3285 | 0.6928 | 0.0238 |
| 0.6919 | 56.0 | 77 | 0.6056 | 0.3197 | 0.6825 | 0.0 |
| 0.6919 | 56.73 | 78 | 0.6074 | 0.3249 | 0.6913 | 0.0 |
| 0.6919 | 57.45 | 79 | 0.6030 | 0.3158 | 0.6775 | 0.0238 |
| 0.6919 | 58.91 | 81 | 0.6001 | 0.3359 | 0.6925 | 0.0238 |
| 0.6919 | 59.64 | 82 | 0.5993 | 0.3409 | 0.6980 | 0.0238 |
| 0.6919 | 60.36 | 83 | 0.6017 | 0.3259 | 0.6884 | 0.0238 |
| 0.6919 | 61.82 | 85 | 0.6009 | 0.3146 | 0.6770 | 0.0238 |
| 0.6919 | 62.55 | 86 | 0.6018 | 0.3197 | 0.6825 | 0.0238 |
| 0.6919 | 64.0 | 88 | 0.5975 | 0.3130 | 0.6731 | 0.0238 |
| 0.6919 | 64.73 | 89 | 0.5978 | 0.3271 | 0.6889 | 0.0238 |
| 0.6919 | 65.45 | 90 | 0.5967 | 0.3424 | 0.6951 | 0.0238 |
| 0.6919 | 66.91 | 92 | 0.5973 | 0.3125 | 0.6698 | 0.0238 |
| 0.6919 | 67.64 | 93 | 0.5956 | 0.3372 | 0.6931 | 0.0238 |
| 0.6919 | 68.36 | 94 | 0.5922 | 0.3373 | 0.6897 | 0.0238 |
| 0.6919 | 69.82 | 96 | 0.5949 | 0.3320 | 0.6843 | 0.0476 |
| 0.6919 | 70.55 | 97 | 0.5959 | 0.3413 | 0.6913 | 0.0476 |
| 0.6919 | 72.0 | 99 | 0.5944 | 0.3420 | 0.7019 | 0.0238 |
| 0.6919 | 72.73 | 100 | 0.5955 | 0.3333 | 0.6881 | 0.0476 |
| 0.6919 | 73.45 | 101 | 0.5933 | 0.3346 | 0.6887 | 0.0238 |
| 0.6919 | 74.91 | 103 | 0.5894 | 0.3543 | 0.7032 | 0.0238 |
| 0.6919 | 75.64 | 104 | 0.5903 | 0.3424 | 0.6951 | 0.0238 |
| 0.6919 | 76.36 | 105 | 0.5890 | 0.3411 | 0.6946 | 0.0476 |
| 0.6919 | 77.82 | 107 | 0.5922 | 0.3346 | 0.6887 | 0.0476 |
| 0.6919 | 78.55 | 108 | 0.5923 | 0.3243 | 0.6812 | 0.0476 |
| 0.6919 | 80.0 | 110 | 0.5908 | 0.3468 | 0.6933 | 0.0476 |
| 0.6919 | 80.73 | 111 | 0.5922 | 0.328 | 0.6793 | 0.0476 |
| 0.6919 | 81.45 | 112 | 0.5892 | 0.3440 | 0.6923 | 0.0238 |
| 0.6919 | 82.91 | 114 | 0.5880 | 0.3506 | 0.6982 | 0.0238 |
| 0.6919 | 83.64 | 115 | 0.5869 | 0.3454 | 0.6928 | 0.0476 |
| 0.6919 | 84.36 | 116 | 0.5841 | 0.3465 | 0.6967 | 0.0238 |
| 0.6919 | 85.82 | 118 | 0.5841 | 0.3568 | 0.6969 | 0.0714 |
| 0.6919 | 86.55 | 119 | 0.5843 | 0.3496 | 0.6944 | 0.0476 |
| 0.6919 | 88.0 | 121 | 0.5860 | 0.3598 | 0.6980 | 0.0476 |
| 0.6919 | 88.73 | 122 | 0.5837 | 0.3457 | 0.6894 | 0.0476 |
| 0.6919 | 89.45 | 123 | 0.5826 | 0.3636 | 0.7029 | 0.0714 |
| 0.6919 | 90.91 | 125 | 0.5822 | 0.3651 | 0.7034 | 0.0714 |
| 0.6919 | 91.64 | 126 | 0.5814 | 0.3607 | 0.7019 | 0.0714 |
| 0.6919 | 92.36 | 127 | 0.5814 | 0.3629 | 0.7063 | 0.0476 |
| 0.6919 | 93.82 | 129 | 0.5818 | 0.3713 | 0.7055 | 0.0714 |
| 0.6919 | 94.55 | 130 | 0.5802 | 0.3766 | 0.7109 | 0.0714 |
| 0.6919 | 96.0 | 132 | 0.5803 | 0.3675 | 0.7006 | 0.0714 |
| 0.6919 | 96.73 | 133 | 0.5825 | 0.3519 | 0.6881 | 0.0714 |
| 0.6919 | 97.45 | 134 | 0.5790 | 0.3629 | 0.6990 | 0.0714 |
| 0.6919 | 98.91 | 136 | 0.5795 | 0.3766 | 0.7109 | 0.0714 |
| 0.6919 | 99.64 | 137 | 0.5784 | 0.3697 | 0.7050 | 0.0714 |
| 0.6919 | 100.36 | 138 | 0.5819 | 0.3583 | 0.6975 | 0.0714 |
| 0.6919 | 101.82 | 140 | 0.5834 | 0.3525 | 0.6954 | 0.0476 |
| 0.6919 | 102.55 | 141 | 0.5825 | 0.3689 | 0.7083 | 0.0238 |
| 0.6919 | 104.0 | 143 | 0.5839 | 0.3460 | 0.6861 | 0.0714 |
| 0.6919 | 104.73 | 144 | 0.5838 | 0.3333 | 0.6814 | 0.0476 |
| 0.6919 | 105.45 | 145 | 0.5801 | 0.3387 | 0.6869 | 0.0238 |
| 0.6919 | 106.91 | 147 | 0.5811 | 0.3515 | 0.6915 | 0.0476 |
| 0.6919 | 107.64 | 148 | 0.5793 | 0.3374 | 0.6830 | 0.0476 |
| 0.6919 | 108.36 | 149 | 0.5766 | 0.3448 | 0.6822 | 0.0714 |
| 0.6919 | 109.82 | 151 | 0.5760 | 0.3445 | 0.6856 | 0.0714 |
| 0.6919 | 110.55 | 152 | 0.5757 | 0.3559 | 0.6931 | 0.0714 |
| 0.6919 | 112.0 | 154 | 0.5760 | 0.3475 | 0.6866 | 0.0714 |
| 0.6919 | 112.73 | 155 | 0.5743 | 0.3629 | 0.6990 | 0.0714 |
| 0.6919 | 113.45 | 156 | 0.5732 | 0.3636 | 0.7029 | 0.0714 |
| 0.6919 | 114.91 | 158 | 0.5736 | 0.3786 | 0.7153 | 0.0476 |
| 0.6919 | 115.64 | 159 | 0.5764 | 0.3667 | 0.7039 | 0.0238 |
| 0.6919 | 116.36 | 160 | 0.5765 | 0.3613 | 0.6985 | 0.0476 |
| 0.6919 | 117.82 | 162 | 0.5749 | 0.3574 | 0.6936 | 0.0714 |
| 0.6919 | 118.55 | 163 | 0.5754 | 0.3592 | 0.7013 | 0.0476 |
| 0.6919 | 120.0 | 165 | 0.5757 | 0.3665 | 0.7112 | 0.0476 |
| 0.6919 | 120.73 | 166 | 0.5771 | 0.3729 | 0.7060 | 0.0714 |
| 0.6919 | 121.45 | 167 | 0.5746 | 0.3629 | 0.6990 | 0.0714 |
| 0.6919 | 122.91 | 169 | 0.5758 | 0.3644 | 0.6995 | 0.0714 |
| 0.6919 | 123.64 | 170 | 0.5745 | 0.3559 | 0.6931 | 0.0714 |
| 0.6919 | 124.36 | 171 | 0.5758 | 0.3544 | 0.6925 | 0.0714 |
| 0.6919 | 125.82 | 173 | 0.5759 | 0.3598 | 0.6980 | 0.0714 |
| 0.6919 | 126.55 | 174 | 0.5772 | 0.3568 | 0.6969 | 0.0714 |
| 0.6919 | 128.0 | 176 | 0.5747 | 0.3583 | 0.6975 | 0.0714 |
| 0.6919 | 128.73 | 177 | 0.5738 | 0.3644 | 0.6995 | 0.0714 |
| 0.6919 | 129.45 | 178 | 0.5751 | 0.3644 | 0.6995 | 0.0714 |
| 0.6919 | 130.91 | 180 | 0.5741 | 0.3713 | 0.7055 | 0.0952 |
| 0.6919 | 131.64 | 181 | 0.5748 | 0.3713 | 0.7055 | 0.0952 |
| 0.6919 | 132.36 | 182 | 0.5767 | 0.3660 | 0.7001 | 0.0714 |
| 0.6919 | 133.82 | 184 | 0.5732 | 0.3660 | 0.7001 | 0.0952 |
| 0.6919 | 134.55 | 185 | 0.5742 | 0.3772 | 0.7037 | 0.0952 |
| 0.6919 | 136.0 | 187 | 0.5690 | 0.3755 | 0.7032 | 0.0952 |
| 0.6919 | 136.73 | 188 | 0.5699 | 0.3805 | 0.7047 | 0.0714 |
| 0.6919 | 137.45 | 189 | 0.5743 | 0.3707 | 0.7016 | 0.0714 |
| 0.6919 | 138.91 | 191 | 0.5740 | 0.3529 | 0.6920 | 0.0952 |
| 0.6919 | 139.64 | 192 | 0.5740 | 0.3660 | 0.7001 | 0.0714 |
| 0.6919 | 140.36 | 193 | 0.5734 | 0.3644 | 0.6995 | 0.0714 |
| 0.6919 | 141.82 | 195 | 0.5740 | 0.3675 | 0.7006 | 0.0714 |
| 0.6919 | 142.55 | 196 | 0.5721 | 0.3707 | 0.7016 | 0.0714 |
| 0.6919 | 144.0 | 198 | 0.5725 | 0.3767 | 0.6998 | 0.0714 |
| 0.6919 | 144.73 | 199 | 0.5734 | 0.3729 | 0.7060 | 0.0952 |
| 0.6919 | 145.45 | 200 | 0.5755 | 0.3629 | 0.6990 | 0.0714 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"black",
"white"
] |
aditnnda/felidae_klasifikasi
|
<!-- 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. -->
# aditnnda/felidae_klasifikasi
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 [Felidae Dataset](https://huggingface.co/datasets/aditnnda/Felidae).
It achieves the following results on the evaluation set:
- Train Loss: 0.5782
- Train Accuracy: 0.8361
- Validation Loss: 0.5283
- Validation Accuracy: 0.8361
- Epoch: 19
## Model description
More information needed
## 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': 3640, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 1.5945 | 0.5574 | 1.5482 | 0.5574 | 0 |
| 1.5213 | 0.7541 | 1.4625 | 0.7541 | 1 |
| 1.4429 | 0.7049 | 1.3574 | 0.7049 | 2 |
| 1.3399 | 0.7869 | 1.2390 | 0.7869 | 3 |
| 1.2264 | 0.6721 | 1.1328 | 0.6721 | 4 |
| 1.1660 | 0.7869 | 1.0287 | 0.7869 | 5 |
| 1.0825 | 0.7377 | 0.9690 | 0.7377 | 6 |
| 1.0005 | 0.8197 | 0.8654 | 0.8197 | 7 |
| 0.9121 | 0.7869 | 0.8303 | 0.7869 | 8 |
| 0.8530 | 0.8525 | 0.7590 | 0.8525 | 9 |
| 0.8602 | 0.8361 | 0.7169 | 0.8361 | 10 |
| 0.8420 | 0.8197 | 0.6993 | 0.8197 | 11 |
| 0.7772 | 0.8689 | 0.6347 | 0.8689 | 12 |
| 0.7447 | 0.8689 | 0.6023 | 0.8689 | 13 |
| 0.7253 | 0.8197 | 0.6458 | 0.8197 | 14 |
| 0.6994 | 0.8361 | 0.6045 | 0.8361 | 15 |
| 0.6761 | 0.8361 | 0.6030 | 0.8361 | 16 |
| 0.5814 | 0.8197 | 0.5523 | 0.8197 | 17 |
| 0.5939 | 0.8689 | 0.5456 | 0.8689 | 18 |
| 0.5782 | 0.8361 | 0.5283 | 0.8361 | 19 |
### Framework versions
- Transformers 4.35.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"cheetah",
"leopard",
"lion",
"puma",
"tiger"
] |
Akshay0706/Cinnamon-Plant-20-Epochs-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. -->
# Rice-Plant-Disease-Detection-Model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2929
- Accuracy: 0.8958
- F1: 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5517 | 1.0 | 18 | 0.5222 | 0.875 | 0.8754 |
| 0.2996 | 2.0 | 36 | 0.3833 | 0.8542 | 0.8564 |
| 0.1529 | 3.0 | 54 | 0.3152 | 0.875 | 0.8763 |
| 0.0843 | 4.0 | 72 | 0.2929 | 0.8958 | 0.8965 |
| 0.0549 | 5.0 | 90 | 0.2756 | 0.875 | 0.8754 |
| 0.0402 | 6.0 | 108 | 0.2765 | 0.875 | 0.8754 |
| 0.0327 | 7.0 | 126 | 0.2875 | 0.875 | 0.8754 |
| 0.0277 | 8.0 | 144 | 0.2938 | 0.875 | 0.8754 |
| 0.0244 | 9.0 | 162 | 0.2992 | 0.875 | 0.8754 |
| 0.0222 | 10.0 | 180 | 0.2996 | 0.8958 | 0.8960 |
| 0.0203 | 11.0 | 198 | 0.3052 | 0.8958 | 0.8960 |
| 0.019 | 12.0 | 216 | 0.3087 | 0.8958 | 0.8960 |
| 0.018 | 13.0 | 234 | 0.3143 | 0.8958 | 0.8960 |
| 0.0171 | 14.0 | 252 | 0.3206 | 0.8958 | 0.8960 |
| 0.0164 | 15.0 | 270 | 0.3227 | 0.8958 | 0.8960 |
| 0.0158 | 16.0 | 288 | 0.3250 | 0.8958 | 0.8960 |
| 0.0155 | 17.0 | 306 | 0.3257 | 0.8958 | 0.8960 |
| 0.0152 | 18.0 | 324 | 0.3264 | 0.8958 | 0.8960 |
| 0.015 | 19.0 | 342 | 0.3276 | 0.8958 | 0.8960 |
| 0.0149 | 20.0 | 360 | 0.3275 | 0.8958 | 0.8960 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cpu
- Datasets 2.14.5
- Tokenizers 0.14.0
|
[
"0",
"1"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8804
- Accuracy: 0.5778
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3266 | 0.3778 |
| 1.1956 | 2.0 | 12 | 1.1674 | 0.4667 |
| 1.1956 | 3.0 | 18 | 1.1849 | 0.4889 |
| 0.4784 | 4.0 | 24 | 1.2723 | 0.4667 |
| 0.1535 | 5.0 | 30 | 1.2811 | 0.4889 |
| 0.1535 | 6.0 | 36 | 1.5643 | 0.4667 |
| 0.0259 | 7.0 | 42 | 1.3477 | 0.5556 |
| 0.0259 | 8.0 | 48 | 1.7927 | 0.4889 |
| 0.0051 | 9.0 | 54 | 1.7277 | 0.5556 |
| 0.0016 | 10.0 | 60 | 1.5795 | 0.6222 |
| 0.0016 | 11.0 | 66 | 1.6103 | 0.6 |
| 0.0008 | 12.0 | 72 | 1.7043 | 0.5778 |
| 0.0008 | 13.0 | 78 | 1.7832 | 0.5778 |
| 0.0005 | 14.0 | 84 | 1.8224 | 0.5778 |
| 0.0004 | 15.0 | 90 | 1.8294 | 0.5778 |
| 0.0004 | 16.0 | 96 | 1.8185 | 0.5778 |
| 0.0004 | 17.0 | 102 | 1.8150 | 0.5778 |
| 0.0004 | 18.0 | 108 | 1.8206 | 0.5778 |
| 0.0004 | 19.0 | 114 | 1.8349 | 0.5778 |
| 0.0003 | 20.0 | 120 | 1.8491 | 0.5778 |
| 0.0003 | 21.0 | 126 | 1.8590 | 0.5778 |
| 0.0003 | 22.0 | 132 | 1.8667 | 0.5556 |
| 0.0003 | 23.0 | 138 | 1.8640 | 0.5556 |
| 0.0003 | 24.0 | 144 | 1.8624 | 0.5556 |
| 0.0003 | 25.0 | 150 | 1.8632 | 0.5778 |
| 0.0003 | 26.0 | 156 | 1.8651 | 0.5556 |
| 0.0003 | 27.0 | 162 | 1.8642 | 0.5778 |
| 0.0003 | 28.0 | 168 | 1.8659 | 0.5778 |
| 0.0003 | 29.0 | 174 | 1.8666 | 0.5778 |
| 0.0003 | 30.0 | 180 | 1.8680 | 0.5778 |
| 0.0003 | 31.0 | 186 | 1.8684 | 0.5778 |
| 0.0002 | 32.0 | 192 | 1.8677 | 0.5778 |
| 0.0002 | 33.0 | 198 | 1.8709 | 0.5778 |
| 0.0002 | 34.0 | 204 | 1.8723 | 0.5778 |
| 0.0002 | 35.0 | 210 | 1.8730 | 0.5778 |
| 0.0002 | 36.0 | 216 | 1.8757 | 0.5778 |
| 0.0002 | 37.0 | 222 | 1.8766 | 0.5778 |
| 0.0002 | 38.0 | 228 | 1.8780 | 0.5778 |
| 0.0002 | 39.0 | 234 | 1.8793 | 0.5778 |
| 0.0002 | 40.0 | 240 | 1.8801 | 0.5778 |
| 0.0002 | 41.0 | 246 | 1.8804 | 0.5778 |
| 0.0002 | 42.0 | 252 | 1.8804 | 0.5778 |
| 0.0002 | 43.0 | 258 | 1.8804 | 0.5778 |
| 0.0002 | 44.0 | 264 | 1.8804 | 0.5778 |
| 0.0002 | 45.0 | 270 | 1.8804 | 0.5778 |
| 0.0002 | 46.0 | 276 | 1.8804 | 0.5778 |
| 0.0002 | 47.0 | 282 | 1.8804 | 0.5778 |
| 0.0002 | 48.0 | 288 | 1.8804 | 0.5778 |
| 0.0002 | 49.0 | 294 | 1.8804 | 0.5778 |
| 0.0002 | 50.0 | 300 | 1.8804 | 0.5778 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6766
- Accuracy: 0.6
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3194 | 0.2889 |
| 1.3705 | 2.0 | 12 | 1.2766 | 0.3778 |
| 1.3705 | 3.0 | 18 | 1.3268 | 0.5333 |
| 0.7361 | 4.0 | 24 | 1.2927 | 0.5556 |
| 0.3404 | 5.0 | 30 | 1.3610 | 0.5556 |
| 0.3404 | 6.0 | 36 | 1.1429 | 0.5778 |
| 0.1188 | 7.0 | 42 | 1.5833 | 0.5333 |
| 0.1188 | 8.0 | 48 | 1.2765 | 0.6667 |
| 0.0229 | 9.0 | 54 | 1.4099 | 0.6222 |
| 0.0046 | 10.0 | 60 | 1.4395 | 0.6 |
| 0.0046 | 11.0 | 66 | 1.6161 | 0.5556 |
| 0.0013 | 12.0 | 72 | 1.5774 | 0.5778 |
| 0.0013 | 13.0 | 78 | 1.5201 | 0.6 |
| 0.0007 | 14.0 | 84 | 1.5608 | 0.6 |
| 0.0005 | 15.0 | 90 | 1.6187 | 0.5778 |
| 0.0005 | 16.0 | 96 | 1.6424 | 0.5778 |
| 0.0004 | 17.0 | 102 | 1.6470 | 0.5778 |
| 0.0004 | 18.0 | 108 | 1.6480 | 0.6 |
| 0.0003 | 19.0 | 114 | 1.6471 | 0.6 |
| 0.0003 | 20.0 | 120 | 1.6450 | 0.6 |
| 0.0003 | 21.0 | 126 | 1.6532 | 0.6 |
| 0.0003 | 22.0 | 132 | 1.6559 | 0.6 |
| 0.0003 | 23.0 | 138 | 1.6612 | 0.6 |
| 0.0003 | 24.0 | 144 | 1.6668 | 0.6 |
| 0.0002 | 25.0 | 150 | 1.6718 | 0.6 |
| 0.0002 | 26.0 | 156 | 1.6748 | 0.6 |
| 0.0002 | 27.0 | 162 | 1.6728 | 0.6 |
| 0.0002 | 28.0 | 168 | 1.6726 | 0.6 |
| 0.0002 | 29.0 | 174 | 1.6718 | 0.6 |
| 0.0002 | 30.0 | 180 | 1.6716 | 0.6 |
| 0.0002 | 31.0 | 186 | 1.6738 | 0.6 |
| 0.0002 | 32.0 | 192 | 1.6734 | 0.6 |
| 0.0002 | 33.0 | 198 | 1.6748 | 0.6 |
| 0.0002 | 34.0 | 204 | 1.6753 | 0.6 |
| 0.0002 | 35.0 | 210 | 1.6740 | 0.6 |
| 0.0002 | 36.0 | 216 | 1.6735 | 0.6 |
| 0.0002 | 37.0 | 222 | 1.6732 | 0.6 |
| 0.0002 | 38.0 | 228 | 1.6740 | 0.6 |
| 0.0002 | 39.0 | 234 | 1.6751 | 0.6 |
| 0.0002 | 40.0 | 240 | 1.6758 | 0.6 |
| 0.0002 | 41.0 | 246 | 1.6766 | 0.6 |
| 0.0002 | 42.0 | 252 | 1.6766 | 0.6 |
| 0.0002 | 43.0 | 258 | 1.6766 | 0.6 |
| 0.0002 | 44.0 | 264 | 1.6766 | 0.6 |
| 0.0002 | 45.0 | 270 | 1.6766 | 0.6 |
| 0.0002 | 46.0 | 276 | 1.6766 | 0.6 |
| 0.0002 | 47.0 | 282 | 1.6766 | 0.6 |
| 0.0002 | 48.0 | 288 | 1.6766 | 0.6 |
| 0.0002 | 49.0 | 294 | 1.6766 | 0.6 |
| 0.0002 | 50.0 | 300 | 1.6766 | 0.6 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5538
- Accuracy: 0.8372
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3426 | 0.4419 |
| 1.3195 | 2.0 | 12 | 1.0931 | 0.5116 |
| 1.3195 | 3.0 | 18 | 0.8535 | 0.6512 |
| 0.6419 | 4.0 | 24 | 0.9249 | 0.6279 |
| 0.325 | 5.0 | 30 | 0.7057 | 0.7674 |
| 0.325 | 6.0 | 36 | 0.5831 | 0.7674 |
| 0.0848 | 7.0 | 42 | 0.6810 | 0.7907 |
| 0.0848 | 8.0 | 48 | 0.5917 | 0.7674 |
| 0.0193 | 9.0 | 54 | 0.6267 | 0.8140 |
| 0.0077 | 10.0 | 60 | 0.4330 | 0.8372 |
| 0.0077 | 11.0 | 66 | 0.5195 | 0.8372 |
| 0.0032 | 12.0 | 72 | 0.6710 | 0.7907 |
| 0.0032 | 13.0 | 78 | 0.6980 | 0.8372 |
| 0.0012 | 14.0 | 84 | 0.5701 | 0.8372 |
| 0.0006 | 15.0 | 90 | 0.5278 | 0.8605 |
| 0.0006 | 16.0 | 96 | 0.5226 | 0.8372 |
| 0.0005 | 17.0 | 102 | 0.5245 | 0.8605 |
| 0.0005 | 18.0 | 108 | 0.5277 | 0.8605 |
| 0.0004 | 19.0 | 114 | 0.5338 | 0.8372 |
| 0.0003 | 20.0 | 120 | 0.5401 | 0.8372 |
| 0.0003 | 21.0 | 126 | 0.5445 | 0.8372 |
| 0.0003 | 22.0 | 132 | 0.5461 | 0.8372 |
| 0.0003 | 23.0 | 138 | 0.5481 | 0.8372 |
| 0.0003 | 24.0 | 144 | 0.5486 | 0.8372 |
| 0.0003 | 25.0 | 150 | 0.5495 | 0.8372 |
| 0.0003 | 26.0 | 156 | 0.5492 | 0.8372 |
| 0.0002 | 27.0 | 162 | 0.5497 | 0.8372 |
| 0.0002 | 28.0 | 168 | 0.5490 | 0.8372 |
| 0.0002 | 29.0 | 174 | 0.5497 | 0.8372 |
| 0.0002 | 30.0 | 180 | 0.5498 | 0.8372 |
| 0.0002 | 31.0 | 186 | 0.5499 | 0.8372 |
| 0.0002 | 32.0 | 192 | 0.5503 | 0.8372 |
| 0.0002 | 33.0 | 198 | 0.5508 | 0.8372 |
| 0.0002 | 34.0 | 204 | 0.5520 | 0.8372 |
| 0.0002 | 35.0 | 210 | 0.5527 | 0.8372 |
| 0.0002 | 36.0 | 216 | 0.5529 | 0.8372 |
| 0.0002 | 37.0 | 222 | 0.5532 | 0.8372 |
| 0.0002 | 38.0 | 228 | 0.5534 | 0.8372 |
| 0.0002 | 39.0 | 234 | 0.5536 | 0.8372 |
| 0.0002 | 40.0 | 240 | 0.5537 | 0.8372 |
| 0.0002 | 41.0 | 246 | 0.5538 | 0.8372 |
| 0.0002 | 42.0 | 252 | 0.5538 | 0.8372 |
| 0.0002 | 43.0 | 258 | 0.5538 | 0.8372 |
| 0.0002 | 44.0 | 264 | 0.5538 | 0.8372 |
| 0.0002 | 45.0 | 270 | 0.5538 | 0.8372 |
| 0.0002 | 46.0 | 276 | 0.5538 | 0.8372 |
| 0.0002 | 47.0 | 282 | 0.5538 | 0.8372 |
| 0.0002 | 48.0 | 288 | 0.5538 | 0.8372 |
| 0.0002 | 49.0 | 294 | 0.5538 | 0.8372 |
| 0.0002 | 50.0 | 300 | 0.5538 | 0.8372 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7761
- Accuracy: 0.8095
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3751 | 0.2619 |
| 1.4552 | 2.0 | 12 | 1.1251 | 0.4048 |
| 1.4552 | 3.0 | 18 | 0.8714 | 0.7143 |
| 0.8827 | 4.0 | 24 | 0.7894 | 0.6190 |
| 0.3505 | 5.0 | 30 | 0.5971 | 0.6905 |
| 0.3505 | 6.0 | 36 | 0.7618 | 0.7143 |
| 0.1054 | 7.0 | 42 | 0.5229 | 0.7619 |
| 0.1054 | 8.0 | 48 | 0.6150 | 0.7857 |
| 0.0181 | 9.0 | 54 | 0.6620 | 0.7619 |
| 0.0039 | 10.0 | 60 | 0.7502 | 0.7619 |
| 0.0039 | 11.0 | 66 | 0.7572 | 0.7143 |
| 0.0013 | 12.0 | 72 | 0.7148 | 0.8095 |
| 0.0013 | 13.0 | 78 | 0.7881 | 0.8095 |
| 0.0007 | 14.0 | 84 | 0.8192 | 0.7857 |
| 0.0005 | 15.0 | 90 | 0.7913 | 0.8095 |
| 0.0005 | 16.0 | 96 | 0.7465 | 0.8095 |
| 0.0004 | 17.0 | 102 | 0.7194 | 0.8095 |
| 0.0004 | 18.0 | 108 | 0.7125 | 0.8095 |
| 0.0003 | 19.0 | 114 | 0.7205 | 0.8095 |
| 0.0003 | 20.0 | 120 | 0.7348 | 0.8095 |
| 0.0003 | 21.0 | 126 | 0.7482 | 0.8095 |
| 0.0003 | 22.0 | 132 | 0.7579 | 0.8095 |
| 0.0003 | 23.0 | 138 | 0.7664 | 0.8095 |
| 0.0003 | 24.0 | 144 | 0.7720 | 0.8095 |
| 0.0003 | 25.0 | 150 | 0.7718 | 0.8095 |
| 0.0003 | 26.0 | 156 | 0.7710 | 0.8095 |
| 0.0003 | 27.0 | 162 | 0.7669 | 0.8095 |
| 0.0003 | 28.0 | 168 | 0.7689 | 0.8095 |
| 0.0003 | 29.0 | 174 | 0.7693 | 0.8095 |
| 0.0002 | 30.0 | 180 | 0.7708 | 0.8095 |
| 0.0002 | 31.0 | 186 | 0.7724 | 0.8095 |
| 0.0002 | 32.0 | 192 | 0.7744 | 0.8095 |
| 0.0002 | 33.0 | 198 | 0.7750 | 0.8095 |
| 0.0002 | 34.0 | 204 | 0.7743 | 0.8095 |
| 0.0002 | 35.0 | 210 | 0.7745 | 0.8095 |
| 0.0002 | 36.0 | 216 | 0.7743 | 0.8095 |
| 0.0002 | 37.0 | 222 | 0.7745 | 0.8095 |
| 0.0002 | 38.0 | 228 | 0.7747 | 0.8095 |
| 0.0002 | 39.0 | 234 | 0.7753 | 0.8095 |
| 0.0002 | 40.0 | 240 | 0.7758 | 0.8095 |
| 0.0002 | 41.0 | 246 | 0.7760 | 0.8095 |
| 0.0002 | 42.0 | 252 | 0.7761 | 0.8095 |
| 0.0002 | 43.0 | 258 | 0.7761 | 0.8095 |
| 0.0002 | 44.0 | 264 | 0.7761 | 0.8095 |
| 0.0002 | 45.0 | 270 | 0.7761 | 0.8095 |
| 0.0002 | 46.0 | 276 | 0.7761 | 0.8095 |
| 0.0002 | 47.0 | 282 | 0.7761 | 0.8095 |
| 0.0002 | 48.0 | 288 | 0.7761 | 0.8095 |
| 0.0002 | 49.0 | 294 | 0.7761 | 0.8095 |
| 0.0002 | 50.0 | 300 | 0.7761 | 0.8095 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9886
- Accuracy: 0.7805
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.2270 | 0.3415 |
| 1.4194 | 2.0 | 12 | 1.0630 | 0.5122 |
| 1.4194 | 3.0 | 18 | 0.7493 | 0.7073 |
| 0.7944 | 4.0 | 24 | 0.7294 | 0.7561 |
| 0.3715 | 5.0 | 30 | 0.6953 | 0.6585 |
| 0.3715 | 6.0 | 36 | 0.5928 | 0.8293 |
| 0.1471 | 7.0 | 42 | 0.5485 | 0.8049 |
| 0.1471 | 8.0 | 48 | 0.8515 | 0.6829 |
| 0.0288 | 9.0 | 54 | 0.5381 | 0.8293 |
| 0.0065 | 10.0 | 60 | 0.8647 | 0.7317 |
| 0.0065 | 11.0 | 66 | 0.7563 | 0.7805 |
| 0.0018 | 12.0 | 72 | 0.7678 | 0.8049 |
| 0.0018 | 13.0 | 78 | 0.8017 | 0.8049 |
| 0.0008 | 14.0 | 84 | 0.8475 | 0.7805 |
| 0.0005 | 15.0 | 90 | 0.8926 | 0.7805 |
| 0.0005 | 16.0 | 96 | 0.9216 | 0.7805 |
| 0.0004 | 17.0 | 102 | 0.9424 | 0.7805 |
| 0.0004 | 18.0 | 108 | 0.9465 | 0.7805 |
| 0.0003 | 19.0 | 114 | 0.9461 | 0.7805 |
| 0.0003 | 20.0 | 120 | 0.9448 | 0.7805 |
| 0.0003 | 21.0 | 126 | 0.9474 | 0.7805 |
| 0.0003 | 22.0 | 132 | 0.9525 | 0.7805 |
| 0.0003 | 23.0 | 138 | 0.9551 | 0.7805 |
| 0.0003 | 24.0 | 144 | 0.9581 | 0.7805 |
| 0.0002 | 25.0 | 150 | 0.9626 | 0.7805 |
| 0.0002 | 26.0 | 156 | 0.9650 | 0.7805 |
| 0.0002 | 27.0 | 162 | 0.9711 | 0.7805 |
| 0.0002 | 28.0 | 168 | 0.9713 | 0.7805 |
| 0.0002 | 29.0 | 174 | 0.9730 | 0.7805 |
| 0.0002 | 30.0 | 180 | 0.9754 | 0.7805 |
| 0.0002 | 31.0 | 186 | 0.9786 | 0.7805 |
| 0.0002 | 32.0 | 192 | 0.9820 | 0.7805 |
| 0.0002 | 33.0 | 198 | 0.9835 | 0.7805 |
| 0.0002 | 34.0 | 204 | 0.9850 | 0.7805 |
| 0.0002 | 35.0 | 210 | 0.9850 | 0.7805 |
| 0.0002 | 36.0 | 216 | 0.9860 | 0.7805 |
| 0.0002 | 37.0 | 222 | 0.9866 | 0.7805 |
| 0.0002 | 38.0 | 228 | 0.9873 | 0.7805 |
| 0.0002 | 39.0 | 234 | 0.9879 | 0.7805 |
| 0.0002 | 40.0 | 240 | 0.9883 | 0.7805 |
| 0.0002 | 41.0 | 246 | 0.9886 | 0.7805 |
| 0.0002 | 42.0 | 252 | 0.9886 | 0.7805 |
| 0.0002 | 43.0 | 258 | 0.9886 | 0.7805 |
| 0.0002 | 44.0 | 264 | 0.9886 | 0.7805 |
| 0.0002 | 45.0 | 270 | 0.9886 | 0.7805 |
| 0.0002 | 46.0 | 276 | 0.9886 | 0.7805 |
| 0.0002 | 47.0 | 282 | 0.9886 | 0.7805 |
| 0.0002 | 48.0 | 288 | 0.9886 | 0.7805 |
| 0.0002 | 49.0 | 294 | 0.9886 | 0.7805 |
| 0.0002 | 50.0 | 300 | 0.9886 | 0.7805 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_00001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1341
- Accuracy: 0.4222
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4260 | 0.2 |
| 1.446 | 2.0 | 12 | 1.3794 | 0.2889 |
| 1.446 | 3.0 | 18 | 1.3570 | 0.3556 |
| 1.184 | 4.0 | 24 | 1.3382 | 0.3111 |
| 1.0671 | 5.0 | 30 | 1.3283 | 0.3111 |
| 1.0671 | 6.0 | 36 | 1.3144 | 0.2889 |
| 0.9249 | 7.0 | 42 | 1.2898 | 0.3333 |
| 0.9249 | 8.0 | 48 | 1.2748 | 0.3556 |
| 0.8443 | 9.0 | 54 | 1.2692 | 0.3333 |
| 0.7477 | 10.0 | 60 | 1.2518 | 0.3778 |
| 0.7477 | 11.0 | 66 | 1.2338 | 0.4 |
| 0.662 | 12.0 | 72 | 1.2193 | 0.3778 |
| 0.662 | 13.0 | 78 | 1.2195 | 0.4 |
| 0.622 | 14.0 | 84 | 1.2039 | 0.3778 |
| 0.5154 | 15.0 | 90 | 1.1949 | 0.4 |
| 0.5154 | 16.0 | 96 | 1.1879 | 0.4 |
| 0.4537 | 17.0 | 102 | 1.1810 | 0.4 |
| 0.4537 | 18.0 | 108 | 1.1670 | 0.4 |
| 0.3859 | 19.0 | 114 | 1.1628 | 0.4 |
| 0.3586 | 20.0 | 120 | 1.1721 | 0.4 |
| 0.3586 | 21.0 | 126 | 1.1698 | 0.4222 |
| 0.3151 | 22.0 | 132 | 1.1603 | 0.4 |
| 0.3151 | 23.0 | 138 | 1.1584 | 0.4222 |
| 0.2881 | 24.0 | 144 | 1.1519 | 0.4222 |
| 0.2498 | 25.0 | 150 | 1.1515 | 0.4222 |
| 0.2498 | 26.0 | 156 | 1.1445 | 0.4222 |
| 0.232 | 27.0 | 162 | 1.1430 | 0.4222 |
| 0.232 | 28.0 | 168 | 1.1452 | 0.4222 |
| 0.2183 | 29.0 | 174 | 1.1406 | 0.4222 |
| 0.1798 | 30.0 | 180 | 1.1348 | 0.4222 |
| 0.1798 | 31.0 | 186 | 1.1304 | 0.4222 |
| 0.1811 | 32.0 | 192 | 1.1281 | 0.4222 |
| 0.1811 | 33.0 | 198 | 1.1317 | 0.4222 |
| 0.1748 | 34.0 | 204 | 1.1302 | 0.4222 |
| 0.1492 | 35.0 | 210 | 1.1303 | 0.4222 |
| 0.1492 | 36.0 | 216 | 1.1319 | 0.4222 |
| 0.1477 | 37.0 | 222 | 1.1328 | 0.4222 |
| 0.1477 | 38.0 | 228 | 1.1366 | 0.4222 |
| 0.1357 | 39.0 | 234 | 1.1362 | 0.4222 |
| 0.1379 | 40.0 | 240 | 1.1351 | 0.4222 |
| 0.1379 | 41.0 | 246 | 1.1344 | 0.4222 |
| 0.1325 | 42.0 | 252 | 1.1341 | 0.4222 |
| 0.1325 | 43.0 | 258 | 1.1341 | 0.4222 |
| 0.1377 | 44.0 | 264 | 1.1341 | 0.4222 |
| 0.1332 | 45.0 | 270 | 1.1341 | 0.4222 |
| 0.1332 | 46.0 | 276 | 1.1341 | 0.4222 |
| 0.1323 | 47.0 | 282 | 1.1341 | 0.4222 |
| 0.1323 | 48.0 | 288 | 1.1341 | 0.4222 |
| 0.1276 | 49.0 | 294 | 1.1341 | 0.4222 |
| 0.1376 | 50.0 | 300 | 1.1341 | 0.4222 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_00001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3630
- Accuracy: 0.5333
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3933 | 0.2889 |
| 1.4502 | 2.0 | 12 | 1.3758 | 0.2889 |
| 1.4502 | 3.0 | 18 | 1.3846 | 0.1556 |
| 1.1864 | 4.0 | 24 | 1.3867 | 0.2 |
| 1.0417 | 5.0 | 30 | 1.4200 | 0.2222 |
| 1.0417 | 6.0 | 36 | 1.4398 | 0.2667 |
| 0.8998 | 7.0 | 42 | 1.4309 | 0.2667 |
| 0.8998 | 8.0 | 48 | 1.4422 | 0.2889 |
| 0.802 | 9.0 | 54 | 1.4525 | 0.3111 |
| 0.7173 | 10.0 | 60 | 1.4451 | 0.3333 |
| 0.7173 | 11.0 | 66 | 1.4170 | 0.3556 |
| 0.6327 | 12.0 | 72 | 1.4262 | 0.3778 |
| 0.6327 | 13.0 | 78 | 1.4500 | 0.3778 |
| 0.5705 | 14.0 | 84 | 1.4362 | 0.3778 |
| 0.4928 | 15.0 | 90 | 1.4119 | 0.3778 |
| 0.4928 | 16.0 | 96 | 1.4031 | 0.4 |
| 0.4272 | 17.0 | 102 | 1.4009 | 0.4 |
| 0.4272 | 18.0 | 108 | 1.4134 | 0.4 |
| 0.3882 | 19.0 | 114 | 1.4007 | 0.4 |
| 0.3396 | 20.0 | 120 | 1.3936 | 0.4 |
