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huggingspark/vit-base-patch16-224-finetuned-flower
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
hkivancoral/smids_10x_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. -->
# smids_10x_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: 2.3327
- Accuracy: 0.8367
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7568 | 1.0 | 750 | 0.7218 | 0.6717 |
| 0.6894 | 2.0 | 1500 | 0.6565 | 0.6583 |
| 0.6167 | 3.0 | 2250 | 0.6175 | 0.7017 |
| 0.6218 | 4.0 | 3000 | 0.6221 | 0.705 |
| 0.5637 | 5.0 | 3750 | 0.5565 | 0.7667 |
| 0.5037 | 6.0 | 4500 | 0.5481 | 0.7533 |
| 0.441 | 7.0 | 5250 | 0.5584 | 0.7667 |
| 0.5186 | 8.0 | 6000 | 0.5150 | 0.78 |
| 0.4475 | 9.0 | 6750 | 0.5478 | 0.775 |
| 0.4117 | 10.0 | 7500 | 0.5408 | 0.7867 |
| 0.4227 | 11.0 | 8250 | 0.4928 | 0.8017 |
| 0.4314 | 12.0 | 9000 | 0.5195 | 0.785 |
| 0.3606 | 13.0 | 9750 | 0.4998 | 0.8033 |
| 0.3206 | 14.0 | 10500 | 0.5323 | 0.8083 |
| 0.2628 | 15.0 | 11250 | 0.5344 | 0.79 |
| 0.2274 | 16.0 | 12000 | 0.5778 | 0.81 |
| 0.1649 | 17.0 | 12750 | 0.5559 | 0.8283 |
| 0.2197 | 18.0 | 13500 | 0.5838 | 0.8017 |
| 0.242 | 19.0 | 14250 | 0.5757 | 0.83 |
| 0.178 | 20.0 | 15000 | 0.6143 | 0.8183 |
| 0.1596 | 21.0 | 15750 | 0.6500 | 0.8267 |
| 0.1681 | 22.0 | 16500 | 0.7191 | 0.83 |
| 0.1356 | 23.0 | 17250 | 0.6652 | 0.83 |
| 0.154 | 24.0 | 18000 | 0.7572 | 0.835 |
| 0.0973 | 25.0 | 18750 | 0.7886 | 0.8283 |
| 0.1206 | 26.0 | 19500 | 0.9030 | 0.8033 |
| 0.0856 | 27.0 | 20250 | 1.0266 | 0.8083 |
| 0.0629 | 28.0 | 21000 | 0.8154 | 0.8333 |
| 0.0803 | 29.0 | 21750 | 1.0582 | 0.8133 |
| 0.0608 | 30.0 | 22500 | 1.1240 | 0.8317 |
| 0.0468 | 31.0 | 23250 | 1.1197 | 0.8183 |
| 0.0343 | 32.0 | 24000 | 1.2322 | 0.8217 |
| 0.0156 | 33.0 | 24750 | 1.3344 | 0.8367 |
| 0.0192 | 34.0 | 25500 | 1.3961 | 0.8133 |
| 0.0219 | 35.0 | 26250 | 1.5315 | 0.8033 |
| 0.0147 | 36.0 | 27000 | 1.5425 | 0.8233 |
| 0.0123 | 37.0 | 27750 | 1.6413 | 0.835 |
| 0.0089 | 38.0 | 28500 | 1.7045 | 0.8167 |
| 0.0003 | 39.0 | 29250 | 1.6054 | 0.8183 |
| 0.0102 | 40.0 | 30000 | 1.6942 | 0.825 |
| 0.0008 | 41.0 | 30750 | 1.7260 | 0.84 |
| 0.0077 | 42.0 | 31500 | 1.9643 | 0.8217 |
| 0.0048 | 43.0 | 32250 | 2.0335 | 0.825 |
| 0.0015 | 44.0 | 33000 | 2.2512 | 0.8367 |
| 0.0015 | 45.0 | 33750 | 2.1796 | 0.8333 |
| 0.0001 | 46.0 | 34500 | 2.2799 | 0.83 |
| 0.0 | 47.0 | 35250 | 2.2493 | 0.8317 |
| 0.0 | 48.0 | 36000 | 2.3177 | 0.8417 |
| 0.0 | 49.0 | 36750 | 2.3130 | 0.8317 |
| 0.0 | 50.0 | 37500 | 2.3327 | 0.8367 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_10x_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. -->
# smids_10x_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: 1.3970
- Accuracy: 0.875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2502 | 1.0 | 750 | 0.3491 | 0.8717 |
| 0.1602 | 2.0 | 1500 | 0.3787 | 0.8617 |
| 0.0975 | 3.0 | 2250 | 0.4137 | 0.8717 |
| 0.0908 | 4.0 | 3000 | 0.4782 | 0.8767 |
| 0.061 | 5.0 | 3750 | 0.6214 | 0.88 |
| 0.0396 | 6.0 | 4500 | 0.7566 | 0.885 |
| 0.062 | 7.0 | 5250 | 0.8425 | 0.8683 |
| 0.0007 | 8.0 | 6000 | 0.8436 | 0.8817 |
| 0.0025 | 9.0 | 6750 | 0.9779 | 0.88 |
| 0.0255 | 10.0 | 7500 | 1.1344 | 0.8683 |
| 0.0352 | 11.0 | 8250 | 1.1423 | 0.8633 |
| 0.0318 | 12.0 | 9000 | 1.0833 | 0.865 |
| 0.0002 | 13.0 | 9750 | 1.1038 | 0.8767 |
| 0.0084 | 14.0 | 10500 | 1.0965 | 0.87 |
| 0.0351 | 15.0 | 11250 | 1.1367 | 0.8717 |
| 0.0252 | 16.0 | 12000 | 1.2686 | 0.875 |
| 0.0093 | 17.0 | 12750 | 1.0965 | 0.8833 |
| 0.0 | 18.0 | 13500 | 1.2326 | 0.8683 |
| 0.0001 | 19.0 | 14250 | 1.3027 | 0.8683 |
| 0.0 | 20.0 | 15000 | 1.1789 | 0.8783 |
| 0.0 | 21.0 | 15750 | 1.2125 | 0.875 |
| 0.0 | 22.0 | 16500 | 1.1748 | 0.8767 |
| 0.0001 | 23.0 | 17250 | 1.1790 | 0.87 |
| 0.0 | 24.0 | 18000 | 1.3742 | 0.8733 |
| 0.0 | 25.0 | 18750 | 1.2377 | 0.8783 |
| 0.0003 | 26.0 | 19500 | 1.3192 | 0.8783 |
| 0.0 | 27.0 | 20250 | 1.2703 | 0.8817 |
| 0.0001 | 28.0 | 21000 | 1.2600 | 0.8833 |
| 0.0 | 29.0 | 21750 | 1.2702 | 0.8867 |
| 0.0 | 30.0 | 22500 | 1.2442 | 0.8917 |
| 0.0 | 31.0 | 23250 | 1.2963 | 0.8817 |
| 0.0 | 32.0 | 24000 | 1.4012 | 0.88 |
| 0.0 | 33.0 | 24750 | 1.4514 | 0.8767 |
| 0.0 | 34.0 | 25500 | 1.4381 | 0.8733 |
| 0.0 | 35.0 | 26250 | 1.3220 | 0.8783 |
| 0.0 | 36.0 | 27000 | 1.3859 | 0.8767 |
| 0.0 | 37.0 | 27750 | 1.2544 | 0.8817 |
| 0.0 | 38.0 | 28500 | 1.2536 | 0.8817 |
| 0.0 | 39.0 | 29250 | 1.3812 | 0.88 |
| 0.0 | 40.0 | 30000 | 1.3350 | 0.8733 |
| 0.0 | 41.0 | 30750 | 1.4121 | 0.8733 |
| 0.0 | 42.0 | 31500 | 1.4110 | 0.8733 |
| 0.0 | 43.0 | 32250 | 1.4115 | 0.875 |
| 0.0 | 44.0 | 33000 | 1.3934 | 0.8783 |
| 0.0 | 45.0 | 33750 | 1.3917 | 0.88 |
| 0.0 | 46.0 | 34500 | 1.3899 | 0.88 |
| 0.0 | 47.0 | 35250 | 1.3925 | 0.88 |
| 0.0 | 48.0 | 36000 | 1.3926 | 0.88 |
| 0.0 | 49.0 | 36750 | 1.3955 | 0.875 |
| 0.0 | 50.0 | 37500 | 1.3970 | 0.875 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_10x_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. -->
# smids_10x_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: 1.4754
- Accuracy: 0.83
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8611 | 1.0 | 750 | 0.8101 | 0.59 |
| 0.7337 | 2.0 | 1500 | 0.7328 | 0.5933 |
| 0.6092 | 3.0 | 2250 | 0.7436 | 0.6133 |
| 0.4788 | 4.0 | 3000 | 0.6067 | 0.7117 |
| 0.5206 | 5.0 | 3750 | 0.5059 | 0.79 |
| 0.4534 | 6.0 | 4500 | 0.5164 | 0.7733 |
| 0.4278 | 7.0 | 5250 | 0.5196 | 0.7883 |
| 0.421 | 8.0 | 6000 | 0.4508 | 0.82 |
| 0.3903 | 9.0 | 6750 | 0.4527 | 0.8067 |
| 0.3955 | 10.0 | 7500 | 0.4845 | 0.7867 |
| 0.4005 | 11.0 | 8250 | 0.4149 | 0.835 |
| 0.3221 | 12.0 | 9000 | 0.4996 | 0.815 |
| 0.3587 | 13.0 | 9750 | 0.4119 | 0.8433 |
| 0.2736 | 14.0 | 10500 | 0.4909 | 0.85 |
| 0.2907 | 15.0 | 11250 | 0.4508 | 0.8217 |
| 0.2288 | 16.0 | 12000 | 0.5192 | 0.825 |
| 0.2378 | 17.0 | 12750 | 0.4692 | 0.8383 |
| 0.2297 | 18.0 | 13500 | 0.5883 | 0.8217 |
| 0.2065 | 19.0 | 14250 | 0.4741 | 0.8483 |
| 0.1668 | 20.0 | 15000 | 0.5264 | 0.82 |
| 0.1867 | 21.0 | 15750 | 0.5865 | 0.8217 |
| 0.1397 | 22.0 | 16500 | 0.5812 | 0.8283 |
| 0.15 | 23.0 | 17250 | 0.7165 | 0.83 |
| 0.083 | 24.0 | 18000 | 0.6760 | 0.8267 |
| 0.3309 | 25.0 | 18750 | 0.8204 | 0.79 |
| 0.6907 | 26.0 | 19500 | 0.6426 | 0.7217 |
| 0.4092 | 27.0 | 20250 | 0.7365 | 0.72 |
| 0.4562 | 28.0 | 21000 | 0.4704 | 0.7817 |
| 0.4588 | 29.0 | 21750 | 0.4367 | 0.7983 |
| 0.4089 | 30.0 | 22500 | 0.5011 | 0.7883 |
| 0.4005 | 31.0 | 23250 | 0.5056 | 0.805 |
| 0.2986 | 32.0 | 24000 | 0.4328 | 0.83 |
| 0.3319 | 33.0 | 24750 | 0.5242 | 0.805 |
| 0.2453 | 34.0 | 25500 | 0.4883 | 0.8383 |
| 0.2459 | 35.0 | 26250 | 0.4886 | 0.8233 |
| 0.2873 | 36.0 | 27000 | 0.5046 | 0.8217 |
| 0.1814 | 37.0 | 27750 | 0.5539 | 0.8133 |
| 0.2073 | 38.0 | 28500 | 0.5379 | 0.8233 |
| 0.2284 | 39.0 | 29250 | 0.5765 | 0.815 |
| 0.1577 | 40.0 | 30000 | 0.6100 | 0.8333 |
| 0.1325 | 41.0 | 30750 | 0.6730 | 0.8333 |
| 0.1239 | 42.0 | 31500 | 0.7561 | 0.8333 |
| 0.0741 | 43.0 | 32250 | 0.8428 | 0.8217 |
| 0.0999 | 44.0 | 33000 | 0.9214 | 0.8167 |
| 0.0863 | 45.0 | 33750 | 0.9203 | 0.8367 |
| 0.0855 | 46.0 | 34500 | 1.0656 | 0.83 |
| 0.064 | 47.0 | 35250 | 1.2523 | 0.815 |
| 0.0407 | 48.0 | 36000 | 1.2659 | 0.83 |
| 0.0522 | 49.0 | 36750 | 1.4174 | 0.835 |
| 0.0025 | 50.0 | 37500 | 1.4754 | 0.83 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/smids_10x_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. -->
# smids_10x_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: 1.0880
- Accuracy: 0.9017
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2267 | 1.0 | 750 | 0.2310 | 0.9017 |
| 0.1688 | 2.0 | 1500 | 0.3133 | 0.8767 |
| 0.1207 | 3.0 | 2250 | 0.3307 | 0.8917 |
| 0.0544 | 4.0 | 3000 | 0.3393 | 0.8933 |
| 0.0685 | 5.0 | 3750 | 0.4794 | 0.8817 |
| 0.0086 | 6.0 | 4500 | 0.5790 | 0.8967 |
| 0.0247 | 7.0 | 5250 | 0.6674 | 0.895 |
| 0.0176 | 8.0 | 6000 | 0.8292 | 0.89 |
| 0.0446 | 9.0 | 6750 | 0.8047 | 0.8933 |
| 0.0267 | 10.0 | 7500 | 0.8263 | 0.8983 |
| 0.0376 | 11.0 | 8250 | 0.8873 | 0.9 |
| 0.0007 | 12.0 | 9000 | 0.9745 | 0.8883 |
| 0.0033 | 13.0 | 9750 | 0.8924 | 0.9033 |
| 0.0025 | 14.0 | 10500 | 0.8544 | 0.905 |
| 0.0004 | 15.0 | 11250 | 1.0444 | 0.8967 |
| 0.0002 | 16.0 | 12000 | 1.0224 | 0.8933 |
| 0.0105 | 17.0 | 12750 | 1.0131 | 0.8883 |
| 0.014 | 18.0 | 13500 | 0.9525 | 0.9033 |
| 0.0 | 19.0 | 14250 | 0.9850 | 0.8983 |
| 0.0 | 20.0 | 15000 | 1.1461 | 0.89 |
| 0.003 | 21.0 | 15750 | 0.9659 | 0.9033 |
| 0.0004 | 22.0 | 16500 | 1.0728 | 0.8967 |
| 0.0257 | 23.0 | 17250 | 1.0751 | 0.9 |
| 0.006 | 24.0 | 18000 | 1.1593 | 0.8933 |
| 0.0242 | 25.0 | 18750 | 1.1220 | 0.8917 |
| 0.0 | 26.0 | 19500 | 1.0931 | 0.9033 |
| 0.0 | 27.0 | 20250 | 1.0693 | 0.9017 |
| 0.0 | 28.0 | 21000 | 1.2173 | 0.88 |
| 0.0 | 29.0 | 21750 | 1.0569 | 0.905 |
| 0.0118 | 30.0 | 22500 | 1.1864 | 0.8917 |
| 0.0 | 31.0 | 23250 | 1.2141 | 0.8967 |
| 0.0 | 32.0 | 24000 | 1.1888 | 0.8933 |
| 0.0 | 33.0 | 24750 | 1.1513 | 0.8983 |
| 0.0 | 34.0 | 25500 | 1.2676 | 0.89 |
| 0.0 | 35.0 | 26250 | 1.1568 | 0.8983 |
| 0.0 | 36.0 | 27000 | 1.1800 | 0.89 |
| 0.0068 | 37.0 | 27750 | 1.1482 | 0.9033 |
| 0.0 | 38.0 | 28500 | 1.0989 | 0.9 |
| 0.0 | 39.0 | 29250 | 1.1226 | 0.9 |
| 0.0 | 40.0 | 30000 | 1.1146 | 0.8983 |
| 0.0 | 41.0 | 30750 | 1.0950 | 0.9 |
| 0.0 | 42.0 | 31500 | 1.0906 | 0.9 |
| 0.0 | 43.0 | 32250 | 1.0973 | 0.9 |
| 0.0 | 44.0 | 33000 | 1.0952 | 0.9 |
| 0.0 | 45.0 | 33750 | 1.0896 | 0.8983 |
| 0.0 | 46.0 | 34500 | 1.0935 | 0.8983 |
| 0.0 | 47.0 | 35250 | 1.0921 | 0.8983 |
| 0.0 | 48.0 | 36000 | 1.0899 | 0.8983 |
| 0.0 | 49.0 | 36750 | 1.0869 | 0.9017 |
| 0.0 | 50.0 | 37500 | 1.0880 | 0.9017 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"abnormal_sperm",
"non-sperm",
"normal_sperm"
] |
hkivancoral/hushem_40x_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_40x_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: 2.2299
- Accuracy: 0.7333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2388 | 1.0 | 215 | 1.5575 | 0.6444 |
| 0.1132 | 2.0 | 430 | 1.5074 | 0.7111 |
| 0.0448 | 3.0 | 645 | 1.5221 | 0.7111 |
| 0.0294 | 4.0 | 860 | 2.1715 | 0.6444 |
| 0.0359 | 5.0 | 1075 | 2.5882 | 0.6667 |
| 0.041 | 6.0 | 1290 | 3.4111 | 0.6667 |
| 0.0762 | 7.0 | 1505 | 2.4705 | 0.6444 |
| 0.014 | 8.0 | 1720 | 2.7547 | 0.6222 |
| 0.0243 | 9.0 | 1935 | 2.3966 | 0.6889 |
| 0.0008 | 10.0 | 2150 | 2.0479 | 0.6889 |
| 0.0216 | 11.0 | 2365 | 2.7964 | 0.6889 |
| 0.0033 | 12.0 | 2580 | 1.0097 | 0.8 |
| 0.0068 | 13.0 | 2795 | 2.2109 | 0.7556 |
| 0.0003 | 14.0 | 3010 | 2.4149 | 0.6889 |
| 0.0104 | 15.0 | 3225 | 2.3252 | 0.6889 |
| 0.0005 | 16.0 | 3440 | 1.9451 | 0.8 |
| 0.0158 | 17.0 | 3655 | 2.3698 | 0.7333 |
| 0.0 | 18.0 | 3870 | 2.5005 | 0.6889 |
| 0.0001 | 19.0 | 4085 | 2.8562 | 0.7333 |
| 0.0002 | 20.0 | 4300 | 2.0511 | 0.7778 |
| 0.0 | 21.0 | 4515 | 2.4064 | 0.6889 |
| 0.0 | 22.0 | 4730 | 2.3086 | 0.7111 |
| 0.0 | 23.0 | 4945 | 2.2846 | 0.7111 |
| 0.0 | 24.0 | 5160 | 2.2636 | 0.7111 |
| 0.0 | 25.0 | 5375 | 2.2498 | 0.7111 |
| 0.0 | 26.0 | 5590 | 2.2376 | 0.7111 |
| 0.0 | 27.0 | 5805 | 2.2259 | 0.7111 |
| 0.0 | 28.0 | 6020 | 2.2168 | 0.7111 |
| 0.0 | 29.0 | 6235 | 2.2093 | 0.7111 |
| 0.0 | 30.0 | 6450 | 2.2030 | 0.7111 |
| 0.0 | 31.0 | 6665 | 2.1968 | 0.7111 |
| 0.0 | 32.0 | 6880 | 2.1940 | 0.7111 |
| 0.0 | 33.0 | 7095 | 2.1890 | 0.7111 |
| 0.0 | 34.0 | 7310 | 2.1860 | 0.7111 |
| 0.0 | 35.0 | 7525 | 2.1850 | 0.7111 |
| 0.0 | 36.0 | 7740 | 2.1848 | 0.7333 |
| 0.0 | 37.0 | 7955 | 2.1855 | 0.7333 |
| 0.0 | 38.0 | 8170 | 2.1862 | 0.7333 |
| 0.0 | 39.0 | 8385 | 2.1884 | 0.7333 |
| 0.0 | 40.0 | 8600 | 2.1906 | 0.7333 |
| 0.0 | 41.0 | 8815 | 2.1950 | 0.7333 |
| 0.0 | 42.0 | 9030 | 2.1985 | 0.7333 |
| 0.0 | 43.0 | 9245 | 2.2034 | 0.7333 |
| 0.0 | 44.0 | 9460 | 2.2083 | 0.7333 |
| 0.0 | 45.0 | 9675 | 2.2137 | 0.7333 |
| 0.0 | 46.0 | 9890 | 2.2189 | 0.7333 |
| 0.0 | 47.0 | 10105 | 2.2227 | 0.7333 |
| 0.0 | 48.0 | 10320 | 2.2266 | 0.7333 |
| 0.0 | 49.0 | 10535 | 2.2294 | 0.7333 |
| 0.0 | 50.0 | 10750 | 2.2299 | 0.7333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_0001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_0001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6972
- Accuracy: 0.8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0059 | 1.0 | 215 | 1.4884 | 0.6667 |
| 0.0007 | 2.0 | 430 | 0.8474 | 0.7556 |
| 0.0002 | 3.0 | 645 | 1.0073 | 0.8 |
| 0.0 | 4.0 | 860 | 0.9250 | 0.8 |
| 0.0 | 5.0 | 1075 | 0.9207 | 0.8 |
| 0.0 | 6.0 | 1290 | 0.9264 | 0.8 |
| 0.0 | 7.0 | 1505 | 0.9356 | 0.8 |
| 0.0 | 8.0 | 1720 | 0.9374 | 0.8 |
| 0.0 | 9.0 | 1935 | 0.9494 | 0.8 |
| 0.0 | 10.0 | 2150 | 0.9562 | 0.8 |
| 0.0 | 11.0 | 2365 | 0.9850 | 0.8 |
| 0.0 | 12.0 | 2580 | 0.9946 | 0.8 |
| 0.0 | 13.0 | 2795 | 1.0078 | 0.8 |
| 0.0 | 14.0 | 3010 | 1.0232 | 0.8 |
| 0.0 | 15.0 | 3225 | 1.0338 | 0.8 |
| 0.0 | 16.0 | 3440 | 1.0587 | 0.8 |
| 0.0 | 17.0 | 3655 | 1.0729 | 0.8 |
| 0.0 | 18.0 | 3870 | 1.0955 | 0.8 |
| 0.0 | 19.0 | 4085 | 1.1141 | 0.8 |
| 0.0 | 20.0 | 4300 | 1.1377 | 0.8 |
| 0.0 | 21.0 | 4515 | 1.1576 | 0.8 |
| 0.0 | 22.0 | 4730 | 1.1774 | 0.8 |
| 0.0 | 23.0 | 4945 | 1.1940 | 0.8 |
| 0.0 | 24.0 | 5160 | 1.2165 | 0.8 |
| 0.0 | 25.0 | 5375 | 1.2327 | 0.8 |
| 0.0 | 26.0 | 5590 | 1.2591 | 0.8 |
| 0.0 | 27.0 | 5805 | 1.2789 | 0.8 |
| 0.0 | 28.0 | 6020 | 1.3041 | 0.8 |
| 0.0 | 29.0 | 6235 | 1.3242 | 0.8 |
| 0.0 | 30.0 | 6450 | 1.3543 | 0.8 |
| 0.0 | 31.0 | 6665 | 1.3823 | 0.8 |
| 0.0 | 32.0 | 6880 | 1.4030 | 0.8 |
| 0.0 | 33.0 | 7095 | 1.4211 | 0.8 |
| 0.0 | 34.0 | 7310 | 1.4411 | 0.8 |
| 0.0 | 35.0 | 7525 | 1.4760 | 0.8 |
| 0.0 | 36.0 | 7740 | 1.4941 | 0.8 |
| 0.0 | 37.0 | 7955 | 1.5110 | 0.8 |
| 0.0 | 38.0 | 8170 | 1.5338 | 0.8 |
| 0.0 | 39.0 | 8385 | 1.5520 | 0.8 |
| 0.0 | 40.0 | 8600 | 1.5687 | 0.8 |
| 0.0 | 41.0 | 8815 | 1.5911 | 0.8 |
| 0.0 | 42.0 | 9030 | 1.6052 | 0.8 |
| 0.0 | 43.0 | 9245 | 1.6245 | 0.8 |
| 0.0 | 44.0 | 9460 | 1.6414 | 0.8 |
| 0.0 | 45.0 | 9675 | 1.6584 | 0.8 |
| 0.0 | 46.0 | 9890 | 1.6706 | 0.8 |
| 0.0 | 47.0 | 10105 | 1.6833 | 0.8 |
| 0.0 | 48.0 | 10320 | 1.6907 | 0.8 |
| 0.0 | 49.0 | 10535 | 1.6958 | 0.8 |
| 0.0 | 50.0 | 10750 | 1.6972 | 0.8 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7272
- Accuracy: 0.8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1818 | 1.0 | 215 | 1.1690 | 0.6667 |
| 0.0471 | 2.0 | 430 | 1.2084 | 0.7556 |
| 0.0323 | 3.0 | 645 | 1.4622 | 0.7556 |
| 0.0035 | 4.0 | 860 | 1.8637 | 0.6667 |
| 0.0576 | 5.0 | 1075 | 1.1848 | 0.7778 |
| 0.0306 | 6.0 | 1290 | 2.1315 | 0.6667 |
| 0.0267 | 7.0 | 1505 | 1.9833 | 0.6889 |
| 0.0299 | 8.0 | 1720 | 2.0731 | 0.6444 |
| 0.0 | 9.0 | 1935 | 1.0026 | 0.7778 |
| 0.0 | 10.0 | 2150 | 1.0394 | 0.7778 |
| 0.0 | 11.0 | 2365 | 1.0442 | 0.8 |
| 0.0 | 12.0 | 2580 | 1.0674 | 0.8 |
| 0.0 | 13.0 | 2795 | 1.0839 | 0.8 |
| 0.0 | 14.0 | 3010 | 1.1001 | 0.8 |
| 0.0 | 15.0 | 3225 | 1.1157 | 0.8 |
| 0.0 | 16.0 | 3440 | 1.1301 | 0.8 |
| 0.0 | 17.0 | 3655 | 1.1460 | 0.8 |
| 0.0 | 18.0 | 3870 | 1.1598 | 0.8 |
| 0.0 | 19.0 | 4085 | 1.1755 | 0.8 |
| 0.0 | 20.0 | 4300 | 1.1926 | 0.8 |
| 0.0 | 21.0 | 4515 | 1.2103 | 0.8 |
| 0.0 | 22.0 | 4730 | 1.2273 | 0.7778 |
| 0.0 | 23.0 | 4945 | 1.2450 | 0.7778 |
| 0.0 | 24.0 | 5160 | 1.2642 | 0.7778 |
| 0.0 | 25.0 | 5375 | 1.2848 | 0.7778 |
| 0.0 | 26.0 | 5590 | 1.3072 | 0.7778 |
| 0.0 | 27.0 | 5805 | 1.3310 | 0.7778 |
| 0.0 | 28.0 | 6020 | 1.3521 | 0.7778 |
| 0.0 | 29.0 | 6235 | 1.3737 | 0.7778 |
| 0.0 | 30.0 | 6450 | 1.3961 | 0.7778 |
| 0.0 | 31.0 | 6665 | 1.4197 | 0.7778 |
| 0.0 | 32.0 | 6880 | 1.4440 | 0.7778 |
| 0.0 | 33.0 | 7095 | 1.4721 | 0.7778 |
| 0.0 | 34.0 | 7310 | 1.5015 | 0.7778 |
| 0.0 | 35.0 | 7525 | 1.5290 | 0.7778 |
| 0.0 | 36.0 | 7740 | 1.5563 | 0.7778 |
| 0.0 | 37.0 | 7955 | 1.5831 | 0.7778 |
| 0.0 | 38.0 | 8170 | 1.6075 | 0.7778 |
| 0.0 | 39.0 | 8385 | 1.6265 | 0.8 |
| 0.0 | 40.0 | 8600 | 1.6444 | 0.8 |
| 0.0 | 41.0 | 8815 | 1.6599 | 0.8 |
| 0.0 | 42.0 | 9030 | 1.6734 | 0.8 |
| 0.0 | 43.0 | 9245 | 1.6847 | 0.8 |
| 0.0 | 44.0 | 9460 | 1.6936 | 0.8 |
| 0.0 | 45.0 | 9675 | 1.7031 | 0.8 |
| 0.0 | 46.0 | 9890 | 1.7109 | 0.8 |
| 0.0 | 47.0 | 10105 | 1.7178 | 0.8 |
| 0.0 | 48.0 | 10320 | 1.7232 | 0.8 |
| 0.0 | 49.0 | 10535 | 1.7265 | 0.8 |
| 0.0 | 50.0 | 10750 | 1.7272 | 0.8 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 1.2217
- 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3354 | 1.0 | 217 | 0.9126 | 0.6744 |
| 0.1561 | 2.0 | 434 | 1.0121 | 0.6977 |
| 0.0656 | 3.0 | 651 | 1.0046 | 0.7442 |
| 0.07 | 4.0 | 868 | 0.8401 | 0.7674 |
| 0.0448 | 5.0 | 1085 | 1.1419 | 0.7907 |
| 0.0667 | 6.0 | 1302 | 1.2686 | 0.8372 |
| 0.0198 | 7.0 | 1519 | 1.1351 | 0.8372 |
| 0.009 | 8.0 | 1736 | 1.6617 | 0.8140 |
| 0.0113 | 9.0 | 1953 | 1.1646 | 0.8372 |
| 0.0284 | 10.0 | 2170 | 0.8325 | 0.9302 |
| 0.0093 | 11.0 | 2387 | 1.2543 | 0.8372 |
| 0.0217 | 12.0 | 2604 | 1.5838 | 0.8140 |
| 0.0059 | 13.0 | 2821 | 1.3486 | 0.8605 |
| 0.0271 | 14.0 | 3038 | 0.9639 | 0.8837 |
| 0.0039 | 15.0 | 3255 | 0.9754 | 0.9070 |
| 0.0 | 16.0 | 3472 | 1.4140 | 0.8140 |
| 0.0 | 17.0 | 3689 | 1.2390 | 0.8837 |
| 0.0011 | 18.0 | 3906 | 1.3250 | 0.8605 |
| 0.0004 | 19.0 | 4123 | 1.2826 | 0.8372 |
| 0.0002 | 20.0 | 4340 | 1.1795 | 0.8605 |
| 0.0 | 21.0 | 4557 | 0.9932 | 0.8837 |
| 0.0 | 22.0 | 4774 | 0.9999 | 0.8837 |
| 0.0 | 23.0 | 4991 | 1.0055 | 0.8837 |
| 0.0 | 24.0 | 5208 | 1.0099 | 0.8837 |
| 0.0 | 25.0 | 5425 | 1.0179 | 0.8837 |
| 0.0 | 26.0 | 5642 | 1.0241 | 0.8837 |
| 0.0 | 27.0 | 5859 | 1.0302 | 0.8837 |
| 0.0 | 28.0 | 6076 | 1.0378 | 0.8837 |
| 0.0 | 29.0 | 6293 | 1.0445 | 0.8837 |
| 0.0 | 30.0 | 6510 | 1.0519 | 0.8837 |
| 0.0 | 31.0 | 6727 | 1.0608 | 0.8837 |
| 0.0 | 32.0 | 6944 | 1.0697 | 0.8837 |
| 0.0 | 33.0 | 7161 | 1.0787 | 0.8837 |
| 0.0 | 34.0 | 7378 | 1.0879 | 0.8837 |
| 0.0 | 35.0 | 7595 | 1.0974 | 0.8837 |
| 0.0 | 36.0 | 7812 | 1.1074 | 0.8837 |
| 0.0 | 37.0 | 8029 | 1.1171 | 0.8837 |
| 0.0 | 38.0 | 8246 | 1.1279 | 0.8837 |
| 0.0 | 39.0 | 8463 | 1.1383 | 0.8837 |
| 0.0 | 40.0 | 8680 | 1.1481 | 0.8837 |
| 0.0 | 41.0 | 8897 | 1.1586 | 0.8837 |
| 0.0 | 42.0 | 9114 | 1.1687 | 0.8837 |
| 0.0 | 43.0 | 9331 | 1.1785 | 0.8837 |
| 0.0 | 44.0 | 9548 | 1.1883 | 0.8837 |
| 0.0 | 45.0 | 9765 | 1.1969 | 0.8837 |
| 0.0 | 46.0 | 9982 | 1.2048 | 0.8837 |
| 0.0 | 47.0 | 10199 | 1.2119 | 0.8837 |
| 0.0 | 48.0 | 10416 | 1.2174 | 0.8837 |
| 0.0 | 49.0 | 10633 | 1.2210 | 0.8837 |
| 0.0 | 50.0 | 10850 | 1.2217 | 0.8837 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_0001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_0001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0670
- Accuracy: 0.7556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.002 | 1.0 | 215 | 1.2697 | 0.7333 |
| 0.0002 | 2.0 | 430 | 0.9917 | 0.8444 |
| 0.0001 | 3.0 | 645 | 1.1998 | 0.7556 |
| 0.0 | 4.0 | 860 | 1.2338 | 0.7778 |
| 0.0 | 5.0 | 1075 | 1.2738 | 0.7778 |
| 0.0 | 6.0 | 1290 | 1.2969 | 0.7556 |
| 0.0 | 7.0 | 1505 | 1.3149 | 0.7556 |
| 0.0 | 8.0 | 1720 | 1.3423 | 0.7333 |
| 0.0 | 9.0 | 1935 | 1.3583 | 0.7333 |
| 0.0 | 10.0 | 2150 | 1.3912 | 0.7333 |
| 0.0 | 11.0 | 2365 | 1.4063 | 0.7333 |
| 0.0 | 12.0 | 2580 | 1.4145 | 0.7333 |
| 0.0 | 13.0 | 2795 | 1.4372 | 0.7333 |
| 0.0 | 14.0 | 3010 | 1.4564 | 0.7333 |
| 0.0 | 15.0 | 3225 | 1.4726 | 0.7556 |
| 0.0 | 16.0 | 3440 | 1.4921 | 0.7556 |
| 0.0 | 17.0 | 3655 | 1.5141 | 0.7556 |
| 0.0 | 18.0 | 3870 | 1.5335 | 0.7556 |
| 0.0 | 19.0 | 4085 | 1.5550 | 0.7556 |
| 0.0 | 20.0 | 4300 | 1.5712 | 0.7556 |
| 0.0 | 21.0 | 4515 | 1.5913 | 0.7556 |
| 0.0 | 22.0 | 4730 | 1.6117 | 0.7556 |
| 0.0 | 23.0 | 4945 | 1.6330 | 0.7556 |
| 0.0 | 24.0 | 5160 | 1.6556 | 0.7556 |
| 0.0 | 25.0 | 5375 | 1.6731 | 0.7556 |
| 0.0 | 26.0 | 5590 | 1.6917 | 0.7333 |
| 0.0 | 27.0 | 5805 | 1.7181 | 0.7556 |
| 0.0 | 28.0 | 6020 | 1.7381 | 0.7556 |
| 0.0 | 29.0 | 6235 | 1.7621 | 0.7333 |
| 0.0 | 30.0 | 6450 | 1.7829 | 0.7556 |
| 0.0 | 31.0 | 6665 | 1.8067 | 0.7556 |
| 0.0 | 32.0 | 6880 | 1.8347 | 0.7556 |
| 0.0 | 33.0 | 7095 | 1.8539 | 0.7556 |
| 0.0 | 34.0 | 7310 | 1.8794 | 0.7556 |
| 0.0 | 35.0 | 7525 | 1.9029 | 0.7556 |
| 0.0 | 36.0 | 7740 | 1.9298 | 0.7556 |
| 0.0 | 37.0 | 7955 | 1.9525 | 0.7556 |
| 0.0 | 38.0 | 8170 | 1.9656 | 0.7556 |
| 0.0 | 39.0 | 8385 | 1.9838 | 0.7556 |
| 0.0 | 40.0 | 8600 | 2.0019 | 0.7556 |
| 0.0 | 41.0 | 8815 | 2.0209 | 0.7556 |
| 0.0 | 42.0 | 9030 | 2.0377 | 0.7556 |
| 0.0 | 43.0 | 9245 | 2.0436 | 0.7556 |
| 0.0 | 44.0 | 9460 | 2.0515 | 0.7556 |
| 0.0 | 45.0 | 9675 | 2.0554 | 0.7556 |
| 0.0 | 46.0 | 9890 | 2.0579 | 0.7556 |
| 0.0 | 47.0 | 10105 | 2.0613 | 0.7556 |
| 0.0 | 48.0 | 10320 | 2.0650 | 0.7556 |
| 0.0 | 49.0 | 10535 | 2.0662 | 0.7556 |
| 0.0 | 50.0 | 10750 | 2.0670 | 0.7556 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0511
- Accuracy: 0.7778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.091 | 1.0 | 215 | 1.8850 | 0.7111 |
| 0.0695 | 2.0 | 430 | 1.9470 | 0.6444 |
| 0.0617 | 3.0 | 645 | 1.5137 | 0.7556 |
| 0.0146 | 4.0 | 860 | 1.8770 | 0.7333 |
| 0.0437 | 5.0 | 1075 | 2.0442 | 0.7333 |
| 0.0116 | 6.0 | 1290 | 1.4759 | 0.8 |
| 0.0035 | 7.0 | 1505 | 2.8704 | 0.6444 |
| 0.0344 | 8.0 | 1720 | 2.7808 | 0.6444 |
| 0.0121 | 9.0 | 1935 | 2.7705 | 0.6889 |
| 0.0003 | 10.0 | 2150 | 2.2137 | 0.7556 |
| 0.0626 | 11.0 | 2365 | 2.2191 | 0.7111 |
| 0.0001 | 12.0 | 2580 | 2.1839 | 0.7111 |
| 0.0106 | 13.0 | 2795 | 2.7238 | 0.6444 |
| 0.0001 | 14.0 | 3010 | 3.0158 | 0.6444 |
| 0.0001 | 15.0 | 3225 | 2.3783 | 0.7556 |
| 0.0 | 16.0 | 3440 | 2.3314 | 0.7556 |
| 0.0 | 17.0 | 3655 | 2.3547 | 0.7556 |
| 0.0 | 18.0 | 3870 | 2.3759 | 0.7556 |
| 0.0 | 19.0 | 4085 | 2.3984 | 0.7556 |
| 0.0 | 20.0 | 4300 | 2.4203 | 0.7556 |
| 0.0 | 21.0 | 4515 | 2.4425 | 0.7556 |
| 0.0 | 22.0 | 4730 | 2.4655 | 0.7556 |
| 0.0 | 23.0 | 4945 | 2.4901 | 0.7556 |
| 0.0 | 24.0 | 5160 | 2.5187 | 0.7556 |
| 0.0 | 25.0 | 5375 | 2.5510 | 0.7556 |
| 0.0 | 26.0 | 5590 | 2.5849 | 0.7556 |
| 0.0 | 27.0 | 5805 | 2.6187 | 0.7556 |
| 0.0 | 28.0 | 6020 | 2.6506 | 0.7556 |
| 0.0 | 29.0 | 6235 | 2.6816 | 0.7556 |
| 0.0 | 30.0 | 6450 | 2.7120 | 0.7556 |
| 0.0 | 31.0 | 6665 | 2.7426 | 0.7778 |
| 0.0 | 32.0 | 6880 | 2.7738 | 0.7778 |
| 0.0 | 33.0 | 7095 | 2.8047 | 0.7778 |
| 0.0 | 34.0 | 7310 | 2.8362 | 0.7778 |
| 0.0 | 35.0 | 7525 | 2.8656 | 0.7778 |
| 0.0 | 36.0 | 7740 | 2.8925 | 0.7778 |
| 0.0 | 37.0 | 7955 | 2.9162 | 0.7778 |
| 0.0 | 38.0 | 8170 | 2.9371 | 0.7778 |
| 0.0 | 39.0 | 8385 | 2.9556 | 0.7778 |
| 0.0 | 40.0 | 8600 | 2.9717 | 0.7778 |
| 0.0 | 41.0 | 8815 | 2.9862 | 0.7778 |
| 0.0 | 42.0 | 9030 | 2.9991 | 0.7778 |
| 0.0 | 43.0 | 9245 | 3.0106 | 0.7778 |
| 0.0 | 44.0 | 9460 | 3.0207 | 0.7778 |
| 0.0 | 45.0 | 9675 | 3.0295 | 0.7778 |
| 0.0 | 46.0 | 9890 | 3.0369 | 0.7778 |
| 0.0 | 47.0 | 10105 | 3.0430 | 0.7778 |
| 0.0 | 48.0 | 10320 | 3.0475 | 0.7778 |
| 0.0 | 49.0 | 10535 | 3.0503 | 0.7778 |
| 0.0 | 50.0 | 10750 | 3.0511 | 0.7778 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 0.5391
- Accuracy: 0.9524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2113 | 1.0 | 219 | 0.5669 | 0.8571 |
| 0.1247 | 2.0 | 438 | 0.3852 | 0.8571 |
| 0.0797 | 3.0 | 657 | 0.3243 | 0.8810 |
| 0.079 | 4.0 | 876 | 0.2935 | 0.9286 |
| 0.1755 | 5.0 | 1095 | 0.3153 | 0.8810 |
| 0.1228 | 6.0 | 1314 | 0.4983 | 0.9048 |
| 0.047 | 7.0 | 1533 | 0.4737 | 0.9048 |
| 0.0236 | 8.0 | 1752 | 0.2530 | 0.9286 |
| 0.0027 | 9.0 | 1971 | 0.9366 | 0.8810 |
| 0.0257 | 10.0 | 2190 | 0.8815 | 0.8810 |
| 0.032 | 11.0 | 2409 | 0.7642 | 0.9048 |
| 0.0025 | 12.0 | 2628 | 0.6321 | 0.9286 |
| 0.0 | 13.0 | 2847 | 0.4805 | 0.9048 |
| 0.0406 | 14.0 | 3066 | 0.7911 | 0.9286 |
| 0.0286 | 15.0 | 3285 | 0.2463 | 0.9048 |
| 0.0029 | 16.0 | 3504 | 0.0537 | 0.9762 |
| 0.0065 | 17.0 | 3723 | 0.3008 | 0.9286 |
| 0.0001 | 18.0 | 3942 | 0.8021 | 0.8810 |
| 0.0 | 19.0 | 4161 | 0.3160 | 0.9762 |
| 0.0084 | 20.0 | 4380 | 1.2037 | 0.8333 |
| 0.0 | 21.0 | 4599 | 0.5426 | 0.9286 |
| 0.0001 | 22.0 | 4818 | 0.3468 | 0.9524 |
| 0.0204 | 23.0 | 5037 | 0.7324 | 0.9286 |
| 0.0 | 24.0 | 5256 | 0.8099 | 0.9048 |
| 0.0 | 25.0 | 5475 | 1.1998 | 0.8810 |
| 0.0 | 26.0 | 5694 | 0.5294 | 0.9524 |
| 0.0 | 27.0 | 5913 | 0.5383 | 0.9524 |
| 0.0 | 28.0 | 6132 | 0.5204 | 0.9524 |
| 0.0 | 29.0 | 6351 | 0.5193 | 0.9524 |
| 0.0 | 30.0 | 6570 | 0.5189 | 0.9524 |
| 0.0 | 31.0 | 6789 | 0.5187 | 0.9524 |
| 0.0 | 32.0 | 7008 | 0.5190 | 0.9524 |
| 0.0 | 33.0 | 7227 | 0.5187 | 0.9524 |
| 0.0 | 34.0 | 7446 | 0.5193 | 0.9524 |
| 0.0 | 35.0 | 7665 | 0.5201 | 0.9524 |
| 0.0 | 36.0 | 7884 | 0.5213 | 0.9524 |
| 0.0 | 37.0 | 8103 | 0.5225 | 0.9524 |
| 0.0 | 38.0 | 8322 | 0.5239 | 0.9524 |
| 0.0 | 39.0 | 8541 | 0.5256 | 0.9524 |
| 0.0 | 40.0 | 8760 | 0.5271 | 0.9524 |
| 0.0 | 41.0 | 8979 | 0.5287 | 0.9524 |
| 0.0 | 42.0 | 9198 | 0.5302 | 0.9524 |
| 0.0 | 43.0 | 9417 | 0.5318 | 0.9524 |
| 0.0 | 44.0 | 9636 | 0.5333 | 0.9524 |
| 0.0 | 45.0 | 9855 | 0.5348 | 0.9524 |
| 0.0 | 46.0 | 10074 | 0.5359 | 0.9524 |
| 0.0 | 47.0 | 10293 | 0.5372 | 0.9524 |
| 0.0 | 48.0 | 10512 | 0.5381 | 0.9524 |
| 0.0 | 49.0 | 10731 | 0.5389 | 0.9524 |
| 0.0 | 50.0 | 10950 | 0.5391 | 0.9524 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_0001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_0001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0266
- Accuracy: 0.9070
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0054 | 1.0 | 217 | 0.4033 | 0.9070 |
| 0.0006 | 2.0 | 434 | 0.7216 | 0.8837 |
| 0.0001 | 3.0 | 651 | 0.6016 | 0.9070 |
| 0.0001 | 4.0 | 868 | 0.5981 | 0.9070 |
| 0.0 | 5.0 | 1085 | 0.6065 | 0.9070 |
| 0.0 | 6.0 | 1302 | 0.6108 | 0.9070 |
| 0.0 | 7.0 | 1519 | 0.6197 | 0.9070 |
| 0.0 | 8.0 | 1736 | 0.6284 | 0.9070 |
| 0.0 | 9.0 | 1953 | 0.6384 | 0.9070 |
| 0.0 | 10.0 | 2170 | 0.6478 | 0.9070 |
| 0.0 | 11.0 | 2387 | 0.6570 | 0.9070 |
| 0.0 | 12.0 | 2604 | 0.6667 | 0.9070 |
| 0.0 | 13.0 | 2821 | 0.6782 | 0.9070 |
| 0.0 | 14.0 | 3038 | 0.6908 | 0.9070 |
| 0.0 | 15.0 | 3255 | 0.6989 | 0.9070 |
| 0.0 | 16.0 | 3472 | 0.7116 | 0.9070 |
| 0.0 | 17.0 | 3689 | 0.7199 | 0.9070 |
| 0.0 | 18.0 | 3906 | 0.7341 | 0.9070 |
| 0.0 | 19.0 | 4123 | 0.7428 | 0.9070 |
| 0.0 | 20.0 | 4340 | 0.7587 | 0.9070 |
| 0.0 | 21.0 | 4557 | 0.7688 | 0.9070 |
| 0.0 | 22.0 | 4774 | 0.7786 | 0.9070 |
| 0.0 | 23.0 | 4991 | 0.7937 | 0.9070 |
| 0.0 | 24.0 | 5208 | 0.8013 | 0.9070 |
| 0.0 | 25.0 | 5425 | 0.8180 | 0.9070 |
| 0.0 | 26.0 | 5642 | 0.8297 | 0.9070 |
| 0.0 | 27.0 | 5859 | 0.8396 | 0.9070 |
| 0.0 | 28.0 | 6076 | 0.8494 | 0.9070 |
| 0.0 | 29.0 | 6293 | 0.8602 | 0.9070 |
| 0.0 | 30.0 | 6510 | 0.8755 | 0.9070 |
| 0.0 | 31.0 | 6727 | 0.8899 | 0.9070 |
| 0.0 | 32.0 | 6944 | 0.8970 | 0.9070 |
| 0.0 | 33.0 | 7161 | 0.9066 | 0.9070 |
| 0.0 | 34.0 | 7378 | 0.9206 | 0.9070 |
| 0.0 | 35.0 | 7595 | 0.9267 | 0.9070 |
| 0.0 | 36.0 | 7812 | 0.9397 | 0.9070 |
| 0.0 | 37.0 | 8029 | 0.9495 | 0.9070 |
| 0.0 | 38.0 | 8246 | 0.9575 | 0.9070 |
| 0.0 | 39.0 | 8463 | 0.9677 | 0.9070 |
| 0.0 | 40.0 | 8680 | 0.9799 | 0.9070 |
| 0.0 | 41.0 | 8897 | 0.9884 | 0.9070 |
| 0.0 | 42.0 | 9114 | 0.9997 | 0.9070 |
| 0.0 | 43.0 | 9331 | 1.0060 | 0.9070 |
| 0.0 | 44.0 | 9548 | 1.0089 | 0.9070 |
| 0.0 | 45.0 | 9765 | 1.0138 | 0.9070 |
| 0.0 | 46.0 | 9982 | 1.0186 | 0.9070 |
| 0.0 | 47.0 | 10199 | 1.0225 | 0.9070 |
| 0.0 | 48.0 | 10416 | 1.0236 | 0.9070 |
| 0.0 | 49.0 | 10633 | 1.0261 | 0.9070 |
| 0.0 | 50.0 | 10850 | 1.0266 | 0.9070 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0078
- 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1615 | 1.0 | 217 | 0.8891 | 0.7674 |
| 0.0732 | 2.0 | 434 | 0.4751 | 0.8372 |
| 0.0924 | 3.0 | 651 | 1.0550 | 0.8140 |
| 0.0215 | 4.0 | 868 | 0.8222 | 0.8140 |
| 0.0108 | 5.0 | 1085 | 1.0431 | 0.7907 |
| 0.0556 | 6.0 | 1302 | 1.0073 | 0.7907 |
| 0.004 | 7.0 | 1519 | 1.0088 | 0.8372 |
| 0.0126 | 8.0 | 1736 | 0.8896 | 0.8605 |
| 0.0277 | 9.0 | 1953 | 1.5634 | 0.8140 |
| 0.0001 | 10.0 | 2170 | 1.2793 | 0.8605 |
| 0.0292 | 11.0 | 2387 | 1.1442 | 0.7674 |
| 0.0544 | 12.0 | 2604 | 1.8210 | 0.7674 |
| 0.0066 | 13.0 | 2821 | 1.1859 | 0.7907 |
| 0.0012 | 14.0 | 3038 | 1.1578 | 0.7907 |
| 0.0282 | 15.0 | 3255 | 1.6928 | 0.7442 |
| 0.0001 | 16.0 | 3472 | 1.3989 | 0.8372 |
| 0.0 | 17.0 | 3689 | 1.4295 | 0.8372 |
| 0.0 | 18.0 | 3906 | 1.4591 | 0.8372 |
| 0.0 | 19.0 | 4123 | 1.4845 | 0.8372 |
| 0.0 | 20.0 | 4340 | 1.5077 | 0.8372 |
| 0.0 | 21.0 | 4557 | 1.5294 | 0.8372 |
| 0.0 | 22.0 | 4774 | 1.5502 | 0.8372 |
| 0.0 | 23.0 | 4991 | 1.5707 | 0.8372 |
| 0.0 | 24.0 | 5208 | 1.5907 | 0.8372 |
| 0.0 | 25.0 | 5425 | 1.6108 | 0.8372 |
| 0.0 | 26.0 | 5642 | 1.6313 | 0.8372 |
| 0.0 | 27.0 | 5859 | 1.6525 | 0.8372 |
| 0.0 | 28.0 | 6076 | 1.6750 | 0.8372 |
| 0.0 | 29.0 | 6293 | 1.6980 | 0.8372 |
| 0.0 | 30.0 | 6510 | 1.7211 | 0.8372 |
| 0.0 | 31.0 | 6727 | 1.7443 | 0.8372 |
| 0.0 | 32.0 | 6944 | 1.7671 | 0.8372 |
| 0.0 | 33.0 | 7161 | 1.7884 | 0.8372 |
| 0.0 | 34.0 | 7378 | 1.8089 | 0.8372 |
| 0.0 | 35.0 | 7595 | 1.8281 | 0.8372 |
| 0.0 | 36.0 | 7812 | 1.8470 | 0.8372 |
| 0.0 | 37.0 | 8029 | 1.8657 | 0.8372 |
| 0.0 | 38.0 | 8246 | 1.8832 | 0.8372 |
| 0.0 | 39.0 | 8463 | 1.9003 | 0.8372 |
| 0.0 | 40.0 | 8680 | 1.9168 | 0.8372 |
| 0.0 | 41.0 | 8897 | 1.9322 | 0.8372 |
| 0.0 | 42.0 | 9114 | 1.9463 | 0.8372 |
| 0.0 | 43.0 | 9331 | 1.9591 | 0.8372 |
| 0.0 | 44.0 | 9548 | 1.9706 | 0.8372 |
| 0.0 | 45.0 | 9765 | 1.9809 | 0.8372 |
| 0.0 | 46.0 | 9982 | 1.9901 | 0.8372 |
| 0.0 | 47.0 | 10199 | 1.9976 | 0.8372 |
| 0.0 | 48.0 | 10416 | 2.0033 | 0.8372 |
| 0.0 | 49.0 | 10633 | 2.0069 | 0.8372 |
| 0.0 | 50.0 | 10850 | 2.0078 | 0.8372 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 1.0843
- Accuracy: 0.8293
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2361 | 1.0 | 220 | 0.7775 | 0.7805 |
| 0.2436 | 2.0 | 440 | 1.3306 | 0.7561 |
| 0.1462 | 3.0 | 660 | 0.9347 | 0.7805 |
| 0.071 | 4.0 | 880 | 0.5663 | 0.8537 |
| 0.1006 | 5.0 | 1100 | 0.2859 | 0.8780 |
| 0.1238 | 6.0 | 1320 | 0.9526 | 0.8293 |
| 0.0201 | 7.0 | 1540 | 1.1774 | 0.8049 |
| 0.0017 | 8.0 | 1760 | 1.4587 | 0.7561 |
| 0.0368 | 9.0 | 1980 | 0.7868 | 0.8537 |
| 0.0002 | 10.0 | 2200 | 0.8716 | 0.8780 |
| 0.0421 | 11.0 | 2420 | 0.9525 | 0.8049 |
| 0.0055 | 12.0 | 2640 | 1.5979 | 0.7805 |
| 0.0103 | 13.0 | 2860 | 0.4608 | 0.9024 |
| 0.0 | 14.0 | 3080 | 1.1806 | 0.8049 |
| 0.0 | 15.0 | 3300 | 1.1203 | 0.8293 |
| 0.0 | 16.0 | 3520 | 1.1285 | 0.8293 |
| 0.0 | 17.0 | 3740 | 1.1228 | 0.8293 |
| 0.0 | 18.0 | 3960 | 1.1188 | 0.8293 |
| 0.0 | 19.0 | 4180 | 1.1166 | 0.8293 |
| 0.0 | 20.0 | 4400 | 1.1122 | 0.8293 |
| 0.0 | 21.0 | 4620 | 1.1096 | 0.8293 |
| 0.0 | 22.0 | 4840 | 1.1085 | 0.8293 |
| 0.0 | 23.0 | 5060 | 1.1063 | 0.8293 |
| 0.0 | 24.0 | 5280 | 1.1040 | 0.8293 |
| 0.0 | 25.0 | 5500 | 1.1028 | 0.8293 |
| 0.0 | 26.0 | 5720 | 1.0996 | 0.8293 |
| 0.0 | 27.0 | 5940 | 1.0984 | 0.8293 |
| 0.0 | 28.0 | 6160 | 1.0966 | 0.8293 |
| 0.0 | 29.0 | 6380 | 1.0939 | 0.8293 |
| 0.0 | 30.0 | 6600 | 1.0930 | 0.8293 |
| 0.0 | 31.0 | 6820 | 1.0903 | 0.8293 |
| 0.0 | 32.0 | 7040 | 1.0890 | 0.8293 |
| 0.0 | 33.0 | 7260 | 1.0876 | 0.8293 |
| 0.0 | 34.0 | 7480 | 1.0855 | 0.8293 |
| 0.0 | 35.0 | 7700 | 1.0853 | 0.8293 |
| 0.0 | 36.0 | 7920 | 1.0829 | 0.8293 |
| 0.0 | 37.0 | 8140 | 1.0834 | 0.8293 |
| 0.0 | 38.0 | 8360 | 1.0821 | 0.8293 |
| 0.0 | 39.0 | 8580 | 1.0819 | 0.8293 |
| 0.0 | 40.0 | 8800 | 1.0819 | 0.8293 |
| 0.0 | 41.0 | 9020 | 1.0821 | 0.8293 |
| 0.0 | 42.0 | 9240 | 1.0825 | 0.8293 |
| 0.0 | 43.0 | 9460 | 1.0825 | 0.8293 |
| 0.0 | 44.0 | 9680 | 1.0818 | 0.8293 |
| 0.0 | 45.0 | 9900 | 1.0822 | 0.8293 |
| 0.0 | 46.0 | 10120 | 1.0832 | 0.8293 |
| 0.0 | 47.0 | 10340 | 1.0843 | 0.8293 |
| 0.0 | 48.0 | 10560 | 1.0840 | 0.8293 |
| 0.0 | 49.0 | 10780 | 1.0843 | 0.8293 |
| 0.0 | 50.0 | 11000 | 1.0843 | 0.8293 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
dima806/hand_gestures_image_detection
|
Returns hand gesture based on image with about 96% accuracy.
See https://www.kaggle.com/code/dima806/hand-gestures-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
call 0.9256 0.9752 0.9498 11825
dislike 0.9784 0.9862 0.9823 11826
fist 0.9833 0.9870 0.9851 11826
four 0.9140 0.9357 0.9247 11826
like 0.9761 0.9101 0.9420 11825
mute 0.9831 0.9964 0.9897 11826
ok 0.9586 0.9658 0.9622 11825
one 0.9708 0.9453 0.9579 11826
palm 0.9764 0.9637 0.9700 11826
peace 0.9187 0.9367 0.9276 11825
peace_inverted 0.9784 0.9748 0.9766 11826
rock 0.9439 0.9361 0.9400 11825
stop 0.9502 0.9723 0.9611 11825
stop_inverted 0.9828 0.9546 0.9685 11826
three 0.9135 0.9068 0.9101 11826
three2 0.9799 0.9670 0.9734 11826
two_up 0.9570 0.9766 0.9667 11826
two_up_inverted 0.9754 0.9703 0.9729 11825
accuracy 0.9589 212861
macro avg 0.9592 0.9589 0.9589 212861
weighted avg 0.9592 0.9589 0.9589 212861
```
|
[
"call",
"dislike",
"fist",
"four",
"like",
"mute",
"ok",
"one",
"palm",
"peace",
"peace_inverted",
"rock",
"stop",
"stop_inverted",
"three",
"three2",
"two_up",
"two_up_inverted"
] |
hkivancoral/hushem_40x_deit_base_adamax_0001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_0001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0988
- Accuracy: 0.9762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0682 | 1.0 | 219 | 0.3355 | 0.9286 |
| 0.0006 | 2.0 | 438 | 0.0818 | 0.9762 |
| 0.0026 | 3.0 | 657 | 0.4435 | 0.8810 |
| 0.0001 | 4.0 | 876 | 0.1410 | 0.9762 |
| 0.0 | 5.0 | 1095 | 0.1325 | 0.9524 |
| 0.0 | 6.0 | 1314 | 0.1311 | 0.9524 |
| 0.0 | 7.0 | 1533 | 0.1285 | 0.9524 |
| 0.0 | 8.0 | 1752 | 0.1304 | 0.9524 |
| 0.0 | 9.0 | 1971 | 0.1305 | 0.9524 |
| 0.0 | 10.0 | 2190 | 0.1306 | 0.9762 |
| 0.0 | 11.0 | 2409 | 0.1299 | 0.9762 |
| 0.0 | 12.0 | 2628 | 0.1308 | 0.9762 |
| 0.0 | 13.0 | 2847 | 0.1297 | 0.9762 |
| 0.0 | 14.0 | 3066 | 0.1299 | 0.9762 |
| 0.0 | 15.0 | 3285 | 0.1283 | 0.9762 |
| 0.0 | 16.0 | 3504 | 0.1307 | 0.9762 |
| 0.0 | 17.0 | 3723 | 0.1314 | 0.9762 |
| 0.0 | 18.0 | 3942 | 0.1318 | 0.9762 |
| 0.0 | 19.0 | 4161 | 0.1316 | 0.9762 |
| 0.0 | 20.0 | 4380 | 0.1321 | 0.9762 |
| 0.0 | 21.0 | 4599 | 0.1311 | 0.9762 |
| 0.0 | 22.0 | 4818 | 0.1309 | 0.9762 |
| 0.0 | 23.0 | 5037 | 0.1314 | 0.9762 |
| 0.0 | 24.0 | 5256 | 0.1307 | 0.9762 |
| 0.0 | 25.0 | 5475 | 0.1305 | 0.9762 |
| 0.0 | 26.0 | 5694 | 0.1328 | 0.9762 |
| 0.0 | 27.0 | 5913 | 0.1314 | 0.9762 |
| 0.0 | 28.0 | 6132 | 0.1318 | 0.9762 |
| 0.0 | 29.0 | 6351 | 0.1318 | 0.9762 |
| 0.0 | 30.0 | 6570 | 0.1304 | 0.9762 |
| 0.0 | 31.0 | 6789 | 0.1298 | 0.9762 |
| 0.0 | 32.0 | 7008 | 0.1302 | 0.9762 |
| 0.0 | 33.0 | 7227 | 0.1299 | 0.9762 |
| 0.0 | 34.0 | 7446 | 0.1307 | 0.9762 |
| 0.0 | 35.0 | 7665 | 0.1289 | 0.9762 |
| 0.0 | 36.0 | 7884 | 0.1272 | 0.9762 |
| 0.0 | 37.0 | 8103 | 0.1257 | 0.9762 |
| 0.0 | 38.0 | 8322 | 0.1265 | 0.9762 |
| 0.0 | 39.0 | 8541 | 0.1244 | 0.9762 |
| 0.0 | 40.0 | 8760 | 0.1224 | 0.9762 |
| 0.0 | 41.0 | 8979 | 0.1200 | 0.9762 |
| 0.0 | 42.0 | 9198 | 0.1172 | 0.9762 |
| 0.0 | 43.0 | 9417 | 0.1170 | 0.9762 |
| 0.0 | 44.0 | 9636 | 0.1123 | 0.9762 |
| 0.0 | 45.0 | 9855 | 0.1089 | 0.9762 |
| 0.0 | 46.0 | 10074 | 0.1076 | 0.9762 |
| 0.0 | 47.0 | 10293 | 0.1032 | 0.9762 |
| 0.0 | 48.0 | 10512 | 0.0999 | 0.9762 |
| 0.0 | 49.0 | 10731 | 0.0983 | 0.9762 |
| 0.0 | 50.0 | 10950 | 0.0988 | 0.9762 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4788
- Accuracy: 0.9286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1955 | 1.0 | 219 | 0.5166 | 0.8571 |
| 0.0615 | 2.0 | 438 | 0.3282 | 0.8810 |
| 0.1238 | 3.0 | 657 | 0.0965 | 0.9524 |
| 0.008 | 4.0 | 876 | 0.3113 | 0.9524 |
| 0.0154 | 5.0 | 1095 | 0.8754 | 0.8810 |
| 0.0116 | 6.0 | 1314 | 0.5560 | 0.9048 |
| 0.0123 | 7.0 | 1533 | 0.5006 | 0.8333 |
| 0.0073 | 8.0 | 1752 | 1.1863 | 0.7857 |
| 0.0145 | 9.0 | 1971 | 0.2536 | 0.9048 |
| 0.0007 | 10.0 | 2190 | 0.3826 | 0.9286 |
| 0.0006 | 11.0 | 2409 | 0.1332 | 0.9762 |
| 0.0002 | 12.0 | 2628 | 0.7313 | 0.8810 |
| 0.0 | 13.0 | 2847 | 0.5030 | 0.9048 |
| 0.0 | 14.0 | 3066 | 0.4838 | 0.9048 |
| 0.0 | 15.0 | 3285 | 0.4726 | 0.9286 |
| 0.0 | 16.0 | 3504 | 0.4645 | 0.9286 |
| 0.0 | 17.0 | 3723 | 0.4599 | 0.9286 |
| 0.0 | 18.0 | 3942 | 0.4563 | 0.9286 |
| 0.0 | 19.0 | 4161 | 0.4541 | 0.9286 |
| 0.0 | 20.0 | 4380 | 0.4522 | 0.9286 |
| 0.0 | 21.0 | 4599 | 0.4527 | 0.9286 |
| 0.0 | 22.0 | 4818 | 0.4513 | 0.9286 |
| 0.0 | 23.0 | 5037 | 0.4519 | 0.9286 |
| 0.0 | 24.0 | 5256 | 0.4525 | 0.9286 |
| 0.0 | 25.0 | 5475 | 0.4545 | 0.9286 |
| 0.0 | 26.0 | 5694 | 0.4558 | 0.9286 |
| 0.0 | 27.0 | 5913 | 0.4548 | 0.9286 |
| 0.0 | 28.0 | 6132 | 0.4567 | 0.9286 |
| 0.0 | 29.0 | 6351 | 0.4592 | 0.9286 |
| 0.0 | 30.0 | 6570 | 0.4613 | 0.9286 |
| 0.0 | 31.0 | 6789 | 0.4633 | 0.9286 |
| 0.0 | 32.0 | 7008 | 0.4657 | 0.9286 |
| 0.0 | 33.0 | 7227 | 0.4670 | 0.9286 |
| 0.0 | 34.0 | 7446 | 0.4694 | 0.9286 |
| 0.0 | 35.0 | 7665 | 0.4721 | 0.9286 |
| 0.0 | 36.0 | 7884 | 0.4739 | 0.9286 |
| 0.0 | 37.0 | 8103 | 0.4760 | 0.9286 |
| 0.0 | 38.0 | 8322 | 0.4771 | 0.9286 |
| 0.0 | 39.0 | 8541 | 0.4783 | 0.9286 |
| 0.0 | 40.0 | 8760 | 0.4790 | 0.9286 |
| 0.0 | 41.0 | 8979 | 0.4792 | 0.9286 |
| 0.0 | 42.0 | 9198 | 0.4797 | 0.9286 |
| 0.0 | 43.0 | 9417 | 0.4798 | 0.9286 |
| 0.0 | 44.0 | 9636 | 0.4799 | 0.9286 |
| 0.0 | 45.0 | 9855 | 0.4797 | 0.9286 |
| 0.0 | 46.0 | 10074 | 0.4797 | 0.9286 |
| 0.0 | 47.0 | 10293 | 0.4794 | 0.9286 |
| 0.0 | 48.0 | 10512 | 0.4789 | 0.9286 |
| 0.0 | 49.0 | 10731 | 0.4785 | 0.9286 |
| 0.0 | 50.0 | 10950 | 0.4788 | 0.9286 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_0001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_0001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0346
- Accuracy: 0.8780
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.031 | 1.0 | 220 | 0.4305 | 0.8537 |
| 0.0086 | 2.0 | 440 | 0.4798 | 0.8537 |
| 0.0001 | 3.0 | 660 | 0.5506 | 0.8537 |
| 0.0001 | 4.0 | 880 | 0.6022 | 0.8537 |
| 0.0 | 5.0 | 1100 | 0.6090 | 0.8537 |
| 0.0 | 6.0 | 1320 | 0.6235 | 0.8537 |
| 0.0 | 7.0 | 1540 | 0.6332 | 0.8537 |
| 0.0 | 8.0 | 1760 | 0.6512 | 0.8537 |
| 0.0 | 9.0 | 1980 | 0.6655 | 0.8780 |
| 0.0 | 10.0 | 2200 | 0.6776 | 0.8780 |
| 0.0 | 11.0 | 2420 | 0.6885 | 0.8780 |
| 0.0 | 12.0 | 2640 | 0.6981 | 0.8780 |
| 0.0 | 13.0 | 2860 | 0.7088 | 0.8780 |
| 0.0 | 14.0 | 3080 | 0.7235 | 0.8780 |
| 0.0 | 15.0 | 3300 | 0.7311 | 0.8780 |
| 0.0 | 16.0 | 3520 | 0.7440 | 0.8780 |
| 0.0 | 17.0 | 3740 | 0.7529 | 0.8780 |
| 0.0 | 18.0 | 3960 | 0.7675 | 0.8780 |
| 0.0 | 19.0 | 4180 | 0.7773 | 0.8780 |
| 0.0 | 20.0 | 4400 | 0.7907 | 0.8780 |
| 0.0 | 21.0 | 4620 | 0.8020 | 0.8780 |
| 0.0 | 22.0 | 4840 | 0.8114 | 0.8780 |
| 0.0 | 23.0 | 5060 | 0.8261 | 0.8780 |
| 0.0 | 24.0 | 5280 | 0.8349 | 0.8780 |
| 0.0 | 25.0 | 5500 | 0.8489 | 0.8780 |
| 0.0 | 26.0 | 5720 | 0.8635 | 0.8780 |
| 0.0 | 27.0 | 5940 | 0.8766 | 0.8780 |
| 0.0 | 28.0 | 6160 | 0.8818 | 0.8780 |
| 0.0 | 29.0 | 6380 | 0.8949 | 0.8780 |
| 0.0 | 30.0 | 6600 | 0.9083 | 0.8780 |
| 0.0 | 31.0 | 6820 | 0.9225 | 0.8780 |
| 0.0 | 32.0 | 7040 | 0.9291 | 0.8780 |
| 0.0 | 33.0 | 7260 | 0.9474 | 0.8780 |
| 0.0 | 34.0 | 7480 | 0.9571 | 0.8780 |
| 0.0 | 35.0 | 7700 | 0.9707 | 0.8780 |
| 0.0 | 36.0 | 7920 | 0.9772 | 0.8780 |
| 0.0 | 37.0 | 8140 | 0.9933 | 0.8780 |
| 0.0 | 38.0 | 8360 | 1.0011 | 0.8780 |
| 0.0 | 39.0 | 8580 | 1.0070 | 0.8780 |
| 0.0 | 40.0 | 8800 | 1.0136 | 0.8780 |
| 0.0 | 41.0 | 9020 | 1.0168 | 0.8780 |
| 0.0 | 42.0 | 9240 | 1.0247 | 0.8780 |
| 0.0 | 43.0 | 9460 | 1.0260 | 0.8780 |
| 0.0 | 44.0 | 9680 | 1.0300 | 0.8780 |
| 0.0 | 45.0 | 9900 | 1.0349 | 0.8780 |
| 0.0 | 46.0 | 10120 | 1.0343 | 0.8780 |
| 0.0 | 47.0 | 10340 | 1.0321 | 0.8780 |
| 0.0 | 48.0 | 10560 | 1.0333 | 0.8780 |
| 0.0 | 49.0 | 10780 | 1.0344 | 0.8780 |
| 0.0 | 50.0 | 11000 | 1.0346 | 0.8780 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4744