| 0.3396 | 21.0 | 126 | 1.3916 | 0.4222 |
| 0.2975 | 22.0 | 132 | 1.3801 | 0.4222 |
| 0.2975 | 23.0 | 138 | 1.3854 | 0.4222 |
| 0.2664 | 24.0 | 144 | 1.3827 | 0.4444 |
| 0.2292 | 25.0 | 150 | 1.3826 | 0.4444 |
| 0.2292 | 26.0 | 156 | 1.3717 | 0.4667 |
| 0.2136 | 27.0 | 162 | 1.3670 | 0.4667 |
| 0.2136 | 28.0 | 168 | 1.3720 | 0.4667 |
| 0.1873 | 29.0 | 174 | 1.3622 | 0.4667 |
| 0.1666 | 30.0 | 180 | 1.3494 | 0.5111 |
| 0.1666 | 31.0 | 186 | 1.3586 | 0.4889 |
| 0.1595 | 32.0 | 192 | 1.3677 | 0.5111 |
| 0.1595 | 33.0 | 198 | 1.3760 | 0.5111 |
| 0.1486 | 34.0 | 204 | 1.3711 | 0.5111 |
| 0.1401 | 35.0 | 210 | 1.3652 | 0.5111 |
| 0.1401 | 36.0 | 216 | 1.3610 | 0.5333 |
| 0.1317 | 37.0 | 222 | 1.3597 | 0.5333 |
| 0.1317 | 38.0 | 228 | 1.3618 | 0.5333 |
| 0.1202 | 39.0 | 234 | 1.3633 | 0.5333 |
| 0.122 | 40.0 | 240 | 1.3628 | 0.5333 |
| 0.122 | 41.0 | 246 | 1.3631 | 0.5333 |
| 0.1214 | 42.0 | 252 | 1.3630 | 0.5333 |
| 0.1214 | 43.0 | 258 | 1.3630 | 0.5333 |
| 0.1203 | 44.0 | 264 | 1.3630 | 0.5333 |
| 0.1185 | 45.0 | 270 | 1.3630 | 0.5333 |
| 0.1185 | 46.0 | 276 | 1.3630 | 0.5333 |
| 0.1174 | 47.0 | 282 | 1.3630 | 0.5333 |
| 0.1174 | 48.0 | 288 | 1.3630 | 0.5333 |
| 0.1152 | 49.0 | 294 | 1.3630 | 0.5333 |
| 0.1204 | 50.0 | 300 | 1.3630 | 0.5333 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_00001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8253
- Accuracy: 0.5581
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4425 | 0.2791 |
| 1.416 | 2.0 | 12 | 1.3728 | 0.3023 |
| 1.416 | 3.0 | 18 | 1.3124 | 0.3488 |
| 1.2388 | 4.0 | 24 | 1.2509 | 0.3721 |
| 1.1051 | 5.0 | 30 | 1.1962 | 0.3488 |
| 1.1051 | 6.0 | 36 | 1.1517 | 0.3721 |
| 0.9682 | 7.0 | 42 | 1.1212 | 0.3721 |
| 0.9682 | 8.0 | 48 | 1.0990 | 0.4186 |
| 0.8769 | 9.0 | 54 | 1.0709 | 0.4884 |
| 0.7643 | 10.0 | 60 | 1.0587 | 0.5116 |
| 0.7643 | 11.0 | 66 | 1.0451 | 0.4884 |
| 0.6717 | 12.0 | 72 | 1.0399 | 0.5581 |
| 0.6717 | 13.0 | 78 | 1.0224 | 0.5349 |
| 0.5988 | 14.0 | 84 | 1.0021 | 0.4884 |
| 0.5291 | 15.0 | 90 | 0.9852 | 0.4884 |
| 0.5291 | 16.0 | 96 | 0.9774 | 0.5116 |
| 0.4581 | 17.0 | 102 | 0.9701 | 0.5116 |
| 0.4581 | 18.0 | 108 | 0.9598 | 0.5116 |
| 0.3895 | 19.0 | 114 | 0.9410 | 0.5814 |
| 0.3415 | 20.0 | 120 | 0.9223 | 0.5581 |
| 0.3415 | 21.0 | 126 | 0.9172 | 0.5349 |
| 0.3044 | 22.0 | 132 | 0.9106 | 0.5349 |
| 0.3044 | 23.0 | 138 | 0.9037 | 0.5581 |
| 0.2632 | 24.0 | 144 | 0.8935 | 0.5581 |
| 0.2425 | 25.0 | 150 | 0.8847 | 0.5814 |
| 0.2425 | 26.0 | 156 | 0.8721 | 0.5581 |
| 0.2102 | 27.0 | 162 | 0.8625 | 0.5581 |
| 0.2102 | 28.0 | 168 | 0.8546 | 0.5581 |
| 0.189 | 29.0 | 174 | 0.8540 | 0.5814 |
| 0.1637 | 30.0 | 180 | 0.8496 | 0.6047 |
| 0.1637 | 31.0 | 186 | 0.8464 | 0.6047 |
| 0.1512 | 32.0 | 192 | 0.8420 | 0.5581 |
| 0.1512 | 33.0 | 198 | 0.8380 | 0.5581 |
| 0.1374 | 34.0 | 204 | 0.8346 | 0.5581 |
| 0.1287 | 35.0 | 210 | 0.8327 | 0.5581 |
| 0.1287 | 36.0 | 216 | 0.8290 | 0.5581 |
| 0.124 | 37.0 | 222 | 0.8276 | 0.5581 |
| 0.124 | 38.0 | 228 | 0.8271 | 0.5581 |
| 0.1186 | 39.0 | 234 | 0.8265 | 0.5581 |
| 0.1159 | 40.0 | 240 | 0.8255 | 0.5581 |
| 0.1159 | 41.0 | 246 | 0.8253 | 0.5581 |
| 0.1139 | 42.0 | 252 | 0.8253 | 0.5581 |
| 0.1139 | 43.0 | 258 | 0.8253 | 0.5581 |
| 0.1142 | 44.0 | 264 | 0.8253 | 0.5581 |
| 0.1107 | 45.0 | 270 | 0.8253 | 0.5581 |
| 0.1107 | 46.0 | 276 | 0.8253 | 0.5581 |
| 0.1118 | 47.0 | 282 | 0.8253 | 0.5581 |
| 0.1118 | 48.0 | 288 | 0.8253 | 0.5581 |
| 0.1159 | 49.0 | 294 | 0.8253 | 0.5581 |
| 0.1095 | 50.0 | 300 | 0.8253 | 0.5581 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_00001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8218
- Accuracy: 0.6667
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3850 | 0.3333 |
| 1.4335 | 2.0 | 12 | 1.3341 | 0.3571 |
| 1.4335 | 3.0 | 18 | 1.2836 | 0.4286 |
| 1.2369 | 4.0 | 24 | 1.2256 | 0.5238 |
| 1.1106 | 5.0 | 30 | 1.1743 | 0.4762 |
| 1.1106 | 6.0 | 36 | 1.1379 | 0.5238 |
| 0.9897 | 7.0 | 42 | 1.1120 | 0.5952 |
| 0.9897 | 8.0 | 48 | 1.0871 | 0.6190 |
| 0.869 | 9.0 | 54 | 1.0617 | 0.5952 |
| 0.7919 | 10.0 | 60 | 1.0389 | 0.5952 |
| 0.7919 | 11.0 | 66 | 1.0206 | 0.5714 |
| 0.7005 | 12.0 | 72 | 1.0005 | 0.5714 |
| 0.7005 | 13.0 | 78 | 0.9876 | 0.5714 |
| 0.6273 | 14.0 | 84 | 0.9709 | 0.5952 |
| 0.5477 | 15.0 | 90 | 0.9546 | 0.5952 |
| 0.5477 | 16.0 | 96 | 0.9438 | 0.5714 |
| 0.4708 | 17.0 | 102 | 0.9277 | 0.5952 |
| 0.4708 | 18.0 | 108 | 0.9166 | 0.6190 |
| 0.4523 | 19.0 | 114 | 0.9086 | 0.6190 |
| 0.3797 | 20.0 | 120 | 0.9051 | 0.5952 |
| 0.3797 | 21.0 | 126 | 0.8956 | 0.6190 |
| 0.3458 | 22.0 | 132 | 0.8852 | 0.6190 |
| 0.3458 | 23.0 | 138 | 0.8841 | 0.6190 |
| 0.3057 | 24.0 | 144 | 0.8804 | 0.5952 |
| 0.2867 | 25.0 | 150 | 0.8683 | 0.6429 |
| 0.2867 | 26.0 | 156 | 0.8580 | 0.6667 |
| 0.2509 | 27.0 | 162 | 0.8515 | 0.6667 |
| 0.2509 | 28.0 | 168 | 0.8546 | 0.6429 |
| 0.2322 | 29.0 | 174 | 0.8500 | 0.6667 |
| 0.2064 | 30.0 | 180 | 0.8396 | 0.6667 |
| 0.2064 | 31.0 | 186 | 0.8363 | 0.6667 |
| 0.1928 | 32.0 | 192 | 0.8371 | 0.6667 |
| 0.1928 | 33.0 | 198 | 0.8332 | 0.6667 |
| 0.1767 | 34.0 | 204 | 0.8261 | 0.6667 |
| 0.1746 | 35.0 | 210 | 0.8249 | 0.6667 |
| 0.1746 | 36.0 | 216 | 0.8258 | 0.6667 |
| 0.1557 | 37.0 | 222 | 0.8248 | 0.6667 |
| 0.1557 | 38.0 | 228 | 0.8243 | 0.6667 |
| 0.1581 | 39.0 | 234 | 0.8225 | 0.6667 |
| 0.1477 | 40.0 | 240 | 0.8219 | 0.6667 |
| 0.1477 | 41.0 | 246 | 0.8217 | 0.6667 |
| 0.149 | 42.0 | 252 | 0.8218 | 0.6667 |
| 0.149 | 43.0 | 258 | 0.8218 | 0.6667 |
| 0.1403 | 44.0 | 264 | 0.8218 | 0.6667 |
| 0.146 | 45.0 | 270 | 0.8218 | 0.6667 |
| 0.146 | 46.0 | 276 | 0.8218 | 0.6667 |
| 0.1461 | 47.0 | 282 | 0.8218 | 0.6667 |
| 0.1461 | 48.0 | 288 | 0.8218 | 0.6667 |
| 0.1422 | 49.0 | 294 | 0.8218 | 0.6667 |
| 0.1494 | 50.0 | 300 | 0.8218 | 0.6667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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. -->
# hushem_1x_deit_tiny_adamax_00001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9982
- Accuracy: 0.5122
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3816 | 0.2927 |
| 1.4331 | 2.0 | 12 | 1.3595 | 0.2195 |
| 1.4331 | 3.0 | 18 | 1.3006 | 0.2927 |
| 1.2071 | 4.0 | 24 | 1.2477 | 0.3415 |
| 1.0931 | 5.0 | 30 | 1.2218 | 0.3659 |
| 1.0931 | 6.0 | 36 | 1.1904 | 0.3415 |
| 0.9583 | 7.0 | 42 | 1.2070 | 0.3659 |
| 0.9583 | 8.0 | 48 | 1.1804 | 0.3415 |
| 0.875 | 9.0 | 54 | 1.1663 | 0.3415 |
| 0.7821 | 10.0 | 60 | 1.1729 | 0.3659 |
| 0.7821 | 11.0 | 66 | 1.1600 | 0.3659 |
| 0.7082 | 12.0 | 72 | 1.1535 | 0.3659 |
| 0.7082 | 13.0 | 78 | 1.1283 | 0.3902 |
| 0.5865 | 14.0 | 84 | 1.1050 | 0.4146 |
| 0.5549 | 15.0 | 90 | 1.0989 | 0.4146 |
| 0.5549 | 16.0 | 96 | 1.0902 | 0.4146 |
| 0.4748 | 17.0 | 102 | 1.0889 | 0.4146 |
| 0.4748 | 18.0 | 108 | 1.0670 | 0.4146 |
| 0.4005 | 19.0 | 114 | 1.0529 | 0.4146 |
| 0.3717 | 20.0 | 120 | 1.0514 | 0.4146 |
| 0.3717 | 21.0 | 126 | 1.0589 | 0.4146 |
| 0.3189 | 22.0 | 132 | 1.0546 | 0.4146 |
| 0.3189 | 23.0 | 138 | 1.0253 | 0.4390 |
| 0.2768 | 24.0 | 144 | 1.0205 | 0.4390 |
| 0.2632 | 25.0 | 150 | 1.0386 | 0.4146 |
| 0.2632 | 26.0 | 156 | 1.0297 | 0.4390 |
| 0.2284 | 27.0 | 162 | 1.0322 | 0.4634 |
| 0.2284 | 28.0 | 168 | 1.0102 | 0.4634 |
| 0.196 | 29.0 | 174 | 1.0015 | 0.4878 |
| 0.1861 | 30.0 | 180 | 1.0070 | 0.4634 |
| 0.1861 | 31.0 | 186 | 1.0149 | 0.4878 |
| 0.1711 | 32.0 | 192 | 1.0173 | 0.4878 |
| 0.1711 | 33.0 | 198 | 1.0083 | 0.4878 |
| 0.1508 | 34.0 | 204 | 1.0068 | 0.5122 |
| 0.1433 | 35.0 | 210 | 0.9998 | 0.5122 |
| 0.1433 | 36.0 | 216 | 0.9984 | 0.5122 |
| 0.1371 | 37.0 | 222 | 0.9985 | 0.5122 |
| 0.1371 | 38.0 | 228 | 0.9983 | 0.5122 |
| 0.1311 | 39.0 | 234 | 0.9983 | 0.5122 |
| 0.1245 | 40.0 | 240 | 0.9977 | 0.5122 |
| 0.1245 | 41.0 | 246 | 0.9980 | 0.5122 |
| 0.1273 | 42.0 | 252 | 0.9982 | 0.5122 |
| 0.1273 | 43.0 | 258 | 0.9982 | 0.5122 |
| 0.1185 | 44.0 | 264 | 0.9982 | 0.5122 |
| 0.1259 | 45.0 | 270 | 0.9982 | 0.5122 |
| 0.1259 | 46.0 | 276 | 0.9982 | 0.5122 |
| 0.1239 | 47.0 | 282 | 0.9982 | 0.5122 |
| 0.1239 | 48.0 | 288 | 0.9982 | 0.5122 |
| 0.1264 | 49.0 | 294 | 0.9982 | 0.5122 |
| 0.1234 | 50.0 | 300 | 0.9982 | 0.5122 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_sgd_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_1x_deit_tiny_sgd_001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3946
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6081 | 0.2889 |
| 1.6517 | 2.0 | 12 | 1.5532 | 0.3333 |
| 1.6517 | 3.0 | 18 | 1.5183 | 0.3111 |
| 1.5073 | 4.0 | 24 | 1.4941 | 0.2 |
| 1.4569 | 5.0 | 30 | 1.4762 | 0.1333 |
| 1.4569 | 6.0 | 36 | 1.4655 | 0.1333 |
| 1.377 | 7.0 | 42 | 1.4570 | 0.1333 |
| 1.377 | 8.0 | 48 | 1.4508 | 0.1333 |
| 1.3495 | 9.0 | 54 | 1.4443 | 0.1333 |
| 1.3234 | 10.0 | 60 | 1.4390 | 0.1333 |
| 1.3234 | 11.0 | 66 | 1.4339 | 0.1778 |
| 1.2813 | 12.0 | 72 | 1.4301 | 0.1778 |
| 1.2813 | 13.0 | 78 | 1.4257 | 0.2 |
| 1.3124 | 14.0 | 84 | 1.4223 | 0.2 |
| 1.2528 | 15.0 | 90 | 1.4195 | 0.2 |
| 1.2528 | 16.0 | 96 | 1.4170 | 0.2222 |
| 1.2252 | 17.0 | 102 | 1.4152 | 0.2 |
| 1.2252 | 18.0 | 108 | 1.4125 | 0.2222 |
| 1.2441 | 19.0 | 114 | 1.4108 | 0.2 |
| 1.1872 | 20.0 | 120 | 1.4088 | 0.2 |
| 1.1872 | 21.0 | 126 | 1.4068 | 0.2 |
| 1.1818 | 22.0 | 132 | 1.4052 | 0.2222 |
| 1.1818 | 23.0 | 138 | 1.4041 | 0.2 |
| 1.1835 | 24.0 | 144 | 1.4032 | 0.2222 |
| 1.1551 | 25.0 | 150 | 1.4021 | 0.2222 |
| 1.1551 | 26.0 | 156 | 1.4013 | 0.2222 |
| 1.1564 | 27.0 | 162 | 1.4008 | 0.2 |
| 1.1564 | 28.0 | 168 | 1.3999 | 0.2222 |
| 1.1662 | 29.0 | 174 | 1.3989 | 0.2222 |
| 1.116 | 30.0 | 180 | 1.3985 | 0.2222 |
| 1.116 | 31.0 | 186 | 1.3976 | 0.2444 |
| 1.153 | 32.0 | 192 | 1.3972 | 0.2444 |
| 1.153 | 33.0 | 198 | 1.3964 | 0.2444 |
| 1.1437 | 34.0 | 204 | 1.3958 | 0.2444 |
| 1.1259 | 35.0 | 210 | 1.3954 | 0.2444 |
| 1.1259 | 36.0 | 216 | 1.3954 | 0.2667 |
| 1.1125 | 37.0 | 222 | 1.3951 | 0.2667 |
| 1.1125 | 38.0 | 228 | 1.3951 | 0.2667 |
| 1.0816 | 39.0 | 234 | 1.3948 | 0.2667 |
| 1.1207 | 40.0 | 240 | 1.3948 | 0.2667 |
| 1.1207 | 41.0 | 246 | 1.3947 | 0.2667 |
| 1.1291 | 42.0 | 252 | 1.3946 | 0.2667 |
| 1.1291 | 43.0 | 258 | 1.3946 | 0.2667 |
| 1.1338 | 44.0 | 264 | 1.3946 | 0.2667 |
| 1.1093 | 45.0 | 270 | 1.3946 | 0.2667 |
| 1.1093 | 46.0 | 276 | 1.3946 | 0.2667 |
| 1.1123 | 47.0 | 282 | 1.3946 | 0.2667 |
| 1.1123 | 48.0 | 288 | 1.3946 | 0.2667 |
| 1.096 | 49.0 | 294 | 1.3946 | 0.2667 |
| 1.1328 | 50.0 | 300 | 1.3946 | 0.2667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_sgd_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_1x_deit_tiny_sgd_001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4913
- Accuracy: 0.1778
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6461 | 0.2222 |
| 1.647 | 2.0 | 12 | 1.5827 | 0.2 |
| 1.647 | 3.0 | 18 | 1.5400 | 0.2 |
| 1.5111 | 4.0 | 24 | 1.5101 | 0.2 |
| 1.4472 | 5.0 | 30 | 1.4855 | 0.1778 |
| 1.4472 | 6.0 | 36 | 1.4711 | 0.1778 |
| 1.3765 | 7.0 | 42 | 1.4618 | 0.2 |
| 1.3765 | 8.0 | 48 | 1.4555 | 0.2 |
| 1.3363 | 9.0 | 54 | 1.4523 | 0.2222 |
| 1.3131 | 10.0 | 60 | 1.4505 | 0.2 |
| 1.3131 | 11.0 | 66 | 1.4495 | 0.2 |
| 1.2743 | 12.0 | 72 | 1.4504 | 0.2 |
| 1.2743 | 13.0 | 78 | 1.4505 | 0.2 |
| 1.2923 | 14.0 | 84 | 1.4516 | 0.2 |
| 1.2475 | 15.0 | 90 | 1.4529 | 0.2 |
| 1.2475 | 16.0 | 96 | 1.4558 | 0.2 |
| 1.2052 | 17.0 | 102 | 1.4591 | 0.1778 |
| 1.2052 | 18.0 | 108 | 1.4603 | 0.1778 |
| 1.2375 | 19.0 | 114 | 1.4628 | 0.1778 |
| 1.1665 | 20.0 | 120 | 1.4654 | 0.1778 |
| 1.1665 | 21.0 | 126 | 1.4668 | 0.1778 |
| 1.1508 | 22.0 | 132 | 1.4681 | 0.1778 |
| 1.1508 | 23.0 | 138 | 1.4710 | 0.1778 |
| 1.1615 | 24.0 | 144 | 1.4735 | 0.1778 |
| 1.1372 | 25.0 | 150 | 1.4742 | 0.1778 |
| 1.1372 | 26.0 | 156 | 1.4775 | 0.1778 |
| 1.1389 | 27.0 | 162 | 1.4787 | 0.1778 |
| 1.1389 | 28.0 | 168 | 1.4813 | 0.1778 |
| 1.1191 | 29.0 | 174 | 1.4821 | 0.1778 |
| 1.106 | 30.0 | 180 | 1.4844 | 0.1778 |
| 1.106 | 31.0 | 186 | 1.4853 | 0.1778 |
| 1.1156 | 32.0 | 192 | 1.4867 | 0.1778 |
| 1.1156 | 33.0 | 198 | 1.4872 | 0.1778 |
| 1.127 | 34.0 | 204 | 1.4879 | 0.1778 |
| 1.1055 | 35.0 | 210 | 1.4887 | 0.1778 |
| 1.1055 | 36.0 | 216 | 1.4895 | 0.1778 |
| 1.089 | 37.0 | 222 | 1.4902 | 0.1778 |
| 1.089 | 38.0 | 228 | 1.4907 | 0.1778 |
| 1.0605 | 39.0 | 234 | 1.4911 | 0.1778 |
| 1.0925 | 40.0 | 240 | 1.4913 | 0.1778 |
| 1.0925 | 41.0 | 246 | 1.4913 | 0.1778 |
| 1.1025 | 42.0 | 252 | 1.4913 | 0.1778 |
| 1.1025 | 43.0 | 258 | 1.4913 | 0.1778 |
| 1.1085 | 44.0 | 264 | 1.4913 | 0.1778 |
| 1.0909 | 45.0 | 270 | 1.4913 | 0.1778 |
| 1.0909 | 46.0 | 276 | 1.4913 | 0.1778 |
| 1.0889 | 47.0 | 282 | 1.4913 | 0.1778 |
| 1.0889 | 48.0 | 288 | 1.4913 | 0.1778 |
| 1.0611 | 49.0 | 294 | 1.4913 | 0.1778 |
| 1.1045 | 50.0 | 300 | 1.4913 | 0.1778 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_sgd_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_1x_deit_tiny_sgd_001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2767
- Accuracy: 0.3488
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6231 | 0.2791 |
| 1.6502 | 2.0 | 12 | 1.5615 | 0.2791 |
| 1.6502 | 3.0 | 18 | 1.5208 | 0.2558 |
| 1.5138 | 4.0 | 24 | 1.4935 | 0.2093 |
| 1.441 | 5.0 | 30 | 1.4720 | 0.2093 |
| 1.441 | 6.0 | 36 | 1.4541 | 0.2326 |
| 1.3942 | 7.0 | 42 | 1.4402 | 0.3023 |
| 1.3942 | 8.0 | 48 | 1.4271 | 0.3023 |
| 1.3895 | 9.0 | 54 | 1.4159 | 0.2791 |
| 1.3382 | 10.0 | 60 | 1.4069 | 0.2791 |
| 1.3382 | 11.0 | 66 | 1.3983 | 0.2558 |
| 1.3326 | 12.0 | 72 | 1.3893 | 0.2558 |
| 1.3326 | 13.0 | 78 | 1.3800 | 0.2558 |
| 1.3102 | 14.0 | 84 | 1.3707 | 0.2558 |
| 1.3163 | 15.0 | 90 | 1.3619 | 0.2791 |
| 1.3163 | 16.0 | 96 | 1.3528 | 0.2791 |
| 1.295 | 17.0 | 102 | 1.3463 | 0.2791 |
| 1.295 | 18.0 | 108 | 1.3391 | 0.2791 |
| 1.2552 | 19.0 | 114 | 1.3325 | 0.3023 |
| 1.2682 | 20.0 | 120 | 1.3269 | 0.3023 |
| 1.2682 | 21.0 | 126 | 1.3221 | 0.3256 |
| 1.2578 | 22.0 | 132 | 1.3173 | 0.3488 |
| 1.2578 | 23.0 | 138 | 1.3126 | 0.3488 |
| 1.2124 | 24.0 | 144 | 1.3087 | 0.3488 |
| 1.2284 | 25.0 | 150 | 1.3049 | 0.3488 |
| 1.2284 | 26.0 | 156 | 1.3017 | 0.3488 |
| 1.2178 | 27.0 | 162 | 1.2982 | 0.3488 |
| 1.2178 | 28.0 | 168 | 1.2955 | 0.3488 |
| 1.2019 | 29.0 | 174 | 1.2931 | 0.3488 |
| 1.2029 | 30.0 | 180 | 1.2906 | 0.3488 |
| 1.2029 | 31.0 | 186 | 1.2886 | 0.3488 |
| 1.1935 | 32.0 | 192 | 1.2863 | 0.3488 |
| 1.1935 | 33.0 | 198 | 1.2843 | 0.3488 |
| 1.164 | 34.0 | 204 | 1.2826 | 0.3488 |
| 1.1999 | 35.0 | 210 | 1.2814 | 0.3488 |
| 1.1999 | 36.0 | 216 | 1.2801 | 0.3488 |
| 1.1813 | 37.0 | 222 | 1.2790 | 0.3488 |
| 1.1813 | 38.0 | 228 | 1.2781 | 0.3488 |
| 1.1753 | 39.0 | 234 | 1.2775 | 0.3488 |
| 1.1877 | 40.0 | 240 | 1.2770 | 0.3488 |
| 1.1877 | 41.0 | 246 | 1.2768 | 0.3488 |
| 1.1774 | 42.0 | 252 | 1.2767 | 0.3488 |
| 1.1774 | 43.0 | 258 | 1.2767 | 0.3488 |
| 1.1704 | 44.0 | 264 | 1.2767 | 0.3488 |
| 1.1843 | 45.0 | 270 | 1.2767 | 0.3488 |
| 1.1843 | 46.0 | 276 | 1.2767 | 0.3488 |
| 1.1726 | 47.0 | 282 | 1.2767 | 0.3488 |
| 1.1726 | 48.0 | 288 | 1.2767 | 0.3488 |
| 1.1541 | 49.0 | 294 | 1.2767 | 0.3488 |
| 1.1928 | 50.0 | 300 | 1.2767 | 0.3488 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_sgd_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_1x_deit_tiny_sgd_001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2335
- Accuracy: 0.4524
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5918 | 0.2857 |
| 1.6404 | 2.0 | 12 | 1.5188 | 0.2857 |
| 1.6404 | 3.0 | 18 | 1.4665 | 0.2857 |
| 1.5241 | 4.0 | 24 | 1.4299 | 0.3333 |
| 1.4755 | 5.0 | 30 | 1.4106 | 0.3571 |
| 1.4755 | 6.0 | 36 | 1.3938 | 0.3095 |
| 1.4186 | 7.0 | 42 | 1.3803 | 0.2857 |
| 1.4186 | 8.0 | 48 | 1.3677 | 0.3810 |
| 1.3819 | 9.0 | 54 | 1.3558 | 0.3810 |
| 1.3541 | 10.0 | 60 | 1.3456 | 0.3810 |
| 1.3541 | 11.0 | 66 | 1.3370 | 0.3810 |
| 1.3363 | 12.0 | 72 | 1.3284 | 0.3810 |
| 1.3363 | 13.0 | 78 | 1.3193 | 0.3571 |
| 1.3168 | 14.0 | 84 | 1.3103 | 0.4048 |
| 1.2875 | 15.0 | 90 | 1.3032 | 0.4048 |
| 1.2875 | 16.0 | 96 | 1.2966 | 0.4048 |
| 1.2638 | 17.0 | 102 | 1.2902 | 0.4048 |
| 1.2638 | 18.0 | 108 | 1.2846 | 0.4048 |
| 1.2758 | 19.0 | 114 | 1.2805 | 0.4048 |
| 1.2611 | 20.0 | 120 | 1.2763 | 0.4048 |
| 1.2611 | 21.0 | 126 | 1.2724 | 0.4048 |
| 1.2411 | 22.0 | 132 | 1.2693 | 0.4048 |
| 1.2411 | 23.0 | 138 | 1.2666 | 0.4048 |
| 1.2357 | 24.0 | 144 | 1.2628 | 0.4048 |
| 1.231 | 25.0 | 150 | 1.2590 | 0.4048 |
| 1.231 | 26.0 | 156 | 1.2555 | 0.4048 |
| 1.2026 | 27.0 | 162 | 1.2531 | 0.4048 |
| 1.2026 | 28.0 | 168 | 1.2508 | 0.4048 |
| 1.2253 | 29.0 | 174 | 1.2482 | 0.4048 |
| 1.1949 | 30.0 | 180 | 1.2457 | 0.4048 |
| 1.1949 | 31.0 | 186 | 1.2436 | 0.4286 |
| 1.2025 | 32.0 | 192 | 1.2420 | 0.4286 |
| 1.2025 | 33.0 | 198 | 1.2406 | 0.4524 |
| 1.1709 | 34.0 | 204 | 1.2390 | 0.4524 |
| 1.1908 | 35.0 | 210 | 1.2376 | 0.4524 |
| 1.1908 | 36.0 | 216 | 1.2365 | 0.4524 |
| 1.1663 | 37.0 | 222 | 1.2358 | 0.4524 |
| 1.1663 | 38.0 | 228 | 1.2349 | 0.4524 |
| 1.1875 | 39.0 | 234 | 1.2342 | 0.4524 |
| 1.1799 | 40.0 | 240 | 1.2338 | 0.4524 |
| 1.1799 | 41.0 | 246 | 1.2336 | 0.4524 |
| 1.1658 | 42.0 | 252 | 1.2335 | 0.4524 |
| 1.1658 | 43.0 | 258 | 1.2335 | 0.4524 |
| 1.1875 | 44.0 | 264 | 1.2335 | 0.4524 |
| 1.1627 | 45.0 | 270 | 1.2335 | 0.4524 |
| 1.1627 | 46.0 | 276 | 1.2335 | 0.4524 |
| 1.1689 | 47.0 | 282 | 1.2335 | 0.4524 |
| 1.1689 | 48.0 | 288 | 1.2335 | 0.4524 |
| 1.1911 | 49.0 | 294 | 1.2335 | 0.4524 |
| 1.1557 | 50.0 | 300 | 1.2335 | 0.4524 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_sgd_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_1x_deit_tiny_sgd_001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2764
- Accuracy: 0.3659
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6481 | 0.2439 |
| 1.6453 | 2.0 | 12 | 1.5595 | 0.2439 |
| 1.6453 | 3.0 | 18 | 1.4979 | 0.2683 |
| 1.5144 | 4.0 | 24 | 1.4546 | 0.2683 |
| 1.4538 | 5.0 | 30 | 1.4262 | 0.2927 |
| 1.4538 | 6.0 | 36 | 1.4074 | 0.2683 |
| 1.3994 | 7.0 | 42 | 1.3954 | 0.2683 |
| 1.3994 | 8.0 | 48 | 1.3847 | 0.2683 |
| 1.3731 | 9.0 | 54 | 1.3749 | 0.2683 |
| 1.3564 | 10.0 | 60 | 1.3671 | 0.2927 |
| 1.3564 | 11.0 | 66 | 1.3612 | 0.3415 |
| 1.3402 | 12.0 | 72 | 1.3541 | 0.3659 |
| 1.3402 | 13.0 | 78 | 1.3472 | 0.3171 |
| 1.2912 | 14.0 | 84 | 1.3416 | 0.3171 |
| 1.304 | 15.0 | 90 | 1.3360 | 0.2927 |
| 1.304 | 16.0 | 96 | 1.3318 | 0.3171 |
| 1.267 | 17.0 | 102 | 1.3278 | 0.3171 |
| 1.267 | 18.0 | 108 | 1.3225 | 0.3171 |
| 1.2687 | 19.0 | 114 | 1.3187 | 0.3415 |
| 1.2447 | 20.0 | 120 | 1.3147 | 0.3415 |
| 1.2447 | 21.0 | 126 | 1.3131 | 0.3171 |
| 1.2262 | 22.0 | 132 | 1.3086 | 0.3171 |
| 1.2262 | 23.0 | 138 | 1.3054 | 0.3171 |
| 1.2132 | 24.0 | 144 | 1.3031 | 0.3171 |
| 1.2231 | 25.0 | 150 | 1.3007 | 0.3171 |
| 1.2231 | 26.0 | 156 | 1.2974 | 0.3171 |
| 1.1895 | 27.0 | 162 | 1.2937 | 0.3171 |
| 1.1895 | 28.0 | 168 | 1.2903 | 0.3415 |
| 1.2062 | 29.0 | 174 | 1.2886 | 0.3415 |
| 1.1907 | 30.0 | 180 | 1.2864 | 0.3415 |
| 1.1907 | 31.0 | 186 | 1.2852 | 0.3415 |
| 1.1836 | 32.0 | 192 | 1.2832 | 0.3415 |
| 1.1836 | 33.0 | 198 | 1.2819 | 0.3415 |
| 1.1632 | 34.0 | 204 | 1.2802 | 0.3415 |
| 1.1553 | 35.0 | 210 | 1.2792 | 0.3659 |
| 1.1553 | 36.0 | 216 | 1.2784 | 0.3659 |
| 1.1703 | 37.0 | 222 | 1.2777 | 0.3659 |
| 1.1703 | 38.0 | 228 | 1.2771 | 0.3659 |
| 1.1625 | 39.0 | 234 | 1.2768 | 0.3659 |
| 1.1523 | 40.0 | 240 | 1.2765 | 0.3659 |
| 1.1523 | 41.0 | 246 | 1.2764 | 0.3659 |
| 1.1617 | 42.0 | 252 | 1.2764 | 0.3659 |
| 1.1617 | 43.0 | 258 | 1.2764 | 0.3659 |
| 1.1427 | 44.0 | 264 | 1.2764 | 0.3659 |
| 1.1631 | 45.0 | 270 | 1.2764 | 0.3659 |
| 1.1631 | 46.0 | 276 | 1.2764 | 0.3659 |
| 1.162 | 47.0 | 282 | 1.2764 | 0.3659 |
| 1.162 | 48.0 | 288 | 1.2764 | 0.3659 |
| 1.1542 | 49.0 | 294 | 1.2764 | 0.3659 |
| 1.1633 | 50.0 | 300 | 1.2764 | 0.3659 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_sgd_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_1x_deit_tiny_sgd_0001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5492
- Accuracy: 0.3333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6732 | 0.2667 |
| 1.7181 | 2.0 | 12 | 1.6650 | 0.2667 |
| 1.7181 | 3.0 | 18 | 1.6576 | 0.2667 |
| 1.6899 | 4.0 | 24 | 1.6505 | 0.2667 |
| 1.7151 | 5.0 | 30 | 1.6435 | 0.2889 |
| 1.7151 | 6.0 | 36 | 1.6372 | 0.2889 |
| 1.6507 | 7.0 | 42 | 1.6312 | 0.2889 |
| 1.6507 | 8.0 | 48 | 1.6255 | 0.2889 |
| 1.626 | 9.0 | 54 | 1.6199 | 0.2889 |
| 1.6566 | 10.0 | 60 | 1.6145 | 0.2889 |
| 1.6566 | 11.0 | 66 | 1.6095 | 0.2889 |
| 1.6122 | 12.0 | 72 | 1.6048 | 0.2889 |
| 1.6122 | 13.0 | 78 | 1.6001 | 0.2889 |
| 1.7016 | 14.0 | 84 | 1.5960 | 0.2889 |
| 1.6075 | 15.0 | 90 | 1.5922 | 0.2889 |
| 1.6075 | 16.0 | 96 | 1.5886 | 0.2889 |
| 1.5839 | 17.0 | 102 | 1.5852 | 0.2889 |
| 1.5839 | 18.0 | 108 | 1.5821 | 0.3111 |
| 1.589 | 19.0 | 114 | 1.5790 | 0.3111 |
| 1.5539 | 20.0 | 120 | 1.5762 | 0.3111 |
| 1.5539 | 21.0 | 126 | 1.5736 | 0.3111 |
| 1.5431 | 22.0 | 132 | 1.5710 | 0.3111 |
| 1.5431 | 23.0 | 138 | 1.5686 | 0.3111 |
| 1.58 | 24.0 | 144 | 1.5665 | 0.3111 |
| 1.5398 | 25.0 | 150 | 1.5646 | 0.3333 |
| 1.5398 | 26.0 | 156 | 1.5627 | 0.3333 |
| 1.5415 | 27.0 | 162 | 1.5611 | 0.3333 |
| 1.5415 | 28.0 | 168 | 1.5596 | 0.3333 |
| 1.5548 | 29.0 | 174 | 1.5581 | 0.3333 |
| 1.5423 | 30.0 | 180 | 1.5567 | 0.3333 |
| 1.5423 | 31.0 | 186 | 1.5553 | 0.3333 |
| 1.5803 | 32.0 | 192 | 1.5542 | 0.3333 |
| 1.5803 | 33.0 | 198 | 1.5532 | 0.3333 |
| 1.4986 | 34.0 | 204 | 1.5522 | 0.3333 |
| 1.5635 | 35.0 | 210 | 1.5514 | 0.3333 |
| 1.5635 | 36.0 | 216 | 1.5508 | 0.3333 |
| 1.5318 | 37.0 | 222 | 1.5503 | 0.3333 |
| 1.5318 | 38.0 | 228 | 1.5499 | 0.3333 |
| 1.4575 | 39.0 | 234 | 1.5495 | 0.3333 |
| 1.527 | 40.0 | 240 | 1.5493 | 0.3333 |
| 1.527 | 41.0 | 246 | 1.5492 | 0.3333 |
| 1.5482 | 42.0 | 252 | 1.5492 | 0.3333 |
| 1.5482 | 43.0 | 258 | 1.5492 | 0.3333 |
| 1.5545 | 44.0 | 264 | 1.5492 | 0.3333 |
| 1.5122 | 45.0 | 270 | 1.5492 | 0.3333 |
| 1.5122 | 46.0 | 276 | 1.5492 | 0.3333 |
| 1.5284 | 47.0 | 282 | 1.5492 | 0.3333 |
| 1.5284 | 48.0 | 288 | 1.5492 | 0.3333 |
| 1.5117 | 49.0 | 294 | 1.5492 | 0.3333 |
| 1.5484 | 50.0 | 300 | 1.5492 | 0.3333 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_sgd_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_1x_deit_tiny_sgd_0001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5785
- Accuracy: 0.2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.7159 | 0.2222 |
| 1.7117 | 2.0 | 12 | 1.7073 | 0.2222 |
| 1.7117 | 3.0 | 18 | 1.6994 | 0.2222 |
| 1.6883 | 4.0 | 24 | 1.6919 | 0.2222 |
| 1.6982 | 5.0 | 30 | 1.6843 | 0.2222 |
| 1.6982 | 6.0 | 36 | 1.6776 | 0.2222 |
| 1.6419 | 7.0 | 42 | 1.6712 | 0.2222 |
| 1.6419 | 8.0 | 48 | 1.6649 | 0.2222 |
| 1.6108 | 9.0 | 54 | 1.6588 | 0.2222 |
| 1.6345 | 10.0 | 60 | 1.6530 | 0.2222 |
| 1.6345 | 11.0 | 66 | 1.6473 | 0.2222 |
| 1.6108 | 12.0 | 72 | 1.6421 | 0.2222 |
| 1.6108 | 13.0 | 78 | 1.6369 | 0.2 |
| 1.6666 | 14.0 | 84 | 1.6323 | 0.2 |
| 1.6138 | 15.0 | 90 | 1.6282 | 0.2 |
| 1.6138 | 16.0 | 96 | 1.6241 | 0.2 |
| 1.5738 | 17.0 | 102 | 1.6203 | 0.2 |
| 1.5738 | 18.0 | 108 | 1.6167 | 0.2 |
| 1.5952 | 19.0 | 114 | 1.6131 | 0.2 |
| 1.555 | 20.0 | 120 | 1.6099 | 0.2 |
| 1.555 | 21.0 | 126 | 1.6070 | 0.2 |
| 1.5267 | 22.0 | 132 | 1.6040 | 0.2 |
| 1.5267 | 23.0 | 138 | 1.6012 | 0.2 |
| 1.5686 | 24.0 | 144 | 1.5988 | 0.2 |
| 1.5444 | 25.0 | 150 | 1.5966 | 0.2 |
| 1.5444 | 26.0 | 156 | 1.5944 | 0.2 |
| 1.544 | 27.0 | 162 | 1.5926 | 0.2 |
| 1.544 | 28.0 | 168 | 1.5907 | 0.2 |
| 1.5375 | 29.0 | 174 | 1.5889 | 0.2 |
| 1.5441 | 30.0 | 180 | 1.5873 | 0.2 |
| 1.5441 | 31.0 | 186 | 1.5857 | 0.2 |
| 1.5614 | 32.0 | 192 | 1.5845 | 0.2 |
| 1.5614 | 33.0 | 198 | 1.5832 | 0.2 |
| 1.5093 | 34.0 | 204 | 1.5821 | 0.2 |
| 1.5478 | 35.0 | 210 | 1.5812 | 0.2 |
| 1.5478 | 36.0 | 216 | 1.5804 | 0.2 |
| 1.5301 | 37.0 | 222 | 1.5798 | 0.2 |
| 1.5301 | 38.0 | 228 | 1.5793 | 0.2 |
| 1.4582 | 39.0 | 234 | 1.5789 | 0.2 |
| 1.5151 | 40.0 | 240 | 1.5786 | 0.2 |
| 1.5151 | 41.0 | 246 | 1.5785 | 0.2 |
| 1.5298 | 42.0 | 252 | 1.5785 | 0.2 |
| 1.5298 | 43.0 | 258 | 1.5785 | 0.2 |
| 1.548 | 44.0 | 264 | 1.5785 | 0.2 |
| 1.5172 | 45.0 | 270 | 1.5785 | 0.2 |
| 1.5172 | 46.0 | 276 | 1.5785 | 0.2 |
| 1.528 | 47.0 | 282 | 1.5785 | 0.2 |
| 1.528 | 48.0 | 288 | 1.5785 | 0.2 |
| 1.4968 | 49.0 | 294 | 1.5785 | 0.2 |
| 1.5413 | 50.0 | 300 | 1.5785 | 0.2 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_sgd_0001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5555
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6995 | 0.2791 |
| 1.7242 | 2.0 | 12 | 1.6902 | 0.2791 |
| 1.7242 | 3.0 | 18 | 1.6819 | 0.2791 |
| 1.6909 | 4.0 | 24 | 1.6741 | 0.2791 |
| 1.6461 | 5.0 | 30 | 1.6664 | 0.2791 |
| 1.6461 | 6.0 | 36 | 1.6587 | 0.2791 |
| 1.6466 | 7.0 | 42 | 1.6518 | 0.2791 |
| 1.6466 | 8.0 | 48 | 1.6448 | 0.2791 |
| 1.6495 | 9.0 | 54 | 1.6384 | 0.2791 |
| 1.6495 | 10.0 | 60 | 1.6323 | 0.2791 |
| 1.6495 | 11.0 | 66 | 1.6267 | 0.2791 |
| 1.6244 | 12.0 | 72 | 1.6213 | 0.2791 |
| 1.6244 | 13.0 | 78 | 1.6166 | 0.2791 |
| 1.593 | 14.0 | 84 | 1.6117 | 0.2791 |
| 1.6183 | 15.0 | 90 | 1.6071 | 0.2791 |
| 1.6183 | 16.0 | 96 | 1.6026 | 0.2791 |
| 1.6105 | 17.0 | 102 | 1.5985 | 0.2558 |
| 1.6105 | 18.0 | 108 | 1.5946 | 0.2558 |
| 1.5599 | 19.0 | 114 | 1.5912 | 0.2558 |
| 1.5756 | 20.0 | 120 | 1.5878 | 0.2558 |
| 1.5756 | 21.0 | 126 | 1.5845 | 0.2558 |
| 1.5692 | 22.0 | 132 | 1.5817 | 0.2558 |
| 1.5692 | 23.0 | 138 | 1.5789 | 0.2558 |
| 1.544 | 24.0 | 144 | 1.5763 | 0.2558 |
| 1.548 | 25.0 | 150 | 1.5738 | 0.2558 |
| 1.548 | 26.0 | 156 | 1.5716 | 0.2791 |
| 1.549 | 27.0 | 162 | 1.5695 | 0.2791 |
| 1.549 | 28.0 | 168 | 1.5675 | 0.2791 |
| 1.5593 | 29.0 | 174 | 1.5658 | 0.2791 |
| 1.528 | 30.0 | 180 | 1.5641 | 0.2791 |
| 1.528 | 31.0 | 186 | 1.5627 | 0.2791 |
| 1.5394 | 32.0 | 192 | 1.5615 | 0.2791 |
| 1.5394 | 33.0 | 198 | 1.5603 | 0.2791 |
| 1.4822 | 34.0 | 204 | 1.5592 | 0.2791 |
| 1.5618 | 35.0 | 210 | 1.5583 | 0.2791 |
| 1.5618 | 36.0 | 216 | 1.5575 | 0.2791 |
| 1.5279 | 37.0 | 222 | 1.5568 | 0.2791 |
| 1.5279 | 38.0 | 228 | 1.5563 | 0.2791 |
| 1.5233 | 39.0 | 234 | 1.5559 | 0.2791 |
| 1.5255 | 40.0 | 240 | 1.5556 | 0.2791 |
| 1.5255 | 41.0 | 246 | 1.5555 | 0.2791 |
| 1.5147 | 42.0 | 252 | 1.5555 | 0.2791 |
| 1.5147 | 43.0 | 258 | 1.5555 | 0.2791 |
| 1.5048 | 44.0 | 264 | 1.5555 | 0.2791 |
| 1.5464 | 45.0 | 270 | 1.5555 | 0.2791 |
| 1.5464 | 46.0 | 276 | 1.5555 | 0.2791 |
| 1.5243 | 47.0 | 282 | 1.5555 | 0.2791 |
| 1.5243 | 48.0 | 288 | 1.5555 | 0.2791 |
| 1.5049 | 49.0 | 294 | 1.5555 | 0.2791 |
| 1.5545 | 50.0 | 300 | 1.5555 | 0.2791 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_sgd_0001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5092
- Accuracy: 0.2857
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6866 | 0.2857 |
| 1.7029 | 2.0 | 12 | 1.6755 | 0.2857 |
| 1.7029 | 3.0 | 18 | 1.6648 | 0.2857 |
| 1.6819 | 4.0 | 24 | 1.6543 | 0.2857 |
| 1.7084 | 5.0 | 30 | 1.6452 | 0.2857 |
| 1.7084 | 6.0 | 36 | 1.6365 | 0.2857 |
| 1.661 | 7.0 | 42 | 1.6277 | 0.2857 |
| 1.661 | 8.0 | 48 | 1.6195 | 0.2857 |
| 1.6506 | 9.0 | 54 | 1.6113 | 0.2857 |
| 1.6321 | 10.0 | 60 | 1.6035 | 0.2857 |
| 1.6321 | 11.0 | 66 | 1.5969 | 0.2857 |
| 1.605 | 12.0 | 72 | 1.5900 | 0.2857 |
| 1.605 | 13.0 | 78 | 1.5837 | 0.2857 |
| 1.6205 | 14.0 | 84 | 1.5775 | 0.2857 |
| 1.6128 | 15.0 | 90 | 1.5717 | 0.2857 |
| 1.6128 | 16.0 | 96 | 1.5663 | 0.2857 |
| 1.5818 | 17.0 | 102 | 1.5613 | 0.2857 |
| 1.5818 | 18.0 | 108 | 1.5566 | 0.2857 |
| 1.6012 | 19.0 | 114 | 1.5522 | 0.2857 |
| 1.6068 | 20.0 | 120 | 1.5482 | 0.2857 |
| 1.6068 | 21.0 | 126 | 1.5443 | 0.2857 |
| 1.5674 | 22.0 | 132 | 1.5409 | 0.2857 |
| 1.5674 | 23.0 | 138 | 1.5376 | 0.2857 |
| 1.565 | 24.0 | 144 | 1.5344 | 0.2857 |
| 1.5842 | 25.0 | 150 | 1.5314 | 0.2857 |
| 1.5842 | 26.0 | 156 | 1.5286 | 0.2857 |
| 1.5593 | 27.0 | 162 | 1.5260 | 0.2857 |
| 1.5593 | 28.0 | 168 | 1.5236 | 0.2857 |
| 1.5824 | 29.0 | 174 | 1.5216 | 0.2857 |
| 1.537 | 30.0 | 180 | 1.5196 | 0.2857 |
| 1.537 | 31.0 | 186 | 1.5181 | 0.2857 |
| 1.5437 | 32.0 | 192 | 1.5165 | 0.2857 |
| 1.5437 | 33.0 | 198 | 1.5150 | 0.2857 |
| 1.5369 | 34.0 | 204 | 1.5137 | 0.2857 |
| 1.5371 | 35.0 | 210 | 1.5125 | 0.2857 |
| 1.5371 | 36.0 | 216 | 1.5116 | 0.2857 |
| 1.5229 | 37.0 | 222 | 1.5109 | 0.2857 |
| 1.5229 | 38.0 | 228 | 1.5102 | 0.2857 |
| 1.5623 | 39.0 | 234 | 1.5097 | 0.2857 |
| 1.5343 | 40.0 | 240 | 1.5094 | 0.2857 |
| 1.5343 | 41.0 | 246 | 1.5093 | 0.2857 |
| 1.5211 | 42.0 | 252 | 1.5092 | 0.2857 |
| 1.5211 | 43.0 | 258 | 1.5092 | 0.2857 |
| 1.5618 | 44.0 | 264 | 1.5092 | 0.2857 |
| 1.5309 | 45.0 | 270 | 1.5092 | 0.2857 |
| 1.5309 | 46.0 | 276 | 1.5092 | 0.2857 |
| 1.5362 | 47.0 | 282 | 1.5092 | 0.2857 |
| 1.5362 | 48.0 | 288 | 1.5092 | 0.2857 |
| 1.5728 | 49.0 | 294 | 1.5092 | 0.2857 |
| 1.5244 | 50.0 | 300 | 1.5092 | 0.2857 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_sgd_0001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5523
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.7547 | 0.2439 |
| 1.7078 | 2.0 | 12 | 1.7422 | 0.2439 |
| 1.7078 | 3.0 | 18 | 1.7303 | 0.2439 |
| 1.6827 | 4.0 | 24 | 1.7187 | 0.2439 |
| 1.6676 | 5.0 | 30 | 1.7076 | 0.2439 |
| 1.6676 | 6.0 | 36 | 1.6970 | 0.2439 |
| 1.6669 | 7.0 | 42 | 1.6882 | 0.2439 |
| 1.6669 | 8.0 | 48 | 1.6793 | 0.2439 |
| 1.5935 | 9.0 | 54 | 1.6701 | 0.2439 |
| 1.6316 | 10.0 | 60 | 1.6617 | 0.2439 |
| 1.6316 | 11.0 | 66 | 1.6538 | 0.2439 |
| 1.6324 | 12.0 | 72 | 1.6460 | 0.2439 |
| 1.6324 | 13.0 | 78 | 1.6387 | 0.2439 |
| 1.5842 | 14.0 | 84 | 1.6318 | 0.2439 |
| 1.5897 | 15.0 | 90 | 1.6256 | 0.2439 |
| 1.5897 | 16.0 | 96 | 1.6199 | 0.2439 |
| 1.5943 | 17.0 | 102 | 1.6144 | 0.2439 |
| 1.5943 | 18.0 | 108 | 1.6092 | 0.2195 |
| 1.5586 | 19.0 | 114 | 1.6040 | 0.2195 |
| 1.5924 | 20.0 | 120 | 1.5990 | 0.2195 |
| 1.5924 | 21.0 | 126 | 1.5945 | 0.2195 |
| 1.5676 | 22.0 | 132 | 1.5902 | 0.2195 |
| 1.5676 | 23.0 | 138 | 1.5862 | 0.2195 |
| 1.5352 | 24.0 | 144 | 1.5823 | 0.2195 |
| 1.5842 | 25.0 | 150 | 1.5786 | 0.2195 |
| 1.5842 | 26.0 | 156 | 1.5752 | 0.2195 |
| 1.5461 | 27.0 | 162 | 1.5723 | 0.2195 |
| 1.5461 | 28.0 | 168 | 1.5695 | 0.2195 |
| 1.551 | 29.0 | 174 | 1.5671 | 0.2439 |
| 1.5549 | 30.0 | 180 | 1.5649 | 0.2439 |
| 1.5549 | 31.0 | 186 | 1.5628 | 0.2439 |
| 1.5532 | 32.0 | 192 | 1.5610 | 0.2439 |
| 1.5532 | 33.0 | 198 | 1.5594 | 0.2439 |
| 1.5006 | 34.0 | 204 | 1.5578 | 0.2439 |
| 1.5134 | 35.0 | 210 | 1.5565 | 0.2439 |
| 1.5134 | 36.0 | 216 | 1.5553 | 0.2439 |
| 1.5386 | 37.0 | 222 | 1.5543 | 0.2439 |
| 1.5386 | 38.0 | 228 | 1.5536 | 0.2439 |
| 1.5372 | 39.0 | 234 | 1.5530 | 0.2439 |
| 1.528 | 40.0 | 240 | 1.5526 | 0.2439 |
| 1.528 | 41.0 | 246 | 1.5524 | 0.2439 |
| 1.5555 | 42.0 | 252 | 1.5523 | 0.2439 |
| 1.5555 | 43.0 | 258 | 1.5523 | 0.2439 |
| 1.509 | 44.0 | 264 | 1.5523 | 0.2439 |
| 1.5379 | 45.0 | 270 | 1.5523 | 0.2439 |
| 1.5379 | 46.0 | 276 | 1.5523 | 0.2439 |
| 1.5588 | 47.0 | 282 | 1.5523 | 0.2439 |
| 1.5588 | 48.0 | 288 | 1.5523 | 0.2439 |
| 1.509 | 49.0 | 294 | 1.5523 | 0.2439 |
| 1.5414 | 50.0 | 300 | 1.5523 | 0.2439 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_sgd_00001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6938
- Accuracy: 0.2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6986 | 0.2 |
| 1.6333 | 2.0 | 12 | 1.6983 | 0.2 |
| 1.6333 | 3.0 | 18 | 1.6981 | 0.2 |
| 1.6088 | 4.0 | 24 | 1.6979 | 0.2 |
| 1.6296 | 5.0 | 30 | 1.6976 | 0.2 |
| 1.6296 | 6.0 | 36 | 1.6974 | 0.2 |
| 1.6252 | 7.0 | 42 | 1.6972 | 0.2 |
| 1.6252 | 8.0 | 48 | 1.6970 | 0.2 |
| 1.6833 | 9.0 | 54 | 1.6968 | 0.2 |
| 1.5983 | 10.0 | 60 | 1.6965 | 0.2 |
| 1.5983 | 11.0 | 66 | 1.6964 | 0.2 |
| 1.61 | 12.0 | 72 | 1.6962 | 0.2 |
| 1.61 | 13.0 | 78 | 1.6960 | 0.2 |
| 1.6125 | 14.0 | 84 | 1.6958 | 0.2 |
| 1.6595 | 15.0 | 90 | 1.6957 | 0.2 |
| 1.6595 | 16.0 | 96 | 1.6956 | 0.2 |
| 1.6372 | 17.0 | 102 | 1.6954 | 0.2 |
| 1.6372 | 18.0 | 108 | 1.6953 | 0.2 |
| 1.6292 | 19.0 | 114 | 1.6951 | 0.2 |
| 1.6414 | 20.0 | 120 | 1.6950 | 0.2 |
| 1.6414 | 21.0 | 126 | 1.6949 | 0.2 |
| 1.6168 | 22.0 | 132 | 1.6948 | 0.2 |
| 1.6168 | 23.0 | 138 | 1.6947 | 0.2 |
| 1.6445 | 24.0 | 144 | 1.6946 | 0.2 |
| 1.6172 | 25.0 | 150 | 1.6945 | 0.2 |
| 1.6172 | 26.0 | 156 | 1.6944 | 0.2 |
| 1.5925 | 27.0 | 162 | 1.6944 | 0.2 |
| 1.5925 | 28.0 | 168 | 1.6943 | 0.2 |
| 1.6351 | 29.0 | 174 | 1.6942 | 0.2 |
| 1.6161 | 30.0 | 180 | 1.6941 | 0.2 |
| 1.6161 | 31.0 | 186 | 1.6941 | 0.2 |
| 1.6095 | 32.0 | 192 | 1.6940 | 0.2 |
| 1.6095 | 33.0 | 198 | 1.6940 | 0.2 |
| 1.6215 | 34.0 | 204 | 1.6939 | 0.2 |
| 1.6213 | 35.0 | 210 | 1.6939 | 0.2 |
| 1.6213 | 36.0 | 216 | 1.6939 | 0.2 |
| 1.6372 | 37.0 | 222 | 1.6938 | 0.2 |
| 1.6372 | 38.0 | 228 | 1.6938 | 0.2 |
| 1.6199 | 39.0 | 234 | 1.6938 | 0.2 |
| 1.6087 | 40.0 | 240 | 1.6938 | 0.2 |
| 1.6087 | 41.0 | 246 | 1.6938 | 0.2 |
| 1.6309 | 42.0 | 252 | 1.6938 | 0.2 |
| 1.6309 | 43.0 | 258 | 1.6938 | 0.2 |
| 1.6203 | 44.0 | 264 | 1.6938 | 0.2 |
| 1.6564 | 45.0 | 270 | 1.6938 | 0.2 |
| 1.6564 | 46.0 | 276 | 1.6938 | 0.2 |
| 1.6178 | 47.0 | 282 | 1.6938 | 0.2 |
| 1.6178 | 48.0 | 288 | 1.6938 | 0.2 |
| 1.6557 | 49.0 | 294 | 1.6938 | 0.2 |
| 1.6181 | 50.0 | 300 | 1.6938 | 0.2 |
### 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_1x_deit_tiny_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_1x_deit_tiny_sgd_00001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7073
- Accuracy: 0.2222
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.7237 | 0.2222 |
| 1.719 | 2.0 | 12 | 1.7228 | 0.2222 |
| 1.719 | 3.0 | 18 | 1.7220 | 0.2222 |
| 1.7128 | 4.0 | 24 | 1.7212 | 0.2222 |
| 1.7405 | 5.0 | 30 | 1.7204 | 0.2222 |
| 1.7405 | 6.0 | 36 | 1.7197 | 0.2222 |
| 1.6943 | 7.0 | 42 | 1.7190 | 0.2222 |
| 1.6943 | 8.0 | 48 | 1.7183 | 0.2222 |
| 1.6759 | 9.0 | 54 | 1.7176 | 0.2222 |
| 1.7158 | 10.0 | 60 | 1.7169 | 0.2222 |
| 1.7158 | 11.0 | 66 | 1.7162 | 0.2222 |
| 1.7024 | 12.0 | 72 | 1.7156 | 0.2222 |
| 1.7024 | 13.0 | 78 | 1.7150 | 0.2222 |
| 1.7744 | 14.0 | 84 | 1.7144 | 0.2222 |
| 1.7251 | 15.0 | 90 | 1.7139 | 0.2222 |
| 1.7251 | 16.0 | 96 | 1.7134 | 0.2222 |
| 1.6942 | 17.0 | 102 | 1.7129 | 0.2222 |
| 1.6942 | 18.0 | 108 | 1.7124 | 0.2222 |
| 1.7154 | 19.0 | 114 | 1.7120 | 0.2222 |
| 1.6829 | 20.0 | 120 | 1.7115 | 0.2222 |
| 1.6829 | 21.0 | 126 | 1.7111 | 0.2222 |
| 1.6559 | 22.0 | 132 | 1.7107 | 0.2222 |
| 1.6559 | 23.0 | 138 | 1.7104 | 0.2222 |
| 1.7194 | 24.0 | 144 | 1.7100 | 0.2222 |
| 1.6925 | 25.0 | 150 | 1.7097 | 0.2222 |
| 1.6925 | 26.0 | 156 | 1.7094 | 0.2222 |
| 1.6919 | 27.0 | 162 | 1.7091 | 0.2222 |
| 1.6919 | 28.0 | 168 | 1.7089 | 0.2222 |
| 1.6948 | 29.0 | 174 | 1.7086 | 0.2222 |
| 1.7059 | 30.0 | 180 | 1.7084 | 0.2222 |
| 1.7059 | 31.0 | 186 | 1.7082 | 0.2222 |
| 1.7337 | 32.0 | 192 | 1.7080 | 0.2222 |
| 1.7337 | 33.0 | 198 | 1.7079 | 0.2222 |
| 1.6587 | 34.0 | 204 | 1.7077 | 0.2222 |
| 1.7172 | 35.0 | 210 | 1.7076 | 0.2222 |
| 1.7172 | 36.0 | 216 | 1.7075 | 0.2222 |
| 1.7051 | 37.0 | 222 | 1.7075 | 0.2222 |
| 1.7051 | 38.0 | 228 | 1.7074 | 0.2222 |
| 1.6141 | 39.0 | 234 | 1.7074 | 0.2222 |
| 1.6784 | 40.0 | 240 | 1.7073 | 0.2222 |
| 1.6784 | 41.0 | 246 | 1.7073 | 0.2222 |
| 1.6991 | 42.0 | 252 | 1.7073 | 0.2222 |
| 1.6991 | 43.0 | 258 | 1.7073 | 0.2222 |
| 1.7247 | 44.0 | 264 | 1.7073 | 0.2222 |
| 1.6773 | 45.0 | 270 | 1.7073 | 0.2222 |
| 1.6773 | 46.0 | 276 | 1.7073 | 0.2222 |
| 1.6939 | 47.0 | 282 | 1.7073 | 0.2222 |
| 1.6939 | 48.0 | 288 | 1.7073 | 0.2222 |
| 1.6622 | 49.0 | 294 | 1.7073 | 0.2222 |
| 1.7192 | 50.0 | 300 | 1.7073 | 0.2222 |
### 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_1x_deit_tiny_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_1x_deit_tiny_sgd_00001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6900
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.7081 | 0.2791 |
| 1.7325 | 2.0 | 12 | 1.7072 | 0.2791 |
| 1.7325 | 3.0 | 18 | 1.7063 | 0.2791 |
| 1.7152 | 4.0 | 24 | 1.7055 | 0.2791 |
| 1.6813 | 5.0 | 30 | 1.7046 | 0.2791 |
| 1.6813 | 6.0 | 36 | 1.7038 | 0.2791 |
| 1.6984 | 7.0 | 42 | 1.7030 | 0.2791 |
| 1.6984 | 8.0 | 48 | 1.7022 | 0.2791 |
| 1.7131 | 9.0 | 54 | 1.7014 | 0.2791 |
| 1.7337 | 10.0 | 60 | 1.7007 | 0.2791 |
| 1.7337 | 11.0 | 66 | 1.7000 | 0.2791 |
| 1.7143 | 12.0 | 72 | 1.6993 | 0.2791 |
| 1.7143 | 13.0 | 78 | 1.6987 | 0.2791 |
| 1.6884 | 14.0 | 84 | 1.6981 | 0.2791 |
| 1.7252 | 15.0 | 90 | 1.6975 | 0.2791 |
| 1.7252 | 16.0 | 96 | 1.6969 | 0.2791 |
| 1.7269 | 17.0 | 102 | 1.6963 | 0.2791 |
| 1.7269 | 18.0 | 108 | 1.6958 | 0.2791 |
| 1.6858 | 19.0 | 114 | 1.6953 | 0.2791 |
| 1.7013 | 20.0 | 120 | 1.6948 | 0.2791 |
| 1.7013 | 21.0 | 126 | 1.6943 | 0.2791 |
| 1.7051 | 22.0 | 132 | 1.6939 | 0.2791 |
| 1.7051 | 23.0 | 138 | 1.6935 | 0.2791 |
| 1.6834 | 24.0 | 144 | 1.6931 | 0.2791 |
| 1.6977 | 25.0 | 150 | 1.6927 | 0.2791 |
| 1.6977 | 26.0 | 156 | 1.6924 | 0.2791 |
| 1.7016 | 27.0 | 162 | 1.6920 | 0.2791 |
| 1.7016 | 28.0 | 168 | 1.6917 | 0.2791 |
| 1.7242 | 29.0 | 174 | 1.6915 | 0.2791 |
| 1.6808 | 30.0 | 180 | 1.6912 | 0.2791 |
| 1.6808 | 31.0 | 186 | 1.6910 | 0.2791 |
| 1.7032 | 32.0 | 192 | 1.6908 | 0.2791 |
| 1.7032 | 33.0 | 198 | 1.6906 | 0.2791 |
| 1.6261 | 34.0 | 204 | 1.6905 | 0.2791 |
| 1.7412 | 35.0 | 210 | 1.6903 | 0.2791 |
| 1.7412 | 36.0 | 216 | 1.6902 | 0.2791 |
| 1.6899 | 37.0 | 222 | 1.6901 | 0.2791 |
| 1.6899 | 38.0 | 228 | 1.6901 | 0.2791 |
| 1.6944 | 39.0 | 234 | 1.6900 | 0.2791 |
| 1.6965 | 40.0 | 240 | 1.6900 | 0.2791 |
| 1.6965 | 41.0 | 246 | 1.6900 | 0.2791 |
| 1.6787 | 42.0 | 252 | 1.6900 | 0.2791 |
| 1.6787 | 43.0 | 258 | 1.6900 | 0.2791 |
| 1.6617 | 44.0 | 264 | 1.6900 | 0.2791 |
| 1.7215 | 45.0 | 270 | 1.6900 | 0.2791 |
| 1.7215 | 46.0 | 276 | 1.6900 | 0.2791 |
| 1.6881 | 47.0 | 282 | 1.6900 | 0.2791 |
| 1.6881 | 48.0 | 288 | 1.6900 | 0.2791 |
| 1.6823 | 49.0 | 294 | 1.6900 | 0.2791 |
| 1.7275 | 50.0 | 300 | 1.6900 | 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"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_sgd_00001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6751
- Accuracy: 0.2857
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6974 | 0.2857 |
| 1.71 | 2.0 | 12 | 1.6962 | 0.2857 |
| 1.71 | 3.0 | 18 | 1.6951 | 0.2857 |
| 1.7036 | 4.0 | 24 | 1.6940 | 0.2857 |
| 1.7465 | 5.0 | 30 | 1.6930 | 0.2857 |
| 1.7465 | 6.0 | 36 | 1.6921 | 0.2857 |
| 1.709 | 7.0 | 42 | 1.6911 | 0.2857 |
| 1.709 | 8.0 | 48 | 1.6901 | 0.2857 |
| 1.712 | 9.0 | 54 | 1.6892 | 0.2857 |
| 1.7048 | 10.0 | 60 | 1.6882 | 0.2857 |
| 1.7048 | 11.0 | 66 | 1.6874 | 0.2857 |
| 1.6828 | 12.0 | 72 | 1.6866 | 0.2857 |
| 1.6828 | 13.0 | 78 | 1.6858 | 0.2857 |
| 1.7139 | 14.0 | 84 | 1.6850 | 0.2857 |
| 1.719 | 15.0 | 90 | 1.6842 | 0.2857 |
| 1.719 | 16.0 | 96 | 1.6835 | 0.2857 |
| 1.6904 | 17.0 | 102 | 1.6828 | 0.2857 |
| 1.6904 | 18.0 | 108 | 1.6821 | 0.2857 |
| 1.7154 | 19.0 | 114 | 1.6815 | 0.2857 |
| 1.7326 | 20.0 | 120 | 1.6809 | 0.2857 |
| 1.7326 | 21.0 | 126 | 1.6804 | 0.2857 |
| 1.6942 | 22.0 | 132 | 1.6799 | 0.2857 |
| 1.6942 | 23.0 | 138 | 1.6794 | 0.2857 |
| 1.6945 | 24.0 | 144 | 1.6789 | 0.2857 |
| 1.728 | 25.0 | 150 | 1.6784 | 0.2857 |
| 1.728 | 26.0 | 156 | 1.6780 | 0.2857 |
| 1.7026 | 27.0 | 162 | 1.6776 | 0.2857 |
| 1.7026 | 28.0 | 168 | 1.6772 | 0.2857 |
| 1.7403 | 29.0 | 174 | 1.6769 | 0.2857 |
| 1.6716 | 30.0 | 180 | 1.6766 | 0.2857 |
| 1.6716 | 31.0 | 186 | 1.6764 | 0.2857 |
| 1.6806 | 32.0 | 192 | 1.6761 | 0.2857 |
| 1.6806 | 33.0 | 198 | 1.6759 | 0.2857 |
| 1.6988 | 34.0 | 204 | 1.6757 | 0.2857 |
| 1.6893 | 35.0 | 210 | 1.6755 | 0.2857 |
| 1.6893 | 36.0 | 216 | 1.6754 | 0.2857 |
| 1.6718 | 37.0 | 222 | 1.6753 | 0.2857 |
| 1.6718 | 38.0 | 228 | 1.6752 | 0.2857 |
| 1.7279 | 39.0 | 234 | 1.6751 | 0.2857 |
| 1.6803 | 40.0 | 240 | 1.6751 | 0.2857 |
| 1.6803 | 41.0 | 246 | 1.6751 | 0.2857 |
| 1.6785 | 42.0 | 252 | 1.6751 | 0.2857 |
| 1.6785 | 43.0 | 258 | 1.6751 | 0.2857 |
| 1.7169 | 44.0 | 264 | 1.6751 | 0.2857 |
| 1.6924 | 45.0 | 270 | 1.6751 | 0.2857 |
| 1.6924 | 46.0 | 276 | 1.6751 | 0.2857 |
| 1.6961 | 47.0 | 282 | 1.6751 | 0.2857 |
| 1.6961 | 48.0 | 288 | 1.6751 | 0.2857 |
| 1.7415 | 49.0 | 294 | 1.6751 | 0.2857 |
| 1.681 | 50.0 | 300 | 1.6751 | 0.2857 |
### 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_1x_deit_tiny_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_1x_deit_tiny_sgd_00001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7419
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.7664 | 0.2439 |
| 1.7149 | 2.0 | 12 | 1.7652 | 0.2439 |
| 1.7149 | 3.0 | 18 | 1.7640 | 0.2439 |
| 1.7055 | 4.0 | 24 | 1.7627 | 0.2439 |
| 1.7032 | 5.0 | 30 | 1.7616 | 0.2439 |
| 1.7032 | 6.0 | 36 | 1.7604 | 0.2439 |
| 1.7195 | 7.0 | 42 | 1.7594 | 0.2439 |
| 1.7195 | 8.0 | 48 | 1.7584 | 0.2439 |
| 1.6458 | 9.0 | 54 | 1.7574 | 0.2439 |
| 1.7017 | 10.0 | 60 | 1.7564 | 0.2439 |
| 1.7017 | 11.0 | 66 | 1.7554 | 0.2439 |
| 1.7123 | 12.0 | 72 | 1.7545 | 0.2439 |
| 1.7123 | 13.0 | 78 | 1.7536 | 0.2439 |
| 1.6713 | 14.0 | 84 | 1.7528 | 0.2439 |
| 1.6849 | 15.0 | 90 | 1.7520 | 0.2439 |
| 1.6849 | 16.0 | 96 | 1.7512 | 0.2439 |
| 1.7051 | 17.0 | 102 | 1.7505 | 0.2439 |
| 1.7051 | 18.0 | 108 | 1.7498 | 0.2439 |
| 1.6541 | 19.0 | 114 | 1.7491 | 0.2439 |
| 1.7161 | 20.0 | 120 | 1.7484 | 0.2439 |
| 1.7161 | 21.0 | 126 | 1.7478 | 0.2439 |
| 1.6901 | 22.0 | 132 | 1.7472 | 0.2439 |
| 1.6901 | 23.0 | 138 | 1.7466 | 0.2439 |
| 1.6528 | 24.0 | 144 | 1.7461 | 0.2439 |
| 1.7234 | 25.0 | 150 | 1.7456 | 0.2439 |
| 1.7234 | 26.0 | 156 | 1.7451 | 0.2439 |
| 1.6839 | 27.0 | 162 | 1.7447 | 0.2439 |
| 1.6839 | 28.0 | 168 | 1.7443 | 0.2439 |
| 1.6859 | 29.0 | 174 | 1.7439 | 0.2439 |
| 1.6955 | 30.0 | 180 | 1.7436 | 0.2439 |
| 1.6955 | 31.0 | 186 | 1.7433 | 0.2439 |
| 1.7014 | 32.0 | 192 | 1.7430 | 0.2439 |
| 1.7014 | 33.0 | 198 | 1.7428 | 0.2439 |
| 1.6319 | 34.0 | 204 | 1.7426 | 0.2439 |
| 1.6586 | 35.0 | 210 | 1.7424 | 0.2439 |
| 1.6586 | 36.0 | 216 | 1.7422 | 0.2439 |
| 1.6897 | 37.0 | 222 | 1.7421 | 0.2439 |
| 1.6897 | 38.0 | 228 | 1.7420 | 0.2439 |
| 1.6863 | 39.0 | 234 | 1.7420 | 0.2439 |
| 1.6801 | 40.0 | 240 | 1.7419 | 0.2439 |
| 1.6801 | 41.0 | 246 | 1.7419 | 0.2439 |
| 1.7183 | 42.0 | 252 | 1.7419 | 0.2439 |
| 1.7183 | 43.0 | 258 | 1.7419 | 0.2439 |
| 1.6529 | 44.0 | 264 | 1.7419 | 0.2439 |
| 1.6913 | 45.0 | 270 | 1.7419 | 0.2439 |
| 1.6913 | 46.0 | 276 | 1.7419 | 0.2439 |
| 1.7139 | 47.0 | 282 | 1.7419 | 0.2439 |
| 1.7139 | 48.0 | 288 | 1.7419 | 0.2439 |
| 1.6464 | 49.0 | 294 | 1.7419 | 0.2439 |
| 1.6966 | 50.0 | 300 | 1.7419 | 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_1x_deit_tiny_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_1x_deit_tiny_rms_001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5885
- Accuracy: 0.2889
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 4.1632 | 0.2444 |
| 4.2585 | 2.0 | 12 | 2.5063 | 0.2444 |
| 4.2585 | 3.0 | 18 | 1.7281 | 0.2444 |
| 1.7534 | 4.0 | 24 | 1.3946 | 0.2444 |
| 1.5909 | 5.0 | 30 | 1.5054 | 0.2444 |
| 1.5909 | 6.0 | 36 | 1.6818 | 0.2444 |
| 1.5201 | 7.0 | 42 | 1.5863 | 0.3556 |
| 1.5201 | 8.0 | 48 | 1.5570 | 0.2667 |
| 1.4849 | 9.0 | 54 | 1.5043 | 0.4667 |
| 1.4118 | 10.0 | 60 | 1.4204 | 0.2444 |
| 1.4118 | 11.0 | 66 | 1.4708 | 0.2667 |
| 1.4258 | 12.0 | 72 | 1.4115 | 0.2444 |
| 1.4258 | 13.0 | 78 | 1.5806 | 0.2667 |
| 1.444 | 14.0 | 84 | 1.3600 | 0.3111 |
| 1.4369 | 15.0 | 90 | 1.4011 | 0.2667 |
| 1.4369 | 16.0 | 96 | 1.2994 | 0.4889 |
| 1.4072 | 17.0 | 102 | 1.3804 | 0.4222 |
| 1.4072 | 18.0 | 108 | 2.3179 | 0.2444 |
| 1.3585 | 19.0 | 114 | 1.4391 | 0.3111 |
| 1.3358 | 20.0 | 120 | 2.0579 | 0.2667 |
| 1.3358 | 21.0 | 126 | 1.3519 | 0.3333 |
| 1.432 | 22.0 | 132 | 1.4609 | 0.2889 |
| 1.432 | 23.0 | 138 | 2.1987 | 0.2444 |
| 1.3028 | 24.0 | 144 | 1.5480 | 0.2444 |
| 1.282 | 25.0 | 150 | 1.3898 | 0.2889 |
| 1.282 | 26.0 | 156 | 1.2611 | 0.2444 |
| 1.2714 | 27.0 | 162 | 1.7016 | 0.2444 |
| 1.2714 | 28.0 | 168 | 1.3743 | 0.2889 |
| 1.2632 | 29.0 | 174 | 1.4836 | 0.3778 |
| 1.176 | 30.0 | 180 | 1.3073 | 0.4 |
| 1.176 | 31.0 | 186 | 1.4096 | 0.2667 |
| 1.1646 | 32.0 | 192 | 1.4023 | 0.4222 |
| 1.1646 | 33.0 | 198 | 1.4449 | 0.4 |
| 1.1055 | 34.0 | 204 | 1.6514 | 0.2889 |
| 1.1692 | 35.0 | 210 | 1.4679 | 0.3111 |
| 1.1692 | 36.0 | 216 | 1.6234 | 0.2667 |
| 1.1228 | 37.0 | 222 | 1.6770 | 0.3333 |
| 1.1228 | 38.0 | 228 | 1.5646 | 0.2667 |
| 1.0125 | 39.0 | 234 | 1.5851 | 0.2889 |
| 1.0301 | 40.0 | 240 | 1.5653 | 0.2889 |
| 1.0301 | 41.0 | 246 | 1.5924 | 0.2667 |
| 1.0049 | 42.0 | 252 | 1.5885 | 0.2889 |
| 1.0049 | 43.0 | 258 | 1.5885 | 0.2889 |
| 1.0088 | 44.0 | 264 | 1.5885 | 0.2889 |
| 0.9822 | 45.0 | 270 | 1.5885 | 0.2889 |
| 0.9822 | 46.0 | 276 | 1.5885 | 0.2889 |
| 0.9822 | 47.0 | 282 | 1.5885 | 0.2889 |
| 0.9822 | 48.0 | 288 | 1.5885 | 0.2889 |
| 0.9898 | 49.0 | 294 | 1.5885 | 0.2889 |
| 0.9935 | 50.0 | 300 | 1.5885 | 0.2889 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2949
- 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 6.6312 | 0.2667 |
| 4.1013 | 2.0 | 12 | 2.1471 | 0.2444 |
| 4.1013 | 3.0 | 18 | 1.7992 | 0.2444 |
| 1.7936 | 4.0 | 24 | 1.5377 | 0.2667 |
| 1.5908 | 5.0 | 30 | 1.6029 | 0.2444 |
| 1.5908 | 6.0 | 36 | 1.5728 | 0.2444 |
| 1.533 | 7.0 | 42 | 1.6272 | 0.2444 |
| 1.533 | 8.0 | 48 | 1.5192 | 0.2667 |
| 1.4887 | 9.0 | 54 | 1.4382 | 0.2444 |
| 1.4288 | 10.0 | 60 | 1.4387 | 0.2444 |
| 1.4288 | 11.0 | 66 | 1.4770 | 0.2667 |
| 1.422 | 12.0 | 72 | 1.3624 | 0.2444 |
| 1.422 | 13.0 | 78 | 1.4332 | 0.2667 |
| 1.4231 | 14.0 | 84 | 1.4892 | 0.2444 |
| 1.385 | 15.0 | 90 | 1.3102 | 0.4222 |
| 1.385 | 16.0 | 96 | 1.3352 | 0.3333 |
| 1.4799 | 17.0 | 102 | 1.6140 | 0.3111 |
| 1.4799 | 18.0 | 108 | 1.4774 | 0.2444 |
| 1.4126 | 19.0 | 114 | 1.3130 | 0.3333 |