- Accuracy: 0.9024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2741 | 1.0 | 220 | 0.7783 | 0.7317 |
| 0.1315 | 2.0 | 440 | 0.8361 | 0.8293 |
| 0.0434 | 3.0 | 660 | 1.2093 | 0.7317 |
| 0.0894 | 4.0 | 880 | 0.3662 | 0.9024 |
| 0.0527 | 5.0 | 1100 | 0.4869 | 0.9024 |
| 0.0501 | 6.0 | 1320 | 0.3025 | 0.9512 |
| 0.001 | 7.0 | 1540 | 0.5424 | 0.9024 |
| 0.023 | 8.0 | 1760 | 0.6857 | 0.9024 |
| 0.0256 | 9.0 | 1980 | 1.0120 | 0.8780 |
| 0.0166 | 10.0 | 2200 | 0.5130 | 0.8780 |
| 0.0004 | 11.0 | 2420 | 0.4559 | 0.8780 |
| 0.0217 | 12.0 | 2640 | 1.2147 | 0.7805 |
| 0.0002 | 13.0 | 2860 | 0.2445 | 0.9268 |
| 0.0 | 14.0 | 3080 | 0.2240 | 0.9268 |
| 0.0 | 15.0 | 3300 | 0.2460 | 0.9268 |
| 0.0 | 16.0 | 3520 | 0.2550 | 0.9268 |
| 0.0 | 17.0 | 3740 | 0.2642 | 0.9268 |
| 0.0 | 18.0 | 3960 | 0.2728 | 0.9268 |
| 0.0 | 19.0 | 4180 | 0.2816 | 0.9268 |
| 0.0 | 20.0 | 4400 | 0.2891 | 0.9268 |
| 0.0 | 21.0 | 4620 | 0.2969 | 0.9268 |
| 0.0 | 22.0 | 4840 | 0.3042 | 0.9268 |
| 0.0 | 23.0 | 5060 | 0.3121 | 0.9268 |
| 0.0 | 24.0 | 5280 | 0.3198 | 0.9268 |
| 0.0 | 25.0 | 5500 | 0.3281 | 0.9268 |
| 0.0 | 26.0 | 5720 | 0.3364 | 0.9268 |
| 0.0 | 27.0 | 5940 | 0.3437 | 0.9268 |
| 0.0 | 28.0 | 6160 | 0.3515 | 0.9268 |
| 0.0 | 29.0 | 6380 | 0.3592 | 0.9268 |
| 0.0 | 30.0 | 6600 | 0.3667 | 0.9268 |
| 0.0 | 31.0 | 6820 | 0.3741 | 0.9268 |
| 0.0 | 32.0 | 7040 | 0.3811 | 0.9268 |
| 0.0 | 33.0 | 7260 | 0.3879 | 0.9268 |
| 0.0 | 34.0 | 7480 | 0.3952 | 0.9268 |
| 0.0 | 35.0 | 7700 | 0.4021 | 0.9268 |
| 0.0 | 36.0 | 7920 | 0.4093 | 0.9268 |
| 0.0 | 37.0 | 8140 | 0.4163 | 0.9268 |
| 0.0 | 38.0 | 8360 | 0.4226 | 0.9268 |
| 0.0 | 39.0 | 8580 | 0.4295 | 0.9268 |
| 0.0 | 40.0 | 8800 | 0.4358 | 0.9268 |
| 0.0 | 41.0 | 9020 | 0.4415 | 0.9268 |
| 0.0 | 42.0 | 9240 | 0.4471 | 0.9268 |
| 0.0 | 43.0 | 9460 | 0.4531 | 0.9268 |
| 0.0 | 44.0 | 9680 | 0.4575 | 0.9268 |
| 0.0 | 45.0 | 9900 | 0.4619 | 0.9268 |
| 0.0 | 46.0 | 10120 | 0.4664 | 0.9268 |
| 0.0 | 47.0 | 10340 | 0.4696 | 0.9268 |
| 0.0 | 48.0 | 10560 | 0.4724 | 0.9024 |
| 0.0 | 49.0 | 10780 | 0.4740 | 0.9024 |
| 0.0 | 50.0 | 11000 | 0.4744 | 0.9024 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_00001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_00001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7464
- Accuracy: 0.7778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.175 | 1.0 | 215 | 0.7361 | 0.7556 |
| 0.0109 | 2.0 | 430 | 0.6497 | 0.8 |
| 0.0023 | 3.0 | 645 | 0.7453 | 0.8222 |
| 0.001 | 4.0 | 860 | 0.7854 | 0.8222 |
| 0.0006 | 5.0 | 1075 | 0.8105 | 0.8222 |
| 0.0004 | 6.0 | 1290 | 0.8328 | 0.8222 |
| 0.0003 | 7.0 | 1505 | 0.8638 | 0.8222 |
| 0.0002 | 8.0 | 1720 | 0.8701 | 0.8222 |
| 0.0002 | 9.0 | 1935 | 0.9048 | 0.8222 |
| 0.0001 | 10.0 | 2150 | 0.9203 | 0.8 |
| 0.0001 | 11.0 | 2365 | 0.9399 | 0.8 |
| 0.0001 | 12.0 | 2580 | 0.9611 | 0.8 |
| 0.0001 | 13.0 | 2795 | 0.9847 | 0.8 |
| 0.0001 | 14.0 | 3010 | 1.0078 | 0.8 |
| 0.0 | 15.0 | 3225 | 1.0165 | 0.8 |
| 0.0 | 16.0 | 3440 | 1.0509 | 0.8 |
| 0.0 | 17.0 | 3655 | 1.0662 | 0.8 |
| 0.0 | 18.0 | 3870 | 1.0960 | 0.8 |
| 0.0 | 19.0 | 4085 | 1.1102 | 0.8 |
| 0.0 | 20.0 | 4300 | 1.1333 | 0.8 |
| 0.0 | 21.0 | 4515 | 1.1560 | 0.8 |
| 0.0 | 22.0 | 4730 | 1.1835 | 0.8 |
| 0.0 | 23.0 | 4945 | 1.2066 | 0.8 |
| 0.0 | 24.0 | 5160 | 1.2238 | 0.8 |
| 0.0 | 25.0 | 5375 | 1.2452 | 0.8 |
| 0.0 | 26.0 | 5590 | 1.2607 | 0.8 |
| 0.0 | 27.0 | 5805 | 1.2985 | 0.8 |
| 0.0 | 28.0 | 6020 | 1.3142 | 0.7778 |
| 0.0 | 29.0 | 6235 | 1.3455 | 0.7778 |
| 0.0 | 30.0 | 6450 | 1.3849 | 0.7778 |
| 0.0 | 31.0 | 6665 | 1.4087 | 0.7778 |
| 0.0 | 32.0 | 6880 | 1.4316 | 0.7778 |
| 0.0 | 33.0 | 7095 | 1.4372 | 0.7778 |
| 0.0 | 34.0 | 7310 | 1.4578 | 0.7778 |
| 0.0 | 35.0 | 7525 | 1.5115 | 0.7778 |
| 0.0 | 36.0 | 7740 | 1.5151 | 0.7778 |
| 0.0 | 37.0 | 7955 | 1.5376 | 0.7778 |
| 0.0 | 38.0 | 8170 | 1.5694 | 0.7778 |
| 0.0 | 39.0 | 8385 | 1.5967 | 0.7778 |
| 0.0 | 40.0 | 8600 | 1.6099 | 0.7778 |
| 0.0 | 41.0 | 8815 | 1.6278 | 0.7778 |
| 0.0 | 42.0 | 9030 | 1.6372 | 0.7778 |
| 0.0 | 43.0 | 9245 | 1.6697 | 0.7778 |
| 0.0 | 44.0 | 9460 | 1.6889 | 0.7778 |
| 0.0 | 45.0 | 9675 | 1.6985 | 0.7778 |
| 0.0 | 46.0 | 9890 | 1.7202 | 0.7778 |
| 0.0 | 47.0 | 10105 | 1.7225 | 0.7778 |
| 0.0 | 48.0 | 10320 | 1.7406 | 0.7778 |
| 0.0 | 49.0 | 10535 | 1.7437 | 0.7778 |
| 0.0 | 50.0 | 10750 | 1.7464 | 0.7778 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_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_40x_deit_base_sgd_001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7430
- Accuracy: 0.7556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2392 | 1.0 | 215 | 1.3895 | 0.2667 |
| 1.1003 | 2.0 | 430 | 1.3294 | 0.3333 |
| 1.0196 | 3.0 | 645 | 1.2624 | 0.4444 |
| 0.8639 | 4.0 | 860 | 1.1946 | 0.4889 |
| 0.731 | 5.0 | 1075 | 1.1313 | 0.5111 |
| 0.6646 | 6.0 | 1290 | 1.0718 | 0.5556 |
| 0.545 | 7.0 | 1505 | 1.0254 | 0.6 |
| 0.4701 | 8.0 | 1720 | 0.9800 | 0.6444 |
| 0.4065 | 9.0 | 1935 | 0.9495 | 0.6222 |
| 0.3851 | 10.0 | 2150 | 0.9148 | 0.6667 |
| 0.3271 | 11.0 | 2365 | 0.8947 | 0.6667 |
| 0.2977 | 12.0 | 2580 | 0.8732 | 0.6889 |
| 0.2671 | 13.0 | 2795 | 0.8416 | 0.7111 |
| 0.2428 | 14.0 | 3010 | 0.8450 | 0.6889 |
| 0.2387 | 15.0 | 3225 | 0.8270 | 0.7111 |
| 0.1988 | 16.0 | 3440 | 0.8218 | 0.7111 |
| 0.1804 | 17.0 | 3655 | 0.8107 | 0.7333 |
| 0.1681 | 18.0 | 3870 | 0.8058 | 0.7333 |
| 0.1475 | 19.0 | 4085 | 0.7968 | 0.7333 |
| 0.1494 | 20.0 | 4300 | 0.7851 | 0.7556 |
| 0.1288 | 21.0 | 4515 | 0.7807 | 0.7556 |
| 0.1265 | 22.0 | 4730 | 0.7751 | 0.7556 |
| 0.1136 | 23.0 | 4945 | 0.7744 | 0.7556 |
| 0.094 | 24.0 | 5160 | 0.7654 | 0.7556 |
| 0.0987 | 25.0 | 5375 | 0.7661 | 0.7556 |
| 0.096 | 26.0 | 5590 | 0.7527 | 0.7556 |
| 0.084 | 27.0 | 5805 | 0.7535 | 0.7556 |
| 0.069 | 28.0 | 6020 | 0.7589 | 0.7556 |
| 0.0764 | 29.0 | 6235 | 0.7612 | 0.7556 |
| 0.067 | 30.0 | 6450 | 0.7558 | 0.7556 |
| 0.0458 | 31.0 | 6665 | 0.7531 | 0.7333 |
| 0.0687 | 32.0 | 6880 | 0.7463 | 0.7556 |
| 0.0414 | 33.0 | 7095 | 0.7445 | 0.7556 |
| 0.0522 | 34.0 | 7310 | 0.7378 | 0.7556 |
| 0.0521 | 35.0 | 7525 | 0.7477 | 0.7556 |
| 0.0458 | 36.0 | 7740 | 0.7370 | 0.7556 |
| 0.0586 | 37.0 | 7955 | 0.7425 | 0.7556 |
| 0.0551 | 38.0 | 8170 | 0.7441 | 0.7556 |
| 0.0389 | 39.0 | 8385 | 0.7437 | 0.7556 |
| 0.0335 | 40.0 | 8600 | 0.7446 | 0.7556 |
| 0.0337 | 41.0 | 8815 | 0.7439 | 0.7556 |
| 0.0431 | 42.0 | 9030 | 0.7421 | 0.7556 |
| 0.0392 | 43.0 | 9245 | 0.7439 | 0.7556 |
| 0.03 | 44.0 | 9460 | 0.7447 | 0.7556 |
| 0.0402 | 45.0 | 9675 | 0.7426 | 0.7556 |
| 0.0313 | 46.0 | 9890 | 0.7416 | 0.7556 |
| 0.0341 | 47.0 | 10105 | 0.7428 | 0.7556 |
| 0.0375 | 48.0 | 10320 | 0.7420 | 0.7556 |
| 0.0432 | 49.0 | 10535 | 0.7428 | 0.7556 |
| 0.0389 | 50.0 | 10750 | 0.7430 | 0.7556 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_tiny_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_40x_deit_tiny_adamax_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: 1.3786
- Accuracy: 0.8444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0587 | 1.0 | 215 | 0.8379 | 0.7778 |
| 0.0029 | 2.0 | 430 | 0.8134 | 0.8222 |
| 0.0061 | 3.0 | 645 | 0.6824 | 0.8667 |
| 0.0003 | 4.0 | 860 | 0.8964 | 0.8444 |
| 0.0004 | 5.0 | 1075 | 1.1389 | 0.8 |
| 0.0069 | 6.0 | 1290 | 0.8847 | 0.8222 |
| 0.0014 | 7.0 | 1505 | 0.9407 | 0.8444 |
| 0.0208 | 8.0 | 1720 | 1.2665 | 0.8 |
| 0.0 | 9.0 | 1935 | 0.7746 | 0.8222 |
| 0.0001 | 10.0 | 2150 | 0.9541 | 0.8222 |
| 0.0 | 11.0 | 2365 | 1.3297 | 0.7556 |
| 0.0 | 12.0 | 2580 | 1.2887 | 0.7778 |
| 0.0 | 13.0 | 2795 | 1.2405 | 0.7778 |
| 0.0 | 14.0 | 3010 | 1.2098 | 0.8 |
| 0.0 | 15.0 | 3225 | 1.1905 | 0.8 |
| 0.0 | 16.0 | 3440 | 1.1775 | 0.8 |
| 0.0 | 17.0 | 3655 | 1.1699 | 0.8 |
| 0.0 | 18.0 | 3870 | 1.1668 | 0.8 |
| 0.0 | 19.0 | 4085 | 1.1651 | 0.8 |
| 0.0 | 20.0 | 4300 | 1.1645 | 0.8 |
| 0.0 | 21.0 | 4515 | 1.1663 | 0.8 |
| 0.0 | 22.0 | 4730 | 1.1709 | 0.8 |
| 0.0 | 23.0 | 4945 | 1.1752 | 0.8 |
| 0.0 | 24.0 | 5160 | 1.1807 | 0.8 |
| 0.0 | 25.0 | 5375 | 1.1874 | 0.8222 |
| 0.0 | 26.0 | 5590 | 1.1925 | 0.8222 |
| 0.0 | 27.0 | 5805 | 1.1999 | 0.8222 |
| 0.0 | 28.0 | 6020 | 1.2057 | 0.8222 |
| 0.0 | 29.0 | 6235 | 1.2150 | 0.8222 |
| 0.0 | 30.0 | 6450 | 1.2228 | 0.8222 |
| 0.0 | 31.0 | 6665 | 1.2334 | 0.8222 |
| 0.0 | 32.0 | 6880 | 1.2399 | 0.8222 |
| 0.0 | 33.0 | 7095 | 1.2440 | 0.8222 |
| 0.0 | 34.0 | 7310 | 1.2539 | 0.8222 |
| 0.0 | 35.0 | 7525 | 1.2643 | 0.8222 |
| 0.0 | 36.0 | 7740 | 1.2752 | 0.8222 |
| 0.0 | 37.0 | 7955 | 1.2837 | 0.8222 |
| 0.0 | 38.0 | 8170 | 1.2941 | 0.8222 |
| 0.0 | 39.0 | 8385 | 1.3057 | 0.8444 |
| 0.0 | 40.0 | 8600 | 1.3171 | 0.8444 |
| 0.0 | 41.0 | 8815 | 1.3233 | 0.8444 |
| 0.0 | 42.0 | 9030 | 1.3334 | 0.8444 |
| 0.0 | 43.0 | 9245 | 1.3422 | 0.8444 |
| 0.0 | 44.0 | 9460 | 1.3487 | 0.8444 |
| 0.0 | 45.0 | 9675 | 1.3569 | 0.8444 |
| 0.0 | 46.0 | 9890 | 1.3629 | 0.8444 |
| 0.0 | 47.0 | 10105 | 1.3713 | 0.8444 |
| 0.0 | 48.0 | 10320 | 1.3761 | 0.8444 |
| 0.0 | 49.0 | 10535 | 1.3795 | 0.8444 |
| 0.0 | 50.0 | 10750 | 1.3786 | 0.8444 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_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_40x_deit_base_sgd_001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9714
- Accuracy: 0.7111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2707 | 1.0 | 215 | 1.3448 | 0.3333 |
| 1.0768 | 2.0 | 430 | 1.2879 | 0.3333 |
| 0.9642 | 3.0 | 645 | 1.2312 | 0.3556 |
| 0.8173 | 4.0 | 860 | 1.1793 | 0.4222 |
| 0.7029 | 5.0 | 1075 | 1.1361 | 0.4889 |
| 0.6404 | 6.0 | 1290 | 1.1006 | 0.5111 |
| 0.5591 | 7.0 | 1505 | 1.0646 | 0.5778 |
| 0.4274 | 8.0 | 1720 | 1.0467 | 0.5778 |
| 0.3944 | 9.0 | 1935 | 1.0267 | 0.6444 |
| 0.3254 | 10.0 | 2150 | 1.0079 | 0.6222 |
| 0.2604 | 11.0 | 2365 | 0.9958 | 0.6222 |
| 0.2631 | 12.0 | 2580 | 0.9759 | 0.6222 |
| 0.2337 | 13.0 | 2795 | 0.9617 | 0.6 |
| 0.1789 | 14.0 | 3010 | 0.9588 | 0.6222 |
| 0.1879 | 15.0 | 3225 | 0.9460 | 0.6222 |
| 0.1684 | 16.0 | 3440 | 0.9372 | 0.6222 |
| 0.1577 | 17.0 | 3655 | 0.9384 | 0.6444 |
| 0.14 | 18.0 | 3870 | 0.9410 | 0.6444 |
| 0.1197 | 19.0 | 4085 | 0.9384 | 0.6444 |
| 0.1254 | 20.0 | 4300 | 0.9412 | 0.6444 |
| 0.1072 | 21.0 | 4515 | 0.9296 | 0.6444 |
| 0.0973 | 22.0 | 4730 | 0.9322 | 0.6444 |
| 0.0821 | 23.0 | 4945 | 0.9340 | 0.6444 |
| 0.0927 | 24.0 | 5160 | 0.9345 | 0.6667 |
| 0.0715 | 25.0 | 5375 | 0.9358 | 0.6667 |
| 0.0724 | 26.0 | 5590 | 0.9414 | 0.6889 |
| 0.0815 | 27.0 | 5805 | 0.9356 | 0.6667 |
| 0.0671 | 28.0 | 6020 | 0.9387 | 0.6889 |
| 0.053 | 29.0 | 6235 | 0.9438 | 0.6889 |
| 0.0671 | 30.0 | 6450 | 0.9381 | 0.7111 |
| 0.0428 | 31.0 | 6665 | 0.9431 | 0.7111 |
| 0.041 | 32.0 | 6880 | 0.9407 | 0.7111 |
| 0.0371 | 33.0 | 7095 | 0.9476 | 0.7111 |
| 0.0372 | 34.0 | 7310 | 0.9501 | 0.7111 |
| 0.0416 | 35.0 | 7525 | 0.9484 | 0.7111 |
| 0.0375 | 36.0 | 7740 | 0.9551 | 0.7111 |
| 0.0443 | 37.0 | 7955 | 0.9530 | 0.7111 |
| 0.031 | 38.0 | 8170 | 0.9549 | 0.7111 |
| 0.0359 | 39.0 | 8385 | 0.9537 | 0.7111 |
| 0.0327 | 40.0 | 8600 | 0.9553 | 0.7111 |
| 0.0313 | 41.0 | 8815 | 0.9602 | 0.7111 |
| 0.0312 | 42.0 | 9030 | 0.9634 | 0.7111 |
| 0.0302 | 43.0 | 9245 | 0.9659 | 0.7111 |
| 0.0284 | 44.0 | 9460 | 0.9687 | 0.7111 |
| 0.0286 | 45.0 | 9675 | 0.9696 | 0.7111 |
| 0.0307 | 46.0 | 9890 | 0.9699 | 0.7111 |
| 0.0251 | 47.0 | 10105 | 0.9708 | 0.7111 |
| 0.0291 | 48.0 | 10320 | 0.9714 | 0.7111 |
| 0.0372 | 49.0 | 10535 | 0.9713 | 0.7111 |
| 0.0296 | 50.0 | 10750 | 0.9714 | 0.7111 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_00001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_00001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1410
- Accuracy: 0.7556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1698 | 1.0 | 215 | 0.9036 | 0.6667 |
| 0.0086 | 2.0 | 430 | 0.8771 | 0.8 |
| 0.0023 | 3.0 | 645 | 0.9467 | 0.8 |
| 0.0009 | 4.0 | 860 | 1.0021 | 0.7778 |
| 0.0006 | 5.0 | 1075 | 1.0414 | 0.7556 |
| 0.0004 | 6.0 | 1290 | 1.0789 | 0.7556 |
| 0.0003 | 7.0 | 1505 | 1.0927 | 0.7778 |
| 0.0002 | 8.0 | 1720 | 1.1233 | 0.7778 |
| 0.0002 | 9.0 | 1935 | 1.1652 | 0.7778 |
| 0.0001 | 10.0 | 2150 | 1.1805 | 0.7778 |
| 0.0001 | 11.0 | 2365 | 1.2046 | 0.7778 |
| 0.0001 | 12.0 | 2580 | 1.2366 | 0.7778 |
| 0.0001 | 13.0 | 2795 | 1.2540 | 0.7778 |
| 0.0001 | 14.0 | 3010 | 1.2856 | 0.7778 |
| 0.0 | 15.0 | 3225 | 1.3104 | 0.7778 |
| 0.0 | 16.0 | 3440 | 1.3434 | 0.7778 |
| 0.0 | 17.0 | 3655 | 1.3705 | 0.7778 |
| 0.0 | 18.0 | 3870 | 1.3922 | 0.7778 |
| 0.0 | 19.0 | 4085 | 1.4221 | 0.7778 |
| 0.0 | 20.0 | 4300 | 1.4557 | 0.7778 |
| 0.0 | 21.0 | 4515 | 1.4854 | 0.7778 |
| 0.0 | 22.0 | 4730 | 1.5092 | 0.7778 |
| 0.0 | 23.0 | 4945 | 1.5343 | 0.7778 |
| 0.0 | 24.0 | 5160 | 1.5541 | 0.7778 |
| 0.0 | 25.0 | 5375 | 1.5830 | 0.7778 |
| 0.0 | 26.0 | 5590 | 1.6177 | 0.7778 |
| 0.0 | 27.0 | 5805 | 1.6474 | 0.7778 |
| 0.0 | 28.0 | 6020 | 1.6634 | 0.7778 |
| 0.0 | 29.0 | 6235 | 1.6875 | 0.7778 |
| 0.0 | 30.0 | 6450 | 1.7106 | 0.7778 |
| 0.0 | 31.0 | 6665 | 1.7484 | 0.7778 |
| 0.0 | 32.0 | 6880 | 1.7797 | 0.7778 |
| 0.0 | 33.0 | 7095 | 1.8167 | 0.7778 |
| 0.0 | 34.0 | 7310 | 1.8422 | 0.7778 |
| 0.0 | 35.0 | 7525 | 1.8678 | 0.7778 |
| 0.0 | 36.0 | 7740 | 1.8865 | 0.7778 |
| 0.0 | 37.0 | 7955 | 1.9143 | 0.7778 |
| 0.0 | 38.0 | 8170 | 1.9225 | 0.7778 |
| 0.0 | 39.0 | 8385 | 1.9621 | 0.7778 |
| 0.0 | 40.0 | 8600 | 1.9777 | 0.7556 |
| 0.0 | 41.0 | 8815 | 2.0240 | 0.7778 |
| 0.0 | 42.0 | 9030 | 2.0141 | 0.7556 |
| 0.0 | 43.0 | 9245 | 2.0463 | 0.7556 |
| 0.0 | 44.0 | 9460 | 2.0688 | 0.7556 |
| 0.0 | 45.0 | 9675 | 2.0919 | 0.7556 |
| 0.0 | 46.0 | 9890 | 2.1123 | 0.7556 |
| 0.0 | 47.0 | 10105 | 2.1294 | 0.7556 |
| 0.0 | 48.0 | 10320 | 2.1354 | 0.7556 |
| 0.0 | 49.0 | 10535 | 2.1448 | 0.7556 |
| 0.0 | 50.0 | 10750 | 2.1410 | 0.7556 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_tiny_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_40x_deit_tiny_adamax_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: 1.9983
- Accuracy: 0.7556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0219 | 1.0 | 215 | 0.6349 | 0.8667 |
| 0.0009 | 2.0 | 430 | 1.0022 | 0.7333 |
| 0.0057 | 3.0 | 645 | 1.0734 | 0.7556 |
| 0.0006 | 4.0 | 860 | 1.3398 | 0.7778 |
| 0.0 | 5.0 | 1075 | 1.6890 | 0.7333 |
| 0.0 | 6.0 | 1290 | 1.6522 | 0.7111 |
| 0.0 | 7.0 | 1505 | 1.6220 | 0.7111 |
| 0.0 | 8.0 | 1720 | 1.6021 | 0.7333 |
| 0.0 | 9.0 | 1935 | 1.5870 | 0.7333 |
| 0.0 | 10.0 | 2150 | 1.5842 | 0.7333 |
| 0.0 | 11.0 | 2365 | 1.5782 | 0.7333 |
| 0.0 | 12.0 | 2580 | 1.5625 | 0.7333 |
| 0.0 | 13.0 | 2795 | 1.5601 | 0.7333 |
| 0.0 | 14.0 | 3010 | 1.5521 | 0.7333 |
| 0.0 | 15.0 | 3225 | 1.5637 | 0.7333 |
| 0.0 | 16.0 | 3440 | 1.5652 | 0.7778 |
| 0.0 | 17.0 | 3655 | 1.5622 | 0.7333 |
| 0.0 | 18.0 | 3870 | 1.5700 | 0.7778 |
| 0.0 | 19.0 | 4085 | 1.5813 | 0.7778 |
| 0.0 | 20.0 | 4300 | 1.5874 | 0.7556 |
| 0.0 | 21.0 | 4515 | 1.5931 | 0.7556 |
| 0.0 | 22.0 | 4730 | 1.6081 | 0.7556 |
| 0.0 | 23.0 | 4945 | 1.6167 | 0.7556 |
| 0.0 | 24.0 | 5160 | 1.6398 | 0.7556 |
| 0.0 | 25.0 | 5375 | 1.6448 | 0.7556 |
| 0.0 | 26.0 | 5590 | 1.6610 | 0.7556 |
| 0.0 | 27.0 | 5805 | 1.6849 | 0.7333 |
| 0.0 | 28.0 | 6020 | 1.6982 | 0.7556 |
| 0.0 | 29.0 | 6235 | 1.7059 | 0.7556 |
| 0.0 | 30.0 | 6450 | 1.7216 | 0.7556 |
| 0.0 | 31.0 | 6665 | 1.7579 | 0.7556 |
| 0.0 | 32.0 | 6880 | 1.7634 | 0.7556 |
| 0.0 | 33.0 | 7095 | 1.7775 | 0.7556 |
| 0.0 | 34.0 | 7310 | 1.8193 | 0.7556 |
| 0.0 | 35.0 | 7525 | 1.8288 | 0.7556 |
| 0.0 | 36.0 | 7740 | 1.8617 | 0.7556 |
| 0.0 | 37.0 | 7955 | 1.8992 | 0.7556 |
| 0.0 | 38.0 | 8170 | 1.9097 | 0.7556 |
| 0.0 | 39.0 | 8385 | 1.9200 | 0.7556 |
| 0.0 | 40.0 | 8600 | 1.9431 | 0.7556 |
| 0.0 | 41.0 | 8815 | 1.9378 | 0.7556 |
| 0.0 | 42.0 | 9030 | 1.9739 | 0.7556 |
| 0.0 | 43.0 | 9245 | 1.9777 | 0.7556 |
| 0.0 | 44.0 | 9460 | 1.9924 | 0.7556 |
| 0.0 | 45.0 | 9675 | 1.9923 | 0.7556 |
| 0.0 | 46.0 | 9890 | 1.9872 | 0.7556 |
| 0.0 | 47.0 | 10105 | 2.0011 | 0.7556 |
| 0.0 | 48.0 | 10320 | 2.0002 | 0.7556 |
| 0.0 | 49.0 | 10535 | 1.9945 | 0.7556 |
| 0.0 | 50.0 | 10750 | 1.9983 | 0.7556 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
vit54155/vit-base-patch16-224-in21k-euroSat
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vit54155/vit-base-patch16-224-in21k-euroSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6316
- Train Accuracy: 0.6693
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.6555
- Validation Accuracy: 0.6320
- Validation Top-3-accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'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': 360, '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 |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.6316 | 0.6693 | 1.0 | 0.6555 | 0.6320 | 1.0 | 0 |
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.13.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
[
"anormal",
"normal"
] |
hkivancoral/hushem_40x_deit_tiny_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_40x_deit_tiny_adamax_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: 0.8703
- Accuracy: 0.9070
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0405 | 1.0 | 217 | 0.7475 | 0.8372 |
| 0.0174 | 2.0 | 434 | 0.3741 | 0.8837 |
| 0.0347 | 3.0 | 651 | 0.7246 | 0.8605 |
| 0.0 | 4.0 | 868 | 0.4948 | 0.9070 |
| 0.0001 | 5.0 | 1085 | 0.5865 | 0.8837 |
| 0.0 | 6.0 | 1302 | 0.7334 | 0.8837 |
| 0.0 | 7.0 | 1519 | 0.7116 | 0.8837 |
| 0.0 | 8.0 | 1736 | 0.7193 | 0.8837 |
| 0.0 | 9.0 | 1953 | 0.7217 | 0.8837 |
| 0.0 | 10.0 | 2170 | 0.7267 | 0.9070 |
| 0.0 | 11.0 | 2387 | 0.7322 | 0.9070 |
| 0.0 | 12.0 | 2604 | 0.7327 | 0.9070 |
| 0.0 | 13.0 | 2821 | 0.7378 | 0.9070 |
| 0.0 | 14.0 | 3038 | 0.7396 | 0.9070 |
| 0.0 | 15.0 | 3255 | 0.7467 | 0.9070 |
| 0.0 | 16.0 | 3472 | 0.7473 | 0.9070 |
| 0.0 | 17.0 | 3689 | 0.7591 | 0.9070 |
| 0.0 | 18.0 | 3906 | 0.7633 | 0.9070 |
| 0.0 | 19.0 | 4123 | 0.7713 | 0.9070 |
| 0.0 | 20.0 | 4340 | 0.7767 | 0.9070 |
| 0.0 | 21.0 | 4557 | 0.7800 | 0.9070 |
| 0.0 | 22.0 | 4774 | 0.7902 | 0.9070 |
| 0.0 | 23.0 | 4991 | 0.7915 | 0.9070 |
| 0.0 | 24.0 | 5208 | 0.8041 | 0.9070 |
| 0.0 | 25.0 | 5425 | 0.8046 | 0.9070 |
| 0.0 | 26.0 | 5642 | 0.8231 | 0.8837 |
| 0.0 | 27.0 | 5859 | 0.8344 | 0.8837 |
| 0.0 | 28.0 | 6076 | 0.8309 | 0.8837 |
| 0.0 | 29.0 | 6293 | 0.8419 | 0.8837 |
| 0.0 | 30.0 | 6510 | 0.8453 | 0.8837 |
| 0.0 | 31.0 | 6727 | 0.8681 | 0.8837 |
| 0.0 | 32.0 | 6944 | 0.8660 | 0.8837 |
| 0.0 | 33.0 | 7161 | 0.8697 | 0.8837 |
| 0.0 | 34.0 | 7378 | 0.8846 | 0.8837 |
| 0.0 | 35.0 | 7595 | 0.8902 | 0.8837 |
| 0.0 | 36.0 | 7812 | 0.8974 | 0.8837 |
| 0.0 | 37.0 | 8029 | 0.8857 | 0.8837 |
| 0.0 | 38.0 | 8246 | 0.8854 | 0.8837 |
| 0.0 | 39.0 | 8463 | 0.8857 | 0.8837 |
| 0.0 | 40.0 | 8680 | 0.8940 | 0.8837 |
| 0.0 | 41.0 | 8897 | 0.8956 | 0.8837 |
| 0.0 | 42.0 | 9114 | 0.8901 | 0.8837 |
| 0.0 | 43.0 | 9331 | 0.8853 | 0.9070 |
| 0.0 | 44.0 | 9548 | 0.8877 | 0.8837 |
| 0.0 | 45.0 | 9765 | 0.8928 | 0.9070 |
| 0.0 | 46.0 | 9982 | 0.8951 | 0.9070 |
| 0.0 | 47.0 | 10199 | 0.8786 | 0.9070 |
| 0.0 | 48.0 | 10416 | 0.8807 | 0.9070 |
| 0.0 | 49.0 | 10633 | 0.8732 | 0.9070 |
| 0.0 | 50.0 | 10850 | 0.8703 | 0.9070 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_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_40x_deit_base_sgd_001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4834
- 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2567 | 1.0 | 217 | 1.3908 | 0.3023 |
| 1.1156 | 2.0 | 434 | 1.3183 | 0.4186 |
| 0.9891 | 3.0 | 651 | 1.2352 | 0.5116 |
| 0.902 | 4.0 | 868 | 1.1401 | 0.5814 |
| 0.7383 | 5.0 | 1085 | 1.0533 | 0.6047 |
| 0.6659 | 6.0 | 1302 | 0.9783 | 0.6279 |
| 0.577 | 7.0 | 1519 | 0.9088 | 0.6047 |
| 0.5084 | 8.0 | 1736 | 0.8504 | 0.6512 |
| 0.4618 | 9.0 | 1953 | 0.8112 | 0.6512 |
| 0.3986 | 10.0 | 2170 | 0.7644 | 0.6744 |
| 0.3262 | 11.0 | 2387 | 0.7405 | 0.6744 |
| 0.3187 | 12.0 | 2604 | 0.7073 | 0.7442 |
| 0.287 | 13.0 | 2821 | 0.6756 | 0.7442 |
| 0.2667 | 14.0 | 3038 | 0.6524 | 0.7674 |
| 0.2566 | 15.0 | 3255 | 0.6373 | 0.7674 |
| 0.2206 | 16.0 | 3472 | 0.6121 | 0.7674 |
| 0.1851 | 17.0 | 3689 | 0.6018 | 0.7674 |
| 0.1802 | 18.0 | 3906 | 0.5901 | 0.7674 |
| 0.1691 | 19.0 | 4123 | 0.5735 | 0.7674 |
| 0.1555 | 20.0 | 4340 | 0.5642 | 0.7674 |
| 0.1532 | 21.0 | 4557 | 0.5647 | 0.7907 |
| 0.1287 | 22.0 | 4774 | 0.5473 | 0.7907 |
| 0.1172 | 23.0 | 4991 | 0.5337 | 0.7907 |
| 0.1215 | 24.0 | 5208 | 0.5344 | 0.7907 |
| 0.1 | 25.0 | 5425 | 0.5177 | 0.7907 |
| 0.1218 | 26.0 | 5642 | 0.5181 | 0.7907 |
| 0.0935 | 27.0 | 5859 | 0.5065 | 0.7907 |
| 0.0833 | 28.0 | 6076 | 0.4985 | 0.7907 |
| 0.0714 | 29.0 | 6293 | 0.4998 | 0.7907 |
| 0.0825 | 30.0 | 6510 | 0.4944 | 0.7907 |
| 0.0754 | 31.0 | 6727 | 0.4956 | 0.7674 |
| 0.0765 | 32.0 | 6944 | 0.4881 | 0.7674 |
| 0.0774 | 33.0 | 7161 | 0.4958 | 0.7674 |
| 0.057 | 34.0 | 7378 | 0.4894 | 0.7674 |
| 0.0663 | 35.0 | 7595 | 0.4882 | 0.7674 |
| 0.059 | 36.0 | 7812 | 0.4848 | 0.7674 |
| 0.0537 | 37.0 | 8029 | 0.4865 | 0.7674 |
| 0.0454 | 38.0 | 8246 | 0.4882 | 0.7674 |
| 0.0514 | 39.0 | 8463 | 0.4854 | 0.7674 |
| 0.0629 | 40.0 | 8680 | 0.4861 | 0.7674 |
| 0.0453 | 41.0 | 8897 | 0.4865 | 0.7674 |
| 0.0447 | 42.0 | 9114 | 0.4837 | 0.7674 |
| 0.0452 | 43.0 | 9331 | 0.4805 | 0.7907 |
| 0.0545 | 44.0 | 9548 | 0.4818 | 0.7907 |
| 0.0444 | 45.0 | 9765 | 0.4816 | 0.7907 |
| 0.0454 | 46.0 | 9982 | 0.4835 | 0.7674 |
| 0.0369 | 47.0 | 10199 | 0.4841 | 0.7674 |
| 0.0401 | 48.0 | 10416 | 0.4827 | 0.7907 |
| 0.0524 | 49.0 | 10633 | 0.4835 | 0.7674 |
| 0.0394 | 50.0 | 10850 | 0.4834 | 0.7674 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_00001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_00001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7075
- Accuracy: 0.9302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2263 | 1.0 | 217 | 0.4817 | 0.7907 |
| 0.0168 | 2.0 | 434 | 0.4089 | 0.8605 |
| 0.0026 | 3.0 | 651 | 0.3730 | 0.9070 |
| 0.0013 | 4.0 | 868 | 0.4093 | 0.9070 |
| 0.0007 | 5.0 | 1085 | 0.4236 | 0.9070 |
| 0.0005 | 6.0 | 1302 | 0.4344 | 0.9070 |
| 0.0004 | 7.0 | 1519 | 0.4366 | 0.9070 |
| 0.0003 | 8.0 | 1736 | 0.4561 | 0.9070 |
| 0.0002 | 9.0 | 1953 | 0.4646 | 0.9070 |
| 0.0001 | 10.0 | 2170 | 0.4712 | 0.9070 |
| 0.0001 | 11.0 | 2387 | 0.4696 | 0.9070 |
| 0.0001 | 12.0 | 2604 | 0.4779 | 0.9070 |
| 0.0001 | 13.0 | 2821 | 0.4883 | 0.9070 |
| 0.0001 | 14.0 | 3038 | 0.4911 | 0.9070 |
| 0.0 | 15.0 | 3255 | 0.4887 | 0.9070 |
| 0.0 | 16.0 | 3472 | 0.5049 | 0.9070 |
| 0.0 | 17.0 | 3689 | 0.5115 | 0.9070 |
| 0.0 | 18.0 | 3906 | 0.5246 | 0.9070 |
| 0.0 | 19.0 | 4123 | 0.5207 | 0.9070 |
| 0.0 | 20.0 | 4340 | 0.5310 | 0.9070 |
| 0.0 | 21.0 | 4557 | 0.5341 | 0.9070 |
| 0.0 | 22.0 | 4774 | 0.5389 | 0.9070 |
| 0.0 | 23.0 | 4991 | 0.5470 | 0.9070 |
| 0.0 | 24.0 | 5208 | 0.5525 | 0.9070 |
| 0.0 | 25.0 | 5425 | 0.5607 | 0.9070 |
| 0.0 | 26.0 | 5642 | 0.5630 | 0.9070 |
| 0.0 | 27.0 | 5859 | 0.5707 | 0.9302 |
| 0.0 | 28.0 | 6076 | 0.5785 | 0.9302 |
| 0.0 | 29.0 | 6293 | 0.5816 | 0.9302 |
| 0.0 | 30.0 | 6510 | 0.5927 | 0.9302 |
| 0.0 | 31.0 | 6727 | 0.6021 | 0.9302 |
| 0.0 | 32.0 | 6944 | 0.6045 | 0.9302 |
| 0.0 | 33.0 | 7161 | 0.6209 | 0.9302 |
| 0.0 | 34.0 | 7378 | 0.6273 | 0.9302 |
| 0.0 | 35.0 | 7595 | 0.6296 | 0.9302 |
| 0.0 | 36.0 | 7812 | 0.6372 | 0.9302 |
| 0.0 | 37.0 | 8029 | 0.6432 | 0.9302 |
| 0.0 | 38.0 | 8246 | 0.6544 | 0.9302 |
| 0.0 | 39.0 | 8463 | 0.6520 | 0.9302 |
| 0.0 | 40.0 | 8680 | 0.6641 | 0.9302 |
| 0.0 | 41.0 | 8897 | 0.6713 | 0.9302 |
| 0.0 | 42.0 | 9114 | 0.6757 | 0.9302 |
| 0.0 | 43.0 | 9331 | 0.6829 | 0.9302 |
| 0.0 | 44.0 | 9548 | 0.6913 | 0.9302 |
| 0.0 | 45.0 | 9765 | 0.6942 | 0.9302 |
| 0.0 | 46.0 | 9982 | 0.7019 | 0.9302 |
| 0.0 | 47.0 | 10199 | 0.7046 | 0.9302 |
| 0.0 | 48.0 | 10416 | 0.7061 | 0.9302 |
| 0.0 | 49.0 | 10633 | 0.7073 | 0.9302 |
| 0.0 | 50.0 | 10850 | 0.7075 | 0.9302 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_tiny_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_40x_deit_tiny_adamax_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: 0.1224
- Accuracy: 0.9762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0999 | 1.0 | 219 | 0.2863 | 0.8810 |
| 0.0106 | 2.0 | 438 | 0.0557 | 0.9524 |
| 0.0032 | 3.0 | 657 | 0.1838 | 0.9762 |
| 0.0003 | 4.0 | 876 | 0.0728 | 0.9762 |
| 0.0109 | 5.0 | 1095 | 0.1935 | 0.9762 |
| 0.0 | 6.0 | 1314 | 0.0601 | 0.9762 |
| 0.0 | 7.0 | 1533 | 0.1576 | 0.9762 |
| 0.0 | 8.0 | 1752 | 0.1618 | 0.9762 |
| 0.0 | 9.0 | 1971 | 0.1684 | 0.9762 |
| 0.0 | 10.0 | 2190 | 0.1720 | 0.9762 |
| 0.0 | 11.0 | 2409 | 0.1705 | 0.9762 |
| 0.0 | 12.0 | 2628 | 0.1761 | 0.9762 |
| 0.0 | 13.0 | 2847 | 0.1758 | 0.9762 |
| 0.0 | 14.0 | 3066 | 0.1752 | 0.9762 |
| 0.0 | 15.0 | 3285 | 0.1769 | 0.9762 |
| 0.0 | 16.0 | 3504 | 0.1750 | 0.9762 |
| 0.0 | 17.0 | 3723 | 0.1767 | 0.9762 |
| 0.0 | 18.0 | 3942 | 0.1778 | 0.9762 |
| 0.0 | 19.0 | 4161 | 0.1748 | 0.9762 |
| 0.0 | 20.0 | 4380 | 0.1777 | 0.9762 |
| 0.0 | 21.0 | 4599 | 0.1775 | 0.9762 |
| 0.0 | 22.0 | 4818 | 0.1734 | 0.9762 |
| 0.0 | 23.0 | 5037 | 0.1752 | 0.9762 |
| 0.0 | 24.0 | 5256 | 0.1709 | 0.9762 |
| 0.0 | 25.0 | 5475 | 0.1680 | 0.9762 |
| 0.0 | 26.0 | 5694 | 0.1718 | 0.9762 |
| 0.0 | 27.0 | 5913 | 0.1738 | 0.9762 |
| 0.0 | 28.0 | 6132 | 0.1754 | 0.9762 |
| 0.0 | 29.0 | 6351 | 0.1694 | 0.9762 |
| 0.0 | 30.0 | 6570 | 0.1671 | 0.9762 |
| 0.0 | 31.0 | 6789 | 0.1676 | 0.9762 |
| 0.0 | 32.0 | 7008 | 0.1684 | 0.9762 |
| 0.0 | 33.0 | 7227 | 0.1579 | 0.9762 |
| 0.0 | 34.0 | 7446 | 0.1646 | 0.9762 |
| 0.0 | 35.0 | 7665 | 0.1705 | 0.9762 |
| 0.0 | 36.0 | 7884 | 0.1608 | 0.9762 |
| 0.0 | 37.0 | 8103 | 0.1657 | 0.9762 |
| 0.0 | 38.0 | 8322 | 0.1625 | 0.9762 |
| 0.0 | 39.0 | 8541 | 0.1523 | 0.9762 |
| 0.0 | 40.0 | 8760 | 0.1553 | 0.9762 |
| 0.0 | 41.0 | 8979 | 0.1442 | 0.9762 |
| 0.0 | 42.0 | 9198 | 0.1409 | 0.9762 |
| 0.0 | 43.0 | 9417 | 0.1436 | 0.9762 |
| 0.0 | 44.0 | 9636 | 0.1410 | 0.9762 |
| 0.0 | 45.0 | 9855 | 0.1340 | 0.9762 |
| 0.0 | 46.0 | 10074 | 0.1301 | 0.9762 |
| 0.0 | 47.0 | 10293 | 0.1236 | 0.9762 |
| 0.0 | 48.0 | 10512 | 0.1220 | 0.9762 |
| 0.0 | 49.0 | 10731 | 0.1222 | 0.9762 |
| 0.0 | 50.0 | 10950 | 0.1224 | 0.9762 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_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_40x_deit_base_sgd_001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2186
- Accuracy: 0.9286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2742 | 1.0 | 219 | 1.3421 | 0.4286 |
| 1.1364 | 2.0 | 438 | 1.2693 | 0.4048 |
| 0.9912 | 3.0 | 657 | 1.1701 | 0.5238 |
| 0.8292 | 4.0 | 876 | 1.0493 | 0.6429 |
| 0.7771 | 5.0 | 1095 | 0.9148 | 0.7143 |
| 0.6956 | 6.0 | 1314 | 0.8048 | 0.7381 |
| 0.519 | 7.0 | 1533 | 0.7062 | 0.8095 |
| 0.5042 | 8.0 | 1752 | 0.6401 | 0.7857 |
| 0.4397 | 9.0 | 1971 | 0.5785 | 0.8333 |
| 0.3933 | 10.0 | 2190 | 0.5338 | 0.8571 |
| 0.341 | 11.0 | 2409 | 0.4959 | 0.8810 |
| 0.3345 | 12.0 | 2628 | 0.4569 | 0.8810 |
| 0.2949 | 13.0 | 2847 | 0.4265 | 0.9048 |
| 0.2608 | 14.0 | 3066 | 0.3999 | 0.9286 |
| 0.2368 | 15.0 | 3285 | 0.3796 | 0.9286 |
| 0.2257 | 16.0 | 3504 | 0.3614 | 0.9286 |
| 0.232 | 17.0 | 3723 | 0.3430 | 0.9286 |
| 0.1928 | 18.0 | 3942 | 0.3249 | 0.9286 |
| 0.1804 | 19.0 | 4161 | 0.3144 | 0.9286 |
| 0.1542 | 20.0 | 4380 | 0.3019 | 0.9048 |
| 0.1333 | 21.0 | 4599 | 0.2915 | 0.9286 |
| 0.1333 | 22.0 | 4818 | 0.2894 | 0.9048 |
| 0.1178 | 23.0 | 5037 | 0.2746 | 0.9286 |
| 0.1098 | 24.0 | 5256 | 0.2771 | 0.9048 |
| 0.1099 | 25.0 | 5475 | 0.2649 | 0.9048 |
| 0.0836 | 26.0 | 5694 | 0.2732 | 0.9048 |
| 0.0751 | 27.0 | 5913 | 0.2625 | 0.9048 |
| 0.0745 | 28.0 | 6132 | 0.2608 | 0.9048 |
| 0.0826 | 29.0 | 6351 | 0.2526 | 0.9048 |
| 0.079 | 30.0 | 6570 | 0.2463 | 0.9286 |
| 0.0659 | 31.0 | 6789 | 0.2439 | 0.9048 |
| 0.0738 | 32.0 | 7008 | 0.2422 | 0.9286 |
| 0.0683 | 33.0 | 7227 | 0.2335 | 0.9286 |
| 0.0674 | 34.0 | 7446 | 0.2343 | 0.9048 |
| 0.0633 | 35.0 | 7665 | 0.2311 | 0.9048 |
| 0.0608 | 36.0 | 7884 | 0.2259 | 0.9286 |
| 0.0543 | 37.0 | 8103 | 0.2239 | 0.9286 |
| 0.0444 | 38.0 | 8322 | 0.2256 | 0.9286 |
| 0.0496 | 39.0 | 8541 | 0.2255 | 0.9286 |
| 0.0513 | 40.0 | 8760 | 0.2253 | 0.9286 |
| 0.0449 | 41.0 | 8979 | 0.2226 | 0.9286 |
| 0.0449 | 42.0 | 9198 | 0.2216 | 0.9286 |
| 0.0549 | 43.0 | 9417 | 0.2202 | 0.9286 |
| 0.0488 | 44.0 | 9636 | 0.2213 | 0.9286 |
| 0.0437 | 45.0 | 9855 | 0.2208 | 0.9286 |
| 0.0362 | 46.0 | 10074 | 0.2201 | 0.9286 |
| 0.0622 | 47.0 | 10293 | 0.2188 | 0.9286 |
| 0.0546 | 48.0 | 10512 | 0.2185 | 0.9286 |
| 0.0472 | 49.0 | 10731 | 0.2186 | 0.9286 |
| 0.0581 | 50.0 | 10950 | 0.2186 | 0.9286 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_00001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_00001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2776
- Accuracy: 0.9524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2891 | 1.0 | 219 | 0.3655 | 0.9048 |
| 0.0271 | 2.0 | 438 | 0.1551 | 0.9762 |
| 0.0059 | 3.0 | 657 | 0.1424 | 0.9762 |
| 0.0011 | 4.0 | 876 | 0.1398 | 0.9762 |
| 0.0007 | 5.0 | 1095 | 0.1496 | 0.9762 |
| 0.0005 | 6.0 | 1314 | 0.1466 | 0.9762 |
| 0.0003 | 7.0 | 1533 | 0.1409 | 0.9762 |
| 0.0002 | 8.0 | 1752 | 0.1498 | 0.9762 |
| 0.0002 | 9.0 | 1971 | 0.1564 | 0.9762 |
| 0.0001 | 10.0 | 2190 | 0.1656 | 0.9524 |
| 0.0001 | 11.0 | 2409 | 0.1807 | 0.9524 |
| 0.0001 | 12.0 | 2628 | 0.1735 | 0.9762 |
| 0.0001 | 13.0 | 2847 | 0.1728 | 0.9762 |
| 0.0001 | 14.0 | 3066 | 0.1752 | 0.9762 |
| 0.0 | 15.0 | 3285 | 0.1830 | 0.9524 |
| 0.0 | 16.0 | 3504 | 0.1909 | 0.9762 |
| 0.0 | 17.0 | 3723 | 0.1856 | 0.9762 |
| 0.0 | 18.0 | 3942 | 0.1931 | 0.9762 |
| 0.0 | 19.0 | 4161 | 0.1937 | 0.9762 |
| 0.0 | 20.0 | 4380 | 0.2012 | 0.9762 |
| 0.0 | 21.0 | 4599 | 0.1972 | 0.9762 |
| 0.0 | 22.0 | 4818 | 0.2059 | 0.9762 |
| 0.0 | 23.0 | 5037 | 0.2072 | 0.9762 |
| 0.0 | 24.0 | 5256 | 0.2139 | 0.9762 |
| 0.0 | 25.0 | 5475 | 0.2220 | 0.9524 |
| 0.0 | 26.0 | 5694 | 0.2242 | 0.9762 |
| 0.0 | 27.0 | 5913 | 0.2291 | 0.9524 |
| 0.0 | 28.0 | 6132 | 0.2302 | 0.9524 |
| 0.0 | 29.0 | 6351 | 0.2283 | 0.9524 |
| 0.0 | 30.0 | 6570 | 0.2384 | 0.9524 |
| 0.0 | 31.0 | 6789 | 0.2437 | 0.9524 |
| 0.0 | 32.0 | 7008 | 0.2389 | 0.9762 |
| 0.0 | 33.0 | 7227 | 0.2474 | 0.9524 |
| 0.0 | 34.0 | 7446 | 0.2474 | 0.9524 |
| 0.0 | 35.0 | 7665 | 0.2453 | 0.9524 |
| 0.0 | 36.0 | 7884 | 0.2498 | 0.9524 |
| 0.0 | 37.0 | 8103 | 0.2535 | 0.9524 |
| 0.0 | 38.0 | 8322 | 0.2499 | 0.9762 |
| 0.0 | 39.0 | 8541 | 0.2607 | 0.9524 |
| 0.0 | 40.0 | 8760 | 0.2656 | 0.9524 |
| 0.0 | 41.0 | 8979 | 0.2652 | 0.9524 |
| 0.0 | 42.0 | 9198 | 0.2609 | 0.9524 |
| 0.0 | 43.0 | 9417 | 0.2697 | 0.9524 |
| 0.0 | 44.0 | 9636 | 0.2693 | 0.9524 |
| 0.0 | 45.0 | 9855 | 0.2763 | 0.9524 |
| 0.0 | 46.0 | 10074 | 0.2779 | 0.9524 |
| 0.0 | 47.0 | 10293 | 0.2750 | 0.9524 |
| 0.0 | 48.0 | 10512 | 0.2730 | 0.9524 |
| 0.0 | 49.0 | 10731 | 0.2766 | 0.9524 |
| 0.0 | 50.0 | 10950 | 0.2776 | 0.9524 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_tiny_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_40x_deit_tiny_adamax_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: 0.9183
- Accuracy: 0.8537
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0769 | 1.0 | 220 | 0.5269 | 0.8293 |
| 0.0227 | 2.0 | 440 | 0.7159 | 0.8293 |
| 0.0008 | 3.0 | 660 | 1.1921 | 0.7805 |
| 0.0031 | 4.0 | 880 | 0.8277 | 0.8537 |
| 0.0006 | 5.0 | 1100 | 0.9658 | 0.8049 |
| 0.0 | 6.0 | 1320 | 0.5880 | 0.8780 |
| 0.0007 | 7.0 | 1540 | 0.9103 | 0.8293 |
| 0.0006 | 8.0 | 1760 | 0.4548 | 0.8780 |
| 0.0029 | 9.0 | 1980 | 1.0525 | 0.8293 |
| 0.0 | 10.0 | 2200 | 0.8563 | 0.8293 |
| 0.0 | 11.0 | 2420 | 0.7625 | 0.8537 |
| 0.0 | 12.0 | 2640 | 0.7582 | 0.8537 |
| 0.0 | 13.0 | 2860 | 0.7509 | 0.8537 |
| 0.0 | 14.0 | 3080 | 0.7628 | 0.8537 |
| 0.0 | 15.0 | 3300 | 0.7694 | 0.8537 |
| 0.0 | 16.0 | 3520 | 0.7739 | 0.8537 |
| 0.0 | 17.0 | 3740 | 0.7753 | 0.8537 |
| 0.0 | 18.0 | 3960 | 0.7841 | 0.8780 |
| 0.0 | 19.0 | 4180 | 0.7863 | 0.8780 |
| 0.0 | 20.0 | 4400 | 0.8002 | 0.8537 |
| 0.0 | 21.0 | 4620 | 0.7935 | 0.8537 |
| 0.0 | 22.0 | 4840 | 0.8153 | 0.8537 |
| 0.0 | 23.0 | 5060 | 0.8135 | 0.8537 |
| 0.0 | 24.0 | 5280 | 0.8169 | 0.8537 |
| 0.0 | 25.0 | 5500 | 0.8219 | 0.8537 |
| 0.0 | 26.0 | 5720 | 0.8262 | 0.8537 |
| 0.0 | 27.0 | 5940 | 0.8371 | 0.8537 |
| 0.0 | 28.0 | 6160 | 0.8311 | 0.8537 |
| 0.0 | 29.0 | 6380 | 0.8379 | 0.8537 |
| 0.0 | 30.0 | 6600 | 0.8615 | 0.8537 |
| 0.0 | 31.0 | 6820 | 0.8776 | 0.8537 |
| 0.0 | 32.0 | 7040 | 0.8507 | 0.8537 |
| 0.0 | 33.0 | 7260 | 0.8611 | 0.8537 |
| 0.0 | 34.0 | 7480 | 0.8621 | 0.8537 |
| 0.0 | 35.0 | 7700 | 0.8793 | 0.8537 |
| 0.0 | 36.0 | 7920 | 0.8811 | 0.8537 |
| 0.0 | 37.0 | 8140 | 0.8839 | 0.8537 |
| 0.0 | 38.0 | 8360 | 0.8830 | 0.8537 |
| 0.0 | 39.0 | 8580 | 0.8720 | 0.8537 |
| 0.0 | 40.0 | 8800 | 0.8855 | 0.8537 |
| 0.0 | 41.0 | 9020 | 0.8862 | 0.8537 |
| 0.0 | 42.0 | 9240 | 0.9183 | 0.8537 |
| 0.0 | 43.0 | 9460 | 0.8971 | 0.8537 |
| 0.0 | 44.0 | 9680 | 0.9190 | 0.8537 |
| 0.0 | 45.0 | 9900 | 0.9193 | 0.8537 |
| 0.0 | 46.0 | 10120 | 0.9157 | 0.8537 |
| 0.0 | 47.0 | 10340 | 0.9115 | 0.8537 |
| 0.0 | 48.0 | 10560 | 0.9160 | 0.8537 |
| 0.0 | 49.0 | 10780 | 0.9155 | 0.8537 |
| 0.0 | 50.0 | 11000 | 0.9183 | 0.8537 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_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_40x_deit_base_sgd_001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5235
- 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2691 | 1.0 | 220 | 1.3176 | 0.3415 |
| 1.1324 | 2.0 | 440 | 1.2295 | 0.4634 |
| 1.003 | 3.0 | 660 | 1.1173 | 0.6341 |
| 0.8718 | 4.0 | 880 | 0.9888 | 0.6585 |
| 0.7662 | 5.0 | 1100 | 0.8700 | 0.6829 |
| 0.6305 | 6.0 | 1320 | 0.7780 | 0.6585 |
| 0.552 | 7.0 | 1540 | 0.7068 | 0.6829 |
| 0.4791 | 8.0 | 1760 | 0.6670 | 0.6829 |
| 0.413 | 9.0 | 1980 | 0.6302 | 0.6829 |
| 0.3827 | 10.0 | 2200 | 0.6050 | 0.7073 |
| 0.3215 | 11.0 | 2420 | 0.5880 | 0.7073 |
| 0.2953 | 12.0 | 2640 | 0.5689 | 0.7073 |
| 0.2691 | 13.0 | 2860 | 0.5551 | 0.7073 |
| 0.255 | 14.0 | 3080 | 0.5391 | 0.7317 |
| 0.2205 | 15.0 | 3300 | 0.5338 | 0.7561 |
| 0.2031 | 16.0 | 3520 | 0.5276 | 0.8049 |
| 0.1827 | 17.0 | 3740 | 0.5158 | 0.8049 |
| 0.178 | 18.0 | 3960 | 0.5117 | 0.8049 |
| 0.1722 | 19.0 | 4180 | 0.5070 | 0.8293 |
| 0.1354 | 20.0 | 4400 | 0.5054 | 0.8293 |
| 0.1154 | 21.0 | 4620 | 0.5008 | 0.8293 |
| 0.1032 | 22.0 | 4840 | 0.5031 | 0.8293 |
| 0.123 | 23.0 | 5060 | 0.5052 | 0.8293 |
| 0.0925 | 24.0 | 5280 | 0.5012 | 0.8049 |
| 0.1004 | 25.0 | 5500 | 0.5002 | 0.8293 |
| 0.1106 | 26.0 | 5720 | 0.5000 | 0.8293 |
| 0.0932 | 27.0 | 5940 | 0.5018 | 0.8293 |
| 0.0974 | 28.0 | 6160 | 0.5069 | 0.8293 |
| 0.0749 | 29.0 | 6380 | 0.5067 | 0.8293 |
| 0.0626 | 30.0 | 6600 | 0.5071 | 0.8293 |
| 0.058 | 31.0 | 6820 | 0.5023 | 0.8293 |
| 0.0771 | 32.0 | 7040 | 0.5068 | 0.8293 |
| 0.0537 | 33.0 | 7260 | 0.5089 | 0.8049 |
| 0.0443 | 34.0 | 7480 | 0.5110 | 0.8049 |
| 0.0529 | 35.0 | 7700 | 0.5102 | 0.8049 |
| 0.056 | 36.0 | 7920 | 0.5123 | 0.8293 |
| 0.0373 | 37.0 | 8140 | 0.5147 | 0.8293 |
| 0.0662 | 38.0 | 8360 | 0.5122 | 0.8293 |
| 0.0489 | 39.0 | 8580 | 0.5155 | 0.8293 |
| 0.0389 | 40.0 | 8800 | 0.5166 | 0.8293 |
| 0.0414 | 41.0 | 9020 | 0.5205 | 0.8049 |
| 0.0455 | 42.0 | 9240 | 0.5225 | 0.8293 |
| 0.0397 | 43.0 | 9460 | 0.5226 | 0.8049 |
| 0.0345 | 44.0 | 9680 | 0.5228 | 0.8049 |
| 0.0281 | 45.0 | 9900 | 0.5217 | 0.8049 |
| 0.0392 | 46.0 | 10120 | 0.5231 | 0.8049 |
| 0.0436 | 47.0 | 10340 | 0.5235 | 0.8293 |
| 0.0347 | 48.0 | 10560 | 0.5238 | 0.8049 |
| 0.0331 | 49.0 | 10780 | 0.5237 | 0.8049 |
| 0.0457 | 50.0 | 11000 | 0.5235 | 0.8049 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_adamax_00001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_adamax_00001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3162
- Accuracy: 0.8780
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2212 | 1.0 | 220 | 0.5037 | 0.7805 |
| 0.0167 | 2.0 | 440 | 0.4288 | 0.8049 |
| 0.0048 | 3.0 | 660 | 0.5660 | 0.8293 |
| 0.001 | 4.0 | 880 | 0.5808 | 0.8049 |
| 0.0006 | 5.0 | 1100 | 0.5916 | 0.8049 |
| 0.0005 | 6.0 | 1320 | 0.6221 | 0.8293 |
| 0.0003 | 7.0 | 1540 | 0.6354 | 0.8293 |
| 0.0002 | 8.0 | 1760 | 0.6592 | 0.8293 |
| 0.0002 | 9.0 | 1980 | 0.6836 | 0.8293 |
| 0.0001 | 10.0 | 2200 | 0.7195 | 0.8537 |
| 0.0001 | 11.0 | 2420 | 0.7292 | 0.8293 |
| 0.0001 | 12.0 | 2640 | 0.7556 | 0.8537 |
| 0.0001 | 13.0 | 2860 | 0.7481 | 0.8537 |
| 0.0001 | 14.0 | 3080 | 0.7541 | 0.8537 |
| 0.0 | 15.0 | 3300 | 0.7642 | 0.8537 |
| 0.0 | 16.0 | 3520 | 0.7944 | 0.8537 |
| 0.0 | 17.0 | 3740 | 0.8081 | 0.8537 |
| 0.0 | 18.0 | 3960 | 0.8431 | 0.8537 |
| 0.0 | 19.0 | 4180 | 0.8377 | 0.8537 |
| 0.0 | 20.0 | 4400 | 0.8619 | 0.8537 |
| 0.0 | 21.0 | 4620 | 0.8688 | 0.8537 |
| 0.0 | 22.0 | 4840 | 0.9067 | 0.8537 |
| 0.0 | 23.0 | 5060 | 0.9298 | 0.8537 |
| 0.0 | 24.0 | 5280 | 0.9319 | 0.8537 |
| 0.0 | 25.0 | 5500 | 0.9416 | 0.8537 |
| 0.0 | 26.0 | 5720 | 0.9575 | 0.8537 |
| 0.0 | 27.0 | 5940 | 0.9826 | 0.8537 |
| 0.0 | 28.0 | 6160 | 0.9800 | 0.8537 |
| 0.0 | 29.0 | 6380 | 0.9999 | 0.8537 |
| 0.0 | 30.0 | 6600 | 1.0189 | 0.8537 |
| 0.0 | 31.0 | 6820 | 1.0648 | 0.8537 |
| 0.0 | 32.0 | 7040 | 1.0627 | 0.8537 |
| 0.0 | 33.0 | 7260 | 1.0899 | 0.8780 |
| 0.0 | 34.0 | 7480 | 1.1141 | 0.8780 |
| 0.0 | 35.0 | 7700 | 1.1351 | 0.8537 |
| 0.0 | 36.0 | 7920 | 1.1265 | 0.8780 |
| 0.0 | 37.0 | 8140 | 1.1654 | 0.8780 |
| 0.0 | 38.0 | 8360 | 1.1754 | 0.8780 |
| 0.0 | 39.0 | 8580 | 1.1881 | 0.8780 |
| 0.0 | 40.0 | 8800 | 1.1930 | 0.8780 |
| 0.0 | 41.0 | 9020 | 1.2376 | 0.8780 |
| 0.0 | 42.0 | 9240 | 1.2450 | 0.8780 |
| 0.0 | 43.0 | 9460 | 1.2371 | 0.8780 |
| 0.0 | 44.0 | 9680 | 1.2839 | 0.8780 |
| 0.0 | 45.0 | 9900 | 1.2844 | 0.8780 |
| 0.0 | 46.0 | 10120 | 1.2849 | 0.8780 |
| 0.0 | 47.0 | 10340 | 1.3098 | 0.8780 |
| 0.0 | 48.0 | 10560 | 1.3232 | 0.8780 |
| 0.0 | 49.0 | 10780 | 1.3105 | 0.8780 |
| 0.0 | 50.0 | 11000 | 1.3162 | 0.8780 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
imfarzanansari/skintelligent-acne
|
# Acne Severity Detection Model
## Overview
This model card provides documentation for the Acne Severity Detection model checkpoint used in the Hugging Face pipeline. The model is designed to assess acne severity levels, ranging from clear skin to very severe acne.
## Model Details
The checkpoint includes the following files:
- **`config.json`**: Model configuration settings.
- **`model.safetensors`**: Serialized model parameters and architecture.
- **`optimizer.pt`**: Optimizer state capturing the current model optimization.
- **`preprocessor_config.json`**: Configuration file for the preprocessor.
- **`rng_state.pth`**: Random number generator state for reproducibility.
- **`scheduler.pt`**: Scheduler state for controlling learning rate schedules.
- **`trainer_state.json`**: Trainer state with information about the training process.
- **`training_args.bin`**: Binary file storing training arguments.
## Usage
To utilize the model checkpoint, follow these steps:
1. Download this repository.
2. Ensure the required dependencies are installed (`pip install -r requirements.txt`).
## Severity Levels
- **Level -1**: Clear Skin
- **Level 0**: Occasional Spots
- **Level 1**: Mild Acne
- **Level 2**: Moderate Acne
- **Level 3**: Severe Acne
- **Level 4**: Very Severe Acne
## Important Notes
- The model card provides insight into the model's purpose, capabilities, and usage instructions.
- Ensure all necessary files are present in the `checkpoint` directory for proper functionality.
## License
This Acne Severity Detection model checkpoint is licensed under the [MIT License](LICENSE). Please review and adhere to the license when using or modifying the code.