| 1.3511 | 20.0 | 120 | 1.2400 | 0.4222 |
| 1.3511 | 21.0 | 126 | 1.5468 | 0.2667 |
| 1.412 | 22.0 | 132 | 1.4525 | 0.2667 |
| 1.412 | 23.0 | 138 | 1.2484 | 0.3778 |
| 1.3184 | 24.0 | 144 | 1.5741 | 0.2444 |
| 1.3429 | 25.0 | 150 | 1.3487 | 0.4444 |
| 1.3429 | 26.0 | 156 | 1.3203 | 0.3111 |
| 1.2824 | 27.0 | 162 | 1.2257 | 0.4222 |
| 1.2824 | 28.0 | 168 | 1.3520 | 0.2222 |
| 1.2504 | 29.0 | 174 | 1.1717 | 0.4667 |
| 1.235 | 30.0 | 180 | 1.2327 | 0.3778 |
| 1.235 | 31.0 | 186 | 1.3371 | 0.4 |
| 1.2286 | 32.0 | 192 | 1.3224 | 0.2889 |
| 1.2286 | 33.0 | 198 | 1.2295 | 0.3778 |
| 1.168 | 34.0 | 204 | 1.2716 | 0.3111 |
| 1.2345 | 35.0 | 210 | 1.2743 | 0.3111 |
| 1.2345 | 36.0 | 216 | 1.3964 | 0.3778 |
| 1.2057 | 37.0 | 222 | 1.3905 | 0.3556 |
| 1.2057 | 38.0 | 228 | 1.2908 | 0.3778 |
| 1.1197 | 39.0 | 234 | 1.2888 | 0.3556 |
| 1.1518 | 40.0 | 240 | 1.2704 | 0.4 |
| 1.1518 | 41.0 | 246 | 1.3067 | 0.3556 |
| 1.1311 | 42.0 | 252 | 1.2949 | 0.3556 |
| 1.1311 | 43.0 | 258 | 1.2949 | 0.3556 |
| 1.109 | 44.0 | 264 | 1.2949 | 0.3556 |
| 1.1464 | 45.0 | 270 | 1.2949 | 0.3556 |
| 1.1464 | 46.0 | 276 | 1.2949 | 0.3556 |
| 1.0982 | 47.0 | 282 | 1.2949 | 0.3556 |
| 1.0982 | 48.0 | 288 | 1.2949 | 0.3556 |
| 1.1635 | 49.0 | 294 | 1.2949 | 0.3556 |
| 1.1115 | 50.0 | 300 | 1.2949 | 0.3556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1536
- Accuracy: 0.4186
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 3.8148 | 0.2558 |
| 4.0682 | 2.0 | 12 | 1.5106 | 0.2558 |
| 4.0682 | 3.0 | 18 | 2.1015 | 0.2558 |
| 1.8697 | 4.0 | 24 | 2.1521 | 0.2558 |
| 1.6204 | 5.0 | 30 | 2.0540 | 0.2558 |
| 1.6204 | 6.0 | 36 | 1.4487 | 0.2558 |
| 1.5557 | 7.0 | 42 | 1.5322 | 0.2326 |
| 1.5557 | 8.0 | 48 | 1.6480 | 0.2558 |
| 1.5276 | 9.0 | 54 | 1.5085 | 0.2558 |
| 1.4446 | 10.0 | 60 | 1.3921 | 0.2558 |
| 1.4446 | 11.0 | 66 | 1.5703 | 0.2558 |
| 1.4728 | 12.0 | 72 | 1.3608 | 0.2791 |
| 1.4728 | 13.0 | 78 | 1.4250 | 0.3488 |
| 1.3652 | 14.0 | 84 | 1.4495 | 0.2558 |
| 1.3593 | 15.0 | 90 | 1.4182 | 0.3023 |
| 1.3593 | 16.0 | 96 | 1.5418 | 0.3023 |
| 1.2943 | 17.0 | 102 | 1.4454 | 0.3256 |
| 1.2943 | 18.0 | 108 | 1.5941 | 0.3721 |
| 1.2915 | 19.0 | 114 | 1.4889 | 0.2558 |
| 1.2591 | 20.0 | 120 | 1.3804 | 0.3488 |
| 1.2591 | 21.0 | 126 | 1.8125 | 0.2558 |
| 1.2263 | 22.0 | 132 | 1.4098 | 0.3023 |
| 1.2263 | 23.0 | 138 | 1.4818 | 0.2558 |
| 1.1885 | 24.0 | 144 | 1.4257 | 0.3721 |
| 1.1814 | 25.0 | 150 | 1.4317 | 0.3023 |
| 1.1814 | 26.0 | 156 | 1.3854 | 0.3488 |
| 1.1163 | 27.0 | 162 | 1.9054 | 0.3256 |
| 1.1163 | 28.0 | 168 | 1.3109 | 0.3488 |
| 1.0609 | 29.0 | 174 | 1.3896 | 0.3488 |
| 1.1038 | 30.0 | 180 | 1.3466 | 0.3256 |
| 1.1038 | 31.0 | 186 | 1.3101 | 0.3256 |
| 1.0099 | 32.0 | 192 | 1.2865 | 0.3721 |
| 1.0099 | 33.0 | 198 | 1.2846 | 0.3721 |
| 1.0297 | 34.0 | 204 | 1.2587 | 0.4186 |
| 0.964 | 35.0 | 210 | 1.2832 | 0.3953 |
| 0.964 | 36.0 | 216 | 1.1929 | 0.3721 |
| 0.9335 | 37.0 | 222 | 1.2162 | 0.3953 |
| 0.9335 | 38.0 | 228 | 1.1906 | 0.4419 |
| 0.8668 | 39.0 | 234 | 1.1859 | 0.4186 |
| 0.8296 | 40.0 | 240 | 1.1516 | 0.4884 |
| 0.8296 | 41.0 | 246 | 1.1577 | 0.4651 |
| 0.8332 | 42.0 | 252 | 1.1536 | 0.4186 |
| 0.8332 | 43.0 | 258 | 1.1536 | 0.4186 |
| 0.8289 | 44.0 | 264 | 1.1536 | 0.4186 |
| 0.8217 | 45.0 | 270 | 1.1536 | 0.4186 |
| 0.8217 | 46.0 | 276 | 1.1536 | 0.4186 |
| 0.8205 | 47.0 | 282 | 1.1536 | 0.4186 |
| 0.8205 | 48.0 | 288 | 1.1536 | 0.4186 |
| 0.8548 | 49.0 | 294 | 1.1536 | 0.4186 |
| 0.8042 | 50.0 | 300 | 1.1536 | 0.4186 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0712
- Accuracy: 0.4762
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 5.0165 | 0.2381 |
| 4.2481 | 2.0 | 12 | 3.3074 | 0.2381 |
| 4.2481 | 3.0 | 18 | 1.5288 | 0.2619 |
| 2.0024 | 4.0 | 24 | 1.5375 | 0.2381 |
| 1.6731 | 5.0 | 30 | 1.4069 | 0.2619 |
| 1.6731 | 6.0 | 36 | 1.8969 | 0.2381 |
| 1.5329 | 7.0 | 42 | 1.4811 | 0.2381 |
| 1.5329 | 8.0 | 48 | 1.4117 | 0.2619 |
| 1.475 | 9.0 | 54 | 1.4704 | 0.2619 |
| 1.4639 | 10.0 | 60 | 1.4459 | 0.2381 |
| 1.4639 | 11.0 | 66 | 1.3572 | 0.4524 |
| 1.4524 | 12.0 | 72 | 1.2630 | 0.4524 |
| 1.4524 | 13.0 | 78 | 1.2843 | 0.4524 |
| 1.4025 | 14.0 | 84 | 1.3420 | 0.2857 |
| 1.3666 | 15.0 | 90 | 1.4060 | 0.2381 |
| 1.3666 | 16.0 | 96 | 1.2621 | 0.3810 |
| 1.3178 | 17.0 | 102 | 1.2969 | 0.2857 |
| 1.3178 | 18.0 | 108 | 1.2881 | 0.3333 |
| 1.3667 | 19.0 | 114 | 1.3980 | 0.2857 |
| 1.3043 | 20.0 | 120 | 1.5195 | 0.2857 |
| 1.3043 | 21.0 | 126 | 1.1841 | 0.4048 |
| 1.2859 | 22.0 | 132 | 1.0567 | 0.5238 |
| 1.2859 | 23.0 | 138 | 1.2258 | 0.2619 |
| 1.2496 | 24.0 | 144 | 1.2372 | 0.2857 |
| 1.252 | 25.0 | 150 | 1.4386 | 0.3333 |
| 1.252 | 26.0 | 156 | 1.1416 | 0.3810 |
| 1.2296 | 27.0 | 162 | 1.0872 | 0.4286 |
| 1.2296 | 28.0 | 168 | 1.4121 | 0.2857 |
| 1.1581 | 29.0 | 174 | 1.0555 | 0.5476 |
| 1.2027 | 30.0 | 180 | 1.1296 | 0.4762 |
| 1.2027 | 31.0 | 186 | 1.2095 | 0.4048 |
| 1.1595 | 32.0 | 192 | 1.0821 | 0.4762 |
| 1.1595 | 33.0 | 198 | 1.1681 | 0.3810 |
| 1.1909 | 34.0 | 204 | 1.1147 | 0.4762 |
| 1.1121 | 35.0 | 210 | 1.0734 | 0.4048 |
| 1.1121 | 36.0 | 216 | 1.0002 | 0.5238 |
| 1.1218 | 37.0 | 222 | 1.1912 | 0.3095 |
| 1.1218 | 38.0 | 228 | 1.0883 | 0.4524 |
| 1.1024 | 39.0 | 234 | 1.1229 | 0.4286 |
| 1.0678 | 40.0 | 240 | 1.0903 | 0.4762 |
| 1.0678 | 41.0 | 246 | 1.0717 | 0.4762 |
| 1.058 | 42.0 | 252 | 1.0712 | 0.4762 |
| 1.058 | 43.0 | 258 | 1.0712 | 0.4762 |
| 1.0512 | 44.0 | 264 | 1.0712 | 0.4762 |
| 1.0743 | 45.0 | 270 | 1.0712 | 0.4762 |
| 1.0743 | 46.0 | 276 | 1.0712 | 0.4762 |
| 1.0691 | 47.0 | 282 | 1.0712 | 0.4762 |
| 1.0691 | 48.0 | 288 | 1.0712 | 0.4762 |
| 1.052 | 49.0 | 294 | 1.0712 | 0.4762 |
| 1.066 | 50.0 | 300 | 1.0712 | 0.4762 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1358
- Accuracy: 0.6098
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 4.7231 | 0.2683 |
| 4.2141 | 2.0 | 12 | 1.8531 | 0.2683 |
| 4.2141 | 3.0 | 18 | 1.6449 | 0.2439 |
| 1.9845 | 4.0 | 24 | 1.4265 | 0.2439 |
| 1.5807 | 5.0 | 30 | 2.0165 | 0.2439 |
| 1.5807 | 6.0 | 36 | 1.5975 | 0.2683 |
| 1.5979 | 7.0 | 42 | 1.4305 | 0.3171 |
| 1.5979 | 8.0 | 48 | 1.4587 | 0.2683 |
| 1.4992 | 9.0 | 54 | 1.2917 | 0.3171 |
| 1.4954 | 10.0 | 60 | 1.2462 | 0.4390 |
| 1.4954 | 11.0 | 66 | 1.2479 | 0.2683 |
| 1.415 | 12.0 | 72 | 1.1246 | 0.5122 |
| 1.415 | 13.0 | 78 | 1.1689 | 0.4878 |
| 1.374 | 14.0 | 84 | 1.3767 | 0.2927 |
| 1.3675 | 15.0 | 90 | 1.1692 | 0.4146 |
| 1.3675 | 16.0 | 96 | 1.6528 | 0.2927 |
| 1.319 | 17.0 | 102 | 1.3151 | 0.3659 |
| 1.319 | 18.0 | 108 | 1.1475 | 0.4146 |
| 1.3335 | 19.0 | 114 | 1.1506 | 0.3415 |
| 1.2819 | 20.0 | 120 | 1.2300 | 0.3902 |
| 1.2819 | 21.0 | 126 | 1.1641 | 0.4146 |
| 1.2507 | 22.0 | 132 | 1.4148 | 0.3659 |
| 1.2507 | 23.0 | 138 | 1.3061 | 0.3415 |
| 1.2134 | 24.0 | 144 | 1.2367 | 0.3415 |
| 1.2611 | 25.0 | 150 | 1.2383 | 0.4878 |
| 1.2611 | 26.0 | 156 | 1.0375 | 0.4878 |
| 1.2053 | 27.0 | 162 | 1.1983 | 0.4878 |
| 1.2053 | 28.0 | 168 | 1.1898 | 0.4146 |
| 1.1593 | 29.0 | 174 | 1.1479 | 0.4878 |
| 1.2426 | 30.0 | 180 | 1.1382 | 0.5610 |
| 1.2426 | 31.0 | 186 | 1.0558 | 0.5610 |
| 1.1866 | 32.0 | 192 | 1.1895 | 0.4390 |
| 1.1866 | 33.0 | 198 | 1.2172 | 0.4146 |
| 1.1453 | 34.0 | 204 | 1.3773 | 0.4146 |
| 1.1026 | 35.0 | 210 | 1.1168 | 0.5122 |
| 1.1026 | 36.0 | 216 | 1.1184 | 0.5610 |
| 1.131 | 37.0 | 222 | 1.1344 | 0.5366 |
| 1.131 | 38.0 | 228 | 1.0932 | 0.5122 |
| 1.1098 | 39.0 | 234 | 1.1070 | 0.6098 |
| 1.0797 | 40.0 | 240 | 1.1237 | 0.5854 |
| 1.0797 | 41.0 | 246 | 1.1366 | 0.6098 |
| 1.0648 | 42.0 | 252 | 1.1358 | 0.6098 |
| 1.0648 | 43.0 | 258 | 1.1358 | 0.6098 |
| 1.0281 | 44.0 | 264 | 1.1358 | 0.6098 |
| 1.0542 | 45.0 | 270 | 1.1358 | 0.6098 |
| 1.0542 | 46.0 | 276 | 1.1358 | 0.6098 |
| 1.0409 | 47.0 | 282 | 1.1358 | 0.6098 |
| 1.0409 | 48.0 | 288 | 1.1358 | 0.6098 |
| 1.0504 | 49.0 | 294 | 1.1358 | 0.6098 |
| 1.0111 | 50.0 | 300 | 1.1358 | 0.6098 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_0001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4166
- Accuracy: 0.5556
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 2.1314 | 0.2444 |
| 2.0481 | 2.0 | 12 | 1.5573 | 0.2444 |
| 2.0481 | 3.0 | 18 | 1.4598 | 0.2444 |
| 1.5099 | 4.0 | 24 | 1.4194 | 0.2444 |
| 1.4253 | 5.0 | 30 | 1.3528 | 0.2667 |
| 1.4253 | 6.0 | 36 | 1.6348 | 0.2444 |
| 1.3319 | 7.0 | 42 | 1.3901 | 0.4444 |
| 1.3319 | 8.0 | 48 | 1.3151 | 0.2889 |
| 1.2142 | 9.0 | 54 | 1.3395 | 0.3333 |
| 1.1416 | 10.0 | 60 | 1.4176 | 0.3556 |
| 1.1416 | 11.0 | 66 | 1.9072 | 0.2667 |
| 0.9889 | 12.0 | 72 | 1.7446 | 0.3111 |
| 0.9889 | 13.0 | 78 | 1.4748 | 0.3778 |
| 0.8552 | 14.0 | 84 | 1.7450 | 0.3778 |
| 0.6798 | 15.0 | 90 | 1.6042 | 0.4889 |
| 0.6798 | 16.0 | 96 | 1.5863 | 0.4222 |
| 0.563 | 17.0 | 102 | 1.9311 | 0.4 |
| 0.563 | 18.0 | 108 | 1.9509 | 0.4444 |
| 0.3845 | 19.0 | 114 | 2.1256 | 0.4667 |
| 0.2041 | 20.0 | 120 | 2.4131 | 0.4889 |
| 0.2041 | 21.0 | 126 | 2.1029 | 0.4667 |
| 0.1874 | 22.0 | 132 | 2.0412 | 0.5778 |
| 0.1874 | 23.0 | 138 | 2.4952 | 0.4889 |
| 0.0735 | 24.0 | 144 | 2.8992 | 0.4667 |
| 0.0229 | 25.0 | 150 | 2.7495 | 0.5556 |
| 0.0229 | 26.0 | 156 | 3.2879 | 0.4667 |
| 0.0293 | 27.0 | 162 | 3.1526 | 0.5111 |
| 0.0293 | 28.0 | 168 | 3.0123 | 0.5333 |
| 0.0023 | 29.0 | 174 | 3.0812 | 0.5556 |
| 0.0008 | 30.0 | 180 | 3.1384 | 0.5556 |
| 0.0008 | 31.0 | 186 | 3.2017 | 0.5556 |
| 0.0005 | 32.0 | 192 | 3.2443 | 0.5556 |
| 0.0005 | 33.0 | 198 | 3.2806 | 0.5556 |
| 0.0005 | 34.0 | 204 | 3.3167 | 0.5556 |
| 0.0004 | 35.0 | 210 | 3.3393 | 0.5556 |
| 0.0004 | 36.0 | 216 | 3.3662 | 0.5556 |
| 0.0004 | 37.0 | 222 | 3.3843 | 0.5556 |
| 0.0004 | 38.0 | 228 | 3.3970 | 0.5556 |
| 0.0003 | 39.0 | 234 | 3.4053 | 0.5556 |
| 0.0003 | 40.0 | 240 | 3.4123 | 0.5556 |
| 0.0003 | 41.0 | 246 | 3.4159 | 0.5556 |
| 0.0003 | 42.0 | 252 | 3.4166 | 0.5556 |
| 0.0003 | 43.0 | 258 | 3.4166 | 0.5556 |
| 0.0003 | 44.0 | 264 | 3.4166 | 0.5556 |
| 0.0003 | 45.0 | 270 | 3.4166 | 0.5556 |
| 0.0003 | 46.0 | 276 | 3.4166 | 0.5556 |
| 0.0003 | 47.0 | 282 | 3.4166 | 0.5556 |
| 0.0003 | 48.0 | 288 | 3.4166 | 0.5556 |
| 0.0003 | 49.0 | 294 | 3.4166 | 0.5556 |
| 0.0003 | 50.0 | 300 | 3.4166 | 0.5556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_0001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1133
- Accuracy: 0.5556
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.9323 | 0.2444 |
| 2.0865 | 2.0 | 12 | 1.4427 | 0.2444 |
| 2.0865 | 3.0 | 18 | 1.4293 | 0.2444 |
| 1.4431 | 4.0 | 24 | 1.3952 | 0.4667 |
| 1.4003 | 5.0 | 30 | 1.2967 | 0.4 |
| 1.4003 | 6.0 | 36 | 1.4719 | 0.2444 |
| 1.3496 | 7.0 | 42 | 1.3224 | 0.3556 |
| 1.3496 | 8.0 | 48 | 1.4673 | 0.3778 |
| 1.2064 | 9.0 | 54 | 1.4551 | 0.2667 |
| 1.1859 | 10.0 | 60 | 1.3687 | 0.3111 |
| 1.1859 | 11.0 | 66 | 1.2313 | 0.4444 |
| 1.0817 | 12.0 | 72 | 1.1514 | 0.4444 |
| 1.0817 | 13.0 | 78 | 1.1701 | 0.4444 |
| 1.0144 | 14.0 | 84 | 1.2204 | 0.4222 |
| 0.8578 | 15.0 | 90 | 1.1603 | 0.4889 |
| 0.8578 | 16.0 | 96 | 1.0987 | 0.5111 |
| 0.8063 | 17.0 | 102 | 0.9277 | 0.5111 |
| 0.8063 | 18.0 | 108 | 1.2038 | 0.5333 |
| 0.601 | 19.0 | 114 | 0.9886 | 0.6 |
| 0.465 | 20.0 | 120 | 1.5667 | 0.5111 |
| 0.465 | 21.0 | 126 | 1.8238 | 0.4889 |
| 0.2956 | 22.0 | 132 | 1.6043 | 0.4222 |
| 0.2956 | 23.0 | 138 | 1.2746 | 0.4889 |
| 0.3513 | 24.0 | 144 | 1.6389 | 0.5556 |
| 0.2137 | 25.0 | 150 | 1.6350 | 0.4889 |
| 0.2137 | 26.0 | 156 | 1.5926 | 0.4667 |
| 0.191 | 27.0 | 162 | 1.8516 | 0.4889 |
| 0.191 | 28.0 | 168 | 2.3628 | 0.4889 |
| 0.0581 | 29.0 | 174 | 2.3998 | 0.4889 |
| 0.0517 | 30.0 | 180 | 2.3913 | 0.5333 |
| 0.0517 | 31.0 | 186 | 2.7108 | 0.5556 |
| 0.005 | 32.0 | 192 | 2.8104 | 0.5556 |
| 0.005 | 33.0 | 198 | 2.8829 | 0.5556 |
| 0.0008 | 34.0 | 204 | 2.9326 | 0.5333 |
| 0.0006 | 35.0 | 210 | 2.9793 | 0.5556 |
| 0.0006 | 36.0 | 216 | 3.0150 | 0.5556 |
| 0.0005 | 37.0 | 222 | 3.0520 | 0.5556 |
| 0.0005 | 38.0 | 228 | 3.0772 | 0.5556 |
| 0.0004 | 39.0 | 234 | 3.0948 | 0.5556 |
| 0.0004 | 40.0 | 240 | 3.1038 | 0.5556 |
| 0.0004 | 41.0 | 246 | 3.1116 | 0.5556 |
| 0.0004 | 42.0 | 252 | 3.1133 | 0.5556 |
| 0.0004 | 43.0 | 258 | 3.1133 | 0.5556 |
| 0.0004 | 44.0 | 264 | 3.1133 | 0.5556 |
| 0.0004 | 45.0 | 270 | 3.1133 | 0.5556 |
| 0.0004 | 46.0 | 276 | 3.1133 | 0.5556 |
| 0.0004 | 47.0 | 282 | 3.1133 | 0.5556 |
| 0.0004 | 48.0 | 288 | 3.1133 | 0.5556 |
| 0.0004 | 49.0 | 294 | 3.1133 | 0.5556 |
| 0.0004 | 50.0 | 300 | 3.1133 | 0.5556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_0001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2114
- Accuracy: 0.5814
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4590 | 0.2558 |
| 2.1915 | 2.0 | 12 | 1.4820 | 0.2558 |
| 2.1915 | 3.0 | 18 | 1.4635 | 0.3488 |
| 1.4733 | 4.0 | 24 | 1.6507 | 0.2558 |
| 1.4003 | 5.0 | 30 | 1.5038 | 0.2558 |
| 1.4003 | 6.0 | 36 | 1.5372 | 0.2093 |
| 1.28 | 7.0 | 42 | 1.4420 | 0.3023 |
| 1.28 | 8.0 | 48 | 1.3681 | 0.3488 |
| 1.2064 | 9.0 | 54 | 1.4133 | 0.3023 |
| 1.1588 | 10.0 | 60 | 1.2991 | 0.4419 |
| 1.1588 | 11.0 | 66 | 1.2547 | 0.4651 |
| 1.133 | 12.0 | 72 | 1.2924 | 0.4884 |
| 1.133 | 13.0 | 78 | 1.2566 | 0.4884 |
| 1.0357 | 14.0 | 84 | 1.1915 | 0.5349 |
| 0.8616 | 15.0 | 90 | 1.2058 | 0.5116 |
| 0.8616 | 16.0 | 96 | 1.1399 | 0.5349 |
| 0.6595 | 17.0 | 102 | 1.1462 | 0.5581 |
| 0.6595 | 18.0 | 108 | 1.2856 | 0.5116 |
| 0.501 | 19.0 | 114 | 1.1528 | 0.6047 |
| 0.3761 | 20.0 | 120 | 1.2487 | 0.6047 |
| 0.3761 | 21.0 | 126 | 1.9335 | 0.5581 |
| 0.1818 | 22.0 | 132 | 2.0855 | 0.5349 |
| 0.1818 | 23.0 | 138 | 2.8198 | 0.5349 |
| 0.0677 | 24.0 | 144 | 1.5837 | 0.6279 |
| 0.0703 | 25.0 | 150 | 2.1739 | 0.5116 |
| 0.0703 | 26.0 | 156 | 2.0640 | 0.5581 |
| 0.0053 | 27.0 | 162 | 2.0886 | 0.5814 |
| 0.0053 | 28.0 | 168 | 2.1352 | 0.5814 |
| 0.0006 | 29.0 | 174 | 2.1434 | 0.5814 |
| 0.0004 | 30.0 | 180 | 2.1524 | 0.5814 |
| 0.0004 | 31.0 | 186 | 2.1594 | 0.5814 |
| 0.0003 | 32.0 | 192 | 2.1659 | 0.5814 |
| 0.0003 | 33.0 | 198 | 2.1759 | 0.5814 |
| 0.0003 | 34.0 | 204 | 2.1825 | 0.5814 |
| 0.0003 | 35.0 | 210 | 2.1918 | 0.5814 |
| 0.0003 | 36.0 | 216 | 2.1964 | 0.5814 |
| 0.0002 | 37.0 | 222 | 2.2014 | 0.5814 |
| 0.0002 | 38.0 | 228 | 2.2049 | 0.5814 |
| 0.0002 | 39.0 | 234 | 2.2075 | 0.5814 |
| 0.0002 | 40.0 | 240 | 2.2099 | 0.5814 |
| 0.0002 | 41.0 | 246 | 2.2110 | 0.5814 |
| 0.0002 | 42.0 | 252 | 2.2114 | 0.5814 |
| 0.0002 | 43.0 | 258 | 2.2114 | 0.5814 |
| 0.0002 | 44.0 | 264 | 2.2114 | 0.5814 |
| 0.0002 | 45.0 | 270 | 2.2114 | 0.5814 |
| 0.0002 | 46.0 | 276 | 2.2114 | 0.5814 |
| 0.0002 | 47.0 | 282 | 2.2114 | 0.5814 |
| 0.0002 | 48.0 | 288 | 2.2114 | 0.5814 |
| 0.0002 | 49.0 | 294 | 2.2114 | 0.5814 |
| 0.0002 | 50.0 | 300 | 2.2114 | 0.5814 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_0001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3903
- Accuracy: 0.5714
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5699 | 0.2619 |
| 2.0801 | 2.0 | 12 | 1.5693 | 0.2381 |
| 2.0801 | 3.0 | 18 | 1.6087 | 0.2619 |
| 1.5352 | 4.0 | 24 | 1.4372 | 0.2619 |
| 1.4323 | 5.0 | 30 | 1.3212 | 0.3095 |
| 1.4323 | 6.0 | 36 | 1.3803 | 0.2381 |
| 1.3894 | 7.0 | 42 | 1.4606 | 0.4524 |
| 1.3894 | 8.0 | 48 | 1.5543 | 0.2619 |
| 1.294 | 9.0 | 54 | 1.1365 | 0.5 |
| 1.1627 | 10.0 | 60 | 1.3219 | 0.3571 |
| 1.1627 | 11.0 | 66 | 1.0508 | 0.5714 |
| 1.0159 | 12.0 | 72 | 1.0736 | 0.5 |
| 1.0159 | 13.0 | 78 | 1.6175 | 0.3571 |
| 0.8051 | 14.0 | 84 | 1.4409 | 0.4524 |
| 0.5869 | 15.0 | 90 | 2.1188 | 0.4286 |
| 0.5869 | 16.0 | 96 | 1.8546 | 0.5476 |
| 0.3044 | 17.0 | 102 | 1.7485 | 0.5 |
| 0.3044 | 18.0 | 108 | 1.6544 | 0.5476 |
| 0.2005 | 19.0 | 114 | 1.7817 | 0.5714 |
| 0.0634 | 20.0 | 120 | 2.6836 | 0.5238 |
| 0.0634 | 21.0 | 126 | 2.3476 | 0.5714 |
| 0.0488 | 22.0 | 132 | 2.3551 | 0.5476 |
| 0.0488 | 23.0 | 138 | 2.4123 | 0.5714 |
| 0.0014 | 24.0 | 144 | 2.3855 | 0.5714 |
| 0.0006 | 25.0 | 150 | 2.3709 | 0.5714 |
| 0.0006 | 26.0 | 156 | 2.3623 | 0.5714 |
| 0.0004 | 27.0 | 162 | 2.3621 | 0.5714 |
| 0.0004 | 28.0 | 168 | 2.3646 | 0.5952 |
| 0.0003 | 29.0 | 174 | 2.3639 | 0.5952 |
| 0.0003 | 30.0 | 180 | 2.3665 | 0.5952 |
| 0.0003 | 31.0 | 186 | 2.3692 | 0.5952 |
| 0.0002 | 32.0 | 192 | 2.3723 | 0.5952 |
| 0.0002 | 33.0 | 198 | 2.3750 | 0.5952 |
| 0.0002 | 34.0 | 204 | 2.3777 | 0.5714 |
| 0.0002 | 35.0 | 210 | 2.3806 | 0.5714 |
| 0.0002 | 36.0 | 216 | 2.3834 | 0.5714 |
| 0.0002 | 37.0 | 222 | 2.3855 | 0.5714 |
| 0.0002 | 38.0 | 228 | 2.3872 | 0.5714 |
| 0.0001 | 39.0 | 234 | 2.3885 | 0.5714 |
| 0.0001 | 40.0 | 240 | 2.3895 | 0.5714 |
| 0.0001 | 41.0 | 246 | 2.3902 | 0.5714 |
| 0.0001 | 42.0 | 252 | 2.3903 | 0.5714 |
| 0.0001 | 43.0 | 258 | 2.3903 | 0.5714 |
| 0.0001 | 44.0 | 264 | 2.3903 | 0.5714 |
| 0.0001 | 45.0 | 270 | 2.3903 | 0.5714 |
| 0.0001 | 46.0 | 276 | 2.3903 | 0.5714 |
| 0.0001 | 47.0 | 282 | 2.3903 | 0.5714 |
| 0.0001 | 48.0 | 288 | 2.3903 | 0.5714 |
| 0.0001 | 49.0 | 294 | 2.3903 | 0.5714 |
| 0.0001 | 50.0 | 300 | 2.3903 | 0.5714 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_0001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7903
- Accuracy: 0.6098
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6663 | 0.2683 |
| 2.1037 | 2.0 | 12 | 1.5022 | 0.2439 |
| 2.1037 | 3.0 | 18 | 1.3886 | 0.2439 |
| 1.5578 | 4.0 | 24 | 1.5306 | 0.2683 |
| 1.3692 | 5.0 | 30 | 1.1860 | 0.4390 |
| 1.3692 | 6.0 | 36 | 1.1738 | 0.4634 |
| 1.2281 | 7.0 | 42 | 1.1634 | 0.4146 |
| 1.2281 | 8.0 | 48 | 1.0062 | 0.4878 |
| 1.0442 | 9.0 | 54 | 1.0814 | 0.5366 |
| 0.7932 | 10.0 | 60 | 1.0549 | 0.5366 |
| 0.7932 | 11.0 | 66 | 1.1757 | 0.5610 |
| 0.3677 | 12.0 | 72 | 1.3513 | 0.6829 |
| 0.3677 | 13.0 | 78 | 1.1722 | 0.6098 |
| 0.2156 | 14.0 | 84 | 1.5096 | 0.5854 |
| 0.0882 | 15.0 | 90 | 1.2491 | 0.6341 |
| 0.0882 | 16.0 | 96 | 1.4974 | 0.6098 |
| 0.0242 | 17.0 | 102 | 1.6715 | 0.6341 |
| 0.0242 | 18.0 | 108 | 1.6860 | 0.5854 |
| 0.0023 | 19.0 | 114 | 1.6856 | 0.5854 |
| 0.0006 | 20.0 | 120 | 1.6918 | 0.5854 |
| 0.0006 | 21.0 | 126 | 1.7001 | 0.5854 |
| 0.0004 | 22.0 | 132 | 1.7120 | 0.5854 |
| 0.0004 | 23.0 | 138 | 1.7178 | 0.5854 |
| 0.0003 | 24.0 | 144 | 1.7236 | 0.6098 |
| 0.0003 | 25.0 | 150 | 1.7313 | 0.6098 |
| 0.0003 | 26.0 | 156 | 1.7370 | 0.6098 |
| 0.0002 | 27.0 | 162 | 1.7449 | 0.6098 |
| 0.0002 | 28.0 | 168 | 1.7492 | 0.6098 |
| 0.0002 | 29.0 | 174 | 1.7547 | 0.6098 |
| 0.0002 | 30.0 | 180 | 1.7601 | 0.6098 |
| 0.0002 | 31.0 | 186 | 1.7659 | 0.6098 |
| 0.0002 | 32.0 | 192 | 1.7694 | 0.6098 |
| 0.0002 | 33.0 | 198 | 1.7734 | 0.6098 |
| 0.0002 | 34.0 | 204 | 1.7771 | 0.6098 |
| 0.0002 | 35.0 | 210 | 1.7802 | 0.6098 |
| 0.0002 | 36.0 | 216 | 1.7829 | 0.6098 |
| 0.0002 | 37.0 | 222 | 1.7850 | 0.6098 |
| 0.0002 | 38.0 | 228 | 1.7868 | 0.6098 |
| 0.0002 | 39.0 | 234 | 1.7883 | 0.6098 |
| 0.0001 | 40.0 | 240 | 1.7895 | 0.6098 |
| 0.0001 | 41.0 | 246 | 1.7900 | 0.6098 |
| 0.0002 | 42.0 | 252 | 1.7903 | 0.6098 |
| 0.0002 | 43.0 | 258 | 1.7903 | 0.6098 |
| 0.0002 | 44.0 | 264 | 1.7903 | 0.6098 |
| 0.0002 | 45.0 | 270 | 1.7903 | 0.6098 |
| 0.0002 | 46.0 | 276 | 1.7903 | 0.6098 |
| 0.0002 | 47.0 | 282 | 1.7903 | 0.6098 |
| 0.0002 | 48.0 | 288 | 1.7903 | 0.6098 |
| 0.0002 | 49.0 | 294 | 1.7903 | 0.6098 |
| 0.0001 | 50.0 | 300 | 1.7903 | 0.6098 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_00001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2620
- Accuracy: 0.6222
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3330 | 0.3111 |
| 1.415 | 2.0 | 12 | 1.1496 | 0.4222 |
| 1.415 | 3.0 | 18 | 1.0095 | 0.6 |
| 0.6844 | 4.0 | 24 | 1.0528 | 0.5333 |
| 0.3289 | 5.0 | 30 | 0.8970 | 0.6 |
| 0.3289 | 6.0 | 36 | 1.2025 | 0.5111 |
| 0.1275 | 7.0 | 42 | 0.9016 | 0.6 |
| 0.1275 | 8.0 | 48 | 1.0450 | 0.5778 |
| 0.049 | 9.0 | 54 | 1.1767 | 0.5556 |
| 0.0201 | 10.0 | 60 | 1.2285 | 0.5333 |
| 0.0201 | 11.0 | 66 | 1.0471 | 0.6 |
| 0.0071 | 12.0 | 72 | 0.9300 | 0.6444 |
| 0.0071 | 13.0 | 78 | 1.1280 | 0.5778 |
| 0.0042 | 14.0 | 84 | 1.1318 | 0.5556 |
| 0.0029 | 15.0 | 90 | 1.1503 | 0.5556 |
| 0.0029 | 16.0 | 96 | 1.0998 | 0.5778 |
| 0.0023 | 17.0 | 102 | 1.1889 | 0.5778 |
| 0.0023 | 18.0 | 108 | 1.2431 | 0.5778 |
| 0.0018 | 19.0 | 114 | 1.2158 | 0.5778 |
| 0.0016 | 20.0 | 120 | 1.2220 | 0.6 |
| 0.0016 | 21.0 | 126 | 1.1974 | 0.6 |
| 0.0014 | 22.0 | 132 | 1.2207 | 0.6 |
| 0.0014 | 23.0 | 138 | 1.2242 | 0.6 |
| 0.0013 | 24.0 | 144 | 1.2118 | 0.6 |
| 0.0011 | 25.0 | 150 | 1.2264 | 0.6222 |
| 0.0011 | 26.0 | 156 | 1.2250 | 0.6 |
| 0.0011 | 27.0 | 162 | 1.2237 | 0.6 |
| 0.0011 | 28.0 | 168 | 1.2290 | 0.6 |
| 0.001 | 29.0 | 174 | 1.2254 | 0.6222 |
| 0.0009 | 30.0 | 180 | 1.2294 | 0.6222 |
| 0.0009 | 31.0 | 186 | 1.2336 | 0.6222 |
| 0.0009 | 32.0 | 192 | 1.2394 | 0.6222 |
| 0.0009 | 33.0 | 198 | 1.2441 | 0.6222 |
| 0.0008 | 34.0 | 204 | 1.2483 | 0.6 |
| 0.0008 | 35.0 | 210 | 1.2484 | 0.6 |
| 0.0008 | 36.0 | 216 | 1.2564 | 0.6 |
| 0.0008 | 37.0 | 222 | 1.2583 | 0.6222 |
| 0.0008 | 38.0 | 228 | 1.2617 | 0.6222 |
| 0.0007 | 39.0 | 234 | 1.2626 | 0.6222 |
| 0.0007 | 40.0 | 240 | 1.2627 | 0.6222 |
| 0.0007 | 41.0 | 246 | 1.2621 | 0.6222 |
| 0.0007 | 42.0 | 252 | 1.2620 | 0.6222 |
| 0.0007 | 43.0 | 258 | 1.2620 | 0.6222 |
| 0.0007 | 44.0 | 264 | 1.2620 | 0.6222 |
| 0.0007 | 45.0 | 270 | 1.2620 | 0.6222 |
| 0.0007 | 46.0 | 276 | 1.2620 | 0.6222 |
| 0.0007 | 47.0 | 282 | 1.2620 | 0.6222 |
| 0.0007 | 48.0 | 288 | 1.2620 | 0.6222 |
| 0.0007 | 49.0 | 294 | 1.2620 | 0.6222 |
| 0.0007 | 50.0 | 300 | 1.2620 | 0.6222 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_00001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4676
- Accuracy: 0.6222
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3067 | 0.4222 |
| 1.3733 | 2.0 | 12 | 1.3951 | 0.4444 |
| 1.3733 | 3.0 | 18 | 1.3740 | 0.4222 |
| 0.6558 | 4.0 | 24 | 1.2467 | 0.5333 |
| 0.3343 | 5.0 | 30 | 1.5107 | 0.4667 |
| 0.3343 | 6.0 | 36 | 1.6079 | 0.4444 |
| 0.1446 | 7.0 | 42 | 1.2227 | 0.5333 |
| 0.1446 | 8.0 | 48 | 1.2018 | 0.5333 |
| 0.0575 | 9.0 | 54 | 1.2408 | 0.5111 |
| 0.0237 | 10.0 | 60 | 1.2581 | 0.5111 |
| 0.0237 | 11.0 | 66 | 1.4007 | 0.6 |
| 0.0072 | 12.0 | 72 | 1.2676 | 0.6444 |
| 0.0072 | 13.0 | 78 | 1.2933 | 0.5778 |
| 0.0036 | 14.0 | 84 | 1.3326 | 0.6222 |
| 0.0025 | 15.0 | 90 | 1.3074 | 0.6444 |
| 0.0025 | 16.0 | 96 | 1.3484 | 0.6222 |
| 0.002 | 17.0 | 102 | 1.3984 | 0.6222 |
| 0.002 | 18.0 | 108 | 1.3916 | 0.6222 |
| 0.0017 | 19.0 | 114 | 1.3871 | 0.6222 |
| 0.0014 | 20.0 | 120 | 1.4171 | 0.6222 |
| 0.0014 | 21.0 | 126 | 1.4207 | 0.6222 |
| 0.0012 | 22.0 | 132 | 1.4218 | 0.6222 |
| 0.0012 | 23.0 | 138 | 1.4371 | 0.6222 |
| 0.0011 | 24.0 | 144 | 1.4404 | 0.6222 |
| 0.001 | 25.0 | 150 | 1.4321 | 0.6222 |
| 0.001 | 26.0 | 156 | 1.4218 | 0.6222 |
| 0.0009 | 27.0 | 162 | 1.4367 | 0.6222 |
| 0.0009 | 28.0 | 168 | 1.4359 | 0.6222 |
| 0.0008 | 29.0 | 174 | 1.4387 | 0.6222 |