|
[
"level -1",
"level 0",
"level 1",
"level 2",
"level 3"
] |
yuanhuaisen/autotrain-0uz9r-dmir6
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
chanhua/autotrain-g6laz-7afl8
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
chanhua/autotrain-zzbhy-dqgkj
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.0984116792678833
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
chanhua/autotrain-7p556-nc0f8
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.0925332307815552
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
chanhua/autotrain-ar615-cxc9m
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
LuoFengBit/autotrain-i6iyx-bc6au
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 3.476023547841741e+16
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
JungleWong/wong
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
lxl2023/autotrain-v7eqd-8qq
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
JungleWong/wong_autotrain
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 3.847293969571684e+27
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
lxl2023/autotrain-lhrqo-h9twf
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
chanhua/autotrain-0uv3s-vxfry
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
lxl2023/autotrain-6l0e2-ufos2
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
JungleWong/quilt_classfication
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.0986123085021973
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body"
] |
hkivancoral/hushem_40x_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_40x_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: 1.7285
- Accuracy: 0.7778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3612 | 1.0 | 215 | 0.8053 | 0.7111 |
| 0.0954 | 2.0 | 430 | 0.7023 | 0.7111 |
| 0.0263 | 3.0 | 645 | 0.7672 | 0.7333 |
| 0.0113 | 4.0 | 860 | 0.8377 | 0.7778 |
| 0.0012 | 5.0 | 1075 | 0.9748 | 0.7778 |
| 0.0007 | 6.0 | 1290 | 0.9997 | 0.7778 |
| 0.0004 | 7.0 | 1505 | 1.1150 | 0.7556 |
| 0.0003 | 8.0 | 1720 | 1.1439 | 0.7556 |
| 0.0002 | 9.0 | 1935 | 1.2019 | 0.7556 |
| 0.0001 | 10.0 | 2150 | 1.2424 | 0.7556 |
| 0.0001 | 11.0 | 2365 | 1.2284 | 0.7556 |
| 0.0001 | 12.0 | 2580 | 1.2809 | 0.7556 |
| 0.0001 | 13.0 | 2795 | 1.3071 | 0.7556 |
| 0.0001 | 14.0 | 3010 | 1.3721 | 0.7556 |
| 0.0 | 15.0 | 3225 | 1.3804 | 0.7556 |
| 0.0 | 16.0 | 3440 | 1.3850 | 0.7556 |
| 0.0 | 17.0 | 3655 | 1.4005 | 0.7556 |
| 0.0 | 18.0 | 3870 | 1.4317 | 0.7556 |
| 0.0 | 19.0 | 4085 | 1.4823 | 0.7556 |
| 0.0 | 20.0 | 4300 | 1.4810 | 0.7556 |
| 0.0 | 21.0 | 4515 | 1.4751 | 0.7556 |
| 0.0 | 22.0 | 4730 | 1.5073 | 0.7556 |
| 0.0 | 23.0 | 4945 | 1.5283 | 0.7333 |
| 0.0 | 24.0 | 5160 | 1.5592 | 0.7556 |
| 0.0 | 25.0 | 5375 | 1.5298 | 0.7556 |
| 0.0 | 26.0 | 5590 | 1.5228 | 0.7778 |
| 0.0 | 27.0 | 5805 | 1.5617 | 0.7556 |
| 0.0 | 28.0 | 6020 | 1.5609 | 0.7778 |
| 0.0 | 29.0 | 6235 | 1.5791 | 0.7556 |
| 0.0 | 30.0 | 6450 | 1.6043 | 0.7778 |
| 0.0 | 31.0 | 6665 | 1.6159 | 0.7556 |
| 0.0 | 32.0 | 6880 | 1.6584 | 0.7556 |
| 0.0 | 33.0 | 7095 | 1.6250 | 0.7778 |
| 0.0 | 34.0 | 7310 | 1.6097 | 0.7778 |
| 0.0 | 35.0 | 7525 | 1.6615 | 0.7778 |
| 0.0 | 36.0 | 7740 | 1.6489 | 0.7778 |
| 0.0 | 37.0 | 7955 | 1.6559 | 0.7778 |
| 0.0 | 38.0 | 8170 | 1.6854 | 0.7778 |
| 0.0 | 39.0 | 8385 | 1.6826 | 0.7778 |
| 0.0 | 40.0 | 8600 | 1.7344 | 0.7333 |
| 0.0 | 41.0 | 8815 | 1.7007 | 0.7778 |
| 0.0 | 42.0 | 9030 | 1.6800 | 0.7778 |
| 0.0 | 43.0 | 9245 | 1.7149 | 0.7778 |
| 0.0 | 44.0 | 9460 | 1.7189 | 0.7556 |
| 0.0 | 45.0 | 9675 | 1.7288 | 0.7778 |
| 0.0 | 46.0 | 9890 | 1.7097 | 0.7778 |
| 0.0 | 47.0 | 10105 | 1.7285 | 0.7778 |
| 0.0 | 48.0 | 10320 | 1.7184 | 0.7778 |
| 0.0 | 49.0 | 10535 | 1.7322 | 0.7778 |
| 0.0 | 50.0 | 10750 | 1.7285 | 0.7778 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_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_40x_deit_base_sgd_0001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2920
- Accuracy: 0.3778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3635 | 1.0 | 215 | 1.4586 | 0.2889 |
| 1.3564 | 2.0 | 430 | 1.4485 | 0.2889 |
| 1.3735 | 3.0 | 645 | 1.4395 | 0.2889 |
| 1.3415 | 4.0 | 860 | 1.4312 | 0.2889 |
| 1.3033 | 5.0 | 1075 | 1.4236 | 0.2889 |
| 1.3111 | 6.0 | 1290 | 1.4165 | 0.2667 |
| 1.2796 | 7.0 | 1505 | 1.4098 | 0.2667 |
| 1.265 | 8.0 | 1720 | 1.4035 | 0.2667 |
| 1.2454 | 9.0 | 1935 | 1.3975 | 0.2667 |
| 1.2437 | 10.0 | 2150 | 1.3919 | 0.2667 |
| 1.2689 | 11.0 | 2365 | 1.3867 | 0.2667 |
| 1.212 | 12.0 | 2580 | 1.3818 | 0.2667 |
| 1.2193 | 13.0 | 2795 | 1.3771 | 0.2667 |
| 1.2167 | 14.0 | 3010 | 1.3726 | 0.2667 |
| 1.205 | 15.0 | 3225 | 1.3683 | 0.2667 |
| 1.2084 | 16.0 | 3440 | 1.3641 | 0.2889 |
| 1.1861 | 17.0 | 3655 | 1.3601 | 0.3333 |
| 1.1898 | 18.0 | 3870 | 1.3563 | 0.3556 |
| 1.1745 | 19.0 | 4085 | 1.3526 | 0.3556 |
| 1.1602 | 20.0 | 4300 | 1.3489 | 0.3556 |
| 1.1523 | 21.0 | 4515 | 1.3454 | 0.3556 |
| 1.1329 | 22.0 | 4730 | 1.3420 | 0.3556 |
| 1.1475 | 23.0 | 4945 | 1.3387 | 0.3556 |
| 1.1333 | 24.0 | 5160 | 1.3354 | 0.3556 |
| 1.1285 | 25.0 | 5375 | 1.3322 | 0.3333 |
| 1.0938 | 26.0 | 5590 | 1.3292 | 0.3333 |
| 1.0832 | 27.0 | 5805 | 1.3262 | 0.3333 |
| 1.0889 | 28.0 | 6020 | 1.3234 | 0.3333 |
| 1.0886 | 29.0 | 6235 | 1.3206 | 0.3333 |
| 1.0684 | 30.0 | 6450 | 1.3180 | 0.3333 |
| 1.0707 | 31.0 | 6665 | 1.3154 | 0.3333 |
| 1.068 | 32.0 | 6880 | 1.3130 | 0.3333 |
| 1.0647 | 33.0 | 7095 | 1.3107 | 0.3556 |
| 1.0516 | 34.0 | 7310 | 1.3085 | 0.3556 |
| 1.0515 | 35.0 | 7525 | 1.3064 | 0.3556 |
| 1.0477 | 36.0 | 7740 | 1.3045 | 0.3556 |
| 1.0685 | 37.0 | 7955 | 1.3027 | 0.3556 |
| 1.0459 | 38.0 | 8170 | 1.3010 | 0.3556 |
| 1.0276 | 39.0 | 8385 | 1.2995 | 0.3556 |
| 1.016 | 40.0 | 8600 | 1.2981 | 0.3556 |
| 1.044 | 41.0 | 8815 | 1.2969 | 0.3556 |
| 1.0849 | 42.0 | 9030 | 1.2957 | 0.3556 |
| 1.0504 | 43.0 | 9245 | 1.2948 | 0.3778 |
| 1.0115 | 44.0 | 9460 | 1.2940 | 0.3778 |
| 1.0336 | 45.0 | 9675 | 1.2933 | 0.3778 |
| 1.0415 | 46.0 | 9890 | 1.2928 | 0.3778 |
| 1.013 | 47.0 | 10105 | 1.2924 | 0.3778 |
| 1.0207 | 48.0 | 10320 | 1.2921 | 0.3778 |
| 1.054 | 49.0 | 10535 | 1.2920 | 0.3778 |
| 1.0317 | 50.0 | 10750 | 1.2920 | 0.3778 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_sgd_00001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_sgd_00001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4455
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.379 | 1.0 | 215 | 1.4681 | 0.2889 |
| 1.3967 | 2.0 | 430 | 1.4670 | 0.2889 |
| 1.423 | 3.0 | 645 | 1.4660 | 0.2889 |
| 1.4018 | 4.0 | 860 | 1.4650 | 0.2889 |
| 1.3899 | 5.0 | 1075 | 1.4640 | 0.2889 |
| 1.4076 | 6.0 | 1290 | 1.4631 | 0.2889 |
| 1.3743 | 7.0 | 1505 | 1.4622 | 0.2889 |
| 1.3724 | 8.0 | 1720 | 1.4613 | 0.2889 |
| 1.3757 | 9.0 | 1935 | 1.4604 | 0.2889 |
| 1.3783 | 10.0 | 2150 | 1.4596 | 0.2889 |
| 1.4141 | 11.0 | 2365 | 1.4589 | 0.2889 |
| 1.3702 | 12.0 | 2580 | 1.4581 | 0.2889 |
| 1.3842 | 13.0 | 2795 | 1.4574 | 0.2889 |
| 1.3926 | 14.0 | 3010 | 1.4567 | 0.2889 |
| 1.3764 | 15.0 | 3225 | 1.4560 | 0.2889 |
| 1.3955 | 16.0 | 3440 | 1.4553 | 0.2889 |
| 1.3752 | 17.0 | 3655 | 1.4547 | 0.2889 |
| 1.3872 | 18.0 | 3870 | 1.4541 | 0.2889 |
| 1.3795 | 19.0 | 4085 | 1.4535 | 0.2889 |
| 1.3768 | 20.0 | 4300 | 1.4530 | 0.2889 |
| 1.3609 | 21.0 | 4515 | 1.4524 | 0.2889 |
| 1.3552 | 22.0 | 4730 | 1.4519 | 0.2889 |
| 1.3869 | 23.0 | 4945 | 1.4514 | 0.2889 |
| 1.3741 | 24.0 | 5160 | 1.4510 | 0.2889 |
| 1.3721 | 25.0 | 5375 | 1.4505 | 0.2889 |
| 1.3593 | 26.0 | 5590 | 1.4501 | 0.2889 |
| 1.3536 | 27.0 | 5805 | 1.4497 | 0.2889 |
| 1.3543 | 28.0 | 6020 | 1.4493 | 0.2889 |
| 1.3589 | 29.0 | 6235 | 1.4489 | 0.2889 |
| 1.3445 | 30.0 | 6450 | 1.4486 | 0.2889 |
| 1.3539 | 31.0 | 6665 | 1.4483 | 0.2889 |
| 1.3535 | 32.0 | 6880 | 1.4480 | 0.2889 |
| 1.3498 | 33.0 | 7095 | 1.4477 | 0.2889 |
| 1.3497 | 34.0 | 7310 | 1.4474 | 0.2889 |
| 1.3582 | 35.0 | 7525 | 1.4472 | 0.2889 |
| 1.354 | 36.0 | 7740 | 1.4469 | 0.2889 |
| 1.3681 | 37.0 | 7955 | 1.4467 | 0.2889 |
| 1.346 | 38.0 | 8170 | 1.4465 | 0.2889 |
| 1.3468 | 39.0 | 8385 | 1.4463 | 0.2889 |
| 1.3488 | 40.0 | 8600 | 1.4462 | 0.2889 |
| 1.3542 | 41.0 | 8815 | 1.4460 | 0.2889 |
| 1.3813 | 42.0 | 9030 | 1.4459 | 0.2889 |
| 1.3585 | 43.0 | 9245 | 1.4458 | 0.2889 |
| 1.3347 | 44.0 | 9460 | 1.4457 | 0.2889 |
| 1.3527 | 45.0 | 9675 | 1.4456 | 0.2889 |
| 1.3601 | 46.0 | 9890 | 1.4456 | 0.2889 |
| 1.3484 | 47.0 | 10105 | 1.4455 | 0.2889 |
| 1.3543 | 48.0 | 10320 | 1.4455 | 0.2889 |
| 1.3639 | 49.0 | 10535 | 1.4455 | 0.2889 |
| 1.3697 | 50.0 | 10750 | 1.4455 | 0.2889 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 3.0098
- Accuracy: 0.6444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3471 | 1.0 | 215 | 1.0795 | 0.5778 |
| 0.0972 | 2.0 | 430 | 0.8936 | 0.6889 |
| 0.0313 | 3.0 | 645 | 0.8957 | 0.6444 |
| 0.0058 | 4.0 | 860 | 1.0591 | 0.6889 |
| 0.0015 | 5.0 | 1075 | 1.2340 | 0.7111 |
| 0.0006 | 6.0 | 1290 | 1.2875 | 0.6889 |
| 0.0004 | 7.0 | 1505 | 1.3860 | 0.6889 |
| 0.0002 | 8.0 | 1720 | 1.4571 | 0.6889 |
| 0.0002 | 9.0 | 1935 | 1.5144 | 0.6667 |
| 0.0001 | 10.0 | 2150 | 1.5648 | 0.6889 |
| 0.0001 | 11.0 | 2365 | 1.6166 | 0.6667 |
| 0.0001 | 12.0 | 2580 | 1.6547 | 0.6889 |
| 0.0001 | 13.0 | 2795 | 1.7064 | 0.6667 |
| 0.0001 | 14.0 | 3010 | 1.7513 | 0.6667 |
| 0.0 | 15.0 | 3225 | 1.7849 | 0.6667 |
| 0.0 | 16.0 | 3440 | 1.8291 | 0.6667 |
| 0.0 | 17.0 | 3655 | 1.8746 | 0.6667 |
| 0.0 | 18.0 | 3870 | 1.9137 | 0.6667 |
| 0.0 | 19.0 | 4085 | 1.9589 | 0.6667 |
| 0.0 | 20.0 | 4300 | 2.0103 | 0.6667 |
| 0.0 | 21.0 | 4515 | 2.0484 | 0.6667 |
| 0.0 | 22.0 | 4730 | 2.0885 | 0.6667 |
| 0.0 | 23.0 | 4945 | 2.1272 | 0.6667 |
| 0.0 | 24.0 | 5160 | 2.1691 | 0.6667 |
| 0.0 | 25.0 | 5375 | 2.2032 | 0.6667 |
| 0.0 | 26.0 | 5590 | 2.2512 | 0.6667 |
| 0.0 | 27.0 | 5805 | 2.2928 | 0.6667 |
| 0.0 | 28.0 | 6020 | 2.3366 | 0.6667 |
| 0.0 | 29.0 | 6235 | 2.3684 | 0.6667 |
| 0.0 | 30.0 | 6450 | 2.4080 | 0.6667 |
| 0.0 | 31.0 | 6665 | 2.4434 | 0.6667 |
| 0.0 | 32.0 | 6880 | 2.4884 | 0.6667 |
| 0.0 | 33.0 | 7095 | 2.5184 | 0.6667 |
| 0.0 | 34.0 | 7310 | 2.5603 | 0.6667 |
| 0.0 | 35.0 | 7525 | 2.6005 | 0.6667 |
| 0.0 | 36.0 | 7740 | 2.6418 | 0.6444 |
| 0.0 | 37.0 | 7955 | 2.6720 | 0.6444 |
| 0.0 | 38.0 | 8170 | 2.7124 | 0.6444 |
| 0.0 | 39.0 | 8385 | 2.7569 | 0.6444 |
| 0.0 | 40.0 | 8600 | 2.7908 | 0.6444 |
| 0.0 | 41.0 | 8815 | 2.8243 | 0.6444 |
| 0.0 | 42.0 | 9030 | 2.8592 | 0.6444 |
| 0.0 | 43.0 | 9245 | 2.8889 | 0.6444 |
| 0.0 | 44.0 | 9460 | 2.9143 | 0.6444 |
| 0.0 | 45.0 | 9675 | 2.9439 | 0.6444 |
| 0.0 | 46.0 | 9890 | 2.9703 | 0.6444 |
| 0.0 | 47.0 | 10105 | 2.9822 | 0.6444 |
| 0.0 | 48.0 | 10320 | 3.0050 | 0.6444 |
| 0.0 | 49.0 | 10535 | 3.0086 | 0.6444 |
| 0.0 | 50.0 | 10750 | 3.0098 | 0.6444 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_sgd_00001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_sgd_00001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3898
- Accuracy: 0.3111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4168 | 1.0 | 215 | 1.4077 | 0.2444 |
| 1.3843 | 2.0 | 430 | 1.4068 | 0.2444 |
| 1.4045 | 3.0 | 645 | 1.4059 | 0.2444 |
| 1.3944 | 4.0 | 860 | 1.4051 | 0.2444 |
| 1.3979 | 5.0 | 1075 | 1.4043 | 0.2444 |
| 1.4212 | 6.0 | 1290 | 1.4036 | 0.2667 |
| 1.4197 | 7.0 | 1505 | 1.4029 | 0.2667 |
| 1.369 | 8.0 | 1720 | 1.4022 | 0.2667 |
| 1.3853 | 9.0 | 1935 | 1.4015 | 0.2667 |
| 1.4053 | 10.0 | 2150 | 1.4008 | 0.2667 |
| 1.3723 | 11.0 | 2365 | 1.4002 | 0.2667 |
| 1.3571 | 12.0 | 2580 | 1.3996 | 0.2667 |
| 1.3936 | 13.0 | 2795 | 1.3990 | 0.2667 |
| 1.3779 | 14.0 | 3010 | 1.3985 | 0.2667 |
| 1.3861 | 15.0 | 3225 | 1.3979 | 0.2667 |
| 1.4005 | 16.0 | 3440 | 1.3974 | 0.2889 |
| 1.3769 | 17.0 | 3655 | 1.3969 | 0.2889 |
| 1.3909 | 18.0 | 3870 | 1.3964 | 0.2889 |
| 1.3834 | 19.0 | 4085 | 1.3960 | 0.2889 |
| 1.3642 | 20.0 | 4300 | 1.3956 | 0.2889 |
| 1.3863 | 21.0 | 4515 | 1.3951 | 0.2889 |
| 1.3863 | 22.0 | 4730 | 1.3947 | 0.2889 |
| 1.3703 | 23.0 | 4945 | 1.3944 | 0.2889 |
| 1.3733 | 24.0 | 5160 | 1.3940 | 0.2889 |
| 1.3751 | 25.0 | 5375 | 1.3937 | 0.3111 |
| 1.3799 | 26.0 | 5590 | 1.3933 | 0.3111 |
| 1.3637 | 27.0 | 5805 | 1.3930 | 0.3111 |
| 1.3658 | 28.0 | 6020 | 1.3927 | 0.3111 |
| 1.3837 | 29.0 | 6235 | 1.3924 | 0.3111 |
| 1.3573 | 30.0 | 6450 | 1.3922 | 0.3111 |
| 1.3483 | 31.0 | 6665 | 1.3919 | 0.3111 |
| 1.3737 | 32.0 | 6880 | 1.3917 | 0.3111 |
| 1.3567 | 33.0 | 7095 | 1.3915 | 0.3111 |
| 1.3764 | 34.0 | 7310 | 1.3913 | 0.3111 |
| 1.3646 | 35.0 | 7525 | 1.3911 | 0.3111 |
| 1.3557 | 36.0 | 7740 | 1.3909 | 0.3111 |
| 1.3829 | 37.0 | 7955 | 1.3907 | 0.3111 |
| 1.3713 | 38.0 | 8170 | 1.3906 | 0.3111 |
| 1.3468 | 39.0 | 8385 | 1.3905 | 0.3111 |
| 1.3527 | 40.0 | 8600 | 1.3903 | 0.3111 |
| 1.3629 | 41.0 | 8815 | 1.3902 | 0.3111 |
| 1.3464 | 42.0 | 9030 | 1.3901 | 0.3111 |
| 1.3709 | 43.0 | 9245 | 1.3901 | 0.3111 |
| 1.3524 | 44.0 | 9460 | 1.3900 | 0.3111 |
| 1.3532 | 45.0 | 9675 | 1.3899 | 0.3111 |
| 1.3657 | 46.0 | 9890 | 1.3899 | 0.3111 |
| 1.3891 | 47.0 | 10105 | 1.3899 | 0.3111 |
| 1.3666 | 48.0 | 10320 | 1.3898 | 0.3111 |
| 1.3713 | 49.0 | 10535 | 1.3898 | 0.3111 |
| 1.3614 | 50.0 | 10750 | 1.3898 | 0.3111 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_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_40x_deit_base_sgd_0001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2543
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3992 | 1.0 | 215 | 1.3998 | 0.2667 |
| 1.3515 | 2.0 | 430 | 1.3921 | 0.3111 |
| 1.3529 | 3.0 | 645 | 1.3851 | 0.3111 |
| 1.3241 | 4.0 | 860 | 1.3787 | 0.3111 |
| 1.3174 | 5.0 | 1075 | 1.3730 | 0.3333 |
| 1.331 | 6.0 | 1290 | 1.3675 | 0.3333 |
| 1.3242 | 7.0 | 1505 | 1.3623 | 0.3333 |
| 1.2565 | 8.0 | 1720 | 1.3574 | 0.3111 |
| 1.2719 | 9.0 | 1935 | 1.3525 | 0.3333 |
| 1.2647 | 10.0 | 2150 | 1.3477 | 0.3333 |
| 1.2293 | 11.0 | 2365 | 1.3430 | 0.3556 |
| 1.1981 | 12.0 | 2580 | 1.3386 | 0.3556 |
| 1.2258 | 13.0 | 2795 | 1.3342 | 0.3556 |
| 1.1901 | 14.0 | 3010 | 1.3299 | 0.3111 |
| 1.1988 | 15.0 | 3225 | 1.3257 | 0.3333 |
| 1.2048 | 16.0 | 3440 | 1.3214 | 0.3333 |
| 1.1848 | 17.0 | 3655 | 1.3173 | 0.3333 |
| 1.1799 | 18.0 | 3870 | 1.3134 | 0.3333 |
| 1.1629 | 19.0 | 4085 | 1.3095 | 0.3333 |
| 1.1482 | 20.0 | 4300 | 1.3058 | 0.3333 |
| 1.1478 | 21.0 | 4515 | 1.3022 | 0.3333 |
| 1.1472 | 22.0 | 4730 | 1.2988 | 0.3333 |
| 1.1143 | 23.0 | 4945 | 1.2955 | 0.3333 |
| 1.1122 | 24.0 | 5160 | 1.2923 | 0.3333 |
| 1.1 | 25.0 | 5375 | 1.2893 | 0.3111 |
| 1.1089 | 26.0 | 5590 | 1.2864 | 0.3111 |
| 1.1001 | 27.0 | 5805 | 1.2836 | 0.3333 |
| 1.0858 | 28.0 | 6020 | 1.2810 | 0.3333 |
| 1.093 | 29.0 | 6235 | 1.2786 | 0.3333 |
| 1.081 | 30.0 | 6450 | 1.2761 | 0.3333 |
| 1.0426 | 31.0 | 6665 | 1.2739 | 0.3333 |
| 1.0757 | 32.0 | 6880 | 1.2718 | 0.3333 |
| 1.0524 | 33.0 | 7095 | 1.2698 | 0.3333 |
| 1.0725 | 34.0 | 7310 | 1.2680 | 0.3333 |
| 1.055 | 35.0 | 7525 | 1.2662 | 0.3333 |
| 1.0308 | 36.0 | 7740 | 1.2646 | 0.3333 |
| 1.0978 | 37.0 | 7955 | 1.2631 | 0.3333 |
| 1.0264 | 38.0 | 8170 | 1.2617 | 0.3333 |
| 1.0508 | 39.0 | 8385 | 1.2604 | 0.3333 |
| 1.0208 | 40.0 | 8600 | 1.2593 | 0.3333 |
| 1.0505 | 41.0 | 8815 | 1.2583 | 0.3333 |
| 1.0147 | 42.0 | 9030 | 1.2574 | 0.3333 |
| 1.0355 | 43.0 | 9245 | 1.2566 | 0.3333 |
| 1.0064 | 44.0 | 9460 | 1.2559 | 0.3333 |
| 1.019 | 45.0 | 9675 | 1.2554 | 0.3333 |
| 1.0292 | 46.0 | 9890 | 1.2549 | 0.3333 |
| 1.0584 | 47.0 | 10105 | 1.2546 | 0.3333 |
| 1.0425 | 48.0 | 10320 | 1.2544 | 0.3333 |
| 1.0211 | 49.0 | 10535 | 1.2543 | 0.3333 |
| 1.0221 | 50.0 | 10750 | 1.2543 | 0.3333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 0.8349
- Accuracy: 0.9070
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.424 | 1.0 | 217 | 0.5360 | 0.8140 |
| 0.1714 | 2.0 | 434 | 0.4093 | 0.8372 |
| 0.024 | 3.0 | 651 | 0.3706 | 0.8372 |
| 0.0076 | 4.0 | 868 | 0.3232 | 0.8605 |
| 0.0117 | 5.0 | 1085 | 0.4002 | 0.8605 |
| 0.0014 | 6.0 | 1302 | 0.3510 | 0.8837 |
| 0.0013 | 7.0 | 1519 | 0.3890 | 0.8837 |
| 0.0003 | 8.0 | 1736 | 0.4966 | 0.8837 |
| 0.0002 | 9.0 | 1953 | 0.4570 | 0.8837 |
| 0.0001 | 10.0 | 2170 | 0.5366 | 0.8837 |
| 0.0001 | 11.0 | 2387 | 0.4687 | 0.8837 |
| 0.0001 | 12.0 | 2604 | 0.5121 | 0.8837 |
| 0.0001 | 13.0 | 2821 | 0.5347 | 0.8837 |
| 0.0001 | 14.0 | 3038 | 0.5583 | 0.8837 |
| 0.0 | 15.0 | 3255 | 0.5404 | 0.8837 |
| 0.0 | 16.0 | 3472 | 0.5914 | 0.8837 |
| 0.0 | 17.0 | 3689 | 0.5903 | 0.8837 |
| 0.0 | 18.0 | 3906 | 0.5962 | 0.8837 |
| 0.0 | 19.0 | 4123 | 0.6082 | 0.8837 |
| 0.0 | 20.0 | 4340 | 0.6491 | 0.9070 |
| 0.0 | 21.0 | 4557 | 0.6647 | 0.8837 |
| 0.0 | 22.0 | 4774 | 0.6416 | 0.8837 |
| 0.0 | 23.0 | 4991 | 0.6353 | 0.9070 |
| 0.0 | 24.0 | 5208 | 0.6866 | 0.9070 |
| 0.0 | 25.0 | 5425 | 0.6552 | 0.9070 |
| 0.0 | 26.0 | 5642 | 0.7023 | 0.9070 |
| 0.0 | 27.0 | 5859 | 0.6738 | 0.9070 |
| 0.0 | 28.0 | 6076 | 0.7119 | 0.9070 |
| 0.0 | 29.0 | 6293 | 0.7453 | 0.9070 |
| 0.0 | 30.0 | 6510 | 0.7641 | 0.9070 |
| 0.0 | 31.0 | 6727 | 0.7753 | 0.9070 |
| 0.0 | 32.0 | 6944 | 0.7598 | 0.9070 |
| 0.0 | 33.0 | 7161 | 0.7952 | 0.9070 |
| 0.0 | 34.0 | 7378 | 0.7621 | 0.9070 |
| 0.0 | 35.0 | 7595 | 0.7849 | 0.9070 |
| 0.0 | 36.0 | 7812 | 0.7647 | 0.9070 |
| 0.0 | 37.0 | 8029 | 0.7761 | 0.9070 |
| 0.0 | 38.0 | 8246 | 0.8153 | 0.9070 |
| 0.0 | 39.0 | 8463 | 0.8099 | 0.9070 |
| 0.0 | 40.0 | 8680 | 0.8036 | 0.9070 |
| 0.0 | 41.0 | 8897 | 0.8358 | 0.9070 |
| 0.0 | 42.0 | 9114 | 0.8036 | 0.9070 |
| 0.0 | 43.0 | 9331 | 0.8414 | 0.9070 |
| 0.0 | 44.0 | 9548 | 0.8111 | 0.9070 |
| 0.0 | 45.0 | 9765 | 0.8271 | 0.9070 |
| 0.0 | 46.0 | 9982 | 0.8237 | 0.9070 |
| 0.0 | 47.0 | 10199 | 0.8249 | 0.9070 |
| 0.0 | 48.0 | 10416 | 0.8315 | 0.9070 |
| 0.0 | 49.0 | 10633 | 0.8343 | 0.9070 |
| 0.0 | 50.0 | 10850 | 0.8349 | 0.9070 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_sgd_00001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_sgd_00001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4462
- Accuracy: 0.2326
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.395 | 1.0 | 217 | 1.4656 | 0.2326 |
| 1.3668 | 2.0 | 434 | 1.4647 | 0.2326 |
| 1.3731 | 3.0 | 651 | 1.4638 | 0.2326 |
| 1.4025 | 4.0 | 868 | 1.4629 | 0.2326 |
| 1.3639 | 5.0 | 1085 | 1.4620 | 0.2326 |
| 1.3879 | 6.0 | 1302 | 1.4612 | 0.2326 |
| 1.3778 | 7.0 | 1519 | 1.4604 | 0.2326 |
| 1.3612 | 8.0 | 1736 | 1.4596 | 0.2326 |
| 1.3705 | 9.0 | 1953 | 1.4589 | 0.2326 |
| 1.3789 | 10.0 | 2170 | 1.4582 | 0.2326 |
| 1.361 | 11.0 | 2387 | 1.4575 | 0.2326 |
| 1.3859 | 12.0 | 2604 | 1.4568 | 0.2326 |
| 1.3896 | 13.0 | 2821 | 1.4562 | 0.2326 |
| 1.3672 | 14.0 | 3038 | 1.4556 | 0.2326 |
| 1.3726 | 15.0 | 3255 | 1.4550 | 0.2326 |
| 1.3714 | 16.0 | 3472 | 1.4544 | 0.2326 |
| 1.3679 | 17.0 | 3689 | 1.4539 | 0.2326 |
| 1.3686 | 18.0 | 3906 | 1.4533 | 0.2326 |
| 1.3759 | 19.0 | 4123 | 1.4528 | 0.2326 |
| 1.3715 | 20.0 | 4340 | 1.4524 | 0.2326 |
| 1.3586 | 21.0 | 4557 | 1.4519 | 0.2326 |
| 1.3602 | 22.0 | 4774 | 1.4515 | 0.2326 |
| 1.3784 | 23.0 | 4991 | 1.4511 | 0.2326 |
| 1.3361 | 24.0 | 5208 | 1.4507 | 0.2326 |
| 1.3639 | 25.0 | 5425 | 1.4503 | 0.2326 |
| 1.3719 | 26.0 | 5642 | 1.4499 | 0.2326 |
| 1.37 | 27.0 | 5859 | 1.4496 | 0.2326 |
| 1.3566 | 28.0 | 6076 | 1.4493 | 0.2326 |
| 1.3167 | 29.0 | 6293 | 1.4490 | 0.2326 |
| 1.3522 | 30.0 | 6510 | 1.4487 | 0.2326 |
| 1.3828 | 31.0 | 6727 | 1.4484 | 0.2326 |
| 1.3664 | 32.0 | 6944 | 1.4481 | 0.2326 |
| 1.3407 | 33.0 | 7161 | 1.4479 | 0.2326 |
| 1.3633 | 34.0 | 7378 | 1.4477 | 0.2326 |
| 1.3611 | 35.0 | 7595 | 1.4475 | 0.2326 |
| 1.3425 | 36.0 | 7812 | 1.4473 | 0.2326 |
| 1.3418 | 37.0 | 8029 | 1.4471 | 0.2326 |
| 1.3479 | 38.0 | 8246 | 1.4470 | 0.2326 |
| 1.3576 | 39.0 | 8463 | 1.4468 | 0.2326 |
| 1.3408 | 40.0 | 8680 | 1.4467 | 0.2326 |
| 1.3433 | 41.0 | 8897 | 1.4466 | 0.2326 |
| 1.3828 | 42.0 | 9114 | 1.4465 | 0.2326 |
| 1.3558 | 43.0 | 9331 | 1.4464 | 0.2326 |
| 1.3616 | 44.0 | 9548 | 1.4463 | 0.2326 |
| 1.36 | 45.0 | 9765 | 1.4463 | 0.2326 |
| 1.3465 | 46.0 | 9982 | 1.4462 | 0.2326 |
| 1.3484 | 47.0 | 10199 | 1.4462 | 0.2326 |
| 1.3826 | 48.0 | 10416 | 1.4462 | 0.2326 |
| 1.3656 | 49.0 | 10633 | 1.4462 | 0.2326 |
| 1.3363 | 50.0 | 10850 | 1.4462 | 0.2326 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_sgd_0001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_sgd_0001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2710
- 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3787 | 1.0 | 217 | 1.4567 | 0.2326 |
| 1.3411 | 2.0 | 434 | 1.4476 | 0.2326 |
| 1.3346 | 3.0 | 651 | 1.4398 | 0.2326 |
| 1.3522 | 4.0 | 868 | 1.4325 | 0.2558 |
| 1.295 | 5.0 | 1085 | 1.4257 | 0.2558 |
| 1.3027 | 6.0 | 1302 | 1.4192 | 0.2791 |
| 1.2908 | 7.0 | 1519 | 1.4129 | 0.3023 |
| 1.2684 | 8.0 | 1736 | 1.4068 | 0.3023 |
| 1.2597 | 9.0 | 1953 | 1.4007 | 0.3023 |
| 1.2504 | 10.0 | 2170 | 1.3948 | 0.3023 |
| 1.2181 | 11.0 | 2387 | 1.3891 | 0.3023 |
| 1.2286 | 12.0 | 2604 | 1.3834 | 0.3023 |
| 1.229 | 13.0 | 2821 | 1.3779 | 0.3023 |
| 1.2118 | 14.0 | 3038 | 1.3725 | 0.3256 |
| 1.1939 | 15.0 | 3255 | 1.3673 | 0.3256 |
| 1.2054 | 16.0 | 3472 | 1.3622 | 0.3488 |
| 1.1836 | 17.0 | 3689 | 1.3572 | 0.3721 |
| 1.1754 | 18.0 | 3906 | 1.3524 | 0.3721 |
| 1.1872 | 19.0 | 4123 | 1.3477 | 0.3721 |
| 1.1652 | 20.0 | 4340 | 1.3431 | 0.3721 |
| 1.1396 | 21.0 | 4557 | 1.3387 | 0.3721 |
| 1.1373 | 22.0 | 4774 | 1.3343 | 0.3953 |
| 1.1381 | 23.0 | 4991 | 1.3300 | 0.3953 |
| 1.101 | 24.0 | 5208 | 1.3259 | 0.3953 |
| 1.1305 | 25.0 | 5425 | 1.3219 | 0.4186 |
| 1.1458 | 26.0 | 5642 | 1.3181 | 0.4186 |
| 1.0969 | 27.0 | 5859 | 1.3143 | 0.4186 |
| 1.092 | 28.0 | 6076 | 1.3106 | 0.4186 |
| 1.0422 | 29.0 | 6293 | 1.3071 | 0.4186 |
| 1.07 | 30.0 | 6510 | 1.3037 | 0.4419 |
| 1.097 | 31.0 | 6727 | 1.3005 | 0.4419 |
| 1.1048 | 32.0 | 6944 | 1.2974 | 0.4419 |
| 1.0657 | 33.0 | 7161 | 1.2945 | 0.4419 |
| 1.0841 | 34.0 | 7378 | 1.2918 | 0.4419 |
| 1.0697 | 35.0 | 7595 | 1.2891 | 0.4419 |
| 1.0586 | 36.0 | 7812 | 1.2867 | 0.4419 |