| 0.0008 | 30.0 | 180 | 1.4566 | 0.6222 |
| 0.0008 | 31.0 | 186 | 1.4528 | 0.6222 |
| 0.0007 | 32.0 | 192 | 1.4517 | 0.6222 |
| 0.0007 | 33.0 | 198 | 1.4535 | 0.6222 |
| 0.0007 | 34.0 | 204 | 1.4488 | 0.6444 |
| 0.0007 | 35.0 | 210 | 1.4494 | 0.6444 |
| 0.0007 | 36.0 | 216 | 1.4561 | 0.6444 |
| 0.0007 | 37.0 | 222 | 1.4595 | 0.6444 |
| 0.0007 | 38.0 | 228 | 1.4667 | 0.6222 |
| 0.0006 | 39.0 | 234 | 1.4671 | 0.6222 |
| 0.0007 | 40.0 | 240 | 1.4686 | 0.6222 |
| 0.0007 | 41.0 | 246 | 1.4681 | 0.6222 |
| 0.0006 | 42.0 | 252 | 1.4676 | 0.6222 |
| 0.0006 | 43.0 | 258 | 1.4676 | 0.6222 |
| 0.0006 | 44.0 | 264 | 1.4676 | 0.6222 |
| 0.0006 | 45.0 | 270 | 1.4676 | 0.6222 |
| 0.0006 | 46.0 | 276 | 1.4676 | 0.6222 |
| 0.0006 | 47.0 | 282 | 1.4676 | 0.6222 |
| 0.0006 | 48.0 | 288 | 1.4676 | 0.6222 |
| 0.0006 | 49.0 | 294 | 1.4676 | 0.6222 |
| 0.0006 | 50.0 | 300 | 1.4676 | 0.6222 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_00001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6755
- Accuracy: 0.7674
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.2450 | 0.3953 |
| 1.3266 | 2.0 | 12 | 1.0282 | 0.4884 |
| 1.3266 | 3.0 | 18 | 0.8766 | 0.6512 |
| 0.6113 | 4.0 | 24 | 0.8143 | 0.6279 |
| 0.301 | 5.0 | 30 | 0.9703 | 0.6047 |
| 0.301 | 6.0 | 36 | 0.7894 | 0.7209 |
| 0.1194 | 7.0 | 42 | 0.8712 | 0.6512 |
| 0.1194 | 8.0 | 48 | 0.7416 | 0.6744 |
| 0.0478 | 9.0 | 54 | 0.7289 | 0.6744 |
| 0.0192 | 10.0 | 60 | 0.6181 | 0.7209 |
| 0.0192 | 11.0 | 66 | 0.7194 | 0.6977 |
| 0.007 | 12.0 | 72 | 0.6519 | 0.6744 |
| 0.007 | 13.0 | 78 | 0.6428 | 0.7209 |
| 0.0038 | 14.0 | 84 | 0.6323 | 0.6977 |
| 0.0027 | 15.0 | 90 | 0.6303 | 0.7209 |
| 0.0027 | 16.0 | 96 | 0.6496 | 0.7209 |
| 0.0021 | 17.0 | 102 | 0.6367 | 0.7209 |
| 0.0021 | 18.0 | 108 | 0.6386 | 0.7209 |
| 0.0018 | 19.0 | 114 | 0.6562 | 0.7442 |
| 0.0015 | 20.0 | 120 | 0.6541 | 0.7442 |
| 0.0015 | 21.0 | 126 | 0.6493 | 0.7442 |
| 0.0014 | 22.0 | 132 | 0.6669 | 0.7442 |
| 0.0014 | 23.0 | 138 | 0.6543 | 0.7674 |
| 0.0012 | 24.0 | 144 | 0.6581 | 0.7442 |
| 0.0011 | 25.0 | 150 | 0.6534 | 0.7442 |
| 0.0011 | 26.0 | 156 | 0.6644 | 0.7442 |
| 0.001 | 27.0 | 162 | 0.6622 | 0.7674 |
| 0.001 | 28.0 | 168 | 0.6583 | 0.7442 |
| 0.001 | 29.0 | 174 | 0.6594 | 0.7674 |
| 0.0009 | 30.0 | 180 | 0.6672 | 0.7674 |
| 0.0009 | 31.0 | 186 | 0.6681 | 0.7674 |
| 0.0008 | 32.0 | 192 | 0.6656 | 0.7674 |
| 0.0008 | 33.0 | 198 | 0.6699 | 0.7674 |
| 0.0008 | 34.0 | 204 | 0.6718 | 0.7674 |
| 0.0008 | 35.0 | 210 | 0.6718 | 0.7674 |
| 0.0008 | 36.0 | 216 | 0.6735 | 0.7674 |
| 0.0008 | 37.0 | 222 | 0.6740 | 0.7674 |
| 0.0008 | 38.0 | 228 | 0.6754 | 0.7674 |
| 0.0007 | 39.0 | 234 | 0.6750 | 0.7674 |
| 0.0007 | 40.0 | 240 | 0.6751 | 0.7674 |
| 0.0007 | 41.0 | 246 | 0.6753 | 0.7674 |
| 0.0007 | 42.0 | 252 | 0.6755 | 0.7674 |
| 0.0007 | 43.0 | 258 | 0.6755 | 0.7674 |
| 0.0007 | 44.0 | 264 | 0.6755 | 0.7674 |
| 0.0007 | 45.0 | 270 | 0.6755 | 0.7674 |
| 0.0007 | 46.0 | 276 | 0.6755 | 0.7674 |
| 0.0007 | 47.0 | 282 | 0.6755 | 0.7674 |
| 0.0007 | 48.0 | 288 | 0.6755 | 0.7674 |
| 0.0007 | 49.0 | 294 | 0.6755 | 0.7674 |
| 0.0007 | 50.0 | 300 | 0.6755 | 0.7674 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_00001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4549
- Accuracy: 0.8571
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4075 | 0.2857 |
| 1.4145 | 2.0 | 12 | 1.3443 | 0.3571 |
| 1.4145 | 3.0 | 18 | 0.8612 | 0.6667 |
| 0.7818 | 4.0 | 24 | 0.9127 | 0.6190 |
| 0.3833 | 5.0 | 30 | 0.5998 | 0.8810 |
| 0.3833 | 6.0 | 36 | 0.5796 | 0.7857 |
| 0.1457 | 7.0 | 42 | 0.5756 | 0.8333 |
| 0.1457 | 8.0 | 48 | 0.5188 | 0.7857 |
| 0.0559 | 9.0 | 54 | 0.5146 | 0.8571 |
| 0.0198 | 10.0 | 60 | 0.5290 | 0.7857 |
| 0.0198 | 11.0 | 66 | 0.4513 | 0.8571 |
| 0.007 | 12.0 | 72 | 0.4696 | 0.8571 |
| 0.007 | 13.0 | 78 | 0.4668 | 0.8333 |
| 0.0039 | 14.0 | 84 | 0.4642 | 0.8333 |
| 0.0028 | 15.0 | 90 | 0.4519 | 0.8571 |
| 0.0028 | 16.0 | 96 | 0.4562 | 0.8333 |
| 0.0022 | 17.0 | 102 | 0.4543 | 0.8571 |
| 0.0022 | 18.0 | 108 | 0.4588 | 0.8571 |
| 0.0018 | 19.0 | 114 | 0.4546 | 0.8571 |
| 0.0016 | 20.0 | 120 | 0.4551 | 0.8333 |
| 0.0016 | 21.0 | 126 | 0.4570 | 0.8333 |
| 0.0013 | 22.0 | 132 | 0.4556 | 0.8333 |
| 0.0013 | 23.0 | 138 | 0.4547 | 0.8333 |
| 0.0012 | 24.0 | 144 | 0.4556 | 0.8571 |
| 0.0011 | 25.0 | 150 | 0.4547 | 0.8571 |
| 0.0011 | 26.0 | 156 | 0.4538 | 0.8571 |
| 0.001 | 27.0 | 162 | 0.4593 | 0.8333 |
| 0.001 | 28.0 | 168 | 0.4560 | 0.8333 |
| 0.0009 | 29.0 | 174 | 0.4555 | 0.8333 |
| 0.0009 | 30.0 | 180 | 0.4554 | 0.8333 |
| 0.0009 | 31.0 | 186 | 0.4563 | 0.8333 |
| 0.0008 | 32.0 | 192 | 0.4547 | 0.8571 |
| 0.0008 | 33.0 | 198 | 0.4545 | 0.8571 |
| 0.0008 | 34.0 | 204 | 0.4547 | 0.8571 |
| 0.0007 | 35.0 | 210 | 0.4541 | 0.8571 |
| 0.0007 | 36.0 | 216 | 0.4545 | 0.8571 |
| 0.0007 | 37.0 | 222 | 0.4550 | 0.8571 |
| 0.0007 | 38.0 | 228 | 0.4547 | 0.8571 |
| 0.0007 | 39.0 | 234 | 0.4549 | 0.8571 |
| 0.0007 | 40.0 | 240 | 0.4549 | 0.8571 |
| 0.0007 | 41.0 | 246 | 0.4549 | 0.8571 |
| 0.0007 | 42.0 | 252 | 0.4549 | 0.8571 |
| 0.0007 | 43.0 | 258 | 0.4549 | 0.8571 |
| 0.0007 | 44.0 | 264 | 0.4549 | 0.8571 |
| 0.0007 | 45.0 | 270 | 0.4549 | 0.8571 |
| 0.0007 | 46.0 | 276 | 0.4549 | 0.8571 |
| 0.0007 | 47.0 | 282 | 0.4549 | 0.8571 |
| 0.0007 | 48.0 | 288 | 0.4549 | 0.8571 |
| 0.0007 | 49.0 | 294 | 0.4549 | 0.8571 |
| 0.0007 | 50.0 | 300 | 0.4549 | 0.8571 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_tiny_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_1x_deit_tiny_rms_00001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1280
- Accuracy: 0.6585
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.2888 | 0.4390 |
| 1.3565 | 2.0 | 12 | 1.0130 | 0.5366 |
| 1.3565 | 3.0 | 18 | 0.9361 | 0.5366 |
| 0.667 | 4.0 | 24 | 0.8831 | 0.6585 |
| 0.2929 | 5.0 | 30 | 0.8739 | 0.5854 |
| 0.2929 | 6.0 | 36 | 0.9329 | 0.5854 |
| 0.1055 | 7.0 | 42 | 0.9159 | 0.6585 |
| 0.1055 | 8.0 | 48 | 1.0700 | 0.5854 |
| 0.04 | 9.0 | 54 | 1.0357 | 0.5854 |
| 0.013 | 10.0 | 60 | 0.9379 | 0.6585 |
| 0.013 | 11.0 | 66 | 0.9964 | 0.6341 |
| 0.0046 | 12.0 | 72 | 1.0009 | 0.6585 |
| 0.0046 | 13.0 | 78 | 0.9889 | 0.6585 |
| 0.0029 | 14.0 | 84 | 1.0074 | 0.6585 |
| 0.0023 | 15.0 | 90 | 1.0258 | 0.6585 |
| 0.0023 | 16.0 | 96 | 1.0330 | 0.6585 |
| 0.0018 | 17.0 | 102 | 1.0391 | 0.6585 |
| 0.0018 | 18.0 | 108 | 1.0476 | 0.6585 |
| 0.0015 | 19.0 | 114 | 1.0552 | 0.6585 |
| 0.0013 | 20.0 | 120 | 1.0615 | 0.6585 |
| 0.0013 | 21.0 | 126 | 1.0642 | 0.6585 |
| 0.0011 | 22.0 | 132 | 1.0600 | 0.6585 |
| 0.0011 | 23.0 | 138 | 1.0791 | 0.6341 |
| 0.001 | 24.0 | 144 | 1.0890 | 0.6585 |
| 0.001 | 25.0 | 150 | 1.0948 | 0.6585 |
| 0.001 | 26.0 | 156 | 1.1067 | 0.6585 |
| 0.0008 | 27.0 | 162 | 1.0949 | 0.6585 |
| 0.0008 | 28.0 | 168 | 1.1017 | 0.6585 |
| 0.0008 | 29.0 | 174 | 1.1094 | 0.6585 |
| 0.0007 | 30.0 | 180 | 1.1105 | 0.6585 |
| 0.0007 | 31.0 | 186 | 1.1156 | 0.6585 |
| 0.0007 | 32.0 | 192 | 1.1158 | 0.6585 |
| 0.0007 | 33.0 | 198 | 1.1174 | 0.6585 |
| 0.0007 | 34.0 | 204 | 1.1167 | 0.6585 |
| 0.0006 | 35.0 | 210 | 1.1206 | 0.6585 |
| 0.0006 | 36.0 | 216 | 1.1224 | 0.6585 |
| 0.0006 | 37.0 | 222 | 1.1230 | 0.6585 |
| 0.0006 | 38.0 | 228 | 1.1253 | 0.6585 |
| 0.0006 | 39.0 | 234 | 1.1272 | 0.6585 |
| 0.0006 | 40.0 | 240 | 1.1276 | 0.6585 |
| 0.0006 | 41.0 | 246 | 1.1278 | 0.6585 |
| 0.0006 | 42.0 | 252 | 1.1280 | 0.6585 |
| 0.0006 | 43.0 | 258 | 1.1280 | 0.6585 |
| 0.0006 | 44.0 | 264 | 1.1280 | 0.6585 |
| 0.0006 | 45.0 | 270 | 1.1280 | 0.6585 |
| 0.0006 | 46.0 | 276 | 1.1280 | 0.6585 |
| 0.0006 | 47.0 | 282 | 1.1280 | 0.6585 |
| 0.0006 | 48.0 | 288 | 1.1280 | 0.6585 |
| 0.0006 | 49.0 | 294 | 1.1280 | 0.6585 |
| 0.0006 | 50.0 | 300 | 1.1280 | 0.6585 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_001_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: 2.0215
- Accuracy: 0.4667
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 2.2870 | 0.2444 |
| 2.1668 | 2.0 | 12 | 1.4669 | 0.2444 |
| 2.1668 | 3.0 | 18 | 1.4980 | 0.2444 |
| 1.4102 | 4.0 | 24 | 1.4751 | 0.2444 |
| 1.4394 | 5.0 | 30 | 1.4286 | 0.2444 |
| 1.4394 | 6.0 | 36 | 1.6019 | 0.2444 |
| 1.3171 | 7.0 | 42 | 1.7291 | 0.2222 |
| 1.3171 | 8.0 | 48 | 1.5314 | 0.3556 |
| 1.2906 | 9.0 | 54 | 1.7281 | 0.2667 |
| 1.2151 | 10.0 | 60 | 1.6012 | 0.2444 |
| 1.2151 | 11.0 | 66 | 1.5621 | 0.4444 |
| 1.1016 | 12.0 | 72 | 1.5069 | 0.2 |
| 1.1016 | 13.0 | 78 | 1.5452 | 0.4222 |
| 1.1085 | 14.0 | 84 | 1.5457 | 0.2889 |
| 0.9838 | 15.0 | 90 | 1.7131 | 0.4 |
| 0.9838 | 16.0 | 96 | 1.9947 | 0.2889 |
| 1.003 | 17.0 | 102 | 1.7538 | 0.4222 |
| 1.003 | 18.0 | 108 | 1.3632 | 0.4444 |
| 0.846 | 19.0 | 114 | 1.7633 | 0.4 |
| 0.7432 | 20.0 | 120 | 1.5259 | 0.4222 |
| 0.7432 | 21.0 | 126 | 1.6982 | 0.4 |
| 0.8111 | 22.0 | 132 | 1.4722 | 0.4 |
| 0.8111 | 23.0 | 138 | 1.5772 | 0.4222 |
| 0.6268 | 24.0 | 144 | 1.6621 | 0.4222 |
| 0.5956 | 25.0 | 150 | 2.2283 | 0.4 |
| 0.5956 | 26.0 | 156 | 1.5965 | 0.4667 |
| 0.863 | 27.0 | 162 | 2.0067 | 0.4 |
| 0.863 | 28.0 | 168 | 2.2609 | 0.3778 |
| 0.575 | 29.0 | 174 | 1.7339 | 0.4222 |
| 0.3505 | 30.0 | 180 | 1.6059 | 0.3778 |
| 0.3505 | 31.0 | 186 | 1.7578 | 0.4444 |
| 0.3884 | 32.0 | 192 | 1.8785 | 0.4444 |
| 0.3884 | 33.0 | 198 | 1.5952 | 0.4222 |
| 0.3742 | 34.0 | 204 | 1.9834 | 0.4444 |
| 0.3113 | 35.0 | 210 | 1.8134 | 0.4222 |
| 0.3113 | 36.0 | 216 | 2.1491 | 0.4 |
| 0.4478 | 37.0 | 222 | 1.9419 | 0.4667 |
| 0.4478 | 38.0 | 228 | 1.8426 | 0.4444 |
| 0.1746 | 39.0 | 234 | 1.9349 | 0.4222 |
| 0.1737 | 40.0 | 240 | 2.0085 | 0.4667 |
| 0.1737 | 41.0 | 246 | 2.0238 | 0.4667 |
| 0.1448 | 42.0 | 252 | 2.0215 | 0.4667 |
| 0.1448 | 43.0 | 258 | 2.0215 | 0.4667 |
| 0.1495 | 44.0 | 264 | 2.0215 | 0.4667 |
| 0.1326 | 45.0 | 270 | 2.0215 | 0.4667 |
| 0.1326 | 46.0 | 276 | 2.0215 | 0.4667 |
| 0.1487 | 47.0 | 282 | 2.0215 | 0.4667 |
| 0.1487 | 48.0 | 288 | 2.0215 | 0.4667 |
| 0.1112 | 49.0 | 294 | 2.0215 | 0.4667 |
| 0.1501 | 50.0 | 300 | 2.0215 | 0.4667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_001_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: 3.0653
- Accuracy: 0.5778
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5866 | 0.2444 |
| 1.9023 | 2.0 | 12 | 1.3764 | 0.2444 |
| 1.9023 | 3.0 | 18 | 1.3051 | 0.4222 |
| 1.349 | 4.0 | 24 | 1.1457 | 0.4889 |
| 1.2765 | 5.0 | 30 | 1.1296 | 0.5333 |
| 1.2765 | 6.0 | 36 | 1.0799 | 0.4667 |
| 0.9532 | 7.0 | 42 | 0.9251 | 0.5778 |
| 0.9532 | 8.0 | 48 | 0.9697 | 0.6 |
| 0.606 | 9.0 | 54 | 1.3926 | 0.4889 |
| 0.572 | 10.0 | 60 | 1.7732 | 0.5778 |
| 0.572 | 11.0 | 66 | 1.3882 | 0.5556 |
| 0.5961 | 12.0 | 72 | 1.7835 | 0.5333 |
| 0.5961 | 13.0 | 78 | 1.6876 | 0.5111 |
| 0.36 | 14.0 | 84 | 2.6292 | 0.5556 |
| 0.1021 | 15.0 | 90 | 3.3955 | 0.4444 |
| 0.1021 | 16.0 | 96 | 2.7199 | 0.5333 |
| 0.0705 | 17.0 | 102 | 3.2188 | 0.5778 |
| 0.0705 | 18.0 | 108 | 2.9572 | 0.5778 |
| 0.1408 | 19.0 | 114 | 3.4311 | 0.6222 |
| 0.0481 | 20.0 | 120 | 3.3680 | 0.5111 |
| 0.0481 | 21.0 | 126 | 3.9440 | 0.4889 |
| 0.0285 | 22.0 | 132 | 3.0805 | 0.5111 |
| 0.0285 | 23.0 | 138 | 3.2788 | 0.4889 |
| 0.0077 | 24.0 | 144 | 3.3798 | 0.5111 |
| 0.0144 | 25.0 | 150 | 3.3118 | 0.5333 |
| 0.0144 | 26.0 | 156 | 3.1251 | 0.5111 |
| 0.0005 | 27.0 | 162 | 2.9134 | 0.5778 |
| 0.0005 | 28.0 | 168 | 2.8352 | 0.6 |
| 0.0006 | 29.0 | 174 | 2.7529 | 0.5778 |
| 0.0002 | 30.0 | 180 | 2.8235 | 0.6 |
| 0.0002 | 31.0 | 186 | 2.8802 | 0.6 |
| 0.0001 | 32.0 | 192 | 2.9253 | 0.5778 |
| 0.0001 | 33.0 | 198 | 2.9651 | 0.5778 |
| 0.0001 | 34.0 | 204 | 2.9943 | 0.5778 |
| 0.0001 | 35.0 | 210 | 3.0146 | 0.5778 |
| 0.0001 | 36.0 | 216 | 3.0314 | 0.5778 |
| 0.0001 | 37.0 | 222 | 3.0446 | 0.5778 |
| 0.0001 | 38.0 | 228 | 3.0538 | 0.5778 |
| 0.0001 | 39.0 | 234 | 3.0596 | 0.5778 |
| 0.0001 | 40.0 | 240 | 3.0631 | 0.5778 |
| 0.0001 | 41.0 | 246 | 3.0649 | 0.5778 |
| 0.0001 | 42.0 | 252 | 3.0653 | 0.5778 |
| 0.0001 | 43.0 | 258 | 3.0653 | 0.5778 |
| 0.0001 | 44.0 | 264 | 3.0653 | 0.5778 |
| 0.0001 | 45.0 | 270 | 3.0653 | 0.5778 |
| 0.0001 | 46.0 | 276 | 3.0653 | 0.5778 |
| 0.0001 | 47.0 | 282 | 3.0653 | 0.5778 |
| 0.0001 | 48.0 | 288 | 3.0653 | 0.5778 |
| 0.0001 | 49.0 | 294 | 3.0653 | 0.5778 |
| 0.0001 | 50.0 | 300 | 3.0653 | 0.5778 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_001_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: 3.7699
- Accuracy: 0.4651
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4218 | 0.2558 |
| 1.7221 | 2.0 | 12 | 1.4061 | 0.3953 |
| 1.7221 | 3.0 | 18 | 1.4801 | 0.3256 |
| 1.2972 | 4.0 | 24 | 1.5453 | 0.3023 |
| 1.2115 | 5.0 | 30 | 1.2993 | 0.3953 |
| 1.2115 | 6.0 | 36 | 1.4486 | 0.3721 |
| 1.1196 | 7.0 | 42 | 1.4881 | 0.3721 |
| 1.1196 | 8.0 | 48 | 1.2031 | 0.4419 |
| 1.0394 | 9.0 | 54 | 1.1825 | 0.4651 |
| 0.9076 | 10.0 | 60 | 1.3831 | 0.3953 |
| 0.9076 | 11.0 | 66 | 1.5606 | 0.3953 |
| 0.8351 | 12.0 | 72 | 1.6879 | 0.3721 |
| 0.8351 | 13.0 | 78 | 1.5744 | 0.5581 |
| 0.7325 | 14.0 | 84 | 2.1220 | 0.5116 |
| 0.5767 | 15.0 | 90 | 2.2458 | 0.4884 |
| 0.5767 | 16.0 | 96 | 2.4745 | 0.3953 |
| 0.487 | 17.0 | 102 | 2.9255 | 0.3953 |
| 0.487 | 18.0 | 108 | 2.8169 | 0.4186 |
| 0.265 | 19.0 | 114 | 2.9600 | 0.4419 |
| 0.2739 | 20.0 | 120 | 3.0131 | 0.3953 |
| 0.2739 | 21.0 | 126 | 3.2413 | 0.4186 |
| 0.1684 | 22.0 | 132 | 4.9920 | 0.3953 |
| 0.1684 | 23.0 | 138 | 3.1514 | 0.5116 |
| 0.3265 | 24.0 | 144 | 4.1598 | 0.3953 |
| 0.2652 | 25.0 | 150 | 3.3248 | 0.4651 |
| 0.2652 | 26.0 | 156 | 3.1898 | 0.4884 |
| 0.1992 | 27.0 | 162 | 3.7937 | 0.3953 |
| 0.1992 | 28.0 | 168 | 3.9838 | 0.4884 |
| 0.1826 | 29.0 | 174 | 3.5764 | 0.3721 |
| 0.124 | 30.0 | 180 | 4.1231 | 0.4419 |
| 0.124 | 31.0 | 186 | 4.1455 | 0.4186 |
| 0.1353 | 32.0 | 192 | 3.9925 | 0.4186 |
| 0.1353 | 33.0 | 198 | 3.7016 | 0.5581 |
| 0.0743 | 34.0 | 204 | 3.7997 | 0.5349 |
| 0.0362 | 35.0 | 210 | 3.6073 | 0.4884 |
| 0.0362 | 36.0 | 216 | 3.6198 | 0.4651 |
| 0.0082 | 37.0 | 222 | 3.6509 | 0.4651 |
| 0.0082 | 38.0 | 228 | 3.7081 | 0.4651 |
| 0.003 | 39.0 | 234 | 3.7432 | 0.4651 |
| 0.002 | 40.0 | 240 | 3.7616 | 0.4651 |
| 0.002 | 41.0 | 246 | 3.7690 | 0.4651 |
| 0.0018 | 42.0 | 252 | 3.7699 | 0.4651 |
| 0.0018 | 43.0 | 258 | 3.7699 | 0.4651 |
| 0.0016 | 44.0 | 264 | 3.7699 | 0.4651 |
| 0.0017 | 45.0 | 270 | 3.7699 | 0.4651 |
| 0.0017 | 46.0 | 276 | 3.7699 | 0.4651 |
| 0.0017 | 47.0 | 282 | 3.7699 | 0.4651 |
| 0.0017 | 48.0 | 288 | 3.7699 | 0.4651 |
| 0.0018 | 49.0 | 294 | 3.7699 | 0.4651 |
| 0.0017 | 50.0 | 300 | 3.7699 | 0.4651 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_001_fold4
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: 3.1234
- Accuracy: 0.5952
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5633 | 0.2381 |
| 1.7898 | 2.0 | 12 | 1.3816 | 0.2381 |
| 1.7898 | 3.0 | 18 | 1.3607 | 0.2619 |
| 1.4334 | 4.0 | 24 | 1.3501 | 0.2619 |
| 1.3732 | 5.0 | 30 | 1.3553 | 0.2381 |
| 1.3732 | 6.0 | 36 | 1.1841 | 0.4762 |
| 1.3036 | 7.0 | 42 | 1.0576 | 0.5952 |
| 1.3036 | 8.0 | 48 | 1.0689 | 0.5952 |
| 1.2142 | 9.0 | 54 | 1.2296 | 0.5 |
| 1.056 | 10.0 | 60 | 0.7879 | 0.6429 |
| 1.056 | 11.0 | 66 | 0.7199 | 0.7143 |
| 0.921 | 12.0 | 72 | 0.9775 | 0.6190 |
| 0.921 | 13.0 | 78 | 0.8809 | 0.5952 |
| 0.6456 | 14.0 | 84 | 1.0792 | 0.5476 |
| 0.6348 | 15.0 | 90 | 1.0335 | 0.6190 |
| 0.6348 | 16.0 | 96 | 1.7853 | 0.5714 |
| 0.4743 | 17.0 | 102 | 1.5872 | 0.5714 |
| 0.4743 | 18.0 | 108 | 2.0651 | 0.5 |
| 0.2408 | 19.0 | 114 | 2.8369 | 0.4762 |
| 0.2271 | 20.0 | 120 | 2.1149 | 0.6190 |
| 0.2271 | 21.0 | 126 | 1.5722 | 0.6190 |
| 0.3385 | 22.0 | 132 | 2.8555 | 0.5476 |
| 0.3385 | 23.0 | 138 | 2.2068 | 0.6667 |
| 0.0822 | 24.0 | 144 | 2.2969 | 0.6190 |
| 0.0932 | 25.0 | 150 | 1.8785 | 0.7143 |
| 0.0932 | 26.0 | 156 | 3.2275 | 0.5714 |
| 0.0807 | 27.0 | 162 | 2.8847 | 0.5952 |
| 0.0807 | 28.0 | 168 | 3.1184 | 0.5952 |
| 0.0424 | 29.0 | 174 | 2.4583 | 0.6190 |
| 0.0287 | 30.0 | 180 | 2.8305 | 0.5714 |
| 0.0287 | 31.0 | 186 | 3.5171 | 0.5476 |
| 0.0333 | 32.0 | 192 | 3.2119 | 0.5952 |
| 0.0333 | 33.0 | 198 | 2.9811 | 0.5952 |
| 0.0008 | 34.0 | 204 | 3.0451 | 0.5952 |
| 0.0004 | 35.0 | 210 | 3.0670 | 0.5952 |
| 0.0004 | 36.0 | 216 | 3.0857 | 0.5952 |
| 0.0003 | 37.0 | 222 | 3.1009 | 0.5952 |
| 0.0003 | 38.0 | 228 | 3.1113 | 0.5952 |
| 0.0003 | 39.0 | 234 | 3.1177 | 0.5952 |
| 0.0003 | 40.0 | 240 | 3.1213 | 0.5952 |
| 0.0003 | 41.0 | 246 | 3.1231 | 0.5952 |
| 0.0002 | 42.0 | 252 | 3.1234 | 0.5952 |
| 0.0002 | 43.0 | 258 | 3.1234 | 0.5952 |
| 0.0002 | 44.0 | 264 | 3.1234 | 0.5952 |
| 0.0002 | 45.0 | 270 | 3.1234 | 0.5952 |
| 0.0002 | 46.0 | 276 | 3.1234 | 0.5952 |
| 0.0002 | 47.0 | 282 | 3.1234 | 0.5952 |
| 0.0002 | 48.0 | 288 | 3.1234 | 0.5952 |
| 0.0002 | 49.0 | 294 | 3.1234 | 0.5952 |
| 0.0002 | 50.0 | 300 | 3.1234 | 0.5952 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_001_fold5
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: 2.9089
- Accuracy: 0.5854
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5709 | 0.2683 |
| 1.6365 | 2.0 | 12 | 1.3231 | 0.3171 |
| 1.6365 | 3.0 | 18 | 1.0858 | 0.4878 |
| 1.2777 | 4.0 | 24 | 1.0527 | 0.4634 |
| 1.0819 | 5.0 | 30 | 2.4025 | 0.4878 |
| 1.0819 | 6.0 | 36 | 1.0776 | 0.6098 |
| 1.1957 | 7.0 | 42 | 1.2491 | 0.4878 |
| 1.1957 | 8.0 | 48 | 1.2390 | 0.4878 |
| 1.0582 | 9.0 | 54 | 2.4696 | 0.3659 |
| 0.9645 | 10.0 | 60 | 0.9800 | 0.6585 |
| 0.9645 | 11.0 | 66 | 1.4465 | 0.4878 |
| 0.7158 | 12.0 | 72 | 1.3709 | 0.4146 |
| 0.7158 | 13.0 | 78 | 1.8787 | 0.5610 |
| 0.4707 | 14.0 | 84 | 2.2003 | 0.4878 |
| 0.3746 | 15.0 | 90 | 2.7652 | 0.4390 |
| 0.3746 | 16.0 | 96 | 1.4738 | 0.6098 |
| 0.3815 | 17.0 | 102 | 2.1297 | 0.4878 |
| 0.3815 | 18.0 | 108 | 2.7358 | 0.4634 |
| 0.2562 | 19.0 | 114 | 2.1602 | 0.6341 |
| 0.215 | 20.0 | 120 | 2.4495 | 0.5122 |
| 0.215 | 21.0 | 126 | 2.2161 | 0.5366 |
| 0.0855 | 22.0 | 132 | 2.6756 | 0.5610 |
| 0.0855 | 23.0 | 138 | 3.4355 | 0.5366 |
| 0.0976 | 24.0 | 144 | 2.8453 | 0.6098 |
| 0.0588 | 25.0 | 150 | 2.9043 | 0.5854 |
| 0.0588 | 26.0 | 156 | 3.0589 | 0.4878 |
| 0.0051 | 27.0 | 162 | 2.7256 | 0.6098 |
| 0.0051 | 28.0 | 168 | 2.6655 | 0.6098 |
| 0.0018 | 29.0 | 174 | 2.6795 | 0.6098 |
| 0.005 | 30.0 | 180 | 2.7568 | 0.6098 |
| 0.005 | 31.0 | 186 | 2.8042 | 0.6098 |
| 0.0004 | 32.0 | 192 | 2.8224 | 0.6098 |
| 0.0004 | 33.0 | 198 | 2.8428 | 0.6098 |
| 0.0002 | 34.0 | 204 | 2.8628 | 0.5854 |
| 0.0002 | 35.0 | 210 | 2.8783 | 0.5854 |
| 0.0002 | 36.0 | 216 | 2.8881 | 0.5854 |
| 0.0002 | 37.0 | 222 | 2.8950 | 0.5854 |
| 0.0002 | 38.0 | 228 | 2.9002 | 0.5854 |
| 0.0002 | 39.0 | 234 | 2.9045 | 0.5854 |
| 0.0002 | 40.0 | 240 | 2.9071 | 0.5854 |
| 0.0002 | 41.0 | 246 | 2.9084 | 0.5854 |
| 0.0001 | 42.0 | 252 | 2.9089 | 0.5854 |
| 0.0001 | 43.0 | 258 | 2.9089 | 0.5854 |
| 0.0002 | 44.0 | 264 | 2.9089 | 0.5854 |
| 0.0001 | 45.0 | 270 | 2.9089 | 0.5854 |
| 0.0001 | 46.0 | 276 | 2.9089 | 0.5854 |
| 0.0002 | 47.0 | 282 | 2.9089 | 0.5854 |
| 0.0002 | 48.0 | 288 | 2.9089 | 0.5854 |
| 0.0001 | 49.0 | 294 | 2.9089 | 0.5854 |
| 0.0001 | 50.0 | 300 | 2.9089 | 0.5854 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_0001_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: 1.3048
- Accuracy: 0.5778
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3083 | 0.3111 |
| 1.2395 | 2.0 | 12 | 1.1116 | 0.5556 |
| 1.2395 | 3.0 | 18 | 0.9370 | 0.6444 |
| 0.5222 | 4.0 | 24 | 0.8939 | 0.6222 |
| 0.1257 | 5.0 | 30 | 1.0613 | 0.6222 |
| 0.1257 | 6.0 | 36 | 1.1544 | 0.6667 |
| 0.0192 | 7.0 | 42 | 1.0970 | 0.6222 |
| 0.0192 | 8.0 | 48 | 1.3834 | 0.5778 |
| 0.0034 | 9.0 | 54 | 1.4273 | 0.6222 |
| 0.0011 | 10.0 | 60 | 1.2955 | 0.6222 |
| 0.0011 | 11.0 | 66 | 1.1578 | 0.6222 |
| 0.0006 | 12.0 | 72 | 1.1209 | 0.6 |
| 0.0006 | 13.0 | 78 | 1.1439 | 0.6 |
| 0.0005 | 14.0 | 84 | 1.1840 | 0.6 |
| 0.0004 | 15.0 | 90 | 1.2222 | 0.5778 |
| 0.0004 | 16.0 | 96 | 1.2485 | 0.5778 |
| 0.0003 | 17.0 | 102 | 1.2638 | 0.5778 |
| 0.0003 | 18.0 | 108 | 1.2689 | 0.5778 |
| 0.0003 | 19.0 | 114 | 1.2732 | 0.5778 |
| 0.0003 | 20.0 | 120 | 1.2771 | 0.5778 |
| 0.0003 | 21.0 | 126 | 1.2803 | 0.5778 |
| 0.0003 | 22.0 | 132 | 1.2805 | 0.5778 |
| 0.0003 | 23.0 | 138 | 1.2805 | 0.5778 |
| 0.0002 | 24.0 | 144 | 1.2807 | 0.5778 |
| 0.0002 | 25.0 | 150 | 1.2825 | 0.5778 |
| 0.0002 | 26.0 | 156 | 1.2850 | 0.5778 |
| 0.0002 | 27.0 | 162 | 1.2856 | 0.5778 |
| 0.0002 | 28.0 | 168 | 1.2878 | 0.5778 |
| 0.0002 | 29.0 | 174 | 1.2904 | 0.5778 |
| 0.0002 | 30.0 | 180 | 1.2922 | 0.5778 |
| 0.0002 | 31.0 | 186 | 1.2931 | 0.5778 |
| 0.0002 | 32.0 | 192 | 1.2945 | 0.5778 |
| 0.0002 | 33.0 | 198 | 1.2963 | 0.5778 |
| 0.0002 | 34.0 | 204 | 1.2983 | 0.5778 |
| 0.0002 | 35.0 | 210 | 1.2995 | 0.5778 |
| 0.0002 | 36.0 | 216 | 1.3007 | 0.5778 |
| 0.0002 | 37.0 | 222 | 1.3018 | 0.5778 |
| 0.0002 | 38.0 | 228 | 1.3034 | 0.5778 |
| 0.0002 | 39.0 | 234 | 1.3042 | 0.5778 |
| 0.0002 | 40.0 | 240 | 1.3046 | 0.5778 |
| 0.0002 | 41.0 | 246 | 1.3047 | 0.5778 |
| 0.0002 | 42.0 | 252 | 1.3048 | 0.5778 |
| 0.0002 | 43.0 | 258 | 1.3048 | 0.5778 |
| 0.0002 | 44.0 | 264 | 1.3048 | 0.5778 |
| 0.0002 | 45.0 | 270 | 1.3048 | 0.5778 |
| 0.0002 | 46.0 | 276 | 1.3048 | 0.5778 |
| 0.0002 | 47.0 | 282 | 1.3048 | 0.5778 |
| 0.0002 | 48.0 | 288 | 1.3048 | 0.5778 |
| 0.0002 | 49.0 | 294 | 1.3048 | 0.5778 |
| 0.0002 | 50.0 | 300 | 1.3048 | 0.5778 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_0001_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: 2.0748
- Accuracy: 0.6
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.2729 | 0.4 |
| 1.2121 | 2.0 | 12 | 1.1250 | 0.6222 |
| 1.2121 | 3.0 | 18 | 1.2362 | 0.5556 |
| 0.4291 | 4.0 | 24 | 1.2042 | 0.6444 |
| 0.1116 | 5.0 | 30 | 1.1861 | 0.6 |
| 0.1116 | 6.0 | 36 | 1.6632 | 0.5556 |
| 0.0196 | 7.0 | 42 | 1.7499 | 0.6 |
| 0.0196 | 8.0 | 48 | 1.7915 | 0.5556 |
| 0.0051 | 9.0 | 54 | 1.8168 | 0.5778 |
| 0.0016 | 10.0 | 60 | 1.8187 | 0.6222 |
| 0.0016 | 11.0 | 66 | 1.8480 | 0.6222 |
| 0.0008 | 12.0 | 72 | 1.8621 | 0.6222 |
| 0.0008 | 13.0 | 78 | 1.8730 | 0.6222 |
| 0.0006 | 14.0 | 84 | 1.8908 | 0.6222 |
| 0.0005 | 15.0 | 90 | 1.9136 | 0.6222 |
| 0.0005 | 16.0 | 96 | 1.9335 | 0.6222 |
| 0.0004 | 17.0 | 102 | 1.9501 | 0.6222 |
| 0.0004 | 18.0 | 108 | 1.9655 | 0.6222 |
| 0.0004 | 19.0 | 114 | 1.9783 | 0.6222 |
| 0.0003 | 20.0 | 120 | 1.9900 | 0.6222 |
| 0.0003 | 21.0 | 126 | 1.9990 | 0.6222 |
| 0.0003 | 22.0 | 132 | 2.0067 | 0.6222 |
| 0.0003 | 23.0 | 138 | 2.0139 | 0.6 |
| 0.0003 | 24.0 | 144 | 2.0208 | 0.6 |
| 0.0003 | 25.0 | 150 | 2.0271 | 0.6 |
| 0.0003 | 26.0 | 156 | 2.0322 | 0.6 |
| 0.0003 | 27.0 | 162 | 2.0367 | 0.6 |
| 0.0003 | 28.0 | 168 | 2.0419 | 0.6 |
| 0.0003 | 29.0 | 174 | 2.0471 | 0.6 |
| 0.0003 | 30.0 | 180 | 2.0520 | 0.6 |
| 0.0003 | 31.0 | 186 | 2.0560 | 0.6 |
| 0.0002 | 32.0 | 192 | 2.0593 | 0.6 |
| 0.0002 | 33.0 | 198 | 2.0621 | 0.6 |
| 0.0003 | 34.0 | 204 | 2.0649 | 0.6 |