| 1.0346 | 37.0 | 8029 | 1.2845 | 0.4419 |
| 1.0364 | 38.0 | 8246 | 1.2824 | 0.4651 |
| 1.055 | 39.0 | 8463 | 1.2804 | 0.4651 |
| 1.0391 | 40.0 | 8680 | 1.2787 | 0.4651 |
| 1.0408 | 41.0 | 8897 | 1.2771 | 0.4651 |
| 1.0911 | 42.0 | 9114 | 1.2757 | 0.4651 |
| 1.042 | 43.0 | 9331 | 1.2745 | 0.4651 |
| 1.0562 | 44.0 | 9548 | 1.2735 | 0.4651 |
| 1.0444 | 45.0 | 9765 | 1.2727 | 0.4651 |
| 1.0551 | 46.0 | 9982 | 1.2720 | 0.4651 |
| 1.0314 | 47.0 | 10199 | 1.2715 | 0.4651 |
| 1.067 | 48.0 | 10416 | 1.2712 | 0.4651 |
| 1.0573 | 49.0 | 10633 | 1.2710 | 0.4651 |
| 1.0022 | 50.0 | 10850 | 1.2710 | 0.4651 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 1.0865
- Accuracy: 0.9048
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5149 | 1.0 | 219 | 0.5408 | 0.7857 |
| 0.1388 | 2.0 | 438 | 0.2883 | 0.8571 |
| 0.0532 | 3.0 | 657 | 0.2259 | 0.9048 |
| 0.0146 | 4.0 | 876 | 0.3103 | 0.8810 |
| 0.0044 | 5.0 | 1095 | 0.2128 | 0.9048 |
| 0.001 | 6.0 | 1314 | 0.4066 | 0.8571 |
| 0.0004 | 7.0 | 1533 | 0.5492 | 0.8571 |
| 0.0003 | 8.0 | 1752 | 0.5191 | 0.8571 |
| 0.0002 | 9.0 | 1971 | 0.5554 | 0.8571 |
| 0.0002 | 10.0 | 2190 | 0.6021 | 0.8571 |
| 0.0001 | 11.0 | 2409 | 0.6325 | 0.8571 |
| 0.0001 | 12.0 | 2628 | 0.5941 | 0.8810 |
| 0.0001 | 13.0 | 2847 | 0.6178 | 0.8810 |
| 0.0 | 14.0 | 3066 | 0.6345 | 0.8810 |
| 0.0 | 15.0 | 3285 | 0.6789 | 0.8810 |
| 0.0 | 16.0 | 3504 | 0.6912 | 0.8810 |
| 0.0 | 17.0 | 3723 | 0.6975 | 0.8810 |
| 0.0 | 18.0 | 3942 | 0.7160 | 0.8810 |
| 0.0 | 19.0 | 4161 | 0.7194 | 0.8810 |
| 0.0 | 20.0 | 4380 | 0.7354 | 0.8810 |
| 0.0 | 21.0 | 4599 | 0.7292 | 0.9048 |
| 0.0 | 22.0 | 4818 | 0.7594 | 0.9048 |
| 0.0 | 23.0 | 5037 | 0.7524 | 0.9048 |
| 0.0 | 24.0 | 5256 | 0.7681 | 0.9048 |
| 0.0 | 25.0 | 5475 | 0.7964 | 0.9048 |
| 0.0 | 26.0 | 5694 | 0.8348 | 0.9048 |
| 0.0 | 27.0 | 5913 | 0.8454 | 0.9048 |
| 0.0 | 28.0 | 6132 | 0.8650 | 0.9048 |
| 0.0 | 29.0 | 6351 | 0.8560 | 0.9048 |
| 0.0 | 30.0 | 6570 | 0.8777 | 0.9048 |
| 0.0 | 31.0 | 6789 | 0.8901 | 0.9048 |
| 0.0 | 32.0 | 7008 | 0.9135 | 0.9048 |
| 0.0 | 33.0 | 7227 | 0.9102 | 0.9048 |
| 0.0 | 34.0 | 7446 | 0.9561 | 0.9048 |
| 0.0 | 35.0 | 7665 | 0.9681 | 0.9048 |
| 0.0 | 36.0 | 7884 | 0.9813 | 0.9048 |
| 0.0 | 37.0 | 8103 | 0.9769 | 0.9048 |
| 0.0 | 38.0 | 8322 | 1.0135 | 0.9048 |
| 0.0 | 39.0 | 8541 | 1.0218 | 0.9048 |
| 0.0 | 40.0 | 8760 | 1.0098 | 0.9048 |
| 0.0 | 41.0 | 8979 | 1.0382 | 0.9048 |
| 0.0 | 42.0 | 9198 | 1.0217 | 0.9048 |
| 0.0 | 43.0 | 9417 | 1.0481 | 0.9048 |
| 0.0 | 44.0 | 9636 | 1.0751 | 0.9048 |
| 0.0 | 45.0 | 9855 | 1.0579 | 0.9048 |
| 0.0 | 46.0 | 10074 | 1.0662 | 0.9048 |
| 0.0 | 47.0 | 10293 | 1.0827 | 0.9048 |
| 0.0 | 48.0 | 10512 | 1.0853 | 0.9048 |
| 0.0 | 49.0 | 10731 | 1.0917 | 0.9048 |
| 0.0 | 50.0 | 10950 | 1.0865 | 0.9048 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_sgd_00001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_sgd_00001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3940
- Accuracy: 0.3095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4075 | 1.0 | 219 | 1.4139 | 0.2857 |
| 1.4006 | 2.0 | 438 | 1.4128 | 0.2857 |
| 1.397 | 3.0 | 657 | 1.4119 | 0.2857 |
| 1.4009 | 4.0 | 876 | 1.4109 | 0.2857 |
| 1.4192 | 5.0 | 1095 | 1.4100 | 0.2857 |
| 1.4068 | 6.0 | 1314 | 1.4091 | 0.2857 |
| 1.4024 | 7.0 | 1533 | 1.4083 | 0.2857 |
| 1.3965 | 8.0 | 1752 | 1.4075 | 0.2857 |
| 1.3783 | 9.0 | 1971 | 1.4067 | 0.3095 |
| 1.3738 | 10.0 | 2190 | 1.4060 | 0.3095 |
| 1.3936 | 11.0 | 2409 | 1.4053 | 0.3095 |
| 1.3746 | 12.0 | 2628 | 1.4046 | 0.3095 |
| 1.3536 | 13.0 | 2847 | 1.4040 | 0.3095 |
| 1.4005 | 14.0 | 3066 | 1.4033 | 0.3095 |
| 1.3798 | 15.0 | 3285 | 1.4027 | 0.3095 |
| 1.3748 | 16.0 | 3504 | 1.4022 | 0.3095 |
| 1.3581 | 17.0 | 3723 | 1.4016 | 0.3095 |
| 1.3695 | 18.0 | 3942 | 1.4011 | 0.3095 |
| 1.366 | 19.0 | 4161 | 1.4006 | 0.3095 |
| 1.3735 | 20.0 | 4380 | 1.4001 | 0.3095 |
| 1.3732 | 21.0 | 4599 | 1.3997 | 0.3095 |
| 1.3632 | 22.0 | 4818 | 1.3992 | 0.3095 |
| 1.3525 | 23.0 | 5037 | 1.3988 | 0.3095 |
| 1.3845 | 24.0 | 5256 | 1.3984 | 0.3095 |
| 1.363 | 25.0 | 5475 | 1.3980 | 0.3095 |
| 1.3693 | 26.0 | 5694 | 1.3977 | 0.3095 |
| 1.3693 | 27.0 | 5913 | 1.3973 | 0.3095 |
| 1.3914 | 28.0 | 6132 | 1.3970 | 0.3095 |
| 1.3857 | 29.0 | 6351 | 1.3967 | 0.3095 |
| 1.3681 | 30.0 | 6570 | 1.3964 | 0.3095 |
| 1.3619 | 31.0 | 6789 | 1.3962 | 0.3095 |
| 1.3666 | 32.0 | 7008 | 1.3959 | 0.3095 |
| 1.3733 | 33.0 | 7227 | 1.3957 | 0.3095 |
| 1.3572 | 34.0 | 7446 | 1.3955 | 0.3095 |
| 1.3715 | 35.0 | 7665 | 1.3953 | 0.3095 |
| 1.3581 | 36.0 | 7884 | 1.3951 | 0.3095 |
| 1.3453 | 37.0 | 8103 | 1.3949 | 0.3095 |
| 1.3666 | 38.0 | 8322 | 1.3948 | 0.3095 |
| 1.3416 | 39.0 | 8541 | 1.3946 | 0.3095 |
| 1.3435 | 40.0 | 8760 | 1.3945 | 0.3095 |
| 1.3731 | 41.0 | 8979 | 1.3944 | 0.3095 |
| 1.3652 | 42.0 | 9198 | 1.3943 | 0.3095 |
| 1.3499 | 43.0 | 9417 | 1.3942 | 0.3095 |
| 1.3629 | 44.0 | 9636 | 1.3941 | 0.3095 |
| 1.3332 | 45.0 | 9855 | 1.3941 | 0.3095 |
| 1.3535 | 46.0 | 10074 | 1.3940 | 0.3095 |
| 1.3876 | 47.0 | 10293 | 1.3940 | 0.3095 |
| 1.363 | 48.0 | 10512 | 1.3940 | 0.3095 |
| 1.3575 | 49.0 | 10731 | 1.3940 | 0.3095 |
| 1.3466 | 50.0 | 10950 | 1.3940 | 0.3095 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_sgd_0001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_sgd_0001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2151
- Accuracy: 0.4286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3918 | 1.0 | 219 | 1.4045 | 0.3095 |
| 1.3704 | 2.0 | 438 | 1.3956 | 0.3095 |
| 1.3491 | 3.0 | 657 | 1.3880 | 0.3333 |
| 1.3369 | 4.0 | 876 | 1.3811 | 0.3333 |
| 1.3406 | 5.0 | 1095 | 1.3747 | 0.3333 |
| 1.3171 | 6.0 | 1314 | 1.3686 | 0.3333 |
| 1.2982 | 7.0 | 1533 | 1.3628 | 0.3571 |
| 1.2896 | 8.0 | 1752 | 1.3571 | 0.3571 |
| 1.2549 | 9.0 | 1971 | 1.3513 | 0.3810 |
| 1.2384 | 10.0 | 2190 | 1.3457 | 0.4048 |
| 1.2507 | 11.0 | 2409 | 1.3401 | 0.4286 |
| 1.2362 | 12.0 | 2628 | 1.3346 | 0.4286 |
| 1.1966 | 13.0 | 2847 | 1.3293 | 0.4286 |
| 1.2279 | 14.0 | 3066 | 1.3240 | 0.4286 |
| 1.2136 | 15.0 | 3285 | 1.3188 | 0.4286 |
| 1.1856 | 16.0 | 3504 | 1.3138 | 0.4286 |
| 1.1941 | 17.0 | 3723 | 1.3088 | 0.4286 |
| 1.1805 | 18.0 | 3942 | 1.3039 | 0.4286 |
| 1.1554 | 19.0 | 4161 | 1.2991 | 0.4048 |
| 1.1709 | 20.0 | 4380 | 1.2943 | 0.4048 |
| 1.1523 | 21.0 | 4599 | 1.2895 | 0.4048 |
| 1.138 | 22.0 | 4818 | 1.2848 | 0.4048 |
| 1.0984 | 23.0 | 5037 | 1.2803 | 0.4048 |
| 1.1405 | 24.0 | 5256 | 1.2759 | 0.4048 |
| 1.1028 | 25.0 | 5475 | 1.2716 | 0.4286 |
| 1.1236 | 26.0 | 5694 | 1.2674 | 0.4286 |
| 1.0819 | 27.0 | 5913 | 1.2634 | 0.4286 |
| 1.1245 | 28.0 | 6132 | 1.2595 | 0.4286 |
| 1.0929 | 29.0 | 6351 | 1.2557 | 0.4286 |
| 1.0861 | 30.0 | 6570 | 1.2521 | 0.4048 |
| 1.082 | 31.0 | 6789 | 1.2486 | 0.4048 |
| 1.0826 | 32.0 | 7008 | 1.2452 | 0.4048 |
| 1.0889 | 33.0 | 7227 | 1.2420 | 0.4048 |
| 1.052 | 34.0 | 7446 | 1.2390 | 0.4286 |
| 1.056 | 35.0 | 7665 | 1.2361 | 0.4286 |
| 1.0391 | 36.0 | 7884 | 1.2333 | 0.4286 |
| 1.0236 | 37.0 | 8103 | 1.2307 | 0.4286 |
| 1.0474 | 38.0 | 8322 | 1.2283 | 0.4286 |
| 1.0069 | 39.0 | 8541 | 1.2261 | 0.4286 |
| 1.0443 | 40.0 | 8760 | 1.2242 | 0.4286 |
| 1.0711 | 41.0 | 8979 | 1.2223 | 0.4048 |
| 1.053 | 42.0 | 9198 | 1.2207 | 0.4286 |
| 1.0356 | 43.0 | 9417 | 1.2193 | 0.4286 |
| 1.0491 | 44.0 | 9636 | 1.2181 | 0.4286 |
| 0.9928 | 45.0 | 9855 | 1.2171 | 0.4286 |
| 1.0402 | 46.0 | 10074 | 1.2163 | 0.4286 |
| 1.0792 | 47.0 | 10293 | 1.2157 | 0.4286 |
| 1.0146 | 48.0 | 10512 | 1.2153 | 0.4286 |
| 1.0325 | 49.0 | 10731 | 1.2152 | 0.4286 |
| 1.0249 | 50.0 | 10950 | 1.2151 | 0.4286 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 0.8352
- Accuracy: 0.8537
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3813 | 1.0 | 220 | 0.6093 | 0.7561 |
| 0.131 | 2.0 | 440 | 0.4372 | 0.8293 |
| 0.0714 | 3.0 | 660 | 0.6223 | 0.7805 |
| 0.0083 | 4.0 | 880 | 0.5773 | 0.8537 |
| 0.0038 | 5.0 | 1100 | 0.5967 | 0.8537 |
| 0.0013 | 6.0 | 1320 | 0.7213 | 0.8537 |
| 0.0005 | 7.0 | 1540 | 0.6555 | 0.8537 |
| 0.0003 | 8.0 | 1760 | 0.7129 | 0.8537 |
| 0.0002 | 9.0 | 1980 | 0.6903 | 0.8537 |
| 0.0001 | 10.0 | 2200 | 0.7139 | 0.8537 |
| 0.0001 | 11.0 | 2420 | 0.7461 | 0.8537 |
| 0.0001 | 12.0 | 2640 | 0.7296 | 0.8537 |
| 0.0001 | 13.0 | 2860 | 0.7461 | 0.8537 |
| 0.0001 | 14.0 | 3080 | 0.7537 | 0.8537 |
| 0.0 | 15.0 | 3300 | 0.7347 | 0.8537 |
| 0.0 | 16.0 | 3520 | 0.7586 | 0.8537 |
| 0.0 | 17.0 | 3740 | 0.7585 | 0.8537 |
| 0.0 | 18.0 | 3960 | 0.7603 | 0.8537 |
| 0.0 | 19.0 | 4180 | 0.7375 | 0.8537 |
| 0.0 | 20.0 | 4400 | 0.7584 | 0.8537 |
| 0.0 | 21.0 | 4620 | 0.7582 | 0.8537 |
| 0.0 | 22.0 | 4840 | 0.7660 | 0.8537 |
| 0.0 | 23.0 | 5060 | 0.7826 | 0.8537 |
| 0.0 | 24.0 | 5280 | 0.7552 | 0.8537 |
| 0.0 | 25.0 | 5500 | 0.7401 | 0.8537 |
| 0.0 | 26.0 | 5720 | 0.7783 | 0.8537 |
| 0.0 | 27.0 | 5940 | 0.7654 | 0.8537 |
| 0.0 | 28.0 | 6160 | 0.7518 | 0.8537 |
| 0.0 | 29.0 | 6380 | 0.7644 | 0.8537 |
| 0.0 | 30.0 | 6600 | 0.7962 | 0.8537 |
| 0.0 | 31.0 | 6820 | 0.8050 | 0.8537 |
| 0.0 | 32.0 | 7040 | 0.7846 | 0.8537 |
| 0.0 | 33.0 | 7260 | 0.7663 | 0.8537 |
| 0.0 | 34.0 | 7480 | 0.7669 | 0.8780 |
| 0.0 | 35.0 | 7700 | 0.7816 | 0.8780 |
| 0.0 | 36.0 | 7920 | 0.7902 | 0.8537 |
| 0.0 | 37.0 | 8140 | 0.7775 | 0.8537 |
| 0.0 | 38.0 | 8360 | 0.8004 | 0.8537 |
| 0.0 | 39.0 | 8580 | 0.7724 | 0.8537 |
| 0.0 | 40.0 | 8800 | 0.7795 | 0.8780 |
| 0.0 | 41.0 | 9020 | 0.8084 | 0.8537 |
| 0.0 | 42.0 | 9240 | 0.8224 | 0.8537 |
| 0.0 | 43.0 | 9460 | 0.8366 | 0.8293 |
| 0.0 | 44.0 | 9680 | 0.8236 | 0.8780 |
| 0.0 | 45.0 | 9900 | 0.8365 | 0.8293 |
| 0.0 | 46.0 | 10120 | 0.8207 | 0.8537 |
| 0.0 | 47.0 | 10340 | 0.8439 | 0.8293 |
| 0.0 | 48.0 | 10560 | 0.8465 | 0.8537 |
| 0.0 | 49.0 | 10780 | 0.8311 | 0.8537 |
| 0.0 | 50.0 | 11000 | 0.8352 | 0.8537 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_sgd_00001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_sgd_00001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3600
- 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4054 | 1.0 | 220 | 1.3726 | 0.2683 |
| 1.4058 | 2.0 | 440 | 1.3720 | 0.2683 |
| 1.4165 | 3.0 | 660 | 1.3713 | 0.2683 |
| 1.3927 | 4.0 | 880 | 1.3707 | 0.2683 |
| 1.4116 | 5.0 | 1100 | 1.3701 | 0.2683 |
| 1.3812 | 6.0 | 1320 | 1.3696 | 0.2683 |
| 1.4048 | 7.0 | 1540 | 1.3690 | 0.2683 |
| 1.3591 | 8.0 | 1760 | 1.3685 | 0.2683 |
| 1.4052 | 9.0 | 1980 | 1.3680 | 0.2683 |
| 1.3882 | 10.0 | 2200 | 1.3676 | 0.2683 |
| 1.3863 | 11.0 | 2420 | 1.3671 | 0.2683 |
| 1.3965 | 12.0 | 2640 | 1.3667 | 0.2683 |
| 1.4035 | 13.0 | 2860 | 1.3663 | 0.2683 |
| 1.3813 | 14.0 | 3080 | 1.3659 | 0.2683 |
| 1.3758 | 15.0 | 3300 | 1.3655 | 0.2683 |
| 1.3822 | 16.0 | 3520 | 1.3651 | 0.2683 |
| 1.3951 | 17.0 | 3740 | 1.3648 | 0.2683 |
| 1.3934 | 18.0 | 3960 | 1.3645 | 0.2683 |
| 1.3858 | 19.0 | 4180 | 1.3642 | 0.2683 |
| 1.3848 | 20.0 | 4400 | 1.3639 | 0.2683 |
| 1.379 | 21.0 | 4620 | 1.3636 | 0.2683 |
| 1.3756 | 22.0 | 4840 | 1.3633 | 0.2683 |
| 1.3759 | 23.0 | 5060 | 1.3630 | 0.2683 |
| 1.3873 | 24.0 | 5280 | 1.3628 | 0.2683 |
| 1.3701 | 25.0 | 5500 | 1.3626 | 0.2683 |
| 1.3779 | 26.0 | 5720 | 1.3623 | 0.2683 |
| 1.3916 | 27.0 | 5940 | 1.3621 | 0.2683 |
| 1.3605 | 28.0 | 6160 | 1.3619 | 0.2927 |
| 1.3832 | 29.0 | 6380 | 1.3617 | 0.2927 |
| 1.3641 | 30.0 | 6600 | 1.3616 | 0.2927 |
| 1.3806 | 31.0 | 6820 | 1.3614 | 0.2927 |
| 1.3762 | 32.0 | 7040 | 1.3612 | 0.2927 |
| 1.3671 | 33.0 | 7260 | 1.3611 | 0.2683 |
| 1.3772 | 34.0 | 7480 | 1.3610 | 0.2683 |
| 1.3577 | 35.0 | 7700 | 1.3608 | 0.2683 |
| 1.3486 | 36.0 | 7920 | 1.3607 | 0.2683 |
| 1.3611 | 37.0 | 8140 | 1.3606 | 0.2683 |
| 1.355 | 38.0 | 8360 | 1.3605 | 0.2683 |
| 1.3682 | 39.0 | 8580 | 1.3604 | 0.2683 |
| 1.3669 | 40.0 | 8800 | 1.3603 | 0.2683 |
| 1.3585 | 41.0 | 9020 | 1.3603 | 0.2683 |
| 1.3549 | 42.0 | 9240 | 1.3602 | 0.2683 |
| 1.3669 | 43.0 | 9460 | 1.3602 | 0.2683 |
| 1.353 | 44.0 | 9680 | 1.3601 | 0.2683 |
| 1.3499 | 45.0 | 9900 | 1.3601 | 0.2683 |
| 1.3573 | 46.0 | 10120 | 1.3600 | 0.2683 |
| 1.384 | 47.0 | 10340 | 1.3600 | 0.2683 |
| 1.367 | 48.0 | 10560 | 1.3600 | 0.2683 |
| 1.3753 | 49.0 | 10780 | 1.3600 | 0.2683 |
| 1.3687 | 50.0 | 11000 | 1.3600 | 0.2683 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_sgd_0001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_sgd_0001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1678
- 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3902 | 1.0 | 220 | 1.3668 | 0.2683 |
| 1.375 | 2.0 | 440 | 1.3610 | 0.2927 |
| 1.3643 | 3.0 | 660 | 1.3560 | 0.2683 |
| 1.3352 | 4.0 | 880 | 1.3513 | 0.2683 |
| 1.343 | 5.0 | 1100 | 1.3466 | 0.2683 |
| 1.2985 | 6.0 | 1320 | 1.3416 | 0.2683 |
| 1.3152 | 7.0 | 1540 | 1.3365 | 0.2927 |
| 1.2618 | 8.0 | 1760 | 1.3311 | 0.3171 |
| 1.2728 | 9.0 | 1980 | 1.3254 | 0.3415 |
| 1.2604 | 10.0 | 2200 | 1.3195 | 0.3415 |
| 1.2446 | 11.0 | 2420 | 1.3136 | 0.3415 |
| 1.2322 | 12.0 | 2640 | 1.3076 | 0.3902 |
| 1.2519 | 13.0 | 2860 | 1.3017 | 0.4146 |
| 1.2115 | 14.0 | 3080 | 1.2958 | 0.4146 |
| 1.2112 | 15.0 | 3300 | 1.2899 | 0.4390 |
| 1.1892 | 16.0 | 3520 | 1.2841 | 0.4390 |
| 1.1942 | 17.0 | 3740 | 1.2784 | 0.4390 |
| 1.2008 | 18.0 | 3960 | 1.2727 | 0.4390 |
| 1.1853 | 19.0 | 4180 | 1.2671 | 0.4390 |
| 1.1573 | 20.0 | 4400 | 1.2615 | 0.4634 |
| 1.1577 | 21.0 | 4620 | 1.2560 | 0.4634 |
| 1.1317 | 22.0 | 4840 | 1.2506 | 0.4634 |
| 1.1597 | 23.0 | 5060 | 1.2453 | 0.4878 |
| 1.1283 | 24.0 | 5280 | 1.2401 | 0.4878 |
| 1.1168 | 25.0 | 5500 | 1.2349 | 0.4634 |
| 1.142 | 26.0 | 5720 | 1.2300 | 0.4634 |
| 1.1324 | 27.0 | 5940 | 1.2251 | 0.4634 |
| 1.1074 | 28.0 | 6160 | 1.2203 | 0.4634 |
| 1.107 | 29.0 | 6380 | 1.2157 | 0.4634 |
| 1.098 | 30.0 | 6600 | 1.2113 | 0.4634 |
| 1.1034 | 31.0 | 6820 | 1.2071 | 0.4634 |
| 1.0941 | 32.0 | 7040 | 1.2031 | 0.4634 |
| 1.0839 | 33.0 | 7260 | 1.1993 | 0.4634 |
| 1.0528 | 34.0 | 7480 | 1.1956 | 0.4634 |
| 1.0292 | 35.0 | 7700 | 1.1922 | 0.4634 |
| 1.0585 | 36.0 | 7920 | 1.1890 | 0.4634 |
| 1.0434 | 37.0 | 8140 | 1.1859 | 0.4634 |
| 1.0597 | 38.0 | 8360 | 1.1831 | 0.4634 |
| 1.0626 | 39.0 | 8580 | 1.1805 | 0.4634 |
| 1.0375 | 40.0 | 8800 | 1.1782 | 0.4634 |
| 1.0422 | 41.0 | 9020 | 1.1761 | 0.4634 |
| 1.0304 | 42.0 | 9240 | 1.1742 | 0.4634 |
| 1.0373 | 43.0 | 9460 | 1.1726 | 0.4878 |
| 1.0134 | 44.0 | 9680 | 1.1712 | 0.4878 |
| 1.0323 | 45.0 | 9900 | 1.1701 | 0.4878 |
| 1.0327 | 46.0 | 10120 | 1.1692 | 0.5122 |
| 1.0599 | 47.0 | 10340 | 1.1685 | 0.5122 |
| 1.0079 | 48.0 | 10560 | 1.1681 | 0.5122 |
| 1.0145 | 49.0 | 10780 | 1.1679 | 0.5122 |
| 1.0358 | 50.0 | 11000 | 1.1678 | 0.5122 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
clewiston/autotrain-vlxo9-2s7eh
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.347914457321167
f1_macro: 0.196969696969697
f1_micro: 0.65
f1_weighted: 0.5121212121212122
precision_macro: 0.1625
precision_micro: 0.65
precision_weighted: 0.42250000000000004
recall_macro: 0.25
recall_micro: 0.65
recall_weighted: 0.65
accuracy: 0.65
|
[
"multiple",
"none",
"partial",
"whole"
] |
yuanhuaisen/autotrain-r6fhf-a4d7f
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.4276469945907593
f1_macro: 0.8530901722391085
f1_micro: 0.875
f1_weighted: 0.878488532743852
precision_macro: 0.8621098104793757
precision_micro: 0.875
precision_weighted: 0.8893636933718457
recall_macro: 0.8544277360066833
recall_micro: 0.875
recall_weighted: 0.875
accuracy: 0.875
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
chanhua/autotrain-6uoy3-zwdlp
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.031825795644891124
f1_micro: 0.10555555555555556
f1_weighted: 0.020156337241764376
precision_macro: 0.017592592592592594
precision_micro: 0.10555555555555556
precision_weighted: 0.011141975308641975
recall_macro: 0.16666666666666666
recall_micro: 0.10555555555555556
recall_weighted: 0.10555555555555556
accuracy: 0.10555555555555556
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above",
"21covered_with_a_quilt,_only_the_head_and_shoulders_exposed",
"22covered_with_a_quilt,_exposed_head_and_shoulders_except_for_other_organs",
"23has_nothing_to_do_with_21_and_22_above"
] |
hkivancoral/hushem_40x_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_40x_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: 0.9268
- Accuracy: 0.7333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1534 | 1.0 | 215 | 1.3121 | 0.3778 |
| 0.8744 | 2.0 | 430 | 1.2305 | 0.5111 |
| 0.7578 | 3.0 | 645 | 1.1023 | 0.5556 |
| 0.6354 | 4.0 | 860 | 0.9584 | 0.5778 |
| 0.4832 | 5.0 | 1075 | 0.8877 | 0.6444 |
| 0.4506 | 6.0 | 1290 | 0.8214 | 0.6889 |
| 0.3619 | 7.0 | 1505 | 0.8077 | 0.6889 |
| 0.3187 | 8.0 | 1720 | 0.7845 | 0.6667 |
| 0.2423 | 9.0 | 1935 | 0.7629 | 0.7111 |
| 0.2351 | 10.0 | 2150 | 0.7464 | 0.7333 |
| 0.2043 | 11.0 | 2365 | 0.7249 | 0.6889 |
| 0.1712 | 12.0 | 2580 | 0.7297 | 0.7111 |
| 0.1294 | 13.0 | 2795 | 0.7280 | 0.7333 |
| 0.1185 | 14.0 | 3010 | 0.7610 | 0.7333 |
| 0.1264 | 15.0 | 3225 | 0.7479 | 0.7333 |
| 0.0869 | 16.0 | 3440 | 0.7617 | 0.7333 |
| 0.0902 | 17.0 | 3655 | 0.7623 | 0.7333 |
| 0.0782 | 18.0 | 3870 | 0.7805 | 0.7333 |
| 0.071 | 19.0 | 4085 | 0.7715 | 0.7333 |
| 0.063 | 20.0 | 4300 | 0.7777 | 0.7333 |
| 0.0587 | 21.0 | 4515 | 0.7497 | 0.7333 |
| 0.0675 | 22.0 | 4730 | 0.7998 | 0.7333 |
| 0.0426 | 23.0 | 4945 | 0.8200 | 0.7333 |
| 0.0373 | 24.0 | 5160 | 0.8281 | 0.7111 |
| 0.0441 | 25.0 | 5375 | 0.8317 | 0.7111 |
| 0.0323 | 26.0 | 5590 | 0.8133 | 0.7111 |
| 0.0359 | 27.0 | 5805 | 0.8214 | 0.7111 |
| 0.0291 | 28.0 | 6020 | 0.8265 | 0.7111 |
| 0.0287 | 29.0 | 6235 | 0.8490 | 0.7111 |
| 0.0271 | 30.0 | 6450 | 0.8534 | 0.7111 |
| 0.0256 | 31.0 | 6665 | 0.8626 | 0.7111 |
| 0.0212 | 32.0 | 6880 | 0.8791 | 0.7111 |
| 0.0155 | 33.0 | 7095 | 0.8740 | 0.7333 |
| 0.0144 | 34.0 | 7310 | 0.8433 | 0.7333 |
| 0.0132 | 35.0 | 7525 | 0.8680 | 0.7333 |
| 0.015 | 36.0 | 7740 | 0.8880 | 0.7333 |
| 0.0129 | 37.0 | 7955 | 0.8931 | 0.7333 |
| 0.018 | 38.0 | 8170 | 0.8891 | 0.7333 |
| 0.0092 | 39.0 | 8385 | 0.9122 | 0.7333 |
| 0.0085 | 40.0 | 8600 | 0.9159 | 0.7333 |
| 0.0124 | 41.0 | 8815 | 0.9199 | 0.7333 |
| 0.0125 | 42.0 | 9030 | 0.9056 | 0.7333 |
| 0.0107 | 43.0 | 9245 | 0.9191 | 0.7333 |
| 0.0095 | 44.0 | 9460 | 0.9083 | 0.7333 |
| 0.0115 | 45.0 | 9675 | 0.9189 | 0.7333 |
| 0.0088 | 46.0 | 9890 | 0.9241 | 0.7333 |
| 0.0065 | 47.0 | 10105 | 0.9299 | 0.7333 |
| 0.007 | 48.0 | 10320 | 0.9257 | 0.7333 |
| 0.0129 | 49.0 | 10535 | 0.9260 | 0.7333 |
| 0.0229 | 50.0 | 10750 | 0.9268 | 0.7333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_rms_001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 7.0637
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3874 | 1.0 | 215 | 1.5845 | 0.2667 |
| 1.2829 | 2.0 | 430 | 1.2221 | 0.4 |
| 0.7962 | 3.0 | 645 | 2.1065 | 0.4 |
| 0.7528 | 4.0 | 860 | 1.0651 | 0.5556 |
| 0.6029 | 5.0 | 1075 | 1.5642 | 0.4889 |
| 0.6246 | 6.0 | 1290 | 1.7962 | 0.4222 |
| 0.589 | 7.0 | 1505 | 1.4819 | 0.4444 |
| 0.6081 | 8.0 | 1720 | 1.4452 | 0.4222 |
| 0.4808 | 9.0 | 1935 | 1.4389 | 0.4444 |
| 0.4155 | 10.0 | 2150 | 1.7698 | 0.4667 |
| 0.4393 | 11.0 | 2365 | 1.4569 | 0.5778 |
| 0.4007 | 12.0 | 2580 | 2.1115 | 0.4 |
| 0.3758 | 13.0 | 2795 | 1.5230 | 0.5556 |
| 0.3244 | 14.0 | 3010 | 2.2901 | 0.4444 |
| 0.3063 | 15.0 | 3225 | 2.0129 | 0.4889 |
| 0.3072 | 16.0 | 3440 | 2.2969 | 0.5333 |
| 0.2444 | 17.0 | 3655 | 2.5054 | 0.4667 |
| 0.2293 | 18.0 | 3870 | 2.3449 | 0.4889 |
| 0.2391 | 19.0 | 4085 | 2.0401 | 0.6444 |
| 0.1843 | 20.0 | 4300 | 2.7271 | 0.5333 |
| 0.2073 | 21.0 | 4515 | 2.2599 | 0.4889 |
| 0.194 | 22.0 | 4730 | 3.1378 | 0.4444 |
| 0.2943 | 23.0 | 4945 | 2.7236 | 0.5333 |
| 0.2089 | 24.0 | 5160 | 2.5054 | 0.5778 |
| 0.2145 | 25.0 | 5375 | 3.8073 | 0.4667 |
| 0.1232 | 26.0 | 5590 | 3.5697 | 0.4889 |
| 0.1349 | 27.0 | 5805 | 3.5985 | 0.5333 |
| 0.1548 | 28.0 | 6020 | 3.0930 | 0.4889 |
| 0.0655 | 29.0 | 6235 | 4.3232 | 0.4889 |
| 0.1304 | 30.0 | 6450 | 3.6994 | 0.5333 |
| 0.0997 | 31.0 | 6665 | 3.7329 | 0.5333 |
| 0.0825 | 32.0 | 6880 | 3.4793 | 0.5333 |
| 0.154 | 33.0 | 7095 | 5.2562 | 0.4667 |
| 0.1206 | 34.0 | 7310 | 4.5299 | 0.4889 |
| 0.1019 | 35.0 | 7525 | 3.6522 | 0.5111 |
| 0.019 | 36.0 | 7740 | 3.9235 | 0.5333 |
| 0.0485 | 37.0 | 7955 | 4.7342 | 0.5556 |
| 0.0155 | 38.0 | 8170 | 4.4779 | 0.5778 |
| 0.0142 | 39.0 | 8385 | 4.2139 | 0.5556 |
| 0.0256 | 40.0 | 8600 | 5.0724 | 0.5333 |
| 0.0211 | 41.0 | 8815 | 4.8895 | 0.4889 |
| 0.019 | 42.0 | 9030 | 4.8291 | 0.5556 |
| 0.0047 | 43.0 | 9245 | 5.9102 | 0.5333 |
| 0.0027 | 44.0 | 9460 | 5.9480 | 0.5556 |
| 0.0009 | 45.0 | 9675 | 6.2260 | 0.5333 |
| 0.0008 | 46.0 | 9890 | 6.6029 | 0.5556 |
| 0.0001 | 47.0 | 10105 | 6.7925 | 0.5556 |
| 0.0001 | 48.0 | 10320 | 6.7039 | 0.5333 |
| 0.0 | 49.0 | 10535 | 7.0556 | 0.5333 |
| 0.0001 | 50.0 | 10750 | 7.0637 | 0.5333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_rms_0001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_0001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4716
- Accuracy: 0.8222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0732 | 1.0 | 215 | 0.6365 | 0.7556 |
| 0.102 | 2.0 | 430 | 1.5996 | 0.6889 |
| 0.0272 | 3.0 | 645 | 0.8191 | 0.8 |
| 0.0001 | 4.0 | 860 | 0.8601 | 0.8 |
| 0.1057 | 5.0 | 1075 | 0.9797 | 0.7778 |
| 0.0863 | 6.0 | 1290 | 1.8140 | 0.6889 |
| 0.0131 | 7.0 | 1505 | 1.1217 | 0.8 |
| 0.0 | 8.0 | 1720 | 1.2704 | 0.8 |
| 0.0 | 9.0 | 1935 | 1.3271 | 0.8222 |
| 0.0 | 10.0 | 2150 | 1.4318 | 0.8222 |
| 0.0 | 11.0 | 2365 | 1.5412 | 0.7778 |
| 0.0 | 12.0 | 2580 | 1.6553 | 0.8 |
| 0.0 | 13.0 | 2795 | 1.7666 | 0.8 |
| 0.0 | 14.0 | 3010 | 1.8788 | 0.8444 |
| 0.0 | 15.0 | 3225 | 1.9590 | 0.8444 |
| 0.0 | 16.0 | 3440 | 2.0334 | 0.8444 |
| 0.0 | 17.0 | 3655 | 2.0934 | 0.8444 |
| 0.0 | 18.0 | 3870 | 2.1509 | 0.8444 |
| 0.0 | 19.0 | 4085 | 2.2044 | 0.8444 |
| 0.0 | 20.0 | 4300 | 2.2497 | 0.8444 |
| 0.0 | 21.0 | 4515 | 2.2856 | 0.8444 |
| 0.0 | 22.0 | 4730 | 2.3133 | 0.8444 |