| 0.0003 | 35.0 | 210 | 2.0672 | 0.6 |
| 0.0003 | 36.0 | 216 | 2.0692 | 0.6 |
| 0.0002 | 37.0 | 222 | 2.0710 | 0.6 |
| 0.0002 | 38.0 | 228 | 2.0723 | 0.6 |
| 0.0002 | 39.0 | 234 | 2.0735 | 0.6 |
| 0.0002 | 40.0 | 240 | 2.0742 | 0.6 |
| 0.0002 | 41.0 | 246 | 2.0747 | 0.6 |
| 0.0002 | 42.0 | 252 | 2.0748 | 0.6 |
| 0.0002 | 43.0 | 258 | 2.0748 | 0.6 |
| 0.0002 | 44.0 | 264 | 2.0748 | 0.6 |
| 0.0002 | 45.0 | 270 | 2.0748 | 0.6 |
| 0.0002 | 46.0 | 276 | 2.0748 | 0.6 |
| 0.0002 | 47.0 | 282 | 2.0748 | 0.6 |
| 0.0002 | 48.0 | 288 | 2.0748 | 0.6 |
| 0.0002 | 49.0 | 294 | 2.0748 | 0.6 |
| 0.0002 | 50.0 | 300 | 2.0748 | 0.6 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_0001_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: 0.4128
- Accuracy: 0.8837
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.1575 | 0.6047 |
| 1.0751 | 2.0 | 12 | 0.7147 | 0.8140 |
| 1.0751 | 3.0 | 18 | 0.4940 | 0.8372 |
| 0.3179 | 4.0 | 24 | 0.5279 | 0.7674 |
| 0.0721 | 5.0 | 30 | 0.4829 | 0.8140 |
| 0.0721 | 6.0 | 36 | 0.3704 | 0.8837 |
| 0.0092 | 7.0 | 42 | 0.4306 | 0.8605 |
| 0.0092 | 8.0 | 48 | 0.4406 | 0.8605 |
| 0.0018 | 9.0 | 54 | 0.4181 | 0.8605 |
| 0.0009 | 10.0 | 60 | 0.4015 | 0.8837 |
| 0.0009 | 11.0 | 66 | 0.3932 | 0.8837 |
| 0.0006 | 12.0 | 72 | 0.3944 | 0.8837 |
| 0.0006 | 13.0 | 78 | 0.3986 | 0.8837 |
| 0.0005 | 14.0 | 84 | 0.4037 | 0.8837 |
| 0.0004 | 15.0 | 90 | 0.4072 | 0.8837 |
| 0.0004 | 16.0 | 96 | 0.4099 | 0.8837 |
| 0.0004 | 17.0 | 102 | 0.4111 | 0.8837 |
| 0.0004 | 18.0 | 108 | 0.4129 | 0.8837 |
| 0.0003 | 19.0 | 114 | 0.4147 | 0.8837 |
| 0.0003 | 20.0 | 120 | 0.4143 | 0.8837 |
| 0.0003 | 21.0 | 126 | 0.4146 | 0.8837 |
| 0.0003 | 22.0 | 132 | 0.4136 | 0.8837 |
| 0.0003 | 23.0 | 138 | 0.4136 | 0.8837 |
| 0.0003 | 24.0 | 144 | 0.4120 | 0.8837 |
| 0.0003 | 25.0 | 150 | 0.4117 | 0.8837 |
| 0.0003 | 26.0 | 156 | 0.4120 | 0.8837 |
| 0.0003 | 27.0 | 162 | 0.4120 | 0.8837 |
| 0.0003 | 28.0 | 168 | 0.4117 | 0.8837 |
| 0.0003 | 29.0 | 174 | 0.4121 | 0.8837 |
| 0.0003 | 30.0 | 180 | 0.4118 | 0.8837 |
| 0.0003 | 31.0 | 186 | 0.4116 | 0.8837 |
| 0.0002 | 32.0 | 192 | 0.4115 | 0.8837 |
| 0.0002 | 33.0 | 198 | 0.4116 | 0.8837 |
| 0.0002 | 34.0 | 204 | 0.4120 | 0.8837 |
| 0.0002 | 35.0 | 210 | 0.4121 | 0.8837 |
| 0.0002 | 36.0 | 216 | 0.4123 | 0.8837 |
| 0.0002 | 37.0 | 222 | 0.4125 | 0.8837 |
| 0.0002 | 38.0 | 228 | 0.4126 | 0.8837 |
| 0.0002 | 39.0 | 234 | 0.4127 | 0.8837 |
| 0.0002 | 40.0 | 240 | 0.4128 | 0.8837 |
| 0.0002 | 41.0 | 246 | 0.4128 | 0.8837 |
| 0.0002 | 42.0 | 252 | 0.4128 | 0.8837 |
| 0.0002 | 43.0 | 258 | 0.4128 | 0.8837 |
| 0.0002 | 44.0 | 264 | 0.4128 | 0.8837 |
| 0.0002 | 45.0 | 270 | 0.4128 | 0.8837 |
| 0.0002 | 46.0 | 276 | 0.4128 | 0.8837 |
| 0.0002 | 47.0 | 282 | 0.4128 | 0.8837 |
| 0.0002 | 48.0 | 288 | 0.4128 | 0.8837 |
| 0.0002 | 49.0 | 294 | 0.4128 | 0.8837 |
| 0.0002 | 50.0 | 300 | 0.4128 | 0.8837 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_0001_fold4
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: 0.3532
- Accuracy: 0.8571
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3457 | 0.4048 |
| 1.2924 | 2.0 | 12 | 0.9334 | 0.5952 |
| 1.2924 | 3.0 | 18 | 0.6841 | 0.6667 |
| 0.6444 | 4.0 | 24 | 0.7302 | 0.6429 |
| 0.1694 | 5.0 | 30 | 0.5848 | 0.7619 |
| 0.1694 | 6.0 | 36 | 0.6575 | 0.7619 |
| 0.0382 | 7.0 | 42 | 0.4727 | 0.8333 |
| 0.0382 | 8.0 | 48 | 0.7729 | 0.7381 |
| 0.0125 | 9.0 | 54 | 0.6089 | 0.7619 |
| 0.0034 | 10.0 | 60 | 0.3189 | 0.9048 |
| 0.0034 | 11.0 | 66 | 0.2852 | 0.8810 |
| 0.0011 | 12.0 | 72 | 0.3340 | 0.8571 |
| 0.0011 | 13.0 | 78 | 0.3522 | 0.8571 |
| 0.0007 | 14.0 | 84 | 0.3495 | 0.8571 |
| 0.0005 | 15.0 | 90 | 0.3442 | 0.8571 |
| 0.0005 | 16.0 | 96 | 0.3406 | 0.8571 |
| 0.0004 | 17.0 | 102 | 0.3391 | 0.8571 |
| 0.0004 | 18.0 | 108 | 0.3391 | 0.8571 |
| 0.0004 | 19.0 | 114 | 0.3401 | 0.8571 |
| 0.0004 | 20.0 | 120 | 0.3412 | 0.8571 |
| 0.0004 | 21.0 | 126 | 0.3433 | 0.8571 |
| 0.0003 | 22.0 | 132 | 0.3444 | 0.8571 |
| 0.0003 | 23.0 | 138 | 0.3456 | 0.8571 |
| 0.0003 | 24.0 | 144 | 0.3474 | 0.8571 |
| 0.0003 | 25.0 | 150 | 0.3486 | 0.8571 |
| 0.0003 | 26.0 | 156 | 0.3489 | 0.8571 |
| 0.0003 | 27.0 | 162 | 0.3489 | 0.8571 |
| 0.0003 | 28.0 | 168 | 0.3500 | 0.8571 |
| 0.0003 | 29.0 | 174 | 0.3510 | 0.8571 |
| 0.0003 | 30.0 | 180 | 0.3511 | 0.8571 |
| 0.0003 | 31.0 | 186 | 0.3517 | 0.8571 |
| 0.0003 | 32.0 | 192 | 0.3522 | 0.8571 |
| 0.0003 | 33.0 | 198 | 0.3523 | 0.8571 |
| 0.0003 | 34.0 | 204 | 0.3526 | 0.8571 |
| 0.0003 | 35.0 | 210 | 0.3526 | 0.8571 |
| 0.0003 | 36.0 | 216 | 0.3527 | 0.8571 |
| 0.0003 | 37.0 | 222 | 0.3530 | 0.8571 |
| 0.0003 | 38.0 | 228 | 0.3531 | 0.8571 |
| 0.0002 | 39.0 | 234 | 0.3531 | 0.8571 |
| 0.0003 | 40.0 | 240 | 0.3531 | 0.8571 |
| 0.0003 | 41.0 | 246 | 0.3531 | 0.8571 |
| 0.0003 | 42.0 | 252 | 0.3532 | 0.8571 |
| 0.0003 | 43.0 | 258 | 0.3532 | 0.8571 |
| 0.0002 | 44.0 | 264 | 0.3532 | 0.8571 |
| 0.0003 | 45.0 | 270 | 0.3532 | 0.8571 |
| 0.0003 | 46.0 | 276 | 0.3532 | 0.8571 |
| 0.0003 | 47.0 | 282 | 0.3532 | 0.8571 |
| 0.0003 | 48.0 | 288 | 0.3532 | 0.8571 |
| 0.0002 | 49.0 | 294 | 0.3532 | 0.8571 |
| 0.0003 | 50.0 | 300 | 0.3532 | 0.8571 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_0001_fold5
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: 0.8185
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.1559 | 0.4878 |
| 1.2441 | 2.0 | 12 | 0.8253 | 0.6829 |
| 1.2441 | 3.0 | 18 | 0.7434 | 0.6098 |
| 0.6071 | 4.0 | 24 | 0.5080 | 0.8293 |
| 0.2296 | 5.0 | 30 | 0.6693 | 0.6829 |
| 0.2296 | 6.0 | 36 | 0.4300 | 0.8293 |
| 0.0509 | 7.0 | 42 | 0.7493 | 0.7317 |
| 0.0509 | 8.0 | 48 | 0.5064 | 0.8537 |
| 0.0088 | 9.0 | 54 | 0.6021 | 0.8780 |
| 0.0021 | 10.0 | 60 | 0.7408 | 0.7805 |
| 0.0021 | 11.0 | 66 | 0.9234 | 0.7073 |
| 0.0009 | 12.0 | 72 | 0.9965 | 0.6829 |
| 0.0009 | 13.0 | 78 | 0.9607 | 0.7317 |
| 0.0006 | 14.0 | 84 | 0.8998 | 0.7561 |
| 0.0004 | 15.0 | 90 | 0.8548 | 0.7561 |
| 0.0004 | 16.0 | 96 | 0.8258 | 0.7561 |
| 0.0004 | 17.0 | 102 | 0.8107 | 0.7805 |
| 0.0004 | 18.0 | 108 | 0.7999 | 0.8049 |
| 0.0003 | 19.0 | 114 | 0.7972 | 0.8049 |
| 0.0003 | 20.0 | 120 | 0.7983 | 0.8049 |
| 0.0003 | 21.0 | 126 | 0.8011 | 0.8049 |
| 0.0003 | 22.0 | 132 | 0.8040 | 0.8049 |
| 0.0003 | 23.0 | 138 | 0.8052 | 0.8049 |
| 0.0003 | 24.0 | 144 | 0.8067 | 0.8049 |
| 0.0003 | 25.0 | 150 | 0.8086 | 0.8049 |
| 0.0003 | 26.0 | 156 | 0.8104 | 0.8049 |
| 0.0003 | 27.0 | 162 | 0.8133 | 0.8049 |
| 0.0003 | 28.0 | 168 | 0.8150 | 0.8049 |
| 0.0003 | 29.0 | 174 | 0.8155 | 0.8049 |
| 0.0002 | 30.0 | 180 | 0.8162 | 0.8049 |
| 0.0002 | 31.0 | 186 | 0.8167 | 0.8049 |
| 0.0002 | 32.0 | 192 | 0.8175 | 0.8049 |
| 0.0002 | 33.0 | 198 | 0.8178 | 0.8049 |
| 0.0002 | 34.0 | 204 | 0.8183 | 0.8049 |
| 0.0002 | 35.0 | 210 | 0.8179 | 0.8049 |
| 0.0002 | 36.0 | 216 | 0.8182 | 0.8049 |
| 0.0002 | 37.0 | 222 | 0.8182 | 0.8049 |
| 0.0002 | 38.0 | 228 | 0.8181 | 0.8049 |
| 0.0002 | 39.0 | 234 | 0.8183 | 0.8049 |
| 0.0002 | 40.0 | 240 | 0.8184 | 0.8049 |
| 0.0002 | 41.0 | 246 | 0.8184 | 0.8049 |
| 0.0002 | 42.0 | 252 | 0.8185 | 0.8049 |
| 0.0002 | 43.0 | 258 | 0.8185 | 0.8049 |
| 0.0002 | 44.0 | 264 | 0.8185 | 0.8049 |
| 0.0002 | 45.0 | 270 | 0.8185 | 0.8049 |
| 0.0002 | 46.0 | 276 | 0.8185 | 0.8049 |
| 0.0002 | 47.0 | 282 | 0.8185 | 0.8049 |
| 0.0002 | 48.0 | 288 | 0.8185 | 0.8049 |
| 0.0002 | 49.0 | 294 | 0.8185 | 0.8049 |
| 0.0002 | 50.0 | 300 | 0.8185 | 0.8049 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_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: 1.1270
- Accuracy: 0.6
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3199 | 0.3333 |
| 1.3414 | 2.0 | 12 | 1.2923 | 0.4667 |
| 1.3414 | 3.0 | 18 | 1.2886 | 0.4667 |
| 1.0791 | 4.0 | 24 | 1.2761 | 0.4667 |
| 0.9244 | 5.0 | 30 | 1.2453 | 0.4889 |
| 0.9244 | 6.0 | 36 | 1.2252 | 0.4667 |
| 0.7694 | 7.0 | 42 | 1.2158 | 0.5111 |
| 0.7694 | 8.0 | 48 | 1.2163 | 0.4667 |
| 0.6552 | 9.0 | 54 | 1.2081 | 0.5111 |
| 0.5314 | 10.0 | 60 | 1.1883 | 0.5556 |
| 0.5314 | 11.0 | 66 | 1.1802 | 0.5556 |
| 0.4407 | 12.0 | 72 | 1.1737 | 0.5778 |
| 0.4407 | 13.0 | 78 | 1.1623 | 0.6222 |
| 0.3864 | 14.0 | 84 | 1.1625 | 0.6222 |
| 0.3093 | 15.0 | 90 | 1.1653 | 0.6222 |
| 0.3093 | 16.0 | 96 | 1.1658 | 0.6222 |
| 0.2597 | 17.0 | 102 | 1.1519 | 0.6444 |
| 0.2597 | 18.0 | 108 | 1.1466 | 0.6222 |
| 0.2099 | 19.0 | 114 | 1.1591 | 0.6 |
| 0.1766 | 20.0 | 120 | 1.1509 | 0.5778 |
| 0.1766 | 21.0 | 126 | 1.1488 | 0.5778 |
| 0.1537 | 22.0 | 132 | 1.1482 | 0.5778 |
| 0.1537 | 23.0 | 138 | 1.1427 | 0.6222 |
| 0.1244 | 24.0 | 144 | 1.1370 | 0.6 |
| 0.103 | 25.0 | 150 | 1.1285 | 0.6 |
| 0.103 | 26.0 | 156 | 1.1323 | 0.6 |
| 0.089 | 27.0 | 162 | 1.1268 | 0.6 |
| 0.089 | 28.0 | 168 | 1.1377 | 0.6 |
| 0.0777 | 29.0 | 174 | 1.1346 | 0.6 |
| 0.068 | 30.0 | 180 | 1.1274 | 0.6 |
| 0.068 | 31.0 | 186 | 1.1199 | 0.6 |
| 0.0597 | 32.0 | 192 | 1.1245 | 0.6 |
| 0.0597 | 33.0 | 198 | 1.1296 | 0.6 |
| 0.0547 | 34.0 | 204 | 1.1270 | 0.6 |
| 0.0493 | 35.0 | 210 | 1.1241 | 0.6 |
| 0.0493 | 36.0 | 216 | 1.1250 | 0.6 |
| 0.0441 | 37.0 | 222 | 1.1253 | 0.6 |
| 0.0441 | 38.0 | 228 | 1.1296 | 0.6 |
| 0.0409 | 39.0 | 234 | 1.1287 | 0.6 |
| 0.0405 | 40.0 | 240 | 1.1275 | 0.6 |
| 0.0405 | 41.0 | 246 | 1.1272 | 0.6 |
| 0.0391 | 42.0 | 252 | 1.1270 | 0.6 |
| 0.0391 | 43.0 | 258 | 1.1270 | 0.6 |
| 0.0395 | 44.0 | 264 | 1.1270 | 0.6 |
| 0.0377 | 45.0 | 270 | 1.1270 | 0.6 |
| 0.0377 | 46.0 | 276 | 1.1270 | 0.6 |
| 0.0388 | 47.0 | 282 | 1.1270 | 0.6 |
| 0.0388 | 48.0 | 288 | 1.1270 | 0.6 |
| 0.0366 | 49.0 | 294 | 1.1270 | 0.6 |
| 0.0396 | 50.0 | 300 | 1.1270 | 0.6 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_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: 1.3101
- Accuracy: 0.6222
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3638 | 0.3556 |
| 1.331 | 2.0 | 12 | 1.3133 | 0.4222 |
| 1.331 | 3.0 | 18 | 1.2851 | 0.4222 |
| 1.0997 | 4.0 | 24 | 1.2715 | 0.4 |
| 0.9418 | 5.0 | 30 | 1.2498 | 0.4444 |
| 0.9418 | 6.0 | 36 | 1.2371 | 0.5111 |
| 0.7701 | 7.0 | 42 | 1.2279 | 0.5111 |
| 0.7701 | 8.0 | 48 | 1.2223 | 0.5556 |
| 0.6624 | 9.0 | 54 | 1.2136 | 0.5333 |
| 0.5468 | 10.0 | 60 | 1.2047 | 0.5111 |
| 0.5468 | 11.0 | 66 | 1.2129 | 0.5333 |
| 0.4638 | 12.0 | 72 | 1.2131 | 0.5556 |
| 0.4638 | 13.0 | 78 | 1.2055 | 0.5778 |
| 0.375 | 14.0 | 84 | 1.2059 | 0.5778 |
| 0.3096 | 15.0 | 90 | 1.2025 | 0.5778 |
| 0.3096 | 16.0 | 96 | 1.2062 | 0.5778 |
| 0.2535 | 17.0 | 102 | 1.2103 | 0.6 |
| 0.2535 | 18.0 | 108 | 1.2313 | 0.5778 |
| 0.2168 | 19.0 | 114 | 1.2293 | 0.5778 |
| 0.1735 | 20.0 | 120 | 1.2169 | 0.6222 |
| 0.1735 | 21.0 | 126 | 1.2306 | 0.6222 |
| 0.1492 | 22.0 | 132 | 1.2370 | 0.6222 |
| 0.1492 | 23.0 | 138 | 1.2467 | 0.6222 |
| 0.1264 | 24.0 | 144 | 1.2411 | 0.6222 |
| 0.1012 | 25.0 | 150 | 1.2438 | 0.6222 |
| 0.1012 | 26.0 | 156 | 1.2523 | 0.6222 |
| 0.0887 | 27.0 | 162 | 1.2537 | 0.6 |
| 0.0887 | 28.0 | 168 | 1.2661 | 0.6222 |
| 0.0734 | 29.0 | 174 | 1.2715 | 0.6222 |
| 0.0647 | 30.0 | 180 | 1.2745 | 0.6 |
| 0.0647 | 31.0 | 186 | 1.2817 | 0.6222 |
| 0.0577 | 32.0 | 192 | 1.2861 | 0.6222 |
| 0.0577 | 33.0 | 198 | 1.2908 | 0.6222 |
| 0.0525 | 34.0 | 204 | 1.2935 | 0.6222 |
| 0.048 | 35.0 | 210 | 1.2969 | 0.6222 |
| 0.048 | 36.0 | 216 | 1.2990 | 0.6 |
| 0.0443 | 37.0 | 222 | 1.3015 | 0.6 |
| 0.0443 | 38.0 | 228 | 1.3052 | 0.6222 |
| 0.0404 | 39.0 | 234 | 1.3082 | 0.6222 |
| 0.0394 | 40.0 | 240 | 1.3089 | 0.6222 |
| 0.0394 | 41.0 | 246 | 1.3101 | 0.6222 |
| 0.0387 | 42.0 | 252 | 1.3101 | 0.6222 |
| 0.0387 | 43.0 | 258 | 1.3101 | 0.6222 |
| 0.0369 | 44.0 | 264 | 1.3101 | 0.6222 |
| 0.0381 | 45.0 | 270 | 1.3101 | 0.6222 |
| 0.0381 | 46.0 | 276 | 1.3101 | 0.6222 |
| 0.0382 | 47.0 | 282 | 1.3101 | 0.6222 |
| 0.0382 | 48.0 | 288 | 1.3101 | 0.6222 |
| 0.037 | 49.0 | 294 | 1.3101 | 0.6222 |
| 0.0386 | 50.0 | 300 | 1.3101 | 0.6222 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_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: 0.6553
- Accuracy: 0.6512
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3641 | 0.3953 |
| 1.3358 | 2.0 | 12 | 1.2934 | 0.4186 |
| 1.3358 | 3.0 | 18 | 1.2307 | 0.4419 |
| 1.1053 | 4.0 | 24 | 1.1728 | 0.5814 |
| 0.9503 | 5.0 | 30 | 1.1200 | 0.5814 |
| 0.9503 | 6.0 | 36 | 1.0691 | 0.5814 |
| 0.8249 | 7.0 | 42 | 1.0268 | 0.6047 |
| 0.8249 | 8.0 | 48 | 1.0002 | 0.6279 |
| 0.6991 | 9.0 | 54 | 0.9588 | 0.6279 |
| 0.62 | 10.0 | 60 | 0.9254 | 0.6279 |
| 0.62 | 11.0 | 66 | 0.8988 | 0.6744 |
| 0.5003 | 12.0 | 72 | 0.8718 | 0.6279 |
| 0.5003 | 13.0 | 78 | 0.8636 | 0.6279 |
| 0.4251 | 14.0 | 84 | 0.8486 | 0.6279 |
| 0.3584 | 15.0 | 90 | 0.8228 | 0.6279 |
| 0.3584 | 16.0 | 96 | 0.8029 | 0.6512 |
| 0.2955 | 17.0 | 102 | 0.7980 | 0.6279 |
| 0.2955 | 18.0 | 108 | 0.7871 | 0.6047 |
| 0.2345 | 19.0 | 114 | 0.7646 | 0.6279 |
| 0.2022 | 20.0 | 120 | 0.7571 | 0.6279 |
| 0.2022 | 21.0 | 126 | 0.7433 | 0.6512 |
| 0.1667 | 22.0 | 132 | 0.7314 | 0.6744 |
| 0.1667 | 23.0 | 138 | 0.7263 | 0.6279 |
| 0.1461 | 24.0 | 144 | 0.7221 | 0.6744 |
| 0.1251 | 25.0 | 150 | 0.7120 | 0.6512 |
| 0.1251 | 26.0 | 156 | 0.6954 | 0.6512 |
| 0.1033 | 27.0 | 162 | 0.6904 | 0.6512 |
| 0.1033 | 28.0 | 168 | 0.6870 | 0.6744 |
| 0.0941 | 29.0 | 174 | 0.6821 | 0.6744 |
| 0.0792 | 30.0 | 180 | 0.6785 | 0.6744 |
| 0.0792 | 31.0 | 186 | 0.6761 | 0.6744 |
| 0.0681 | 32.0 | 192 | 0.6723 | 0.6744 |
| 0.0681 | 33.0 | 198 | 0.6679 | 0.6744 |
| 0.0621 | 34.0 | 204 | 0.6648 | 0.6512 |
| 0.0554 | 35.0 | 210 | 0.6628 | 0.6512 |
| 0.0554 | 36.0 | 216 | 0.6584 | 0.6744 |
| 0.0533 | 37.0 | 222 | 0.6569 | 0.6744 |
| 0.0533 | 38.0 | 228 | 0.6569 | 0.6512 |
| 0.0487 | 39.0 | 234 | 0.6565 | 0.6512 |
| 0.0478 | 40.0 | 240 | 0.6552 | 0.6512 |
| 0.0478 | 41.0 | 246 | 0.6553 | 0.6512 |
| 0.0459 | 42.0 | 252 | 0.6553 | 0.6512 |
| 0.0459 | 43.0 | 258 | 0.6553 | 0.6512 |
| 0.0488 | 44.0 | 264 | 0.6553 | 0.6512 |
| 0.0454 | 45.0 | 270 | 0.6553 | 0.6512 |
| 0.0454 | 46.0 | 276 | 0.6553 | 0.6512 |
| 0.0445 | 47.0 | 282 | 0.6553 | 0.6512 |
| 0.0445 | 48.0 | 288 | 0.6553 | 0.6512 |
| 0.0487 | 49.0 | 294 | 0.6553 | 0.6512 |
| 0.0463 | 50.0 | 300 | 0.6553 | 0.6512 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_00001_fold4
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: 0.7508
- Accuracy: 0.6667
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3350 | 0.3571 |
| 1.346 | 2.0 | 12 | 1.2810 | 0.3810 |
| 1.346 | 3.0 | 18 | 1.2346 | 0.4048 |
| 1.107 | 4.0 | 24 | 1.1917 | 0.4048 |
| 0.9637 | 5.0 | 30 | 1.1623 | 0.3571 |
| 0.9637 | 6.0 | 36 | 1.1357 | 0.4048 |
| 0.8241 | 7.0 | 42 | 1.1137 | 0.4286 |
| 0.8241 | 8.0 | 48 | 1.0906 | 0.4286 |
| 0.6746 | 9.0 | 54 | 1.0721 | 0.4286 |
| 0.594 | 10.0 | 60 | 1.0502 | 0.4286 |
| 0.594 | 11.0 | 66 | 1.0303 | 0.4286 |
| 0.4897 | 12.0 | 72 | 1.0072 | 0.4524 |
| 0.4897 | 13.0 | 78 | 0.9837 | 0.4762 |
| 0.4223 | 14.0 | 84 | 0.9800 | 0.4762 |
| 0.3482 | 15.0 | 90 | 0.9580 | 0.5 |
| 0.3482 | 16.0 | 96 | 0.9315 | 0.5238 |
| 0.2808 | 17.0 | 102 | 0.9182 | 0.5238 |
| 0.2808 | 18.0 | 108 | 0.9032 | 0.5714 |
| 0.2441 | 19.0 | 114 | 0.8918 | 0.6190 |
| 0.2119 | 20.0 | 120 | 0.8729 | 0.6190 |
| 0.2119 | 21.0 | 126 | 0.8574 | 0.6190 |
| 0.1699 | 22.0 | 132 | 0.8454 | 0.6190 |
| 0.1699 | 23.0 | 138 | 0.8308 | 0.6190 |
| 0.1443 | 24.0 | 144 | 0.8166 | 0.6190 |
| 0.1255 | 25.0 | 150 | 0.8066 | 0.6905 |
| 0.1255 | 26.0 | 156 | 0.8088 | 0.6905 |
| 0.1078 | 27.0 | 162 | 0.7901 | 0.6905 |
| 0.1078 | 28.0 | 168 | 0.7892 | 0.6667 |
| 0.094 | 29.0 | 174 | 0.7900 | 0.6667 |
| 0.0785 | 30.0 | 180 | 0.7761 | 0.6667 |
| 0.0785 | 31.0 | 186 | 0.7673 | 0.6667 |
| 0.071 | 32.0 | 192 | 0.7632 | 0.6667 |
| 0.071 | 33.0 | 198 | 0.7572 | 0.6667 |
| 0.066 | 34.0 | 204 | 0.7549 | 0.6667 |
| 0.0595 | 35.0 | 210 | 0.7582 | 0.6667 |
| 0.0595 | 36.0 | 216 | 0.7573 | 0.6667 |
| 0.0553 | 37.0 | 222 | 0.7569 | 0.6667 |
| 0.0553 | 38.0 | 228 | 0.7526 | 0.6667 |
| 0.0524 | 39.0 | 234 | 0.7502 | 0.6667 |
| 0.0501 | 40.0 | 240 | 0.7502 | 0.6667 |
| 0.0501 | 41.0 | 246 | 0.7508 | 0.6667 |
| 0.0507 | 42.0 | 252 | 0.7508 | 0.6667 |
| 0.0507 | 43.0 | 258 | 0.7508 | 0.6667 |
| 0.0466 | 44.0 | 264 | 0.7508 | 0.6667 |
| 0.0501 | 45.0 | 270 | 0.7508 | 0.6667 |
| 0.0501 | 46.0 | 276 | 0.7508 | 0.6667 |
| 0.0512 | 47.0 | 282 | 0.7508 | 0.6667 |
| 0.0512 | 48.0 | 288 | 0.7508 | 0.6667 |
| 0.0478 | 49.0 | 294 | 0.7508 | 0.6667 |
| 0.0501 | 50.0 | 300 | 0.7508 | 0.6667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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. -->
# hushem_1x_deit_small_adamax_00001_fold5
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: 0.7730
- Accuracy: 0.6585
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3080 | 0.3171 |
| 1.348 | 2.0 | 12 | 1.2421 | 0.3659 |
| 1.348 | 3.0 | 18 | 1.1840 | 0.4634 |
| 1.1221 | 4.0 | 24 | 1.1443 | 0.4634 |
| 0.9962 | 5.0 | 30 | 1.1209 | 0.4634 |
| 0.9962 | 6.0 | 36 | 1.0884 | 0.5366 |
| 0.8532 | 7.0 | 42 | 1.0512 | 0.5122 |
| 0.8532 | 8.0 | 48 | 1.0147 | 0.5366 |
| 0.73 | 9.0 | 54 | 0.9886 | 0.5366 |
| 0.61 | 10.0 | 60 | 0.9683 | 0.5610 |
| 0.61 | 11.0 | 66 | 0.9452 | 0.5854 |
| 0.5241 | 12.0 | 72 | 0.9201 | 0.6341 |
| 0.5241 | 13.0 | 78 | 0.9013 | 0.6341 |
| 0.4293 | 14.0 | 84 | 0.8851 | 0.6341 |
| 0.3674 | 15.0 | 90 | 0.8707 | 0.6341 |
| 0.3674 | 16.0 | 96 | 0.8542 | 0.6341 |
| 0.304 | 17.0 | 102 | 0.8474 | 0.6341 |
| 0.304 | 18.0 | 108 | 0.8370 | 0.6341 |
| 0.2449 | 19.0 | 114 | 0.8233 | 0.6341 |
| 0.2119 | 20.0 | 120 | 0.8193 | 0.6341 |
| 0.2119 | 21.0 | 126 | 0.8116 | 0.6341 |
| 0.1788 | 22.0 | 132 | 0.8051 | 0.6341 |
| 0.1788 | 23.0 | 138 | 0.7954 | 0.6341 |
| 0.1445 | 24.0 | 144 | 0.7897 | 0.6341 |
| 0.1262 | 25.0 | 150 | 0.7881 | 0.6829 |
| 0.1262 | 26.0 | 156 | 0.7818 | 0.6585 |
| 0.1066 | 27.0 | 162 | 0.7872 | 0.6829 |
| 0.1066 | 28.0 | 168 | 0.7762 | 0.6585 |
| 0.0891 | 29.0 | 174 | 0.7687 | 0.6585 |
| 0.0806 | 30.0 | 180 | 0.7658 | 0.6829 |
| 0.0806 | 31.0 | 186 | 0.7688 | 0.6829 |
| 0.0692 | 32.0 | 192 | 0.7732 | 0.6829 |
| 0.0692 | 33.0 | 198 | 0.7763 | 0.6585 |
| 0.0592 | 34.0 | 204 | 0.7749 | 0.6585 |
| 0.0587 | 35.0 | 210 | 0.7694 | 0.6829 |
| 0.0587 | 36.0 | 216 | 0.7701 | 0.6829 |
| 0.0549 | 37.0 | 222 | 0.7733 | 0.6585 |
| 0.0549 | 38.0 | 228 | 0.7741 | 0.6585 |
| 0.0463 | 39.0 | 234 | 0.7744 | 0.6585 |
| 0.0481 | 40.0 | 240 | 0.7732 | 0.6585 |
| 0.0481 | 41.0 | 246 | 0.7732 | 0.6585 |
| 0.0468 | 42.0 | 252 | 0.7730 | 0.6585 |
| 0.0468 | 43.0 | 258 | 0.7730 | 0.6585 |
| 0.0455 | 44.0 | 264 | 0.7730 | 0.6585 |
| 0.0473 | 45.0 | 270 | 0.7730 | 0.6585 |
| 0.0473 | 46.0 | 276 | 0.7730 | 0.6585 |
| 0.0444 | 47.0 | 282 | 0.7730 | 0.6585 |
| 0.0444 | 48.0 | 288 | 0.7730 | 0.6585 |
| 0.048 | 49.0 | 294 | 0.7730 | 0.6585 |
| 0.0476 | 50.0 | 300 | 0.7730 | 0.6585 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_sgd_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_1x_deit_small_sgd_001_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: 1.2536
- Accuracy: 0.4667
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3733 | 0.3778 |
| 1.5546 | 2.0 | 12 | 1.3601 | 0.4 |
| 1.5546 | 3.0 | 18 | 1.3490 | 0.4222 |
| 1.5316 | 4.0 | 24 | 1.3414 | 0.4222 |
| 1.4864 | 5.0 | 30 | 1.3332 | 0.4222 |
| 1.4864 | 6.0 | 36 | 1.3258 | 0.4222 |
| 1.4723 | 7.0 | 42 | 1.3198 | 0.4222 |
| 1.4723 | 8.0 | 48 | 1.3148 | 0.4 |
| 1.4485 | 9.0 | 54 | 1.3096 | 0.4 |
| 1.4339 | 10.0 | 60 | 1.3042 | 0.4 |
| 1.4339 | 11.0 | 66 | 1.3005 | 0.4222 |
| 1.4182 | 12.0 | 72 | 1.2965 | 0.4222 |
| 1.4182 | 13.0 | 78 | 1.2931 | 0.4 |
| 1.3944 | 14.0 | 84 | 1.2902 | 0.4222 |
| 1.3955 | 15.0 | 90 | 1.2868 | 0.4444 |
| 1.3955 | 16.0 | 96 | 1.2841 | 0.4444 |
| 1.3685 | 17.0 | 102 | 1.2813 | 0.4444 |
| 1.3685 | 18.0 | 108 | 1.2791 | 0.4444 |
| 1.351 | 19.0 | 114 | 1.2769 | 0.4444 |
| 1.3583 | 20.0 | 120 | 1.2750 | 0.4667 |
| 1.3583 | 21.0 | 126 | 1.2734 | 0.4444 |
| 1.3432 | 22.0 | 132 | 1.2719 | 0.4444 |
| 1.3432 | 23.0 | 138 | 1.2696 | 0.4444 |
| 1.3309 | 24.0 | 144 | 1.2677 | 0.4444 |
| 1.3166 | 25.0 | 150 | 1.2667 | 0.4444 |
| 1.3166 | 26.0 | 156 | 1.2651 | 0.4667 |
| 1.3168 | 27.0 | 162 | 1.2639 | 0.4667 |
| 1.3168 | 28.0 | 168 | 1.2624 | 0.4667 |
| 1.3102 | 29.0 | 174 | 1.2615 | 0.4667 |
| 1.3034 | 30.0 | 180 | 1.2602 | 0.4667 |
| 1.3034 | 31.0 | 186 | 1.2590 | 0.4667 |
| 1.3106 | 32.0 | 192 | 1.2580 | 0.4667 |
| 1.3106 | 33.0 | 198 | 1.2570 | 0.4667 |
| 1.2903 | 34.0 | 204 | 1.2562 | 0.4667 |
| 1.2915 | 35.0 | 210 | 1.2554 | 0.4667 |
| 1.2915 | 36.0 | 216 | 1.2549 | 0.4667 |
| 1.2913 | 37.0 | 222 | 1.2546 | 0.4667 |
| 1.2913 | 38.0 | 228 | 1.2542 | 0.4667 |
| 1.2715 | 39.0 | 234 | 1.2539 | 0.4667 |
| 1.2929 | 40.0 | 240 | 1.2538 | 0.4667 |
| 1.2929 | 41.0 | 246 | 1.2537 | 0.4667 |
| 1.2815 | 42.0 | 252 | 1.2536 | 0.4667 |
| 1.2815 | 43.0 | 258 | 1.2536 | 0.4667 |
| 1.2834 | 44.0 | 264 | 1.2536 | 0.4667 |
| 1.2687 | 45.0 | 270 | 1.2536 | 0.4667 |
| 1.2687 | 46.0 | 276 | 1.2536 | 0.4667 |
| 1.2845 | 47.0 | 282 | 1.2536 | 0.4667 |
| 1.2845 | 48.0 | 288 | 1.2536 | 0.4667 |
| 1.2639 | 49.0 | 294 | 1.2536 | 0.4667 |
| 1.2911 | 50.0 | 300 | 1.2536 | 0.4667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_sgd_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_1x_deit_small_sgd_001_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: 1.3470
- 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4885 | 0.2 |
| 1.5055 | 2.0 | 12 | 1.4667 | 0.2667 |
| 1.5055 | 3.0 | 18 | 1.4496 | 0.2444 |
| 1.4394 | 4.0 | 24 | 1.4374 | 0.2444 |
| 1.4154 | 5.0 | 30 | 1.4269 | 0.2444 |
| 1.4154 | 6.0 | 36 | 1.4185 | 0.2667 |
| 1.3643 | 7.0 | 42 | 1.4107 | 0.3333 |
| 1.3643 | 8.0 | 48 | 1.4053 | 0.3556 |
| 1.3559 | 9.0 | 54 | 1.4001 | 0.3556 |
| 1.3227 | 10.0 | 60 | 1.3952 | 0.3556 |
| 1.3227 | 11.0 | 66 | 1.3910 | 0.3556 |
| 1.3197 | 12.0 | 72 | 1.3872 | 0.3556 |
| 1.3197 | 13.0 | 78 | 1.3837 | 0.3556 |
| 1.2846 | 14.0 | 84 | 1.3804 | 0.3556 |
| 1.2901 | 15.0 | 90 | 1.3773 | 0.3556 |
| 1.2901 | 16.0 | 96 | 1.3743 | 0.3333 |
| 1.2643 | 17.0 | 102 | 1.3716 | 0.3333 |
| 1.2643 | 18.0 | 108 | 1.3691 | 0.3333 |
| 1.2844 | 19.0 | 114 | 1.3667 | 0.3333 |
| 1.2293 | 20.0 | 120 | 1.3643 | 0.3333 |
| 1.2293 | 21.0 | 126 | 1.3623 | 0.3333 |
| 1.2404 | 22.0 | 132 | 1.3607 | 0.3333 |
| 1.2404 | 23.0 | 138 | 1.3587 | 0.3333 |
| 1.2359 | 24.0 | 144 | 1.3573 | 0.3333 |
| 1.2062 | 25.0 | 150 | 1.3561 | 0.3333 |
| 1.2062 | 26.0 | 156 | 1.3548 | 0.3333 |
| 1.2199 | 27.0 | 162 | 1.3536 | 0.3333 |
| 1.2199 | 28.0 | 168 | 1.3527 | 0.3333 |
| 1.2151 | 29.0 | 174 | 1.3520 | 0.3556 |
| 1.2005 | 30.0 | 180 | 1.3511 | 0.3556 |
| 1.2005 | 31.0 | 186 | 1.3504 | 0.3556 |
| 1.1928 | 32.0 | 192 | 1.3498 | 0.3556 |
| 1.1928 | 33.0 | 198 | 1.3492 | 0.3556 |
| 1.1891 | 34.0 | 204 | 1.3487 | 0.3556 |
| 1.1974 | 35.0 | 210 | 1.3482 | 0.3556 |
| 1.1974 | 36.0 | 216 | 1.3478 | 0.3556 |
| 1.1657 | 37.0 | 222 | 1.3476 | 0.3556 |
| 1.1657 | 38.0 | 228 | 1.3474 | 0.3556 |
| 1.1722 | 39.0 | 234 | 1.3472 | 0.3556 |
| 1.2031 | 40.0 | 240 | 1.3471 | 0.3556 |
| 1.2031 | 41.0 | 246 | 1.3470 | 0.3556 |
| 1.1899 | 42.0 | 252 | 1.3470 | 0.3556 |
| 1.1899 | 43.0 | 258 | 1.3470 | 0.3556 |
| 1.1761 | 44.0 | 264 | 1.3470 | 0.3556 |
| 1.1715 | 45.0 | 270 | 1.3470 | 0.3556 |
| 1.1715 | 46.0 | 276 | 1.3470 | 0.3556 |
| 1.1816 | 47.0 | 282 | 1.3470 | 0.3556 |
| 1.1816 | 48.0 | 288 | 1.3470 | 0.3556 |
| 1.1504 | 49.0 | 294 | 1.3470 | 0.3556 |
| 1.1896 | 50.0 | 300 | 1.3470 | 0.3556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_sgd_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_1x_deit_small_sgd_001_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: 1.3255
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5642 | 0.2326 |
| 1.4806 | 2.0 | 12 | 1.5332 | 0.3256 |
| 1.4806 | 3.0 | 18 | 1.5110 | 0.3256 |
| 1.4127 | 4.0 | 24 | 1.4910 | 0.3256 |
| 1.3859 | 5.0 | 30 | 1.4734 | 0.3256 |
| 1.3859 | 6.0 | 36 | 1.4581 | 0.3256 |
| 1.372 | 7.0 | 42 | 1.4448 | 0.3256 |
| 1.372 | 8.0 | 48 | 1.4360 | 0.3256 |
| 1.3407 | 9.0 | 54 | 1.4268 | 0.3256 |
| 1.3476 | 10.0 | 60 | 1.4184 | 0.3256 |
| 1.3476 | 11.0 | 66 | 1.4115 | 0.3256 |
| 1.3176 | 12.0 | 72 | 1.4055 | 0.3488 |
| 1.3176 | 13.0 | 78 | 1.3989 | 0.3488 |
| 1.3009 | 14.0 | 84 | 1.3926 | 0.3256 |
| 1.3032 | 15.0 | 90 | 1.3870 | 0.3256 |
| 1.3032 | 16.0 | 96 | 1.3815 | 0.3256 |
| 1.2893 | 17.0 | 102 | 1.3768 | 0.3256 |
| 1.2893 | 18.0 | 108 | 1.3723 | 0.3023 |
| 1.252 | 19.0 | 114 | 1.3680 | 0.3023 |
| 1.2643 | 20.0 | 120 | 1.3638 | 0.3023 |
| 1.2643 | 21.0 | 126 | 1.3601 | 0.2791 |
| 1.2642 | 22.0 | 132 | 1.3567 | 0.2791 |
| 1.2642 | 23.0 | 138 | 1.3535 | 0.2791 |
| 1.2369 | 24.0 | 144 | 1.3502 | 0.2791 |
| 1.2315 | 25.0 | 150 | 1.3476 | 0.2791 |
| 1.2315 | 26.0 | 156 | 1.3450 | 0.2791 |
| 1.2236 | 27.0 | 162 | 1.3424 | 0.2558 |
| 1.2236 | 28.0 | 168 | 1.3403 | 0.2558 |
| 1.2327 | 29.0 | 174 | 1.3382 | 0.2558 |
| 1.2254 | 30.0 | 180 | 1.3363 | 0.2558 |
| 1.2254 | 31.0 | 186 | 1.3347 | 0.2558 |
| 1.2165 | 32.0 | 192 | 1.3331 | 0.2558 |
| 1.2165 | 33.0 | 198 | 1.3315 | 0.2558 |
| 1.2003 | 34.0 | 204 | 1.3303 | 0.2558 |
| 1.2034 | 35.0 | 210 | 1.3292 | 0.2558 |
| 1.2034 | 36.0 | 216 | 1.3282 | 0.2558 |
| 1.2052 | 37.0 | 222 | 1.3273 | 0.2558 |
| 1.2052 | 38.0 | 228 | 1.3266 | 0.2558 |
| 1.2216 | 39.0 | 234 | 1.3261 | 0.2558 |
| 1.2003 | 40.0 | 240 | 1.3258 | 0.2558 |
| 1.2003 | 41.0 | 246 | 1.3256 | 0.2558 |
| 1.1856 | 42.0 | 252 | 1.3255 | 0.2558 |
| 1.1856 | 43.0 | 258 | 1.3255 | 0.2558 |
| 1.2091 | 44.0 | 264 | 1.3255 | 0.2558 |
| 1.1987 | 45.0 | 270 | 1.3255 | 0.2558 |
| 1.1987 | 46.0 | 276 | 1.3255 | 0.2558 |
| 1.1885 | 47.0 | 282 | 1.3255 | 0.2558 |
| 1.1885 | 48.0 | 288 | 1.3255 | 0.2558 |
| 1.2076 | 49.0 | 294 | 1.3255 | 0.2558 |
| 1.2139 | 50.0 | 300 | 1.3255 | 0.2558 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_sgd_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_1x_deit_small_sgd_001_fold4
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: 1.3002
- Accuracy: 0.3333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4504 | 0.2857 |
| 1.4996 | 2.0 | 12 | 1.4256 | 0.2619 |
| 1.4996 | 3.0 | 18 | 1.4065 | 0.3095 |
| 1.4661 | 4.0 | 24 | 1.3909 | 0.3333 |
| 1.4137 | 5.0 | 30 | 1.3815 | 0.3333 |
| 1.4137 | 6.0 | 36 | 1.3736 | 0.3810 |
| 1.3923 | 7.0 | 42 | 1.3662 | 0.3571 |
| 1.3923 | 8.0 | 48 | 1.3602 | 0.3095 |
| 1.3511 | 9.0 | 54 | 1.3552 | 0.3333 |
| 1.3471 | 10.0 | 60 | 1.3505 | 0.3333 |
| 1.3471 | 11.0 | 66 | 1.3464 | 0.3333 |
| 1.3212 | 12.0 | 72 | 1.3425 | 0.3333 |
| 1.3212 | 13.0 | 78 | 1.3391 | 0.3333 |
| 1.3151 | 14.0 | 84 | 1.3358 | 0.3333 |
| 1.2949 | 15.0 | 90 | 1.3328 | 0.3333 |
| 1.2949 | 16.0 | 96 | 1.3296 | 0.3333 |
| 1.282 | 17.0 | 102 | 1.3270 | 0.3333 |
| 1.282 | 18.0 | 108 | 1.3243 | 0.3333 |
| 1.2637 | 19.0 | 114 | 1.3223 | 0.3333 |
| 1.2828 | 20.0 | 120 | 1.3203 | 0.3333 |
| 1.2828 | 21.0 | 126 | 1.3182 | 0.3333 |
| 1.2384 | 22.0 | 132 | 1.3165 | 0.3333 |
| 1.2384 | 23.0 | 138 | 1.3149 | 0.3333 |
| 1.2419 | 24.0 | 144 | 1.3133 | 0.3333 |
| 1.2404 | 25.0 | 150 | 1.3117 | 0.3571 |
| 1.2404 | 26.0 | 156 | 1.3102 | 0.3571 |
| 1.2294 | 27.0 | 162 | 1.3091 | 0.3571 |
| 1.2294 | 28.0 | 168 | 1.3080 | 0.3571 |
| 1.2327 | 29.0 | 174 | 1.3070 | 0.3571 |
| 1.2115 | 30.0 | 180 | 1.3061 | 0.3571 |
| 1.2115 | 31.0 | 186 | 1.3052 | 0.3333 |
| 1.2091 | 32.0 | 192 | 1.3043 | 0.3333 |
| 1.2091 | 33.0 | 198 | 1.3036 | 0.3333 |
| 1.2111 | 34.0 | 204 | 1.3028 | 0.3333 |
| 1.2001 | 35.0 | 210 | 1.3022 | 0.3333 |
| 1.2001 | 36.0 | 216 | 1.3016 | 0.3333 |
| 1.2048 | 37.0 | 222 | 1.3012 | 0.3333 |
| 1.2048 | 38.0 | 228 | 1.3009 | 0.3333 |
| 1.1981 | 39.0 | 234 | 1.3006 | 0.3333 |
| 1.1973 | 40.0 | 240 | 1.3004 | 0.3333 |
| 1.1973 | 41.0 | 246 | 1.3003 | 0.3333 |
| 1.2009 | 42.0 | 252 | 1.3002 | 0.3333 |
| 1.2009 | 43.0 | 258 | 1.3002 | 0.3333 |
| 1.1848 | 44.0 | 264 | 1.3002 | 0.3333 |
| 1.2 | 45.0 | 270 | 1.3002 | 0.3333 |
| 1.2 | 46.0 | 276 | 1.3002 | 0.3333 |
| 1.2026 | 47.0 | 282 | 1.3002 | 0.3333 |
| 1.2026 | 48.0 | 288 | 1.3002 | 0.3333 |
| 1.1883 | 49.0 | 294 | 1.3002 | 0.3333 |
| 1.2097 | 50.0 | 300 | 1.3002 | 0.3333 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_sgd_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_1x_deit_small_sgd_001_fold5
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: 1.2874
- 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4789 | 0.2683 |
| 1.5098 | 2.0 | 12 | 1.4475 | 0.2927 |
| 1.5098 | 3.0 | 18 | 1.4244 | 0.2683 |
| 1.4415 | 4.0 | 24 | 1.4086 | 0.2683 |
| 1.4228 | 5.0 | 30 | 1.3943 | 0.2927 |
| 1.4228 | 6.0 | 36 | 1.3837 | 0.2683 |
| 1.3818 | 7.0 | 42 | 1.3755 | 0.2439 |
| 1.3818 | 8.0 | 48 | 1.3687 | 0.2195 |
| 1.3662 | 9.0 | 54 | 1.3625 | 0.2439 |
| 1.3382 | 10.0 | 60 | 1.3567 | 0.2439 |
| 1.3382 | 11.0 | 66 | 1.3518 | 0.2439 |
| 1.3324 | 12.0 | 72 | 1.3466 | 0.2439 |
| 1.3324 | 13.0 | 78 | 1.3420 | 0.2439 |
| 1.3002 | 14.0 | 84 | 1.3382 | 0.2439 |
| 1.2845 | 15.0 | 90 | 1.3339 | 0.2683 |
| 1.2845 | 16.0 | 96 | 1.3305 | 0.2683 |
| 1.2783 | 17.0 | 102 | 1.3271 | 0.2927 |
| 1.2783 | 18.0 | 108 | 1.3237 | 0.3171 |
| 1.2896 | 19.0 | 114 | 1.3207 | 0.3171 |
| 1.2581 | 20.0 | 120 | 1.3176 | 0.3171 |
| 1.2581 | 21.0 | 126 | 1.3151 | 0.3415 |
| 1.2555 | 22.0 | 132 | 1.3123 | 0.3415 |
| 1.2555 | 23.0 | 138 | 1.3099 | 0.3415 |
| 1.2563 | 24.0 | 144 | 1.3076 | 0.3415 |
| 1.2461 | 25.0 | 150 | 1.3050 | 0.3415 |
| 1.2461 | 26.0 | 156 | 1.3029 | 0.3171 |
| 1.2294 | 27.0 | 162 | 1.3009 | 0.3171 |
| 1.2294 | 28.0 | 168 | 1.2991 | 0.3171 |
| 1.2223 | 29.0 | 174 | 1.2975 | 0.3171 |
| 1.2396 | 30.0 | 180 | 1.2961 | 0.3171 |
| 1.2396 | 31.0 | 186 | 1.2948 | 0.3171 |
| 1.2235 | 32.0 | 192 | 1.2934 | 0.3171 |
| 1.2235 | 33.0 | 198 | 1.2923 | 0.3171 |
| 1.2018 | 34.0 | 204 | 1.2911 | 0.3171 |
| 1.2131 | 35.0 | 210 | 1.2902 | 0.3171 |
| 1.2131 | 36.0 | 216 | 1.2895 | 0.3171 |
| 1.2105 | 37.0 | 222 | 1.2888 | 0.3171 |
| 1.2105 | 38.0 | 228 | 1.2883 | 0.3171 |
| 1.1724 | 39.0 | 234 | 1.2879 | 0.3171 |
| 1.2168 | 40.0 | 240 | 1.2876 | 0.3171 |
| 1.2168 | 41.0 | 246 | 1.2875 | 0.3171 |
| 1.1977 | 42.0 | 252 | 1.2874 | 0.3171 |
| 1.1977 | 43.0 | 258 | 1.2874 | 0.3171 |
| 1.1916 | 44.0 | 264 | 1.2874 | 0.3171 |
| 1.21 | 45.0 | 270 | 1.2874 | 0.3171 |
| 1.21 | 46.0 | 276 | 1.2874 | 0.3171 |
| 1.1885 | 47.0 | 282 | 1.2874 | 0.3171 |
| 1.1885 | 48.0 | 288 | 1.2874 | 0.3171 |
| 1.2083 | 49.0 | 294 | 1.2874 | 0.3171 |
| 1.2106 | 50.0 | 300 | 1.2874 | 0.3171 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
camiloTel0410/bean-classifier
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bean-classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0051
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0592 | 3.85 | 500 | 0.0051 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
hkivancoral/hushem_1x_deit_small_sgd_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_1x_deit_small_sgd_0001_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: 1.4520
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5076 | 0.2889 |
| 1.5379 | 2.0 | 12 | 1.5042 | 0.2889 |
| 1.5379 | 3.0 | 18 | 1.5010 | 0.2889 |
| 1.5099 | 4.0 | 24 | 1.4983 | 0.2889 |
| 1.521 | 5.0 | 30 | 1.4955 | 0.2889 |
| 1.521 | 6.0 | 36 | 1.4929 | 0.2889 |
| 1.4972 | 7.0 | 42 | 1.4902 | 0.2889 |
| 1.4972 | 8.0 | 48 | 1.4879 | 0.2889 |
| 1.5152 | 9.0 | 54 | 1.4855 | 0.2889 |
| 1.4839 | 10.0 | 60 | 1.4831 | 0.2889 |
| 1.4839 | 11.0 | 66 | 1.4810 | 0.2889 |
| 1.478 | 12.0 | 72 | 1.4788 | 0.2889 |
| 1.478 | 13.0 | 78 | 1.4768 | 0.2889 |
| 1.4972 | 14.0 | 84 | 1.4749 | 0.2889 |
| 1.4822 | 15.0 | 90 | 1.4732 | 0.2889 |
| 1.4822 | 16.0 | 96 | 1.4714 | 0.2889 |
| 1.4784 | 17.0 | 102 | 1.4698 | 0.2889 |
| 1.4784 | 18.0 | 108 | 1.4684 | 0.2889 |
| 1.4862 | 19.0 | 114 | 1.4671 | 0.2889 |
| 1.4536 | 20.0 | 120 | 1.4657 | 0.2889 |
| 1.4536 | 21.0 | 126 | 1.4645 | 0.2889 |
| 1.4751 | 22.0 | 132 | 1.4634 | 0.2889 |
| 1.4751 | 23.0 | 138 | 1.4621 | 0.2889 |
| 1.4645 | 24.0 | 144 | 1.4610 | 0.2889 |
| 1.4518 | 25.0 | 150 | 1.4602 | 0.2889 |
| 1.4518 | 26.0 | 156 | 1.4592 | 0.2889 |
| 1.4648 | 27.0 | 162 | 1.4583 | 0.2889 |
| 1.4648 | 28.0 | 168 | 1.4575 | 0.2667 |
| 1.47 | 29.0 | 174 | 1.4568 | 0.2667 |
| 1.4692 | 30.0 | 180 | 1.4560 | 0.2667 |
| 1.4692 | 31.0 | 186 | 1.4553 | 0.2667 |
| 1.4701 | 32.0 | 192 | 1.4547 | 0.2667 |
| 1.4701 | 33.0 | 198 | 1.4541 | 0.2667 |
| 1.4745 | 34.0 | 204 | 1.4536 | 0.2667 |
| 1.4582 | 35.0 | 210 | 1.4532 | 0.2667 |
| 1.4582 | 36.0 | 216 | 1.4528 | 0.2667 |
| 1.4443 | 37.0 | 222 | 1.4526 | 0.2667 |
| 1.4443 | 38.0 | 228 | 1.4523 | 0.2667 |
| 1.44 | 39.0 | 234 | 1.4522 | 0.2667 |
| 1.4727 | 40.0 | 240 | 1.4521 | 0.2667 |
| 1.4727 | 41.0 | 246 | 1.4520 | 0.2667 |
| 1.4651 | 42.0 | 252 | 1.4520 | 0.2667 |
| 1.4651 | 43.0 | 258 | 1.4520 | 0.2667 |
| 1.4764 | 44.0 | 264 | 1.4520 | 0.2667 |
| 1.4313 | 45.0 | 270 | 1.4520 | 0.2667 |
| 1.4313 | 46.0 | 276 | 1.4520 | 0.2667 |
| 1.4565 | 47.0 | 282 | 1.4520 | 0.2667 |
| 1.4565 | 48.0 | 288 | 1.4520 | 0.2667 |
| 1.4277 | 49.0 | 294 | 1.4520 | 0.2667 |
| 1.4569 | 50.0 | 300 | 1.4520 | 0.2667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_sgd_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_1x_deit_small_sgd_0001_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: 1.4641
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5104 | 0.1778 |
| 1.5321 | 2.0 | 12 | 1.5075 | 0.1778 |
| 1.5321 | 3.0 | 18 | 1.5047 | 0.1778 |
| 1.5118 | 4.0 | 24 | 1.5023 | 0.2 |
| 1.5295 | 5.0 | 30 | 1.4998 | 0.2 |
| 1.5295 | 6.0 | 36 | 1.4976 | 0.2 |
| 1.4893 | 7.0 | 42 | 1.4953 | 0.2 |
| 1.4893 | 8.0 | 48 | 1.4932 | 0.2 |
| 1.5068 | 9.0 | 54 | 1.4912 | 0.2 |
| 1.4876 | 10.0 | 60 | 1.4893 | 0.2 |
| 1.4876 | 11.0 | 66 | 1.4876 | 0.2222 |
| 1.4872 | 12.0 | 72 | 1.4858 | 0.2222 |
| 1.4872 | 13.0 | 78 | 1.4842 | 0.2444 |
| 1.482 | 14.0 | 84 | 1.4826 | 0.2444 |
| 1.4925 | 15.0 | 90 | 1.4811 | 0.2444 |
| 1.4925 | 16.0 | 96 | 1.4797 | 0.2444 |
| 1.4692 | 17.0 | 102 | 1.4783 | 0.2444 |
| 1.4692 | 18.0 | 108 | 1.4772 | 0.2444 |
| 1.4971 | 19.0 | 114 | 1.4761 | 0.2444 |
| 1.4368 | 20.0 | 120 | 1.4750 | 0.2444 |
| 1.4368 | 21.0 | 126 | 1.4740 | 0.2444 |
| 1.4645 | 22.0 | 132 | 1.4731 | 0.2444 |
| 1.4645 | 23.0 | 138 | 1.4721 | 0.2444 |
| 1.4558 | 24.0 | 144 | 1.4712 | 0.2667 |
| 1.4397 | 25.0 | 150 | 1.4705 | 0.2667 |
| 1.4397 | 26.0 | 156 | 1.4698 | 0.2667 |
| 1.4566 | 27.0 | 162 | 1.4691 | 0.2667 |
| 1.4566 | 28.0 | 168 | 1.4684 | 0.2667 |
| 1.4686 | 29.0 | 174 | 1.4678 | 0.2667 |
| 1.4549 | 30.0 | 180 | 1.4672 | 0.2667 |
| 1.4549 | 31.0 | 186 | 1.4667 | 0.2667 |
| 1.4527 | 32.0 | 192 | 1.4662 | 0.2667 |
| 1.4527 | 33.0 | 198 | 1.4658 | 0.2667 |
| 1.4549 | 34.0 | 204 | 1.4654 | 0.2667 |
| 1.4704 | 35.0 | 210 | 1.4650 | 0.2667 |
| 1.4704 | 36.0 | 216 | 1.4648 | 0.2667 |
| 1.4264 | 37.0 | 222 | 1.4646 | 0.2667 |
| 1.4264 | 38.0 | 228 | 1.4644 | 0.2667 |
| 1.4286 | 39.0 | 234 | 1.4642 | 0.2667 |
| 1.4743 | 40.0 | 240 | 1.4642 | 0.2667 |
| 1.4743 | 41.0 | 246 | 1.4641 | 0.2667 |
| 1.4713 | 42.0 | 252 | 1.4641 | 0.2667 |
| 1.4713 | 43.0 | 258 | 1.4641 | 0.2667 |
| 1.4345 | 44.0 | 264 | 1.4641 | 0.2667 |
| 1.4282 | 45.0 | 270 | 1.4641 | 0.2667 |
| 1.4282 | 46.0 | 276 | 1.4641 | 0.2667 |
| 1.4413 | 47.0 | 282 | 1.4641 | 0.2667 |
| 1.4413 | 48.0 | 288 | 1.4641 | 0.2667 |
| 1.4233 | 49.0 | 294 | 1.4641 | 0.2667 |
| 1.4542 | 50.0 | 300 | 1.4641 | 0.2667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_sgd_0001_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: 1.5323
- Accuracy: 0.3023
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5973 | 0.1860 |
| 1.5067 | 2.0 | 12 | 1.5935 | 0.1860 |
| 1.5067 | 3.0 | 18 | 1.5902 | 0.1860 |
| 1.4808 | 4.0 | 24 | 1.5868 | 0.2326 |
| 1.4805 | 5.0 | 30 | 1.5835 | 0.2326 |
| 1.4805 | 6.0 | 36 | 1.5802 | 0.2558 |
| 1.4884 | 7.0 | 42 | 1.5770 | 0.2558 |
| 1.4884 | 8.0 | 48 | 1.5745 | 0.2558 |
| 1.4701 | 9.0 | 54 | 1.5717 | 0.2558 |
| 1.4909 | 10.0 | 60 | 1.5691 | 0.2558 |
| 1.4909 | 11.0 | 66 | 1.5667 | 0.2558 |
| 1.4719 | 12.0 | 72 | 1.5643 | 0.2558 |
| 1.4719 | 13.0 | 78 | 1.5620 | 0.2558 |
| 1.4695 | 14.0 | 84 | 1.5598 | 0.3023 |
| 1.4633 | 15.0 | 90 | 1.5576 | 0.3023 |
| 1.4633 | 16.0 | 96 | 1.5555 | 0.3023 |
| 1.4805 | 17.0 | 102 | 1.5536 | 0.3023 |
| 1.4805 | 18.0 | 108 | 1.5518 | 0.3023 |
| 1.4265 | 19.0 | 114 | 1.5500 | 0.3023 |
| 1.4558 | 20.0 | 120 | 1.5483 | 0.3023 |
| 1.4558 | 21.0 | 126 | 1.5468 | 0.3023 |
| 1.4538 | 22.0 | 132 | 1.5454 | 0.3023 |
| 1.4538 | 23.0 | 138 | 1.5441 | 0.3023 |
| 1.4345 | 24.0 | 144 | 1.5427 | 0.3023 |
| 1.435 | 25.0 | 150 | 1.5416 | 0.3023 |
| 1.435 | 26.0 | 156 | 1.5405 | 0.3023 |
| 1.4381 | 27.0 | 162 | 1.5394 | 0.3023 |
| 1.4381 | 28.0 | 168 | 1.5384 | 0.3023 |
| 1.4397 | 29.0 | 174 | 1.5376 | 0.3023 |
| 1.4251 | 30.0 | 180 | 1.5368 | 0.3023 |
| 1.4251 | 31.0 | 186 | 1.5361 | 0.3023 |
| 1.4272 | 32.0 | 192 | 1.5354 | 0.3023 |
| 1.4272 | 33.0 | 198 | 1.5348 | 0.3023 |
| 1.4277 | 34.0 | 204 | 1.5343 | 0.3023 |
| 1.4249 | 35.0 | 210 | 1.5338 | 0.3023 |
| 1.4249 | 36.0 | 216 | 1.5334 | 0.3023 |
| 1.4476 | 37.0 | 222 | 1.5330 | 0.3023 |
| 1.4476 | 38.0 | 228 | 1.5328 | 0.3023 |
| 1.4487 | 39.0 | 234 | 1.5326 | 0.3023 |
| 1.4294 | 40.0 | 240 | 1.5324 | 0.3023 |
| 1.4294 | 41.0 | 246 | 1.5324 | 0.3023 |
| 1.4087 | 42.0 | 252 | 1.5323 | 0.3023 |
| 1.4087 | 43.0 | 258 | 1.5323 | 0.3023 |
| 1.4561 | 44.0 | 264 | 1.5323 | 0.3023 |
| 1.4317 | 45.0 | 270 | 1.5323 | 0.3023 |
| 1.4317 | 46.0 | 276 | 1.5323 | 0.3023 |
| 1.4154 | 47.0 | 282 | 1.5323 | 0.3023 |
| 1.4154 | 48.0 | 288 | 1.5323 | 0.3023 |
| 1.4386 | 49.0 | 294 | 1.5323 | 0.3023 |
| 1.4625 | 50.0 | 300 | 1.5323 | 0.3023 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_sgd_0001_fold4
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: 1.4227
- Accuracy: 0.2619
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4804 | 0.2619 |
| 1.5213 | 2.0 | 12 | 1.4770 | 0.2857 |
| 1.5213 | 3.0 | 18 | 1.4737 | 0.2857 |
| 1.5439 | 4.0 | 24 | 1.4702 | 0.2857 |
| 1.5226 | 5.0 | 30 | 1.4673 | 0.2857 |
| 1.5226 | 6.0 | 36 | 1.4646 | 0.2857 |
| 1.52 | 7.0 | 42 | 1.4618 | 0.2857 |
| 1.52 | 8.0 | 48 | 1.4591 | 0.2857 |
| 1.5076 | 9.0 | 54 | 1.4566 | 0.2857 |
| 1.5003 | 10.0 | 60 | 1.4541 | 0.2857 |
| 1.5003 | 11.0 | 66 | 1.4520 | 0.2857 |
| 1.4856 | 12.0 | 72 | 1.4497 | 0.2857 |
| 1.4856 | 13.0 | 78 | 1.4476 | 0.2857 |
| 1.5104 | 14.0 | 84 | 1.4457 | 0.2857 |
| 1.4726 | 15.0 | 90 | 1.4438 | 0.2857 |
| 1.4726 | 16.0 | 96 | 1.4420 | 0.2857 |
| 1.4844 | 17.0 | 102 | 1.4403 | 0.2857 |
| 1.4844 | 18.0 | 108 | 1.4387 | 0.2619 |
| 1.4456 | 19.0 | 114 | 1.4373 | 0.2619 |
| 1.5242 | 20.0 | 120 | 1.4359 | 0.2619 |
| 1.5242 | 21.0 | 126 | 1.4347 | 0.2619 |
| 1.4484 | 22.0 | 132 | 1.4335 | 0.2619 |
| 1.4484 | 23.0 | 138 | 1.4324 | 0.2619 |
| 1.4722 | 24.0 | 144 | 1.4314 | 0.2619 |
| 1.4802 | 25.0 | 150 | 1.4303 | 0.2619 |
| 1.4802 | 26.0 | 156 | 1.4294 | 0.2619 |
| 1.4658 | 27.0 | 162 | 1.4284 | 0.2619 |
| 1.4658 | 28.0 | 168 | 1.4276 | 0.2619 |
| 1.4705 | 29.0 | 174 | 1.4269 | 0.2619 |
| 1.4629 | 30.0 | 180 | 1.4263 | 0.2619 |
| 1.4629 | 31.0 | 186 | 1.4256 | 0.2619 |
| 1.4786 | 32.0 | 192 | 1.4251 | 0.2619 |
| 1.4786 | 33.0 | 198 | 1.4246 | 0.2619 |
| 1.4444 | 34.0 | 204 | 1.4242 | 0.2619 |
| 1.435 | 35.0 | 210 | 1.4238 | 0.2619 |
| 1.435 | 36.0 | 216 | 1.4235 | 0.2619 |
| 1.4653 | 37.0 | 222 | 1.4232 | 0.2619 |
| 1.4653 | 38.0 | 228 | 1.4230 | 0.2619 |
| 1.4482 | 39.0 | 234 | 1.4228 | 0.2619 |
| 1.4598 | 40.0 | 240 | 1.4227 | 0.2619 |
| 1.4598 | 41.0 | 246 | 1.4227 | 0.2619 |
| 1.4528 | 42.0 | 252 | 1.4227 | 0.2619 |
| 1.4528 | 43.0 | 258 | 1.4227 | 0.2619 |
| 1.4661 | 44.0 | 264 | 1.4227 | 0.2619 |
| 1.4575 | 45.0 | 270 | 1.4227 | 0.2619 |
| 1.4575 | 46.0 | 276 | 1.4227 | 0.2619 |
| 1.4719 | 47.0 | 282 | 1.4227 | 0.2619 |
| 1.4719 | 48.0 | 288 | 1.4227 | 0.2619 |
| 1.4602 | 49.0 | 294 | 1.4227 | 0.2619 |
| 1.465 | 50.0 | 300 | 1.4227 | 0.2619 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_sgd_0001_fold5
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: 1.4475
- Accuracy: 0.2683
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5161 | 0.2439 |
| 1.5359 | 2.0 | 12 | 1.5118 | 0.2439 |
| 1.5359 | 3.0 | 18 | 1.5076 | 0.2439 |
| 1.5171 | 4.0 | 24 | 1.5040 | 0.2439 |
| 1.5208 | 5.0 | 30 | 1.5002 | 0.2683 |
| 1.5208 | 6.0 | 36 | 1.4969 | 0.2683 |
| 1.5066 | 7.0 | 42 | 1.4937 | 0.2683 |
| 1.5066 | 8.0 | 48 | 1.4908 | 0.2683 |
| 1.4941 | 9.0 | 54 | 1.4878 | 0.2683 |
| 1.4953 | 10.0 | 60 | 1.4851 | 0.2683 |
| 1.4953 | 11.0 | 66 | 1.4825 | 0.2683 |
| 1.498 | 12.0 | 72 | 1.4798 | 0.2683 |
| 1.498 | 13.0 | 78 | 1.4774 | 0.2683 |
| 1.465 | 14.0 | 84 | 1.4753 | 0.2683 |
| 1.4811 | 15.0 | 90 | 1.4730 | 0.2683 |
| 1.4811 | 16.0 | 96 | 1.4709 | 0.2683 |
| 1.476 | 17.0 | 102 | 1.4689 | 0.2683 |
| 1.476 | 18.0 | 108 | 1.4672 | 0.2683 |
| 1.4977 | 19.0 | 114 | 1.4656 | 0.2683 |
| 1.4745 | 20.0 | 120 | 1.4639 | 0.2683 |
| 1.4745 | 21.0 | 126 | 1.4624 | 0.2683 |
| 1.4662 | 22.0 | 132 | 1.4609 | 0.2683 |
| 1.4662 | 23.0 | 138 | 1.4594 | 0.2683 |
| 1.4905 | 24.0 | 144 | 1.4581 | 0.2683 |
| 1.465 | 25.0 | 150 | 1.4568 | 0.2683 |
| 1.465 | 26.0 | 156 | 1.4556 | 0.2683 |
| 1.4499 | 27.0 | 162 | 1.4545 | 0.2683 |
| 1.4499 | 28.0 | 168 | 1.4535 | 0.2683 |
| 1.473 | 29.0 | 174 | 1.4527 | 0.2683 |
| 1.4704 | 30.0 | 180 | 1.4520 | 0.2683 |
| 1.4704 | 31.0 | 186 | 1.4512 | 0.2683 |
| 1.4654 | 32.0 | 192 | 1.4506 | 0.2683 |
| 1.4654 | 33.0 | 198 | 1.4500 | 0.2683 |
| 1.4322 | 34.0 | 204 | 1.4494 | 0.2683 |
| 1.459 | 35.0 | 210 | 1.4490 | 0.2683 |
| 1.459 | 36.0 | 216 | 1.4486 | 0.2683 |
| 1.4499 | 37.0 | 222 | 1.4482 | 0.2683 |
| 1.4499 | 38.0 | 228 | 1.4480 | 0.2683 |
| 1.4314 | 39.0 | 234 | 1.4477 | 0.2683 |
| 1.4745 | 40.0 | 240 | 1.4476 | 0.2683 |
| 1.4745 | 41.0 | 246 | 1.4476 | 0.2683 |
| 1.4482 | 42.0 | 252 | 1.4475 | 0.2683 |
| 1.4482 | 43.0 | 258 | 1.4475 | 0.2683 |
| 1.4526 | 44.0 | 264 | 1.4475 | 0.2683 |
| 1.4693 | 45.0 | 270 | 1.4475 | 0.2683 |
| 1.4693 | 46.0 | 276 | 1.4475 | 0.2683 |
| 1.4506 | 47.0 | 282 | 1.4475 | 0.2683 |
| 1.4506 | 48.0 | 288 | 1.4475 | 0.2683 |
| 1.4529 | 49.0 | 294 | 1.4475 | 0.2683 |
| 1.4667 | 50.0 | 300 | 1.4475 | 0.2683 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_sgd_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: 1.5045
- Accuracy: 0.2889
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5103 | 0.2889 |
| 1.5406 | 2.0 | 12 | 1.5100 | 0.2889 |
| 1.5406 | 3.0 | 18 | 1.5097 | 0.2889 |
| 1.5187 | 4.0 | 24 | 1.5094 | 0.2889 |
| 1.5371 | 5.0 | 30 | 1.5091 | 0.2889 |
| 1.5371 | 6.0 | 36 | 1.5089 | 0.2889 |
| 1.517 | 7.0 | 42 | 1.5086 | 0.2889 |
| 1.517 | 8.0 | 48 | 1.5084 | 0.2889 |
| 1.5407 | 9.0 | 54 | 1.5081 | 0.2889 |
| 1.5157 | 10.0 | 60 | 1.5079 | 0.2889 |
| 1.5157 | 11.0 | 66 | 1.5077 | 0.2889 |
| 1.5121 | 12.0 | 72 | 1.5074 | 0.2889 |
| 1.5121 | 13.0 | 78 | 1.5072 | 0.2889 |
| 1.538 | 14.0 | 84 | 1.5070 | 0.2889 |
| 1.5262 | 15.0 | 90 | 1.5068 | 0.2889 |
| 1.5262 | 16.0 | 96 | 1.5066 | 0.2889 |
| 1.5233 | 17.0 | 102 | 1.5064 | 0.2889 |
| 1.5233 | 18.0 | 108 | 1.5063 | 0.2889 |
| 1.5376 | 19.0 | 114 | 1.5061 | 0.2889 |
| 1.5005 | 20.0 | 120 | 1.5060 | 0.2889 |
| 1.5005 | 21.0 | 126 | 1.5058 | 0.2889 |
| 1.5271 | 22.0 | 132 | 1.5057 | 0.2889 |
| 1.5271 | 23.0 | 138 | 1.5056 | 0.2889 |
| 1.5205 | 24.0 | 144 | 1.5055 | 0.2889 |
| 1.5085 | 25.0 | 150 | 1.5054 | 0.2889 |
| 1.5085 | 26.0 | 156 | 1.5053 | 0.2889 |
| 1.5221 | 27.0 | 162 | 1.5052 | 0.2889 |
| 1.5221 | 28.0 | 168 | 1.5051 | 0.2889 |
| 1.5344 | 29.0 | 174 | 1.5050 | 0.2889 |
| 1.5325 | 30.0 | 180 | 1.5049 | 0.2889 |
| 1.5325 | 31.0 | 186 | 1.5048 | 0.2889 |
| 1.5365 | 32.0 | 192 | 1.5048 | 0.2889 |
| 1.5365 | 33.0 | 198 | 1.5047 | 0.2889 |
| 1.5421 | 34.0 | 204 | 1.5046 | 0.2889 |
| 1.5276 | 35.0 | 210 | 1.5046 | 0.2889 |
| 1.5276 | 36.0 | 216 | 1.5046 | 0.2889 |
| 1.5101 | 37.0 | 222 | 1.5045 | 0.2889 |
| 1.5101 | 38.0 | 228 | 1.5045 | 0.2889 |
| 1.5025 | 39.0 | 234 | 1.5045 | 0.2889 |
| 1.5405 | 40.0 | 240 | 1.5045 | 0.2889 |
| 1.5405 | 41.0 | 246 | 1.5045 | 0.2889 |
| 1.5373 | 42.0 | 252 | 1.5045 | 0.2889 |
| 1.5373 | 43.0 | 258 | 1.5045 | 0.2889 |
| 1.5465 | 44.0 | 264 | 1.5045 | 0.2889 |
| 1.4924 | 45.0 | 270 | 1.5045 | 0.2889 |
| 1.4924 | 46.0 | 276 | 1.5045 | 0.2889 |
| 1.521 | 47.0 | 282 | 1.5045 | 0.2889 |
| 1.521 | 48.0 | 288 | 1.5045 | 0.2889 |
| 1.494 | 49.0 | 294 | 1.5045 | 0.2889 |
| 1.5268 | 50.0 | 300 | 1.5045 | 0.2889 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_sgd_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: 1.5076
- Accuracy: 0.1778
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5128 | 0.1778 |
| 1.5351 | 2.0 | 12 | 1.5126 | 0.1778 |
| 1.5351 | 3.0 | 18 | 1.5123 | 0.1778 |
| 1.521 | 4.0 | 24 | 1.5120 | 0.1778 |
| 1.5462 | 5.0 | 30 | 1.5118 | 0.1778 |
| 1.5462 | 6.0 | 36 | 1.5116 | 0.1778 |
| 1.5099 | 7.0 | 42 | 1.5113 | 0.1778 |
| 1.5099 | 8.0 | 48 | 1.5111 | 0.1778 |
| 1.5333 | 9.0 | 54 | 1.5109 | 0.1778 |
| 1.5206 | 10.0 | 60 | 1.5106 | 0.1778 |
| 1.5206 | 11.0 | 66 | 1.5105 | 0.1778 |
| 1.5227 | 12.0 | 72 | 1.5103 | 0.1778 |
| 1.5227 | 13.0 | 78 | 1.5101 | 0.1778 |
| 1.5256 | 14.0 | 84 | 1.5099 | 0.1778 |
| 1.5395 | 15.0 | 90 | 1.5097 | 0.1778 |
| 1.5395 | 16.0 | 96 | 1.5095 | 0.1778 |
| 1.5169 | 17.0 | 102 | 1.5094 | 0.1778 |
| 1.5169 | 18.0 | 108 | 1.5092 | 0.1778 |
| 1.5502 | 19.0 | 114 | 1.5091 | 0.1778 |
| 1.4882 | 20.0 | 120 | 1.5090 | 0.1778 |
| 1.4882 | 21.0 | 126 | 1.5088 | 0.1778 |
| 1.5202 | 22.0 | 132 | 1.5087 | 0.1778 |
| 1.5202 | 23.0 | 138 | 1.5086 | 0.1778 |
| 1.5139 | 24.0 | 144 | 1.5085 | 0.1778 |
| 1.4995 | 25.0 | 150 | 1.5084 | 0.1778 |
| 1.4995 | 26.0 | 156 | 1.5083 | 0.1778 |