| 0.0 | 23.0 | 4945 | 2.3351 | 0.8444 |
| 0.0 | 24.0 | 5160 | 2.3530 | 0.8444 |
| 0.0 | 25.0 | 5375 | 2.3678 | 0.8444 |
| 0.0 | 26.0 | 5590 | 2.3804 | 0.8444 |
| 0.0 | 27.0 | 5805 | 2.3914 | 0.8444 |
| 0.0 | 28.0 | 6020 | 2.4010 | 0.8222 |
| 0.0 | 29.0 | 6235 | 2.4094 | 0.8222 |
| 0.0 | 30.0 | 6450 | 2.4170 | 0.8222 |
| 0.0 | 31.0 | 6665 | 2.4237 | 0.8222 |
| 0.0 | 32.0 | 6880 | 2.4297 | 0.8222 |
| 0.0 | 33.0 | 7095 | 2.4351 | 0.8222 |
| 0.0 | 34.0 | 7310 | 2.4400 | 0.8222 |
| 0.0 | 35.0 | 7525 | 2.4444 | 0.8222 |
| 0.0 | 36.0 | 7740 | 2.4484 | 0.8222 |
| 0.0 | 37.0 | 7955 | 2.4519 | 0.8222 |
| 0.0 | 38.0 | 8170 | 2.4551 | 0.8222 |
| 0.0 | 39.0 | 8385 | 2.4580 | 0.8222 |
| 0.0 | 40.0 | 8600 | 2.4605 | 0.8222 |
| 0.0 | 41.0 | 8815 | 2.4628 | 0.8222 |
| 0.0 | 42.0 | 9030 | 2.4647 | 0.8222 |
| 0.0 | 43.0 | 9245 | 2.4664 | 0.8222 |
| 0.0 | 44.0 | 9460 | 2.4679 | 0.8222 |
| 0.0 | 45.0 | 9675 | 2.4691 | 0.8222 |
| 0.0 | 46.0 | 9890 | 2.4700 | 0.8222 |
| 0.0 | 47.0 | 10105 | 2.4708 | 0.8222 |
| 0.0 | 48.0 | 10320 | 2.4713 | 0.8222 |
| 0.0 | 49.0 | 10535 | 2.4716 | 0.8222 |
| 0.0 | 50.0 | 10750 | 2.4716 | 0.8222 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
chanhua/autotrain-wo576-85kuw
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.0984116792678833
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
hkivancoral/hushem_40x_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_40x_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: 1.0440
- Accuracy: 0.6889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0975 | 1.0 | 215 | 1.3370 | 0.3778 |
| 0.8761 | 2.0 | 430 | 1.2895 | 0.4444 |
| 0.7359 | 3.0 | 645 | 1.2565 | 0.4889 |
| 0.6277 | 4.0 | 860 | 1.2398 | 0.5556 |
| 0.5094 | 5.0 | 1075 | 1.2052 | 0.5556 |
| 0.4187 | 6.0 | 1290 | 1.1950 | 0.5778 |
| 0.3909 | 7.0 | 1505 | 1.1310 | 0.6 |
| 0.3137 | 8.0 | 1720 | 1.1412 | 0.5556 |
| 0.2817 | 9.0 | 1935 | 1.0706 | 0.5778 |
| 0.2108 | 10.0 | 2150 | 1.0537 | 0.6 |
| 0.1785 | 11.0 | 2365 | 1.0606 | 0.5778 |
| 0.1677 | 12.0 | 2580 | 1.0202 | 0.5778 |
| 0.1602 | 13.0 | 2795 | 1.0251 | 0.5778 |
| 0.1355 | 14.0 | 3010 | 1.0164 | 0.6 |
| 0.1234 | 15.0 | 3225 | 1.0019 | 0.5778 |
| 0.0937 | 16.0 | 3440 | 0.9960 | 0.6 |
| 0.0963 | 17.0 | 3655 | 0.9708 | 0.5778 |
| 0.0998 | 18.0 | 3870 | 0.9907 | 0.5778 |
| 0.0604 | 19.0 | 4085 | 0.9932 | 0.6 |
| 0.0724 | 20.0 | 4300 | 0.9792 | 0.5556 |
| 0.0616 | 21.0 | 4515 | 0.9528 | 0.5556 |
| 0.0591 | 22.0 | 4730 | 0.9741 | 0.5556 |
| 0.0433 | 23.0 | 4945 | 0.9824 | 0.5556 |
| 0.0476 | 24.0 | 5160 | 0.9907 | 0.5556 |
| 0.0326 | 25.0 | 5375 | 0.9714 | 0.5778 |
| 0.0325 | 26.0 | 5590 | 0.9834 | 0.6 |
| 0.0352 | 27.0 | 5805 | 0.9903 | 0.5778 |
| 0.0319 | 28.0 | 6020 | 0.9831 | 0.5778 |
| 0.0242 | 29.0 | 6235 | 0.9872 | 0.6 |
| 0.0238 | 30.0 | 6450 | 1.0027 | 0.6222 |
| 0.0166 | 31.0 | 6665 | 0.9985 | 0.5778 |
| 0.0151 | 32.0 | 6880 | 1.0088 | 0.6 |
| 0.0176 | 33.0 | 7095 | 1.0180 | 0.6 |
| 0.0221 | 34.0 | 7310 | 1.0038 | 0.6444 |
| 0.0159 | 35.0 | 7525 | 0.9868 | 0.6667 |
| 0.0115 | 36.0 | 7740 | 1.0104 | 0.6444 |
| 0.017 | 37.0 | 7955 | 1.0128 | 0.6889 |
| 0.0105 | 38.0 | 8170 | 1.0250 | 0.6444 |
| 0.0144 | 39.0 | 8385 | 1.0115 | 0.6889 |
| 0.0092 | 40.0 | 8600 | 1.0202 | 0.6667 |
| 0.0131 | 41.0 | 8815 | 1.0296 | 0.6444 |
| 0.0108 | 42.0 | 9030 | 1.0274 | 0.6889 |
| 0.0089 | 43.0 | 9245 | 1.0423 | 0.6889 |
| 0.0153 | 44.0 | 9460 | 1.0420 | 0.6889 |
| 0.0077 | 45.0 | 9675 | 1.0387 | 0.6667 |
| 0.0096 | 46.0 | 9890 | 1.0413 | 0.6889 |
| 0.0073 | 47.0 | 10105 | 1.0431 | 0.6889 |
| 0.0112 | 48.0 | 10320 | 1.0453 | 0.6889 |
| 0.0085 | 49.0 | 10535 | 1.0438 | 0.6889 |
| 0.01 | 50.0 | 10750 | 1.0440 | 0.6889 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
chanhua/autotrain-82nel-cfd2f
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.06666666666666668
f1_micro: 0.20000000000000004
f1_weighted: 0.06666666666666668
precision_macro: 0.04
precision_micro: 0.2
precision_weighted: 0.04
recall_macro: 0.2
recall_micro: 0.2
recall_weighted: 0.2
accuracy: 0.2
|
[
"10_just_a_pure_cotton_quilt",
"11_cover_the_quilt_with_only_the_head_exposed",
"12_just_a_pure_face",
"13_cover_the_quilt_to_expose_the_head_and_shoulders",
"14_has_nothing_to_do_with_11_and_13_above"
] |
Yura32000/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6394
- Accuracy: 0.896
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7761 | 0.99 | 62 | 2.5927 | 0.824 |
| 1.8745 | 2.0 | 125 | 1.8134 | 0.868 |
| 1.5945 | 2.98 | 186 | 1.6394 | 0.896 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
hkivancoral/hushem_40x_deit_base_rms_001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 8.5594
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1732 | 1.0 | 215 | 1.0108 | 0.4667 |
| 0.7763 | 2.0 | 430 | 1.2138 | 0.5333 |
| 0.7021 | 3.0 | 645 | 1.2446 | 0.4 |
| 0.6002 | 4.0 | 860 | 1.7707 | 0.4444 |
| 0.4988 | 5.0 | 1075 | 2.1116 | 0.4667 |
| 0.4269 | 6.0 | 1290 | 2.3849 | 0.5556 |
| 0.3366 | 7.0 | 1505 | 2.4322 | 0.5556 |
| 0.2961 | 8.0 | 1720 | 3.2646 | 0.5556 |
| 0.2377 | 9.0 | 1935 | 3.1438 | 0.5333 |
| 0.2435 | 10.0 | 2150 | 3.6031 | 0.5778 |
| 0.2593 | 11.0 | 2365 | 3.5951 | 0.4889 |
| 0.1482 | 12.0 | 2580 | 3.8372 | 0.5111 |
| 0.1871 | 13.0 | 2795 | 3.7490 | 0.6222 |
| 0.1246 | 14.0 | 3010 | 3.7977 | 0.5333 |
| 0.166 | 15.0 | 3225 | 3.7321 | 0.5778 |
| 0.1672 | 16.0 | 3440 | 4.6413 | 0.4889 |
| 0.1752 | 17.0 | 3655 | 4.9330 | 0.5556 |
| 0.1214 | 18.0 | 3870 | 4.3615 | 0.5556 |
| 0.0488 | 19.0 | 4085 | 4.4231 | 0.5111 |
| 0.1336 | 20.0 | 4300 | 4.4451 | 0.5778 |
| 0.1002 | 21.0 | 4515 | 3.7455 | 0.5778 |
| 0.0734 | 22.0 | 4730 | 4.4970 | 0.5556 |
| 0.0322 | 23.0 | 4945 | 4.8990 | 0.5333 |
| 0.214 | 24.0 | 5160 | 5.1865 | 0.5778 |
| 0.1242 | 25.0 | 5375 | 5.0088 | 0.5333 |
| 0.0033 | 26.0 | 5590 | 4.9606 | 0.5556 |
| 0.0333 | 27.0 | 5805 | 4.4063 | 0.5778 |
| 0.0592 | 28.0 | 6020 | 4.1719 | 0.5556 |
| 0.0444 | 29.0 | 6235 | 6.2342 | 0.5111 |
| 0.0039 | 30.0 | 6450 | 5.9834 | 0.5333 |
| 0.003 | 31.0 | 6665 | 6.2329 | 0.5333 |
| 0.0008 | 32.0 | 6880 | 6.2499 | 0.6 |
| 0.1078 | 33.0 | 7095 | 5.2542 | 0.6222 |
| 0.0258 | 34.0 | 7310 | 6.7980 | 0.4889 |
| 0.0052 | 35.0 | 7525 | 6.6849 | 0.5333 |
| 0.0003 | 36.0 | 7740 | 6.1342 | 0.5556 |
| 0.0005 | 37.0 | 7955 | 5.4920 | 0.5778 |
| 0.0004 | 38.0 | 8170 | 5.3684 | 0.5778 |
| 0.0148 | 39.0 | 8385 | 5.3551 | 0.5556 |
| 0.0054 | 40.0 | 8600 | 7.4300 | 0.5111 |
| 0.0 | 41.0 | 8815 | 6.8539 | 0.5556 |
| 0.0 | 42.0 | 9030 | 6.8688 | 0.5556 |
| 0.0 | 43.0 | 9245 | 7.1702 | 0.5778 |
| 0.0 | 44.0 | 9460 | 7.4631 | 0.5778 |
| 0.0 | 45.0 | 9675 | 7.7338 | 0.5778 |
| 0.0 | 46.0 | 9890 | 7.9825 | 0.5778 |
| 0.0 | 47.0 | 10105 | 8.2172 | 0.5778 |
| 0.0 | 48.0 | 10320 | 8.4047 | 0.5778 |
| 0.0 | 49.0 | 10535 | 8.5267 | 0.5778 |
| 0.0 | 50.0 | 10750 | 8.5594 | 0.5778 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
chanhua/autotrain-xcbf5-99oqk
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.094420313835144
f1_macro: 0.45714285714285713
f1_micro: 0.5714285714285714
f1_weighted: 0.5061224489795919
precision_macro: 0.4666666666666666
precision_micro: 0.5714285714285714
precision_weighted: 0.5428571428571428
recall_macro: 0.5555555555555555
recall_micro: 0.5714285714285714
recall_weighted: 0.5714285714285714
accuracy: 0.5714285714285714
|
[
"11_cover_the_quilt_with_only_the_head_exposed",
"13_cover_the_quilt_to_expose_the_head_and_shoulders",
"14_has_nothing_to_do_with_11_and_13_above"
] |
hkivancoral/hushem_40x_deit_base_rms_0001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_0001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0887
- Accuracy: 0.7556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0825 | 1.0 | 215 | 1.5281 | 0.7111 |
| 0.0311 | 2.0 | 430 | 1.2158 | 0.8 |
| 0.0011 | 3.0 | 645 | 1.8306 | 0.6889 |
| 0.0414 | 4.0 | 860 | 2.0416 | 0.7333 |
| 0.0002 | 5.0 | 1075 | 2.3340 | 0.6444 |
| 0.0027 | 6.0 | 1290 | 1.1579 | 0.7556 |
| 0.0001 | 7.0 | 1505 | 2.3412 | 0.6889 |
| 0.0 | 8.0 | 1720 | 2.3885 | 0.7111 |
| 0.0 | 9.0 | 1935 | 2.4917 | 0.7333 |
| 0.0 | 10.0 | 2150 | 2.6169 | 0.7333 |
| 0.0 | 11.0 | 2365 | 2.7660 | 0.7333 |
| 0.0 | 12.0 | 2580 | 2.9176 | 0.7333 |
| 0.0 | 13.0 | 2795 | 3.0652 | 0.7333 |
| 0.0 | 14.0 | 3010 | 3.1998 | 0.7556 |
| 0.0 | 15.0 | 3225 | 3.3068 | 0.7556 |
| 0.0 | 16.0 | 3440 | 3.4034 | 0.7556 |
| 0.0 | 17.0 | 3655 | 3.4958 | 0.7556 |
| 0.0 | 18.0 | 3870 | 3.5902 | 0.7556 |
| 0.0 | 19.0 | 4085 | 3.6748 | 0.7556 |
| 0.0 | 20.0 | 4300 | 3.7449 | 0.7556 |
| 0.0 | 21.0 | 4515 | 3.7990 | 0.7556 |
| 0.0 | 22.0 | 4730 | 3.8408 | 0.7556 |
| 0.0 | 23.0 | 4945 | 3.8743 | 0.7556 |
| 0.0 | 24.0 | 5160 | 3.9017 | 0.7556 |
| 0.0 | 25.0 | 5375 | 3.9247 | 0.7556 |
| 0.0 | 26.0 | 5590 | 3.9444 | 0.7556 |
| 0.0 | 27.0 | 5805 | 3.9616 | 0.7556 |
| 0.0 | 28.0 | 6020 | 3.9766 | 0.7556 |
| 0.0 | 29.0 | 6235 | 3.9899 | 0.7556 |
| 0.0 | 30.0 | 6450 | 4.0018 | 0.7556 |
| 0.0 | 31.0 | 6665 | 4.0124 | 0.7556 |
| 0.0 | 32.0 | 6880 | 4.0219 | 0.7556 |
| 0.0 | 33.0 | 7095 | 4.0305 | 0.7556 |
| 0.0 | 34.0 | 7310 | 4.0382 | 0.7556 |
| 0.0 | 35.0 | 7525 | 4.0452 | 0.7556 |
| 0.0 | 36.0 | 7740 | 4.0514 | 0.7556 |
| 0.0 | 37.0 | 7955 | 4.0571 | 0.7556 |
| 0.0 | 38.0 | 8170 | 4.0622 | 0.7556 |
| 0.0 | 39.0 | 8385 | 4.0668 | 0.7556 |
| 0.0 | 40.0 | 8600 | 4.0708 | 0.7556 |
| 0.0 | 41.0 | 8815 | 4.0744 | 0.7556 |
| 0.0 | 42.0 | 9030 | 4.0776 | 0.7556 |
| 0.0 | 43.0 | 9245 | 4.0803 | 0.7556 |
| 0.0 | 44.0 | 9460 | 4.0826 | 0.7556 |
| 0.0 | 45.0 | 9675 | 4.0846 | 0.7556 |
| 0.0 | 46.0 | 9890 | 4.0861 | 0.7556 |
| 0.0 | 47.0 | 10105 | 4.0873 | 0.7556 |
| 0.0 | 48.0 | 10320 | 4.0881 | 0.7556 |
| 0.0 | 49.0 | 10535 | 4.0886 | 0.7556 |
| 0.0 | 50.0 | 10750 | 4.0887 | 0.7556 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 0.4899
- 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1798 | 1.0 | 217 | 1.2889 | 0.4186 |
| 1.0098 | 2.0 | 434 | 1.1067 | 0.6047 |
| 0.7827 | 3.0 | 651 | 0.9427 | 0.6977 |
| 0.6326 | 4.0 | 868 | 0.7917 | 0.6977 |
| 0.5443 | 5.0 | 1085 | 0.6647 | 0.7907 |
| 0.4438 | 6.0 | 1302 | 0.5810 | 0.8140 |
| 0.3761 | 7.0 | 1519 | 0.5185 | 0.8372 |
| 0.3386 | 8.0 | 1736 | 0.4778 | 0.8140 |
| 0.2796 | 9.0 | 1953 | 0.4431 | 0.8605 |
| 0.2037 | 10.0 | 2170 | 0.4372 | 0.8605 |
| 0.1624 | 11.0 | 2387 | 0.3943 | 0.8837 |
| 0.1477 | 12.0 | 2604 | 0.4019 | 0.8605 |
| 0.1485 | 13.0 | 2821 | 0.3856 | 0.8605 |
| 0.1192 | 14.0 | 3038 | 0.3686 | 0.8605 |
| 0.1115 | 15.0 | 3255 | 0.3722 | 0.8605 |
| 0.0891 | 16.0 | 3472 | 0.3567 | 0.8837 |
| 0.0776 | 17.0 | 3689 | 0.3631 | 0.8605 |
| 0.1039 | 18.0 | 3906 | 0.3600 | 0.8605 |
| 0.0608 | 19.0 | 4123 | 0.3514 | 0.8605 |
| 0.0639 | 20.0 | 4340 | 0.3706 | 0.8605 |
| 0.0555 | 21.0 | 4557 | 0.3773 | 0.8605 |
| 0.0552 | 22.0 | 4774 | 0.3713 | 0.8372 |
| 0.0457 | 23.0 | 4991 | 0.3749 | 0.8372 |
| 0.0383 | 24.0 | 5208 | 0.3901 | 0.8372 |
| 0.0332 | 25.0 | 5425 | 0.3933 | 0.8372 |
| 0.0322 | 26.0 | 5642 | 0.3995 | 0.8372 |
| 0.0278 | 27.0 | 5859 | 0.4012 | 0.8372 |
| 0.0212 | 28.0 | 6076 | 0.3938 | 0.8372 |
| 0.0224 | 29.0 | 6293 | 0.4080 | 0.8372 |
| 0.0218 | 30.0 | 6510 | 0.4237 | 0.8372 |
| 0.0278 | 31.0 | 6727 | 0.4231 | 0.8372 |
| 0.0212 | 32.0 | 6944 | 0.4330 | 0.8372 |
| 0.021 | 33.0 | 7161 | 0.4507 | 0.8372 |
| 0.0127 | 34.0 | 7378 | 0.4390 | 0.8372 |
| 0.0158 | 35.0 | 7595 | 0.4566 | 0.8372 |
| 0.0178 | 36.0 | 7812 | 0.4594 | 0.8372 |
| 0.0109 | 37.0 | 8029 | 0.4570 | 0.8372 |
| 0.0096 | 38.0 | 8246 | 0.4635 | 0.8372 |
| 0.0113 | 39.0 | 8463 | 0.4700 | 0.8372 |
| 0.0149 | 40.0 | 8680 | 0.4815 | 0.8372 |
| 0.0111 | 41.0 | 8897 | 0.4769 | 0.8372 |
| 0.0075 | 42.0 | 9114 | 0.4756 | 0.8372 |
| 0.0093 | 43.0 | 9331 | 0.4800 | 0.8372 |
| 0.009 | 44.0 | 9548 | 0.4851 | 0.8372 |
| 0.0065 | 45.0 | 9765 | 0.4808 | 0.8372 |
| 0.011 | 46.0 | 9982 | 0.4835 | 0.8372 |
| 0.0064 | 47.0 | 10199 | 0.4871 | 0.8372 |
| 0.0093 | 48.0 | 10416 | 0.4902 | 0.8372 |
| 0.0136 | 49.0 | 10633 | 0.4899 | 0.8372 |
| 0.0058 | 50.0 | 10850 | 0.4899 | 0.8372 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_rms_001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0356
- 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1943 | 1.0 | 217 | 1.3862 | 0.3488 |
| 1.2108 | 2.0 | 434 | 1.3456 | 0.3721 |
| 0.8764 | 3.0 | 651 | 1.3683 | 0.4884 |
| 0.7995 | 4.0 | 868 | 0.8441 | 0.5814 |
| 0.8665 | 5.0 | 1085 | 1.2083 | 0.5116 |
| 0.7433 | 6.0 | 1302 | 0.7858 | 0.7209 |
| 0.7205 | 7.0 | 1519 | 0.8439 | 0.6744 |
| 0.6415 | 8.0 | 1736 | 0.6198 | 0.6512 |
| 0.6773 | 9.0 | 1953 | 0.8169 | 0.6744 |
| 0.5449 | 10.0 | 2170 | 0.8224 | 0.6512 |
| 0.5225 | 11.0 | 2387 | 0.7556 | 0.7209 |
| 0.5268 | 12.0 | 2604 | 0.8703 | 0.6744 |
| 0.41 | 13.0 | 2821 | 0.7919 | 0.6512 |
| 0.4695 | 14.0 | 3038 | 0.9473 | 0.6744 |
| 0.3173 | 15.0 | 3255 | 1.2235 | 0.6512 |
| 0.3283 | 16.0 | 3472 | 1.3091 | 0.6512 |
| 0.3212 | 17.0 | 3689 | 1.0773 | 0.6047 |
| 0.3662 | 18.0 | 3906 | 0.9193 | 0.6279 |
| 0.3712 | 19.0 | 4123 | 0.9811 | 0.6744 |
| 0.3483 | 20.0 | 4340 | 1.5620 | 0.5814 |
| 0.2594 | 21.0 | 4557 | 1.8035 | 0.5814 |
| 0.3019 | 22.0 | 4774 | 1.3880 | 0.6744 |
| 0.2498 | 23.0 | 4991 | 1.6113 | 0.5814 |
| 0.2349 | 24.0 | 5208 | 1.2780 | 0.6047 |
| 0.1589 | 25.0 | 5425 | 1.6674 | 0.6512 |
| 0.2341 | 26.0 | 5642 | 1.6966 | 0.6512 |
| 0.1986 | 27.0 | 5859 | 1.4673 | 0.6047 |
| 0.1141 | 28.0 | 6076 | 1.6993 | 0.6512 |
| 0.1291 | 29.0 | 6293 | 2.0265 | 0.5581 |
| 0.1273 | 30.0 | 6510 | 1.8689 | 0.6279 |
| 0.0887 | 31.0 | 6727 | 1.4863 | 0.6977 |
| 0.101 | 32.0 | 6944 | 2.2258 | 0.6279 |
| 0.09 | 33.0 | 7161 | 1.6918 | 0.5814 |
| 0.063 | 34.0 | 7378 | 2.4040 | 0.5349 |
| 0.0263 | 35.0 | 7595 | 2.2869 | 0.5814 |
| 0.0357 | 36.0 | 7812 | 2.0118 | 0.6047 |
| 0.033 | 37.0 | 8029 | 2.5046 | 0.6279 |
| 0.0417 | 38.0 | 8246 | 2.0462 | 0.6512 |
| 0.0049 | 39.0 | 8463 | 3.1349 | 0.5814 |
| 0.0034 | 40.0 | 8680 | 2.4922 | 0.6279 |
| 0.0115 | 41.0 | 8897 | 2.7021 | 0.5581 |
| 0.0248 | 42.0 | 9114 | 3.1496 | 0.5116 |
| 0.0078 | 43.0 | 9331 | 2.6336 | 0.6279 |
| 0.0022 | 44.0 | 9548 | 3.2458 | 0.5349 |
| 0.0015 | 45.0 | 9765 | 3.3966 | 0.5349 |
| 0.0031 | 46.0 | 9982 | 4.1353 | 0.5116 |
| 0.0 | 47.0 | 10199 | 3.5481 | 0.5814 |
| 0.0002 | 48.0 | 10416 | 3.8712 | 0.5349 |
| 0.0 | 49.0 | 10633 | 4.0305 | 0.5581 |
| 0.0 | 50.0 | 10850 | 4.0356 | 0.5581 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 0.2388
- Accuracy: 0.8810
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2231 | 1.0 | 219 | 1.3846 | 0.2857 |
| 0.9485 | 2.0 | 438 | 1.1776 | 0.5238 |
| 0.8421 | 3.0 | 657 | 0.9985 | 0.6429 |
| 0.6802 | 4.0 | 876 | 0.8236 | 0.7381 |
| 0.5815 | 5.0 | 1095 | 0.6866 | 0.7857 |
| 0.5091 | 6.0 | 1314 | 0.5853 | 0.8095 |
| 0.3792 | 7.0 | 1533 | 0.5105 | 0.8333 |
| 0.3552 | 8.0 | 1752 | 0.4443 | 0.8333 |
| 0.3174 | 9.0 | 1971 | 0.4029 | 0.8810 |
| 0.2621 | 10.0 | 2190 | 0.3730 | 0.8571 |
| 0.2168 | 11.0 | 2409 | 0.3473 | 0.8571 |
| 0.2263 | 12.0 | 2628 | 0.3296 | 0.9048 |
| 0.1689 | 13.0 | 2847 | 0.3233 | 0.9048 |
| 0.171 | 14.0 | 3066 | 0.3040 | 0.8810 |
| 0.1176 | 15.0 | 3285 | 0.3059 | 0.8810 |
| 0.1241 | 16.0 | 3504 | 0.2811 | 0.8571 |
| 0.1343 | 17.0 | 3723 | 0.2712 | 0.8571 |
| 0.0953 | 18.0 | 3942 | 0.2802 | 0.8571 |
| 0.0918 | 19.0 | 4161 | 0.2700 | 0.8571 |
| 0.0691 | 20.0 | 4380 | 0.2755 | 0.8571 |
| 0.088 | 21.0 | 4599 | 0.2615 | 0.8571 |
| 0.0857 | 22.0 | 4818 | 0.2483 | 0.8571 |
| 0.0654 | 23.0 | 5037 | 0.2562 | 0.8571 |
| 0.0661 | 24.0 | 5256 | 0.2789 | 0.8571 |
| 0.0463 | 25.0 | 5475 | 0.2435 | 0.8571 |
| 0.0362 | 26.0 | 5694 | 0.2633 | 0.8571 |
| 0.0272 | 27.0 | 5913 | 0.2844 | 0.8571 |
| 0.041 | 28.0 | 6132 | 0.2942 | 0.8571 |
| 0.034 | 29.0 | 6351 | 0.2744 | 0.8571 |
| 0.0352 | 30.0 | 6570 | 0.2644 | 0.8810 |
| 0.0212 | 31.0 | 6789 | 0.2648 | 0.8810 |
| 0.0359 | 32.0 | 7008 | 0.2431 | 0.8810 |
| 0.0203 | 33.0 | 7227 | 0.2434 | 0.8810 |
| 0.0209 | 34.0 | 7446 | 0.2577 | 0.8810 |
| 0.0254 | 35.0 | 7665 | 0.2645 | 0.8810 |
| 0.0178 | 36.0 | 7884 | 0.2497 | 0.8810 |
| 0.0232 | 37.0 | 8103 | 0.2639 | 0.8810 |
| 0.015 | 38.0 | 8322 | 0.2391 | 0.8810 |
| 0.0246 | 39.0 | 8541 | 0.2615 | 0.8810 |
| 0.0228 | 40.0 | 8760 | 0.2445 | 0.8810 |
| 0.0203 | 41.0 | 8979 | 0.2448 | 0.8810 |
| 0.014 | 42.0 | 9198 | 0.2402 | 0.8810 |
| 0.0231 | 43.0 | 9417 | 0.2372 | 0.8810 |
| 0.0179 | 44.0 | 9636 | 0.2499 | 0.8810 |
| 0.0197 | 45.0 | 9855 | 0.2540 | 0.8810 |
| 0.0118 | 46.0 | 10074 | 0.2416 | 0.8810 |
| 0.0134 | 47.0 | 10293 | 0.2401 | 0.8810 |
| 0.0155 | 48.0 | 10512 | 0.2412 | 0.8810 |
| 0.0103 | 49.0 | 10731 | 0.2400 | 0.8810 |
| 0.0156 | 50.0 | 10950 | 0.2388 | 0.8810 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_rms_0001_fold3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_0001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3610
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0864 | 1.0 | 217 | 0.7004 | 0.8140 |
| 0.0159 | 2.0 | 434 | 0.8536 | 0.8372 |
| 0.116 | 3.0 | 651 | 0.4860 | 0.9070 |
| 0.0015 | 4.0 | 868 | 0.3942 | 0.9302 |
| 0.0089 | 5.0 | 1085 | 0.6012 | 0.8372 |
| 0.0001 | 6.0 | 1302 | 0.5930 | 0.8605 |
| 0.0006 | 7.0 | 1519 | 0.5592 | 0.8837 |
| 0.15 | 8.0 | 1736 | 0.5307 | 0.8837 |
| 0.0001 | 9.0 | 1953 | 0.5223 | 0.8372 |
| 0.0766 | 10.0 | 2170 | 0.7047 | 0.8372 |
| 0.0001 | 11.0 | 2387 | 1.3810 | 0.8140 |
| 0.0061 | 12.0 | 2604 | 1.1687 | 0.8140 |
| 0.0 | 13.0 | 2821 | 1.4554 | 0.8140 |
| 0.0 | 14.0 | 3038 | 1.4775 | 0.8372 |
| 0.0 | 15.0 | 3255 | 1.5402 | 0.8140 |
| 0.0 | 16.0 | 3472 | 1.6119 | 0.8140 |
| 0.0 | 17.0 | 3689 | 1.6931 | 0.8140 |
| 0.0 | 18.0 | 3906 | 1.7745 | 0.8140 |
| 0.0 | 19.0 | 4123 | 1.8507 | 0.8372 |
| 0.0 | 20.0 | 4340 | 1.9114 | 0.8372 |
| 0.0 | 21.0 | 4557 | 1.9677 | 0.8372 |
| 0.0 | 22.0 | 4774 | 2.0255 | 0.8372 |
| 0.0 | 23.0 | 4991 | 2.0805 | 0.8372 |
| 0.0 | 24.0 | 5208 | 2.1308 | 0.8372 |
| 0.0 | 25.0 | 5425 | 2.1719 | 0.8372 |
| 0.0 | 26.0 | 5642 | 2.2040 | 0.8372 |
| 0.0 | 27.0 | 5859 | 2.2288 | 0.8372 |
| 0.0 | 28.0 | 6076 | 2.2485 | 0.8372 |
| 0.0 | 29.0 | 6293 | 2.2646 | 0.8372 |
| 0.0 | 30.0 | 6510 | 2.2781 | 0.8372 |
| 0.0 | 31.0 | 6727 | 2.2896 | 0.8372 |
| 0.0 | 32.0 | 6944 | 2.2995 | 0.8372 |
| 0.0 | 33.0 | 7161 | 2.3082 | 0.8372 |
| 0.0 | 34.0 | 7378 | 2.3158 | 0.8372 |
| 0.0 | 35.0 | 7595 | 2.3224 | 0.8372 |
| 0.0 | 36.0 | 7812 | 2.3283 | 0.8372 |
| 0.0 | 37.0 | 8029 | 2.3335 | 0.8372 |
| 0.0 | 38.0 | 8246 | 2.3381 | 0.8372 |
| 0.0 | 39.0 | 8463 | 2.3422 | 0.8372 |
| 0.0 | 40.0 | 8680 | 2.3458 | 0.8372 |
| 0.0 | 41.0 | 8897 | 2.3489 | 0.8372 |
| 0.0 | 42.0 | 9114 | 2.3516 | 0.8372 |
| 0.0 | 43.0 | 9331 | 2.3540 | 0.8372 |
| 0.0 | 44.0 | 9548 | 2.3560 | 0.8372 |
| 0.0 | 45.0 | 9765 | 2.3576 | 0.8372 |
| 0.0 | 46.0 | 9982 | 2.3589 | 0.8372 |
| 0.0 | 47.0 | 10199 | 2.3599 | 0.8372 |
| 0.0 | 48.0 | 10416 | 2.3606 | 0.8372 |
| 0.0 | 49.0 | 10633 | 2.3610 | 0.8372 |
| 0.0 | 50.0 | 10850 | 2.3610 | 0.8372 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 0.5908
- Accuracy: 0.8293
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1602 | 1.0 | 220 | 1.4008 | 0.2927 |
| 0.8954 | 2.0 | 440 | 1.2274 | 0.3902 |
| 0.7859 | 3.0 | 660 | 1.0703 | 0.5366 |
| 0.6718 | 4.0 | 880 | 0.9455 | 0.6098 |
| 0.5505 | 5.0 | 1100 | 0.8399 | 0.6341 |
| 0.4372 | 6.0 | 1320 | 0.7728 | 0.7073 |
| 0.3616 | 7.0 | 1540 | 0.7172 | 0.7317 |
| 0.291 | 8.0 | 1760 | 0.7018 | 0.7317 |
| 0.2597 | 9.0 | 1980 | 0.6678 | 0.7317 |
| 0.2339 | 10.0 | 2200 | 0.6575 | 0.7073 |
| 0.2227 | 11.0 | 2420 | 0.6389 | 0.7073 |
| 0.179 | 12.0 | 2640 | 0.6500 | 0.7073 |
| 0.1598 | 13.0 | 2860 | 0.6290 | 0.7073 |
| 0.1448 | 14.0 | 3080 | 0.6491 | 0.6585 |
| 0.1209 | 15.0 | 3300 | 0.6174 | 0.7073 |
| 0.1192 | 16.0 | 3520 | 0.6084 | 0.7073 |
| 0.1037 | 17.0 | 3740 | 0.6013 | 0.7317 |
| 0.0848 | 18.0 | 3960 | 0.5985 | 0.7073 |
| 0.1048 | 19.0 | 4180 | 0.5896 | 0.7317 |
| 0.0665 | 20.0 | 4400 | 0.6043 | 0.7073 |
| 0.0723 | 21.0 | 4620 | 0.5932 | 0.7561 |
| 0.0444 | 22.0 | 4840 | 0.5749 | 0.8049 |
| 0.0448 | 23.0 | 5060 | 0.5862 | 0.7805 |
| 0.0396 | 24.0 | 5280 | 0.5758 | 0.8049 |
| 0.0378 | 25.0 | 5500 | 0.5566 | 0.8293 |
| 0.0428 | 26.0 | 5720 | 0.5740 | 0.8293 |
| 0.0345 | 27.0 | 5940 | 0.5631 | 0.8049 |
| 0.0515 | 28.0 | 6160 | 0.5844 | 0.8049 |
| 0.0324 | 29.0 | 6380 | 0.5872 | 0.8293 |
| 0.0292 | 30.0 | 6600 | 0.5789 | 0.8293 |
| 0.0208 | 31.0 | 6820 | 0.5688 | 0.8293 |
| 0.0421 | 32.0 | 7040 | 0.5703 | 0.8293 |
| 0.0246 | 33.0 | 7260 | 0.5663 | 0.8293 |
| 0.0318 | 34.0 | 7480 | 0.5726 | 0.8293 |
| 0.0151 | 35.0 | 7700 | 0.5751 | 0.8293 |
| 0.0169 | 36.0 | 7920 | 0.5772 | 0.8293 |
| 0.017 | 37.0 | 8140 | 0.5665 | 0.8293 |
| 0.0393 | 38.0 | 8360 | 0.5815 | 0.8293 |
| 0.0218 | 39.0 | 8580 | 0.5765 | 0.8293 |
| 0.0156 | 40.0 | 8800 | 0.5742 | 0.8293 |
| 0.0183 | 41.0 | 9020 | 0.5956 | 0.8293 |
| 0.0155 | 42.0 | 9240 | 0.5886 | 0.8293 |
| 0.0134 | 43.0 | 9460 | 0.5775 | 0.8293 |
| 0.0186 | 44.0 | 9680 | 0.5921 | 0.8293 |
| 0.0177 | 45.0 | 9900 | 0.5863 | 0.8293 |
| 0.0115 | 46.0 | 10120 | 0.5918 | 0.8293 |
| 0.0196 | 47.0 | 10340 | 0.5892 | 0.8293 |
| 0.0172 | 48.0 | 10560 | 0.5892 | 0.8293 |
| 0.0129 | 49.0 | 10780 | 0.5910 | 0.8293 |
| 0.0197 | 50.0 | 11000 | 0.5908 | 0.8293 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_rms_001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7808
- 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4095 | 1.0 | 219 | 1.4091 | 0.2381 |
| 1.3846 | 2.0 | 438 | 1.3865 | 0.2381 |
| 1.2802 | 3.0 | 657 | 1.3372 | 0.2381 |
| 1.1537 | 4.0 | 876 | 1.4032 | 0.2619 |
| 1.177 | 5.0 | 1095 | 1.3147 | 0.4286 |
| 1.1719 | 6.0 | 1314 | 0.9703 | 0.6667 |
| 1.0403 | 7.0 | 1533 | 1.2271 | 0.4762 |
| 0.9188 | 8.0 | 1752 | 0.9431 | 0.5714 |
| 0.8565 | 9.0 | 1971 | 1.0056 | 0.5952 |
| 0.8519 | 10.0 | 2190 | 0.7845 | 0.6429 |
| 0.7519 | 11.0 | 2409 | 0.7049 | 0.6905 |
| 0.8514 | 12.0 | 2628 | 0.6628 | 0.7857 |
| 0.8808 | 13.0 | 2847 | 0.8006 | 0.7381 |