| 1.5175 | 27.0 | 162 | 1.5082 | 0.1778 |
| 1.5175 | 28.0 | 168 | 1.5081 | 0.1778 |
| 1.5365 | 29.0 | 174 | 1.5081 | 0.1778 |
| 1.5232 | 30.0 | 180 | 1.5080 | 0.1778 |
| 1.5232 | 31.0 | 186 | 1.5079 | 0.1778 |
| 1.5236 | 32.0 | 192 | 1.5079 | 0.1778 |
| 1.5236 | 33.0 | 198 | 1.5078 | 0.1778 |
| 1.5292 | 34.0 | 204 | 1.5078 | 0.1778 |
| 1.544 | 35.0 | 210 | 1.5077 | 0.1778 |
| 1.544 | 36.0 | 216 | 1.5077 | 0.1778 |
| 1.4971 | 37.0 | 222 | 1.5077 | 0.1778 |
| 1.4971 | 38.0 | 228 | 1.5077 | 0.1778 |
| 1.4951 | 39.0 | 234 | 1.5076 | 0.1778 |
| 1.5452 | 40.0 | 240 | 1.5076 | 0.1778 |
| 1.5452 | 41.0 | 246 | 1.5076 | 0.1778 |
| 1.5473 | 42.0 | 252 | 1.5076 | 0.1778 |
| 1.5473 | 43.0 | 258 | 1.5076 | 0.1778 |
| 1.5095 | 44.0 | 264 | 1.5076 | 0.1778 |
| 1.495 | 45.0 | 270 | 1.5076 | 0.1778 |
| 1.495 | 46.0 | 276 | 1.5076 | 0.1778 |
| 1.5118 | 47.0 | 282 | 1.5076 | 0.1778 |
| 1.5118 | 48.0 | 288 | 1.5076 | 0.1778 |
| 1.493 | 49.0 | 294 | 1.5076 | 0.1778 |
| 1.528 | 50.0 | 300 | 1.5076 | 0.1778 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_sgd_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: 1.5939
- Accuracy: 0.1860
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6009 | 0.1860 |
| 1.5096 | 2.0 | 12 | 1.6005 | 0.1860 |
| 1.5096 | 3.0 | 18 | 1.6002 | 0.1860 |
| 1.4896 | 4.0 | 24 | 1.5998 | 0.1860 |
| 1.4946 | 5.0 | 30 | 1.5995 | 0.1860 |
| 1.4946 | 6.0 | 36 | 1.5992 | 0.1860 |
| 1.508 | 7.0 | 42 | 1.5988 | 0.1860 |
| 1.508 | 8.0 | 48 | 1.5986 | 0.1860 |
| 1.4945 | 9.0 | 54 | 1.5983 | 0.1860 |
| 1.5205 | 10.0 | 60 | 1.5980 | 0.1860 |
| 1.5205 | 11.0 | 66 | 1.5977 | 0.1860 |
| 1.5058 | 12.0 | 72 | 1.5975 | 0.1860 |
| 1.5058 | 13.0 | 78 | 1.5972 | 0.1860 |
| 1.5082 | 14.0 | 84 | 1.5970 | 0.1860 |
| 1.502 | 15.0 | 90 | 1.5967 | 0.1860 |
| 1.502 | 16.0 | 96 | 1.5965 | 0.1860 |
| 1.5281 | 17.0 | 102 | 1.5963 | 0.1860 |
| 1.5281 | 18.0 | 108 | 1.5961 | 0.1860 |
| 1.4713 | 19.0 | 114 | 1.5959 | 0.1860 |
| 1.5067 | 20.0 | 120 | 1.5957 | 0.1860 |
| 1.5067 | 21.0 | 126 | 1.5955 | 0.1860 |
| 1.5046 | 22.0 | 132 | 1.5953 | 0.1860 |
| 1.5046 | 23.0 | 138 | 1.5952 | 0.1860 |
| 1.4884 | 24.0 | 144 | 1.5950 | 0.1860 |
| 1.4923 | 25.0 | 150 | 1.5949 | 0.1860 |
| 1.4923 | 26.0 | 156 | 1.5948 | 0.1860 |
| 1.4973 | 27.0 | 162 | 1.5947 | 0.1860 |
| 1.4973 | 28.0 | 168 | 1.5945 | 0.1860 |
| 1.5002 | 29.0 | 174 | 1.5945 | 0.1860 |
| 1.4807 | 30.0 | 180 | 1.5944 | 0.1860 |
| 1.4807 | 31.0 | 186 | 1.5943 | 0.1860 |
| 1.486 | 32.0 | 192 | 1.5942 | 0.1860 |
| 1.486 | 33.0 | 198 | 1.5941 | 0.1860 |
| 1.4927 | 34.0 | 204 | 1.5941 | 0.1860 |
| 1.4875 | 35.0 | 210 | 1.5940 | 0.1860 |
| 1.4875 | 36.0 | 216 | 1.5940 | 0.1860 |
| 1.5166 | 37.0 | 222 | 1.5940 | 0.1860 |
| 1.5166 | 38.0 | 228 | 1.5939 | 0.1860 |
| 1.5127 | 39.0 | 234 | 1.5939 | 0.1860 |
| 1.4974 | 40.0 | 240 | 1.5939 | 0.1860 |
| 1.4974 | 41.0 | 246 | 1.5939 | 0.1860 |
| 1.4716 | 42.0 | 252 | 1.5939 | 0.1860 |
| 1.4716 | 43.0 | 258 | 1.5939 | 0.1860 |
| 1.5277 | 44.0 | 264 | 1.5939 | 0.1860 |
| 1.501 | 45.0 | 270 | 1.5939 | 0.1860 |
| 1.501 | 46.0 | 276 | 1.5939 | 0.1860 |
| 1.4805 | 47.0 | 282 | 1.5939 | 0.1860 |
| 1.4805 | 48.0 | 288 | 1.5939 | 0.1860 |
| 1.5052 | 49.0 | 294 | 1.5939 | 0.1860 |
| 1.536 | 50.0 | 300 | 1.5939 | 0.1860 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_sgd_00001_fold4
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: 1.4772
- Accuracy: 0.2857
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4838 | 0.2619 |
| 1.5238 | 2.0 | 12 | 1.4835 | 0.2619 |
| 1.5238 | 3.0 | 18 | 1.4831 | 0.2619 |
| 1.5534 | 4.0 | 24 | 1.4828 | 0.2619 |
| 1.5381 | 5.0 | 30 | 1.4825 | 0.2619 |
| 1.5381 | 6.0 | 36 | 1.4822 | 0.2619 |
| 1.5402 | 7.0 | 42 | 1.4819 | 0.2619 |
| 1.5402 | 8.0 | 48 | 1.4816 | 0.2619 |
| 1.5343 | 9.0 | 54 | 1.4813 | 0.2619 |
| 1.5296 | 10.0 | 60 | 1.4811 | 0.2619 |
| 1.5296 | 11.0 | 66 | 1.4808 | 0.2619 |
| 1.5185 | 12.0 | 72 | 1.4805 | 0.2619 |
| 1.5185 | 13.0 | 78 | 1.4803 | 0.2619 |
| 1.5511 | 14.0 | 84 | 1.4801 | 0.2619 |
| 1.5137 | 15.0 | 90 | 1.4798 | 0.2619 |
| 1.5137 | 16.0 | 96 | 1.4796 | 0.2619 |
| 1.5299 | 17.0 | 102 | 1.4794 | 0.2619 |
| 1.5299 | 18.0 | 108 | 1.4792 | 0.2857 |
| 1.4899 | 19.0 | 114 | 1.4790 | 0.2857 |
| 1.5822 | 20.0 | 120 | 1.4789 | 0.2857 |
| 1.5822 | 21.0 | 126 | 1.4787 | 0.2857 |
| 1.5002 | 22.0 | 132 | 1.4786 | 0.2857 |
| 1.5002 | 23.0 | 138 | 1.4784 | 0.2857 |
| 1.5297 | 24.0 | 144 | 1.4783 | 0.2857 |
| 1.5406 | 25.0 | 150 | 1.4781 | 0.2857 |
| 1.5406 | 26.0 | 156 | 1.4780 | 0.2857 |
| 1.5241 | 27.0 | 162 | 1.4779 | 0.2857 |
| 1.5241 | 28.0 | 168 | 1.4778 | 0.2857 |
| 1.5379 | 29.0 | 174 | 1.4777 | 0.2857 |
| 1.5253 | 30.0 | 180 | 1.4776 | 0.2857 |
| 1.5253 | 31.0 | 186 | 1.4775 | 0.2857 |
| 1.549 | 32.0 | 192 | 1.4775 | 0.2857 |
| 1.549 | 33.0 | 198 | 1.4774 | 0.2857 |
| 1.5016 | 34.0 | 204 | 1.4774 | 0.2857 |
| 1.4996 | 35.0 | 210 | 1.4773 | 0.2857 |
| 1.4996 | 36.0 | 216 | 1.4773 | 0.2857 |
| 1.533 | 37.0 | 222 | 1.4772 | 0.2857 |
| 1.533 | 38.0 | 228 | 1.4772 | 0.2857 |
| 1.5136 | 39.0 | 234 | 1.4772 | 0.2857 |
| 1.5288 | 40.0 | 240 | 1.4772 | 0.2857 |
| 1.5288 | 41.0 | 246 | 1.4772 | 0.2857 |
| 1.5195 | 42.0 | 252 | 1.4772 | 0.2857 |
| 1.5195 | 43.0 | 258 | 1.4772 | 0.2857 |
| 1.5432 | 44.0 | 264 | 1.4772 | 0.2857 |
| 1.5238 | 45.0 | 270 | 1.4772 | 0.2857 |
| 1.5238 | 46.0 | 276 | 1.4772 | 0.2857 |
| 1.544 | 47.0 | 282 | 1.4772 | 0.2857 |
| 1.544 | 48.0 | 288 | 1.4772 | 0.2857 |
| 1.5337 | 49.0 | 294 | 1.4772 | 0.2857 |
| 1.5345 | 50.0 | 300 | 1.4772 | 0.2857 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_sgd_00001_fold5
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: 1.5124
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5202 | 0.2195 |
| 1.5388 | 2.0 | 12 | 1.5198 | 0.2195 |
| 1.5388 | 3.0 | 18 | 1.5194 | 0.2195 |
| 1.5264 | 4.0 | 24 | 1.5190 | 0.2195 |
| 1.5345 | 5.0 | 30 | 1.5186 | 0.2195 |
| 1.5345 | 6.0 | 36 | 1.5183 | 0.2195 |
| 1.526 | 7.0 | 42 | 1.5179 | 0.2195 |
| 1.526 | 8.0 | 48 | 1.5176 | 0.2195 |
| 1.5157 | 9.0 | 54 | 1.5173 | 0.2195 |
| 1.5235 | 10.0 | 60 | 1.5170 | 0.2195 |
| 1.5235 | 11.0 | 66 | 1.5167 | 0.2195 |
| 1.5297 | 12.0 | 72 | 1.5164 | 0.2439 |
| 1.5297 | 13.0 | 78 | 1.5161 | 0.2439 |
| 1.4988 | 14.0 | 84 | 1.5158 | 0.2439 |
| 1.5228 | 15.0 | 90 | 1.5155 | 0.2439 |
| 1.5228 | 16.0 | 96 | 1.5153 | 0.2439 |
| 1.5206 | 17.0 | 102 | 1.5150 | 0.2439 |
| 1.5206 | 18.0 | 108 | 1.5148 | 0.2439 |
| 1.5425 | 19.0 | 114 | 1.5146 | 0.2439 |
| 1.5252 | 20.0 | 120 | 1.5144 | 0.2439 |
| 1.5252 | 21.0 | 126 | 1.5142 | 0.2439 |
| 1.5165 | 22.0 | 132 | 1.5140 | 0.2439 |
| 1.5165 | 23.0 | 138 | 1.5139 | 0.2439 |
| 1.5451 | 24.0 | 144 | 1.5137 | 0.2439 |
| 1.5198 | 25.0 | 150 | 1.5135 | 0.2439 |
| 1.5198 | 26.0 | 156 | 1.5134 | 0.2439 |
| 1.5047 | 27.0 | 162 | 1.5132 | 0.2439 |
| 1.5047 | 28.0 | 168 | 1.5131 | 0.2439 |
| 1.5384 | 29.0 | 174 | 1.5130 | 0.2439 |
| 1.5271 | 30.0 | 180 | 1.5129 | 0.2439 |
| 1.5271 | 31.0 | 186 | 1.5128 | 0.2439 |
| 1.5283 | 32.0 | 192 | 1.5127 | 0.2439 |
| 1.5283 | 33.0 | 198 | 1.5127 | 0.2439 |
| 1.4864 | 34.0 | 204 | 1.5126 | 0.2439 |
| 1.5229 | 35.0 | 210 | 1.5125 | 0.2439 |
| 1.5229 | 36.0 | 216 | 1.5125 | 0.2439 |
| 1.513 | 37.0 | 222 | 1.5125 | 0.2439 |
| 1.513 | 38.0 | 228 | 1.5124 | 0.2439 |
| 1.4969 | 39.0 | 234 | 1.5124 | 0.2439 |
| 1.5399 | 40.0 | 240 | 1.5124 | 0.2439 |
| 1.5399 | 41.0 | 246 | 1.5124 | 0.2439 |
| 1.5142 | 42.0 | 252 | 1.5124 | 0.2439 |
| 1.5142 | 43.0 | 258 | 1.5124 | 0.2439 |
| 1.5226 | 44.0 | 264 | 1.5124 | 0.2439 |
| 1.538 | 45.0 | 270 | 1.5124 | 0.2439 |
| 1.538 | 46.0 | 276 | 1.5124 | 0.2439 |
| 1.5217 | 47.0 | 282 | 1.5124 | 0.2439 |
| 1.5217 | 48.0 | 288 | 1.5124 | 0.2439 |
| 1.5124 | 49.0 | 294 | 1.5124 | 0.2439 |
| 1.5354 | 50.0 | 300 | 1.5124 | 0.2439 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_rms_001_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: 1.5587
- 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 5.6616 | 0.2444 |
| 4.5403 | 2.0 | 12 | 1.9139 | 0.2444 |
| 4.5403 | 3.0 | 18 | 1.7372 | 0.2444 |
| 1.8724 | 4.0 | 24 | 1.4323 | 0.2667 |
| 1.5505 | 5.0 | 30 | 1.5541 | 0.2444 |
| 1.5505 | 6.0 | 36 | 1.5305 | 0.2444 |
| 1.4992 | 7.0 | 42 | 1.5286 | 0.2444 |
| 1.4992 | 8.0 | 48 | 1.5617 | 0.2444 |
| 1.4899 | 9.0 | 54 | 1.4717 | 0.2444 |
| 1.4501 | 10.0 | 60 | 1.4440 | 0.2444 |
| 1.4501 | 11.0 | 66 | 1.4155 | 0.2667 |
| 1.4052 | 12.0 | 72 | 1.3606 | 0.2444 |
| 1.4052 | 13.0 | 78 | 1.4215 | 0.3333 |
| 1.4555 | 14.0 | 84 | 1.3356 | 0.3333 |
| 1.4209 | 15.0 | 90 | 1.4688 | 0.2667 |
| 1.4209 | 16.0 | 96 | 1.2956 | 0.4444 |
| 1.4079 | 17.0 | 102 | 1.4012 | 0.2444 |
| 1.4079 | 18.0 | 108 | 1.4817 | 0.2444 |
| 1.4101 | 19.0 | 114 | 1.4296 | 0.2667 |
| 1.6129 | 20.0 | 120 | 1.5601 | 0.2444 |
| 1.6129 | 21.0 | 126 | 1.8216 | 0.2667 |
| 1.5349 | 22.0 | 132 | 1.6109 | 0.2667 |
| 1.5349 | 23.0 | 138 | 1.6663 | 0.2444 |
| 1.4443 | 24.0 | 144 | 1.4166 | 0.2444 |
| 1.3949 | 25.0 | 150 | 1.5159 | 0.2444 |
| 1.3949 | 26.0 | 156 | 1.5557 | 0.2444 |
| 1.2549 | 27.0 | 162 | 1.2710 | 0.3333 |
| 1.2549 | 28.0 | 168 | 1.4661 | 0.3333 |
| 1.2756 | 29.0 | 174 | 1.3759 | 0.3111 |
| 1.2244 | 30.0 | 180 | 1.3243 | 0.4222 |
| 1.2244 | 31.0 | 186 | 1.1877 | 0.4222 |
| 1.1482 | 32.0 | 192 | 1.1943 | 0.4667 |
| 1.1482 | 33.0 | 198 | 1.3644 | 0.3111 |
| 1.0904 | 34.0 | 204 | 1.3812 | 0.3778 |
| 1.051 | 35.0 | 210 | 1.3131 | 0.4444 |
| 1.051 | 36.0 | 216 | 1.7518 | 0.2667 |
| 1.0583 | 37.0 | 222 | 1.8440 | 0.3556 |
| 1.0583 | 38.0 | 228 | 1.7450 | 0.2889 |
| 0.8766 | 39.0 | 234 | 1.5767 | 0.3556 |
| 0.9084 | 40.0 | 240 | 1.5052 | 0.3778 |
| 0.9084 | 41.0 | 246 | 1.5534 | 0.3556 |
| 0.8553 | 42.0 | 252 | 1.5587 | 0.3556 |
| 0.8553 | 43.0 | 258 | 1.5587 | 0.3556 |
| 0.8404 | 44.0 | 264 | 1.5587 | 0.3556 |
| 0.8432 | 45.0 | 270 | 1.5587 | 0.3556 |
| 0.8432 | 46.0 | 276 | 1.5587 | 0.3556 |
| 0.8133 | 47.0 | 282 | 1.5587 | 0.3556 |
| 0.8133 | 48.0 | 288 | 1.5587 | 0.3556 |
| 0.8467 | 49.0 | 294 | 1.5587 | 0.3556 |
| 0.8396 | 50.0 | 300 | 1.5587 | 0.3556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_rms_001_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: 1.2524
- Accuracy: 0.4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 7.5665 | 0.2444 |
| 4.6598 | 2.0 | 12 | 1.8034 | 0.2444 |
| 4.6598 | 3.0 | 18 | 1.7719 | 0.2444 |
| 1.754 | 4.0 | 24 | 1.5619 | 0.2667 |
| 1.5561 | 5.0 | 30 | 1.5155 | 0.2444 |
| 1.5561 | 6.0 | 36 | 1.5905 | 0.2444 |
| 1.5161 | 7.0 | 42 | 1.4606 | 0.2444 |
| 1.5161 | 8.0 | 48 | 1.5057 | 0.2667 |
| 1.4837 | 9.0 | 54 | 1.4997 | 0.2444 |
| 1.456 | 10.0 | 60 | 1.4411 | 0.2444 |
| 1.456 | 11.0 | 66 | 1.4980 | 0.2667 |
| 1.4256 | 12.0 | 72 | 1.4097 | 0.2444 |
| 1.4256 | 13.0 | 78 | 1.4518 | 0.2667 |
| 1.4488 | 14.0 | 84 | 1.3937 | 0.2667 |
| 1.4354 | 15.0 | 90 | 1.4044 | 0.2444 |
| 1.4354 | 16.0 | 96 | 1.3767 | 0.2667 |
| 1.4383 | 17.0 | 102 | 1.4222 | 0.2444 |
| 1.4383 | 18.0 | 108 | 1.4806 | 0.2444 |
| 1.4107 | 19.0 | 114 | 1.4789 | 0.2444 |
| 1.3761 | 20.0 | 120 | 1.2485 | 0.4444 |
| 1.3761 | 21.0 | 126 | 1.3600 | 0.2667 |
| 1.3385 | 22.0 | 132 | 1.4500 | 0.4 |
| 1.3385 | 23.0 | 138 | 1.3814 | 0.3778 |
| 1.3465 | 24.0 | 144 | 1.4692 | 0.2667 |
| 1.323 | 25.0 | 150 | 1.1674 | 0.4667 |
| 1.323 | 26.0 | 156 | 1.3636 | 0.2889 |
| 1.2871 | 27.0 | 162 | 1.3963 | 0.4 |
| 1.2871 | 28.0 | 168 | 1.3023 | 0.4444 |
| 1.1938 | 29.0 | 174 | 1.2034 | 0.4222 |
| 1.2252 | 30.0 | 180 | 1.2237 | 0.4444 |
| 1.2252 | 31.0 | 186 | 1.2906 | 0.4 |
| 1.2127 | 32.0 | 192 | 1.2853 | 0.4 |
| 1.2127 | 33.0 | 198 | 1.3006 | 0.3556 |
| 1.131 | 34.0 | 204 | 1.3803 | 0.2889 |
| 1.1689 | 35.0 | 210 | 1.2981 | 0.3556 |
| 1.1689 | 36.0 | 216 | 1.4728 | 0.2889 |
| 1.1285 | 37.0 | 222 | 1.3455 | 0.3333 |
| 1.1285 | 38.0 | 228 | 1.2593 | 0.4 |
| 1.0174 | 39.0 | 234 | 1.2539 | 0.3556 |
| 1.0651 | 40.0 | 240 | 1.2296 | 0.4 |
| 1.0651 | 41.0 | 246 | 1.2510 | 0.3778 |
| 1.0297 | 42.0 | 252 | 1.2524 | 0.4 |
| 1.0297 | 43.0 | 258 | 1.2524 | 0.4 |
| 0.9982 | 44.0 | 264 | 1.2524 | 0.4 |
| 1.047 | 45.0 | 270 | 1.2524 | 0.4 |
| 1.047 | 46.0 | 276 | 1.2524 | 0.4 |
| 0.9969 | 47.0 | 282 | 1.2524 | 0.4 |
| 0.9969 | 48.0 | 288 | 1.2524 | 0.4 |
| 1.0686 | 49.0 | 294 | 1.2524 | 0.4 |
| 1.0034 | 50.0 | 300 | 1.2524 | 0.4 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_rms_001_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: 1.4032
- 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 7.8981 | 0.2558 |
| 4.5842 | 2.0 | 12 | 2.6640 | 0.2558 |
| 4.5842 | 3.0 | 18 | 1.6613 | 0.2558 |
| 1.9697 | 4.0 | 24 | 1.9731 | 0.2558 |
| 1.667 | 5.0 | 30 | 1.6222 | 0.2558 |
| 1.667 | 6.0 | 36 | 1.5650 | 0.2558 |
| 1.5493 | 7.0 | 42 | 1.7126 | 0.2326 |
| 1.5493 | 8.0 | 48 | 1.7198 | 0.2558 |
| 1.5158 | 9.0 | 54 | 1.4567 | 0.2558 |
| 1.4878 | 10.0 | 60 | 1.4210 | 0.2558 |
| 1.4878 | 11.0 | 66 | 1.4952 | 0.2558 |
| 1.4957 | 12.0 | 72 | 1.3917 | 0.2558 |
| 1.4957 | 13.0 | 78 | 1.4819 | 0.2558 |
| 1.4611 | 14.0 | 84 | 1.4450 | 0.2558 |
| 1.4163 | 15.0 | 90 | 1.4214 | 0.2558 |
| 1.4163 | 16.0 | 96 | 1.4612 | 0.2326 |
| 1.4195 | 17.0 | 102 | 1.4036 | 0.2558 |
| 1.4195 | 18.0 | 108 | 1.5088 | 0.2558 |
| 1.4297 | 19.0 | 114 | 1.4009 | 0.2558 |
| 1.4404 | 20.0 | 120 | 1.3994 | 0.2558 |
| 1.4404 | 21.0 | 126 | 1.4434 | 0.2326 |
| 1.4296 | 22.0 | 132 | 1.4098 | 0.2326 |
| 1.4296 | 23.0 | 138 | 1.4135 | 0.2558 |
| 1.4052 | 24.0 | 144 | 1.4035 | 0.3023 |
| 1.3982 | 25.0 | 150 | 1.3795 | 0.3023 |
| 1.3982 | 26.0 | 156 | 1.3917 | 0.2558 |
| 1.358 | 27.0 | 162 | 1.5442 | 0.2326 |
| 1.358 | 28.0 | 168 | 1.3715 | 0.3256 |
| 1.3753 | 29.0 | 174 | 1.4626 | 0.2791 |
| 1.3737 | 30.0 | 180 | 1.4033 | 0.3023 |
| 1.3737 | 31.0 | 186 | 1.4221 | 0.3488 |
| 1.2553 | 32.0 | 192 | 1.5495 | 0.2791 |
| 1.2553 | 33.0 | 198 | 1.4332 | 0.2791 |
| 1.2089 | 34.0 | 204 | 1.4065 | 0.2791 |
| 1.2158 | 35.0 | 210 | 1.4613 | 0.2791 |
| 1.2158 | 36.0 | 216 | 1.4360 | 0.3256 |
| 1.1733 | 37.0 | 222 | 1.4966 | 0.3256 |
| 1.1733 | 38.0 | 228 | 1.4024 | 0.2791 |
| 1.1359 | 39.0 | 234 | 1.3752 | 0.2791 |
| 1.1239 | 40.0 | 240 | 1.4121 | 0.3023 |
| 1.1239 | 41.0 | 246 | 1.4047 | 0.2791 |
| 1.0932 | 42.0 | 252 | 1.4032 | 0.2791 |
| 1.0932 | 43.0 | 258 | 1.4032 | 0.2791 |
| 1.0875 | 44.0 | 264 | 1.4032 | 0.2791 |
| 1.102 | 45.0 | 270 | 1.4032 | 0.2791 |
| 1.102 | 46.0 | 276 | 1.4032 | 0.2791 |
| 1.0783 | 47.0 | 282 | 1.4032 | 0.2791 |
| 1.0783 | 48.0 | 288 | 1.4032 | 0.2791 |
| 1.1264 | 49.0 | 294 | 1.4032 | 0.2791 |
| 1.0785 | 50.0 | 300 | 1.4032 | 0.2791 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_rms_001_fold4
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: 1.2235
- 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 6.3906 | 0.2381 |
| 3.8063 | 2.0 | 12 | 1.7015 | 0.2619 |
| 3.8063 | 3.0 | 18 | 2.0641 | 0.2619 |
| 1.9221 | 4.0 | 24 | 1.7697 | 0.2381 |
| 1.6782 | 5.0 | 30 | 1.4022 | 0.2619 |
| 1.6782 | 6.0 | 36 | 1.7511 | 0.2381 |
| 1.5442 | 7.0 | 42 | 1.4627 | 0.2381 |
| 1.5442 | 8.0 | 48 | 1.4402 | 0.2619 |
| 1.4869 | 9.0 | 54 | 1.4717 | 0.2619 |
| 1.4572 | 10.0 | 60 | 1.4285 | 0.2381 |
| 1.4572 | 11.0 | 66 | 1.4073 | 0.2619 |
| 1.4861 | 12.0 | 72 | 1.4071 | 0.3095 |
| 1.4861 | 13.0 | 78 | 1.3676 | 0.3095 |
| 1.4283 | 14.0 | 84 | 1.4281 | 0.2381 |
| 1.4135 | 15.0 | 90 | 1.4437 | 0.2381 |
| 1.4135 | 16.0 | 96 | 1.3561 | 0.3095 |
| 1.375 | 17.0 | 102 | 1.3574 | 0.2857 |
| 1.375 | 18.0 | 108 | 1.2368 | 0.2857 |
| 1.3639 | 19.0 | 114 | 1.4601 | 0.2857 |
| 1.2891 | 20.0 | 120 | 1.7927 | 0.2381 |
| 1.2891 | 21.0 | 126 | 1.2451 | 0.4048 |
| 1.3173 | 22.0 | 132 | 1.1578 | 0.4762 |
| 1.3173 | 23.0 | 138 | 1.3222 | 0.3095 |
| 1.2505 | 24.0 | 144 | 1.3748 | 0.2381 |
| 1.263 | 25.0 | 150 | 1.3699 | 0.2857 |
| 1.263 | 26.0 | 156 | 1.2508 | 0.3810 |
| 1.2132 | 27.0 | 162 | 1.1843 | 0.4048 |
| 1.2132 | 28.0 | 168 | 1.4161 | 0.2619 |
| 1.1485 | 29.0 | 174 | 1.1305 | 0.4524 |
| 1.181 | 30.0 | 180 | 1.1818 | 0.4524 |
| 1.181 | 31.0 | 186 | 1.2906 | 0.4048 |
| 1.131 | 32.0 | 192 | 1.1623 | 0.4762 |
| 1.131 | 33.0 | 198 | 1.2826 | 0.4524 |
| 1.164 | 34.0 | 204 | 1.1932 | 0.4524 |
| 1.0879 | 35.0 | 210 | 1.1104 | 0.4286 |
| 1.0879 | 36.0 | 216 | 1.0661 | 0.5714 |
| 1.1012 | 37.0 | 222 | 1.2594 | 0.4048 |
| 1.1012 | 38.0 | 228 | 1.1459 | 0.4286 |
| 1.0505 | 39.0 | 234 | 1.1918 | 0.4524 |
| 1.0052 | 40.0 | 240 | 1.2662 | 0.4286 |
| 1.0052 | 41.0 | 246 | 1.2165 | 0.4048 |
| 0.9631 | 42.0 | 252 | 1.2235 | 0.4048 |
| 0.9631 | 43.0 | 258 | 1.2235 | 0.4048 |
| 0.9397 | 44.0 | 264 | 1.2235 | 0.4048 |
| 0.9545 | 45.0 | 270 | 1.2235 | 0.4048 |
| 0.9545 | 46.0 | 276 | 1.2235 | 0.4048 |
| 0.9591 | 47.0 | 282 | 1.2235 | 0.4048 |
| 0.9591 | 48.0 | 288 | 1.2235 | 0.4048 |
| 0.9579 | 49.0 | 294 | 1.2235 | 0.4048 |
| 0.9362 | 50.0 | 300 | 1.2235 | 0.4048 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_1x_deit_small_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_1x_deit_small_rms_001_fold5
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: 1.0780
- Accuracy: 0.5854
## Model description
More information needed
## Intended uses & 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 5.8408 | 0.2683 |
| 4.7719 | 2.0 | 12 | 1.7477 | 0.2683 |
| 4.7719 | 3.0 | 18 | 1.7384 | 0.2683 |
| 1.932 | 4.0 | 24 | 1.4924 | 0.2439 |
| 1.6238 | 5.0 | 30 | 1.5127 | 0.2439 |
| 1.6238 | 6.0 | 36 | 1.7015 | 0.2683 |
| 1.5494 | 7.0 | 42 | 1.5012 | 0.2439 |
| 1.5494 | 8.0 | 48 | 1.4936 | 0.2683 |
| 1.5044 | 9.0 | 54 | 1.4046 | 0.2683 |
| 1.5179 | 10.0 | 60 | 1.4005 | 0.2439 |
| 1.5179 | 11.0 | 66 | 1.4179 | 0.2683 |
| 1.475 | 12.0 | 72 | 1.4436 | 0.2439 |
| 1.475 | 13.0 | 78 | 1.6356 | 0.2439 |
| 1.4371 | 14.0 | 84 | 1.3958 | 0.2683 |
| 1.4353 | 15.0 | 90 | 1.3814 | 0.2439 |
| 1.4353 | 16.0 | 96 | 1.5489 | 0.2439 |
| 1.4229 | 17.0 | 102 | 1.4209 | 0.2439 |
| 1.4229 | 18.0 | 108 | 1.3777 | 0.2439 |
| 1.4337 | 19.0 | 114 | 1.3611 | 0.3171 |
| 1.399 | 20.0 | 120 | 1.3904 | 0.4146 |
| 1.399 | 21.0 | 126 | 1.3106 | 0.3415 |
| 1.3792 | 22.0 | 132 | 1.5271 | 0.3171 |
| 1.3792 | 23.0 | 138 | 1.4354 | 0.3171 |
| 1.353 | 24.0 | 144 | 1.2465 | 0.3171 |
| 1.3278 | 25.0 | 150 | 1.4779 | 0.2683 |
| 1.3278 | 26.0 | 156 | 1.1238 | 0.6585 |
| 1.2815 | 27.0 | 162 | 1.0700 | 0.4878 |
| 1.2815 | 28.0 | 168 | 1.4309 | 0.2927 |
| 1.2766 | 29.0 | 174 | 1.1073 | 0.5854 |
| 1.2458 | 30.0 | 180 | 1.0518 | 0.5366 |
| 1.2458 | 31.0 | 186 | 1.0678 | 0.5366 |
| 1.196 | 32.0 | 192 | 1.0365 | 0.5122 |
| 1.196 | 33.0 | 198 | 1.0762 | 0.4878 |
| 1.1298 | 34.0 | 204 | 1.1843 | 0.4390 |
| 1.1053 | 35.0 | 210 | 0.9867 | 0.5610 |
| 1.1053 | 36.0 | 216 | 0.9844 | 0.5854 |
| 1.0778 | 37.0 | 222 | 1.2314 | 0.4878 |
| 1.0778 | 38.0 | 228 | 0.9827 | 0.5854 |
| 1.0269 | 39.0 | 234 | 1.0882 | 0.5854 |
| 0.9486 | 40.0 | 240 | 1.0901 | 0.5854 |
| 0.9486 | 41.0 | 246 | 1.0899 | 0.5854 |
| 0.9443 | 42.0 | 252 | 1.0780 | 0.5854 |
| 0.9443 | 43.0 | 258 | 1.0780 | 0.5854 |
| 0.8824 | 44.0 | 264 | 1.0780 | 0.5854 |
| 0.8971 | 45.0 | 270 | 1.0780 | 0.5854 |
| 0.8971 | 46.0 | 276 | 1.0780 | 0.5854 |
| 0.8963 | 47.0 | 282 | 1.0780 | 0.5854 |
| 0.8963 | 48.0 | 288 | 1.0780 | 0.5854 |
| 0.9026 | 49.0 | 294 | 1.0780 | 0.5854 |
| 0.9008 | 50.0 | 300 | 1.0780 | 0.5854 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
Akshay0706/Rice-Plant-50-Epochs-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. -->
# Rice-Plant-50-Epochs-Model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1649
- Accuracy: 0.9688
- F1: 0.9686
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0399 | 1.0 | 115 | 0.6185 | 0.8910 | 0.8933 |
| 0.3392 | 2.0 | 230 | 0.2849 | 0.9502 | 0.9497 |
| 0.1633 | 3.0 | 345 | 0.2230 | 0.9439 | 0.9440 |
| 0.104 | 4.0 | 460 | 0.2022 | 0.9502 | 0.9495 |
| 0.0828 | 5.0 | 575 | 0.2081 | 0.9408 | 0.9406 |
| 0.0603 | 6.0 | 690 | 0.2301 | 0.9408 | 0.9403 |
| 0.0513 | 7.0 | 805 | 0.1704 | 0.9595 | 0.9593 |
| 0.042 | 8.0 | 920 | 0.1587 | 0.9626 | 0.9626 |
| 0.0356 | 9.0 | 1035 | 0.1606 | 0.9626 | 0.9625 |
| 0.0299 | 10.0 | 1150 | 0.1608 | 0.9657 | 0.9656 |
| 0.0262 | 11.0 | 1265 | 0.1553 | 0.9626 | 0.9625 |
| 0.0232 | 12.0 | 1380 | 0.1582 | 0.9657 | 0.9656 |
| 0.0207 | 13.0 | 1495 | 0.1588 | 0.9657 | 0.9656 |
| 0.0186 | 14.0 | 1610 | 0.1618 | 0.9657 | 0.9656 |
| 0.0168 | 15.0 | 1725 | 0.1618 | 0.9657 | 0.9656 |
| 0.0152 | 16.0 | 1840 | 0.1639 | 0.9657 | 0.9656 |
| 0.0139 | 17.0 | 1955 | 0.1649 | 0.9688 | 0.9686 |
| 0.0127 | 18.0 | 2070 | 0.1676 | 0.9657 | 0.9656 |
| 0.0117 | 19.0 | 2185 | 0.1688 | 0.9688 | 0.9686 |
| 0.0108 | 20.0 | 2300 | 0.1710 | 0.9626 | 0.9622 |
| 0.01 | 21.0 | 2415 | 0.1723 | 0.9657 | 0.9654 |
| 0.0093 | 22.0 | 2530 | 0.1739 | 0.9657 | 0.9654 |
| 0.0087 | 23.0 | 2645 | 0.1758 | 0.9626 | 0.9622 |
| 0.0081 | 24.0 | 2760 | 0.1776 | 0.9626 | 0.9622 |
| 0.0076 | 25.0 | 2875 | 0.1777 | 0.9657 | 0.9654 |
| 0.0071 | 26.0 | 2990 | 0.1792 | 0.9657 | 0.9654 |
| 0.0067 | 27.0 | 3105 | 0.1808 | 0.9657 | 0.9654 |
| 0.0063 | 28.0 | 3220 | 0.1822 | 0.9657 | 0.9654 |
| 0.006 | 29.0 | 3335 | 0.1834 | 0.9657 | 0.9654 |
| 0.0057 | 30.0 | 3450 | 0.1840 | 0.9657 | 0.9654 |
| 0.0054 | 31.0 | 3565 | 0.1855 | 0.9657 | 0.9654 |
| 0.0051 | 32.0 | 3680 | 0.1868 | 0.9657 | 0.9654 |
| 0.0049 | 33.0 | 3795 | 0.1877 | 0.9657 | 0.9654 |
| 0.0047 | 34.0 | 3910 | 0.1892 | 0.9657 | 0.9654 |
| 0.0045 | 35.0 | 4025 | 0.1900 | 0.9657 | 0.9654 |
| 0.0043 | 36.0 | 4140 | 0.1914 | 0.9657 | 0.9654 |
| 0.0042 | 37.0 | 4255 | 0.1919 | 0.9657 | 0.9654 |
| 0.004 | 38.0 | 4370 | 0.1929 | 0.9657 | 0.9654 |
| 0.0039 | 39.0 | 4485 | 0.1938 | 0.9657 | 0.9654 |
| 0.0037 | 40.0 | 4600 | 0.1953 | 0.9657 | 0.9654 |
| 0.0036 | 41.0 | 4715 | 0.1956 | 0.9657 | 0.9654 |
| 0.0035 | 42.0 | 4830 | 0.1965 | 0.9657 | 0.9654 |
| 0.0035 | 43.0 | 4945 | 0.1974 | 0.9657 | 0.9654 |
| 0.0034 | 44.0 | 5060 | 0.1981 | 0.9657 | 0.9654 |
| 0.0033 | 45.0 | 5175 | 0.1984 | 0.9657 | 0.9654 |
| 0.0032 | 46.0 | 5290 | 0.1986 | 0.9657 | 0.9654 |
| 0.0032 | 47.0 | 5405 | 0.1989 | 0.9657 | 0.9654 |
| 0.0032 | 48.0 | 5520 | 0.1993 | 0.9657 | 0.9654 |
| 0.0031 | 49.0 | 5635 | 0.1993 | 0.9657 | 0.9654 |
| 0.0031 | 50.0 | 5750 | 0.1993 | 0.9657 | 0.9654 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"0",
"1",
"2",
"3",
"4",
"5"
] |
zkdeng/resnet-50-finetuned-combinedSpiders
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-50-finetuned-combinedSpiders
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3794
- eval_accuracy: 0.8996
- eval_precision: 0.8983
- eval_recall: 0.8934
- eval_f1: 0.8943
- eval_runtime: 14.9052
- eval_samples_per_second: 181.145
- eval_steps_per_second: 11.338
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"annual crop",
"forest",
"herbaceous vegetation",
"highway",
"industrial",
"pasture",
"permanent crop",
"residential",
"river",
"sea or lake"
] |
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