| 0.796 | 14.0 | 3066 | 0.7332 | 0.6905 |
| 0.7213 | 15.0 | 3285 | 0.7486 | 0.6905 |
| 0.663 | 16.0 | 3504 | 0.4390 | 0.7857 |
| 0.5845 | 17.0 | 3723 | 0.9856 | 0.5952 |
| 0.5228 | 18.0 | 3942 | 0.6588 | 0.7381 |
| 0.5581 | 19.0 | 4161 | 0.6093 | 0.8571 |
| 0.518 | 20.0 | 4380 | 0.5316 | 0.6905 |
| 0.5058 | 21.0 | 4599 | 0.7052 | 0.7381 |
| 0.453 | 22.0 | 4818 | 0.6155 | 0.7143 |
| 0.4128 | 23.0 | 5037 | 0.7141 | 0.7381 |
| 0.44 | 24.0 | 5256 | 0.6896 | 0.7619 |
| 0.3933 | 25.0 | 5475 | 0.6353 | 0.7619 |
| 0.3648 | 26.0 | 5694 | 0.7225 | 0.8095 |
| 0.2677 | 27.0 | 5913 | 0.6987 | 0.8810 |
| 0.3023 | 28.0 | 6132 | 0.8143 | 0.8333 |
| 0.332 | 29.0 | 6351 | 0.8300 | 0.8333 |
| 0.2772 | 30.0 | 6570 | 0.6339 | 0.7619 |
| 0.1878 | 31.0 | 6789 | 0.6694 | 0.8333 |
| 0.2152 | 32.0 | 7008 | 0.7930 | 0.7619 |
| 0.2378 | 33.0 | 7227 | 0.7856 | 0.7619 |
| 0.1874 | 34.0 | 7446 | 0.6614 | 0.8571 |
| 0.2043 | 35.0 | 7665 | 0.7218 | 0.8095 |
| 0.122 | 36.0 | 7884 | 1.0415 | 0.8333 |
| 0.1837 | 37.0 | 8103 | 1.2016 | 0.7381 |
| 0.1148 | 38.0 | 8322 | 0.8289 | 0.7857 |
| 0.0825 | 39.0 | 8541 | 1.4711 | 0.7381 |
| 0.0828 | 40.0 | 8760 | 0.9405 | 0.8810 |
| 0.0736 | 41.0 | 8979 | 1.4104 | 0.8810 |
| 0.0864 | 42.0 | 9198 | 1.1297 | 0.8333 |
| 0.0176 | 43.0 | 9417 | 1.2293 | 0.7857 |
| 0.0392 | 44.0 | 9636 | 1.3878 | 0.8095 |
| 0.0272 | 45.0 | 9855 | 1.2021 | 0.8571 |
| 0.0125 | 46.0 | 10074 | 2.3102 | 0.7619 |
| 0.0149 | 47.0 | 10293 | 1.8621 | 0.7857 |
| 0.0032 | 48.0 | 10512 | 1.7899 | 0.8333 |
| 0.0016 | 49.0 | 10731 | 1.9528 | 0.8095 |
| 0.0001 | 50.0 | 10950 | 1.7808 | 0.8095 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_rms_0001_fold4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_0001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3583
- Accuracy: 0.9524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.104 | 1.0 | 219 | 0.2949 | 0.9286 |
| 0.01 | 2.0 | 438 | 0.1700 | 0.9524 |
| 0.0196 | 3.0 | 657 | 0.6452 | 0.8571 |
| 0.0007 | 4.0 | 876 | 0.1624 | 0.9762 |
| 0.0273 | 5.0 | 1095 | 0.4258 | 0.9048 |
| 0.0129 | 6.0 | 1314 | 0.1517 | 0.9524 |
| 0.0582 | 7.0 | 1533 | 0.6754 | 0.9048 |
| 0.0004 | 8.0 | 1752 | 0.2532 | 0.9286 |
| 0.0 | 9.0 | 1971 | 0.2443 | 0.9286 |
| 0.0 | 10.0 | 2190 | 0.2524 | 0.9286 |
| 0.0 | 11.0 | 2409 | 0.2616 | 0.9286 |
| 0.0 | 12.0 | 2628 | 0.2792 | 0.9286 |
| 0.0 | 13.0 | 2847 | 0.2936 | 0.9524 |
| 0.0 | 14.0 | 3066 | 0.3043 | 0.9524 |
| 0.0 | 15.0 | 3285 | 0.3082 | 0.9524 |
| 0.0 | 16.0 | 3504 | 0.3110 | 0.9524 |
| 0.0 | 17.0 | 3723 | 0.3145 | 0.9524 |
| 0.0 | 18.0 | 3942 | 0.3171 | 0.9524 |
| 0.0 | 19.0 | 4161 | 0.3231 | 0.9524 |
| 0.0 | 20.0 | 4380 | 0.3294 | 0.9524 |
| 0.0 | 21.0 | 4599 | 0.3348 | 0.9524 |
| 0.0 | 22.0 | 4818 | 0.3389 | 0.9524 |
| 0.0 | 23.0 | 5037 | 0.3420 | 0.9524 |
| 0.0 | 24.0 | 5256 | 0.3443 | 0.9524 |
| 0.0 | 25.0 | 5475 | 0.3462 | 0.9524 |
| 0.0 | 26.0 | 5694 | 0.3477 | 0.9524 |
| 0.0 | 27.0 | 5913 | 0.3490 | 0.9524 |
| 0.0 | 28.0 | 6132 | 0.3502 | 0.9524 |
| 0.0 | 29.0 | 6351 | 0.3512 | 0.9524 |
| 0.0 | 30.0 | 6570 | 0.3521 | 0.9524 |
| 0.0 | 31.0 | 6789 | 0.3528 | 0.9524 |
| 0.0 | 32.0 | 7008 | 0.3535 | 0.9524 |
| 0.0 | 33.0 | 7227 | 0.3541 | 0.9524 |
| 0.0 | 34.0 | 7446 | 0.3547 | 0.9524 |
| 0.0 | 35.0 | 7665 | 0.3552 | 0.9524 |
| 0.0 | 36.0 | 7884 | 0.3556 | 0.9524 |
| 0.0 | 37.0 | 8103 | 0.3560 | 0.9524 |
| 0.0 | 38.0 | 8322 | 0.3564 | 0.9524 |
| 0.0 | 39.0 | 8541 | 0.3567 | 0.9524 |
| 0.0 | 40.0 | 8760 | 0.3570 | 0.9524 |
| 0.0 | 41.0 | 8979 | 0.3573 | 0.9524 |
| 0.0 | 42.0 | 9198 | 0.3575 | 0.9524 |
| 0.0 | 43.0 | 9417 | 0.3577 | 0.9524 |
| 0.0 | 44.0 | 9636 | 0.3578 | 0.9524 |
| 0.0 | 45.0 | 9855 | 0.3580 | 0.9524 |
| 0.0 | 46.0 | 10074 | 0.3581 | 0.9524 |
| 0.0 | 47.0 | 10293 | 0.3582 | 0.9524 |
| 0.0 | 48.0 | 10512 | 0.3582 | 0.9524 |
| 0.0 | 49.0 | 10731 | 0.3583 | 0.9524 |
| 0.0 | 50.0 | 10950 | 0.3583 | 0.9524 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 1.1471
- 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4892 | 1.0 | 215 | 1.3892 | 0.2222 |
| 1.3775 | 2.0 | 430 | 1.3751 | 0.2889 |
| 1.3266 | 3.0 | 645 | 1.3631 | 0.3111 |
| 1.2619 | 4.0 | 860 | 1.3523 | 0.3111 |
| 1.235 | 5.0 | 1075 | 1.3429 | 0.3111 |
| 1.1826 | 6.0 | 1290 | 1.3347 | 0.3778 |
| 1.2015 | 7.0 | 1505 | 1.3284 | 0.3778 |
| 1.2072 | 8.0 | 1720 | 1.3225 | 0.4 |
| 1.1254 | 9.0 | 1935 | 1.3170 | 0.4 |
| 1.1293 | 10.0 | 2150 | 1.3118 | 0.3556 |
| 1.0925 | 11.0 | 2365 | 1.3069 | 0.3778 |
| 1.0731 | 12.0 | 2580 | 1.3024 | 0.3778 |
| 1.0421 | 13.0 | 2795 | 1.2976 | 0.3778 |
| 1.0531 | 14.0 | 3010 | 1.2928 | 0.3778 |
| 1.0284 | 15.0 | 3225 | 1.2877 | 0.3778 |
| 1.0283 | 16.0 | 3440 | 1.2824 | 0.4 |
| 1.0283 | 17.0 | 3655 | 1.2767 | 0.4222 |
| 1.0038 | 18.0 | 3870 | 1.2712 | 0.4444 |
| 0.9952 | 19.0 | 4085 | 1.2654 | 0.4444 |
| 0.9413 | 20.0 | 4300 | 1.2587 | 0.4444 |
| 0.9562 | 21.0 | 4515 | 1.2529 | 0.4667 |
| 1.0163 | 22.0 | 4730 | 1.2467 | 0.4667 |
| 0.9391 | 23.0 | 4945 | 1.2401 | 0.4667 |
| 0.955 | 24.0 | 5160 | 1.2340 | 0.4889 |
| 0.9454 | 25.0 | 5375 | 1.2281 | 0.4889 |
| 0.9013 | 26.0 | 5590 | 1.2229 | 0.4889 |
| 0.8818 | 27.0 | 5805 | 1.2169 | 0.5111 |
| 0.8594 | 28.0 | 6020 | 1.2115 | 0.5111 |
| 0.8984 | 29.0 | 6235 | 1.2064 | 0.5111 |
| 0.8277 | 30.0 | 6450 | 1.2009 | 0.5111 |
| 0.8636 | 31.0 | 6665 | 1.1955 | 0.5111 |
| 0.8466 | 32.0 | 6880 | 1.1910 | 0.5111 |
| 0.8955 | 33.0 | 7095 | 1.1866 | 0.5111 |
| 0.817 | 34.0 | 7310 | 1.1825 | 0.5111 |
| 0.8132 | 35.0 | 7525 | 1.1781 | 0.5111 |
| 0.7914 | 36.0 | 7740 | 1.1742 | 0.5111 |
| 0.835 | 37.0 | 7955 | 1.1705 | 0.5111 |
| 0.8383 | 38.0 | 8170 | 1.1668 | 0.5111 |
| 0.828 | 39.0 | 8385 | 1.1638 | 0.5111 |
| 0.7822 | 40.0 | 8600 | 1.1606 | 0.5111 |
| 0.8243 | 41.0 | 8815 | 1.1580 | 0.5333 |
| 0.9371 | 42.0 | 9030 | 1.1556 | 0.5333 |
| 0.8482 | 43.0 | 9245 | 1.1533 | 0.5333 |
| 0.8054 | 44.0 | 9460 | 1.1516 | 0.5333 |
| 0.8152 | 45.0 | 9675 | 1.1501 | 0.5333 |
| 0.8013 | 46.0 | 9890 | 1.1489 | 0.5333 |
| 0.7786 | 47.0 | 10105 | 1.1481 | 0.5333 |
| 0.7918 | 48.0 | 10320 | 1.1474 | 0.5333 |
| 0.8671 | 49.0 | 10535 | 1.1471 | 0.5333 |
| 0.8286 | 50.0 | 10750 | 1.1471 | 0.5333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
yuanhuaisen/autotrain-9oj9k-0pndc
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.5109696984291077
f1_macro: 0.7355182828867041
f1_micro: 0.7840909090909092
f1_weighted: 0.7828294512505038
precision_macro: 0.7308866944925176
precision_micro: 0.7840909090909091
precision_weighted: 0.782664525741997
recall_macro: 0.7416666666666667
recall_micro: 0.7840909090909091
recall_weighted: 0.7840909090909091
accuracy: 0.7840909090909091
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
hkivancoral/hushem_40x_deit_base_rms_001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5781
- Accuracy: 0.7561
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2271 | 1.0 | 220 | 1.5032 | 0.3902 |
| 0.9545 | 2.0 | 440 | 1.5087 | 0.3902 |
| 0.8667 | 3.0 | 660 | 1.0714 | 0.4878 |
| 0.8154 | 4.0 | 880 | 0.7851 | 0.6098 |
| 0.6309 | 5.0 | 1100 | 1.0215 | 0.4878 |
| 0.5655 | 6.0 | 1320 | 0.8556 | 0.6098 |
| 0.4033 | 7.0 | 1540 | 0.7849 | 0.7073 |
| 0.3567 | 8.0 | 1760 | 1.1431 | 0.6585 |
| 0.3869 | 9.0 | 1980 | 0.7273 | 0.7561 |
| 0.2867 | 10.0 | 2200 | 0.9025 | 0.6341 |
| 0.2933 | 11.0 | 2420 | 1.0767 | 0.6829 |
| 0.2822 | 12.0 | 2640 | 0.9054 | 0.7561 |
| 0.2576 | 13.0 | 2860 | 1.1701 | 0.7073 |
| 0.1424 | 14.0 | 3080 | 1.2265 | 0.7317 |
| 0.1597 | 15.0 | 3300 | 1.2021 | 0.7317 |
| 0.0822 | 16.0 | 3520 | 1.5652 | 0.7073 |
| 0.0859 | 17.0 | 3740 | 1.0512 | 0.7561 |
| 0.1048 | 18.0 | 3960 | 1.9377 | 0.6341 |
| 0.0506 | 19.0 | 4180 | 1.4302 | 0.7561 |
| 0.0595 | 20.0 | 4400 | 1.2065 | 0.7073 |
| 0.1492 | 21.0 | 4620 | 1.7891 | 0.7073 |
| 0.0835 | 22.0 | 4840 | 1.5550 | 0.7561 |
| 0.0475 | 23.0 | 5060 | 1.2142 | 0.7317 |
| 0.0941 | 24.0 | 5280 | 1.4080 | 0.7073 |
| 0.0186 | 25.0 | 5500 | 1.5889 | 0.7561 |
| 0.0776 | 26.0 | 5720 | 1.8453 | 0.6829 |
| 0.0752 | 27.0 | 5940 | 1.5817 | 0.7805 |
| 0.0113 | 28.0 | 6160 | 1.6776 | 0.7805 |
| 0.0011 | 29.0 | 6380 | 2.1296 | 0.7317 |
| 0.0107 | 30.0 | 6600 | 1.9807 | 0.7073 |
| 0.0181 | 31.0 | 6820 | 1.9248 | 0.7073 |
| 0.0106 | 32.0 | 7040 | 2.5784 | 0.7317 |
| 0.0002 | 33.0 | 7260 | 1.8180 | 0.8049 |
| 0.0013 | 34.0 | 7480 | 1.5976 | 0.8049 |
| 0.0031 | 35.0 | 7700 | 1.9747 | 0.7317 |
| 0.0094 | 36.0 | 7920 | 2.4830 | 0.7317 |
| 0.0006 | 37.0 | 8140 | 2.9074 | 0.7561 |
| 0.0049 | 38.0 | 8360 | 2.6503 | 0.6829 |
| 0.0002 | 39.0 | 8580 | 2.4189 | 0.7561 |
| 0.0 | 40.0 | 8800 | 2.4124 | 0.7561 |
| 0.0 | 41.0 | 9020 | 2.5470 | 0.7561 |
| 0.0 | 42.0 | 9240 | 2.6196 | 0.7805 |
| 0.0 | 43.0 | 9460 | 2.7251 | 0.7805 |
| 0.0 | 44.0 | 9680 | 2.9457 | 0.7805 |
| 0.0 | 45.0 | 9900 | 3.1311 | 0.7805 |
| 0.0 | 46.0 | 10120 | 3.2547 | 0.7805 |
| 0.0 | 47.0 | 10340 | 3.3567 | 0.7317 |
| 0.0 | 48.0 | 10560 | 3.5689 | 0.7561 |
| 0.0 | 49.0 | 10780 | 3.5825 | 0.7561 |
| 0.0 | 50.0 | 11000 | 3.5781 | 0.7561 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_rms_0001_fold5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_0001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7342
- Accuracy: 0.8537
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0617 | 1.0 | 220 | 0.4998 | 0.8780 |
| 0.0015 | 2.0 | 440 | 0.4091 | 0.9024 |
| 0.067 | 3.0 | 660 | 0.7752 | 0.9024 |
| 0.0007 | 4.0 | 880 | 0.7710 | 0.8293 |
| 0.0006 | 5.0 | 1100 | 0.9905 | 0.8293 |
| 0.0109 | 6.0 | 1320 | 1.1163 | 0.8049 |
| 0.0 | 7.0 | 1540 | 1.0399 | 0.8049 |
| 0.0 | 8.0 | 1760 | 1.0747 | 0.8293 |
| 0.0 | 9.0 | 1980 | 1.1399 | 0.8537 |
| 0.0 | 10.0 | 2200 | 1.2260 | 0.8537 |
| 0.0 | 11.0 | 2420 | 1.3150 | 0.8537 |
| 0.0 | 12.0 | 2640 | 1.3880 | 0.8537 |
| 0.0 | 13.0 | 2860 | 1.4421 | 0.8537 |
| 0.0 | 14.0 | 3080 | 1.4689 | 0.8537 |
| 0.0 | 15.0 | 3300 | 1.4886 | 0.8537 |
| 0.0 | 16.0 | 3520 | 1.5214 | 0.8537 |
| 0.0 | 17.0 | 3740 | 1.5517 | 0.8537 |
| 0.0 | 18.0 | 3960 | 1.5796 | 0.8537 |
| 0.0 | 19.0 | 4180 | 1.6055 | 0.8537 |
| 0.0 | 20.0 | 4400 | 1.6255 | 0.8537 |
| 0.0 | 21.0 | 4620 | 1.6409 | 0.8537 |
| 0.0 | 22.0 | 4840 | 1.6535 | 0.8537 |
| 0.0 | 23.0 | 5060 | 1.6637 | 0.8537 |
| 0.0 | 24.0 | 5280 | 1.6723 | 0.8537 |
| 0.0 | 25.0 | 5500 | 1.6796 | 0.8537 |
| 0.0 | 26.0 | 5720 | 1.6858 | 0.8537 |
| 0.0 | 27.0 | 5940 | 1.6914 | 0.8537 |
| 0.0 | 28.0 | 6160 | 1.6963 | 0.8537 |
| 0.0 | 29.0 | 6380 | 1.7007 | 0.8537 |
| 0.0 | 30.0 | 6600 | 1.7046 | 0.8537 |
| 0.0 | 31.0 | 6820 | 1.7081 | 0.8537 |
| 0.0 | 32.0 | 7040 | 1.7112 | 0.8537 |
| 0.0 | 33.0 | 7260 | 1.7141 | 0.8537 |
| 0.0 | 34.0 | 7480 | 1.7167 | 0.8537 |
| 0.0 | 35.0 | 7700 | 1.7191 | 0.8537 |
| 0.0 | 36.0 | 7920 | 1.7212 | 0.8537 |
| 0.0 | 37.0 | 8140 | 1.7232 | 0.8537 |
| 0.0 | 38.0 | 8360 | 1.7249 | 0.8537 |
| 0.0 | 39.0 | 8580 | 1.7265 | 0.8537 |
| 0.0 | 40.0 | 8800 | 1.7279 | 0.8537 |
| 0.0 | 41.0 | 9020 | 1.7292 | 0.8537 |
| 0.0 | 42.0 | 9240 | 1.7303 | 0.8537 |
| 0.0 | 43.0 | 9460 | 1.7312 | 0.8537 |
| 0.0 | 44.0 | 9680 | 1.7321 | 0.8537 |
| 0.0 | 45.0 | 9900 | 1.7328 | 0.8537 |
| 0.0 | 46.0 | 10120 | 1.7333 | 0.8537 |
| 0.0 | 47.0 | 10340 | 1.7337 | 0.8537 |
| 0.0 | 48.0 | 10560 | 1.7340 | 0.8537 |
| 0.0 | 49.0 | 10780 | 1.7342 | 0.8537 |
| 0.0 | 50.0 | 11000 | 1.7342 | 0.8537 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 1.2757
- 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3997 | 1.0 | 215 | 1.4647 | 0.2444 |
| 1.3365 | 2.0 | 430 | 1.4330 | 0.2222 |
| 1.2917 | 3.0 | 645 | 1.4094 | 0.2444 |
| 1.2427 | 4.0 | 860 | 1.3927 | 0.2667 |
| 1.2441 | 5.0 | 1075 | 1.3794 | 0.2667 |
| 1.1872 | 6.0 | 1290 | 1.3684 | 0.2667 |
| 1.1795 | 7.0 | 1505 | 1.3589 | 0.3111 |
| 1.1209 | 8.0 | 1720 | 1.3504 | 0.3333 |
| 1.1403 | 9.0 | 1935 | 1.3427 | 0.3778 |
| 1.0825 | 10.0 | 2150 | 1.3360 | 0.3778 |
| 1.0205 | 11.0 | 2365 | 1.3306 | 0.3778 |
| 1.0287 | 12.0 | 2580 | 1.3256 | 0.4222 |
| 1.0526 | 13.0 | 2795 | 1.3201 | 0.4444 |
| 0.979 | 14.0 | 3010 | 1.3158 | 0.4444 |
| 1.009 | 15.0 | 3225 | 1.3119 | 0.4444 |
| 1.0242 | 16.0 | 3440 | 1.3068 | 0.4444 |
| 0.9586 | 17.0 | 3655 | 1.3041 | 0.4222 |
| 0.9705 | 18.0 | 3870 | 1.3009 | 0.4222 |
| 0.9559 | 19.0 | 4085 | 1.2993 | 0.4222 |
| 0.95 | 20.0 | 4300 | 1.2983 | 0.4444 |
| 0.9501 | 21.0 | 4515 | 1.2955 | 0.4444 |
| 0.9287 | 22.0 | 4730 | 1.2949 | 0.4444 |
| 0.8978 | 23.0 | 4945 | 1.2936 | 0.4444 |
| 0.8221 | 24.0 | 5160 | 1.2913 | 0.4444 |
| 0.8642 | 25.0 | 5375 | 1.2902 | 0.4444 |
| 0.8893 | 26.0 | 5590 | 1.2888 | 0.4444 |
| 0.8888 | 27.0 | 5805 | 1.2875 | 0.4444 |
| 0.8399 | 28.0 | 6020 | 1.2872 | 0.4444 |
| 0.8384 | 29.0 | 6235 | 1.2862 | 0.4444 |
| 0.8557 | 30.0 | 6450 | 1.2852 | 0.4444 |
| 0.8264 | 31.0 | 6665 | 1.2846 | 0.4444 |
| 0.7947 | 32.0 | 6880 | 1.2839 | 0.4222 |
| 0.7889 | 33.0 | 7095 | 1.2827 | 0.4222 |
| 0.829 | 34.0 | 7310 | 1.2822 | 0.4444 |
| 0.754 | 35.0 | 7525 | 1.2813 | 0.4444 |
| 0.7758 | 36.0 | 7740 | 1.2807 | 0.4444 |
| 0.8928 | 37.0 | 7955 | 1.2794 | 0.4444 |
| 0.734 | 38.0 | 8170 | 1.2794 | 0.4444 |
| 0.7594 | 39.0 | 8385 | 1.2785 | 0.4444 |
| 0.775 | 40.0 | 8600 | 1.2779 | 0.4444 |
| 0.7835 | 41.0 | 8815 | 1.2773 | 0.4667 |
| 0.7569 | 42.0 | 9030 | 1.2769 | 0.4667 |
| 0.7974 | 43.0 | 9245 | 1.2769 | 0.4667 |
| 0.7959 | 44.0 | 9460 | 1.2766 | 0.4667 |
| 0.8113 | 45.0 | 9675 | 1.2762 | 0.4667 |
| 0.7344 | 46.0 | 9890 | 1.2759 | 0.4667 |
| 0.7955 | 47.0 | 10105 | 1.2758 | 0.4667 |
| 0.7831 | 48.0 | 10320 | 1.2757 | 0.4667 |
| 0.7467 | 49.0 | 10535 | 1.2757 | 0.4667 |
| 0.8192 | 50.0 | 10750 | 1.2757 | 0.4667 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 0.9959
- Accuracy: 0.6744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4187 | 1.0 | 217 | 1.4585 | 0.3023 |
| 1.3875 | 2.0 | 434 | 1.4331 | 0.2791 |
| 1.3134 | 3.0 | 651 | 1.4120 | 0.3256 |
| 1.3061 | 4.0 | 868 | 1.3930 | 0.3488 |
| 1.3015 | 5.0 | 1085 | 1.3762 | 0.3721 |
| 1.2507 | 6.0 | 1302 | 1.3597 | 0.3721 |
| 1.2542 | 7.0 | 1519 | 1.3427 | 0.3721 |
| 1.2153 | 8.0 | 1736 | 1.3276 | 0.3721 |
| 1.2187 | 9.0 | 1953 | 1.3127 | 0.4186 |
| 1.1894 | 10.0 | 2170 | 1.2981 | 0.4186 |
| 1.1545 | 11.0 | 2387 | 1.2843 | 0.4186 |
| 1.1296 | 12.0 | 2604 | 1.2697 | 0.4419 |
| 1.1425 | 13.0 | 2821 | 1.2546 | 0.4651 |
| 1.1006 | 14.0 | 3038 | 1.2420 | 0.4884 |
| 1.101 | 15.0 | 3255 | 1.2295 | 0.4884 |
| 1.0751 | 16.0 | 3472 | 1.2159 | 0.4884 |
| 1.0907 | 17.0 | 3689 | 1.2031 | 0.4884 |
| 1.047 | 18.0 | 3906 | 1.1903 | 0.5116 |
| 1.0396 | 19.0 | 4123 | 1.1781 | 0.5581 |
| 1.0151 | 20.0 | 4340 | 1.1663 | 0.5581 |
| 1.0071 | 21.0 | 4557 | 1.1547 | 0.5581 |
| 0.9605 | 22.0 | 4774 | 1.1441 | 0.5814 |
| 0.9825 | 23.0 | 4991 | 1.1328 | 0.6047 |
| 0.9877 | 24.0 | 5208 | 1.1238 | 0.6047 |
| 0.944 | 25.0 | 5425 | 1.1139 | 0.6047 |
| 1.0028 | 26.0 | 5642 | 1.1046 | 0.6047 |
| 0.9583 | 27.0 | 5859 | 1.0948 | 0.6279 |
| 0.9319 | 28.0 | 6076 | 1.0861 | 0.6279 |
| 0.8861 | 29.0 | 6293 | 1.0779 | 0.6279 |
| 0.9631 | 30.0 | 6510 | 1.0704 | 0.6512 |
| 0.8801 | 31.0 | 6727 | 1.0625 | 0.6512 |
| 0.9404 | 32.0 | 6944 | 1.0548 | 0.6512 |
| 0.9252 | 33.0 | 7161 | 1.0485 | 0.6512 |
| 0.8258 | 34.0 | 7378 | 1.0422 | 0.6512 |
| 0.8739 | 35.0 | 7595 | 1.0361 | 0.6744 |
| 0.8975 | 36.0 | 7812 | 1.0306 | 0.6744 |
| 0.8371 | 37.0 | 8029 | 1.0260 | 0.6744 |
| 0.8695 | 38.0 | 8246 | 1.0212 | 0.6744 |
| 0.8346 | 39.0 | 8463 | 1.0171 | 0.6744 |
| 0.8685 | 40.0 | 8680 | 1.0135 | 0.6744 |
| 0.8448 | 41.0 | 8897 | 1.0098 | 0.6744 |
| 0.8514 | 42.0 | 9114 | 1.0067 | 0.6744 |
| 0.8326 | 43.0 | 9331 | 1.0041 | 0.6744 |
| 0.8323 | 44.0 | 9548 | 1.0018 | 0.6744 |
| 0.8178 | 45.0 | 9765 | 0.9998 | 0.6744 |
| 0.8479 | 46.0 | 9982 | 0.9982 | 0.6744 |
| 0.8512 | 47.0 | 10199 | 0.9971 | 0.6744 |
| 0.851 | 48.0 | 10416 | 0.9963 | 0.6744 |
| 0.839 | 49.0 | 10633 | 0.9959 | 0.6744 |
| 0.7968 | 50.0 | 10850 | 0.9959 | 0.6744 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
yuanhuaisen/autotrain-55x6s-5uwoq
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 0.12345679012345678
f1_micro: 0.22727272727272727
f1_weighted: 0.08417508417508417
precision_macro: 0.07575757575757576
precision_micro: 0.22727272727272727
precision_weighted: 0.051652892561983466
recall_macro: 0.3333333333333333
recall_micro: 0.22727272727272727
recall_weighted: 0.22727272727272727
accuracy: 0.22727272727272727
|
[
"11covered_with_a_quilt_and_only_the_head_exposed",
"12covered_with_a_quilt_and_exposed_other_parts_of_the_body",
"13has_nothing_to_do_with_11_and_12_above"
] |
hkivancoral/hushem_40x_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_40x_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: 1.0643
- 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4577 | 1.0 | 219 | 1.6228 | 0.1667 |
| 1.3428 | 2.0 | 438 | 1.5859 | 0.1429 |
| 1.3515 | 3.0 | 657 | 1.5534 | 0.1667 |
| 1.3164 | 4.0 | 876 | 1.5241 | 0.1905 |
| 1.262 | 5.0 | 1095 | 1.5001 | 0.1905 |
| 1.2747 | 6.0 | 1314 | 1.4781 | 0.1905 |
| 1.2005 | 7.0 | 1533 | 1.4587 | 0.1905 |
| 1.2174 | 8.0 | 1752 | 1.4397 | 0.2143 |
| 1.1711 | 9.0 | 1971 | 1.4197 | 0.2143 |
| 1.1443 | 10.0 | 2190 | 1.4007 | 0.2619 |
| 1.1123 | 11.0 | 2409 | 1.3821 | 0.2857 |
| 1.164 | 12.0 | 2628 | 1.3642 | 0.4048 |
| 1.0774 | 13.0 | 2847 | 1.3471 | 0.3810 |
| 1.1066 | 14.0 | 3066 | 1.3290 | 0.3810 |
| 1.055 | 15.0 | 3285 | 1.3135 | 0.4286 |
| 1.0496 | 16.0 | 3504 | 1.2984 | 0.4048 |
| 1.112 | 17.0 | 3723 | 1.2838 | 0.4286 |
| 1.0058 | 18.0 | 3942 | 1.2696 | 0.4286 |
| 1.0363 | 19.0 | 4161 | 1.2563 | 0.4286 |
| 1.0446 | 20.0 | 4380 | 1.2431 | 0.4286 |
| 1.0301 | 21.0 | 4599 | 1.2308 | 0.4286 |
| 1.0066 | 22.0 | 4818 | 1.2182 | 0.4524 |
| 0.9188 | 23.0 | 5037 | 1.2068 | 0.4524 |
| 0.9729 | 24.0 | 5256 | 1.1969 | 0.5238 |
| 0.9215 | 25.0 | 5475 | 1.1858 | 0.5238 |
| 0.9604 | 26.0 | 5694 | 1.1753 | 0.5476 |
| 0.9173 | 27.0 | 5913 | 1.1663 | 0.5714 |
| 0.9314 | 28.0 | 6132 | 1.1573 | 0.5714 |
| 0.8654 | 29.0 | 6351 | 1.1486 | 0.5714 |
| 0.9372 | 30.0 | 6570 | 1.1410 | 0.5714 |
| 0.9028 | 31.0 | 6789 | 1.1331 | 0.5714 |
| 0.9732 | 32.0 | 7008 | 1.1254 | 0.5714 |
| 0.9146 | 33.0 | 7227 | 1.1186 | 0.5714 |
| 0.8712 | 34.0 | 7446 | 1.1126 | 0.5714 |
| 0.8981 | 35.0 | 7665 | 1.1068 | 0.5714 |
| 0.8626 | 36.0 | 7884 | 1.1011 | 0.5714 |
| 0.884 | 37.0 | 8103 | 1.0956 | 0.5714 |
| 0.9119 | 38.0 | 8322 | 1.0906 | 0.5714 |
| 0.8378 | 39.0 | 8541 | 1.0862 | 0.5714 |
| 0.8095 | 40.0 | 8760 | 1.0823 | 0.5952 |
| 0.9067 | 41.0 | 8979 | 1.0785 | 0.5952 |
| 0.874 | 42.0 | 9198 | 1.0755 | 0.5714 |
| 0.8784 | 43.0 | 9417 | 1.0728 | 0.5714 |
| 0.8408 | 44.0 | 9636 | 1.0704 | 0.5714 |
| 0.8315 | 45.0 | 9855 | 1.0684 | 0.5714 |
| 0.8598 | 46.0 | 10074 | 1.0667 | 0.5714 |
| 0.8452 | 47.0 | 10293 | 1.0654 | 0.5952 |
| 0.863 | 48.0 | 10512 | 1.0647 | 0.5952 |
| 0.8292 | 49.0 | 10731 | 1.0643 | 0.5952 |
| 0.7869 | 50.0 | 10950 | 1.0643 | 0.5952 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_deit_base_rms_00001_fold1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_00001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1476
- Accuracy: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0061 | 1.0 | 215 | 0.5807 | 0.8667 |
| 0.0003 | 2.0 | 430 | 0.6211 | 0.8444 |
| 0.0001 | 3.0 | 645 | 0.8059 | 0.8222 |
| 0.0 | 4.0 | 860 | 0.8142 | 0.8444 |
| 0.0 | 5.0 | 1075 | 0.8755 | 0.8222 |
| 0.0 | 6.0 | 1290 | 0.9063 | 0.8444 |
| 0.0 | 7.0 | 1505 | 0.9620 | 0.8667 |
| 0.0 | 8.0 | 1720 | 0.9896 | 0.8667 |
| 0.0 | 9.0 | 1935 | 1.0818 | 0.8667 |
| 0.0 | 10.0 | 2150 | 1.1238 | 0.8667 |
| 0.0 | 11.0 | 2365 | 1.1782 | 0.8667 |
| 0.0 | 12.0 | 2580 | 1.2105 | 0.8667 |
| 0.0 | 13.0 | 2795 | 1.2229 | 0.8667 |
| 0.0 | 14.0 | 3010 | 1.2497 | 0.8667 |
| 0.0 | 15.0 | 3225 | 1.2395 | 0.8667 |
| 0.0 | 16.0 | 3440 | 1.2297 | 0.8889 |
| 0.0 | 17.0 | 3655 | 1.2382 | 0.8889 |
| 0.0 | 18.0 | 3870 | 1.2316 | 0.8667 |
| 0.0 | 19.0 | 4085 | 1.2222 | 0.8889 |
| 0.0 | 20.0 | 4300 | 1.2098 | 0.8889 |
| 0.0 | 21.0 | 4515 | 1.2108 | 0.8889 |
| 0.0 | 22.0 | 4730 | 1.2160 | 0.8667 |
| 0.0 | 23.0 | 4945 | 1.1914 | 0.8889 |
| 0.0 | 24.0 | 5160 | 1.2067 | 0.8667 |
| 0.0 | 25.0 | 5375 | 1.1881 | 0.8667 |
| 0.0 | 26.0 | 5590 | 1.1754 | 0.8667 |
| 0.0 | 27.0 | 5805 | 1.1838 | 0.8667 |
| 0.0 | 28.0 | 6020 | 1.1945 | 0.8444 |
| 0.0 | 29.0 | 6235 | 1.1919 | 0.8444 |
| 0.0 | 30.0 | 6450 | 1.1709 | 0.8444 |
| 0.0 | 31.0 | 6665 | 1.1710 | 0.8444 |
| 0.0 | 32.0 | 6880 | 1.1725 | 0.8444 |
| 0.0 | 33.0 | 7095 | 1.1648 | 0.8444 |
| 0.0 | 34.0 | 7310 | 1.1652 | 0.8444 |
| 0.0 | 35.0 | 7525 | 1.1685 | 0.8444 |
| 0.0 | 36.0 | 7740 | 1.1632 | 0.8444 |
| 0.0 | 37.0 | 7955 | 1.1596 | 0.8667 |
| 0.0 | 38.0 | 8170 | 1.1545 | 0.8667 |
| 0.0 | 39.0 | 8385 | 1.1576 | 0.8444 |
| 0.0 | 40.0 | 8600 | 1.1585 | 0.8667 |
| 0.0 | 41.0 | 8815 | 1.1448 | 0.8667 |
| 0.0 | 42.0 | 9030 | 1.1428 | 0.8667 |
| 0.0 | 43.0 | 9245 | 1.1526 | 0.8667 |
| 0.0 | 44.0 | 9460 | 1.1466 | 0.8667 |
| 0.0 | 45.0 | 9675 | 1.1454 | 0.8667 |
| 0.0 | 46.0 | 9890 | 1.1467 | 0.8667 |
| 0.0 | 47.0 | 10105 | 1.1498 | 0.8667 |
| 0.0 | 48.0 | 10320 | 1.1458 | 0.8667 |
| 0.0 | 49.0 | 10535 | 1.1472 | 0.8667 |
| 0.0 | 50.0 | 10750 | 1.1476 | 0.8667 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 2.1541
- Accuracy: 0.7556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1447 | 1.0 | 215 | 1.0279 | 0.7556 |
| 0.0939 | 2.0 | 430 | 1.6244 | 0.7333 |
| 0.025 | 3.0 | 645 | 1.1738 | 0.8 |
| 0.0505 | 4.0 | 860 | 1.8318 | 0.6667 |
| 0.02 | 5.0 | 1075 | 1.9882 | 0.7333 |
| 0.1072 | 6.0 | 1290 | 1.2076 | 0.7333 |
| 0.0633 | 7.0 | 1505 | 1.6747 | 0.7333 |
| 0.0604 | 8.0 | 1720 | 1.1018 | 0.8 |
| 0.0484 | 9.0 | 1935 | 2.2857 | 0.6444 |
| 0.0045 | 10.0 | 2150 | 2.0338 | 0.7556 |
| 0.0317 | 11.0 | 2365 | 2.1474 | 0.7556 |
| 0.0239 | 12.0 | 2580 | 1.5303 | 0.7778 |
| 0.0001 | 13.0 | 2795 | 2.3569 | 0.6444 |
| 0.0001 | 14.0 | 3010 | 2.2079 | 0.7556 |
| 0.0 | 15.0 | 3225 | 1.6648 | 0.7778 |
| 0.0065 | 16.0 | 3440 | 1.7779 | 0.7778 |
| 0.0 | 17.0 | 3655 | 1.9802 | 0.7556 |
| 0.0 | 18.0 | 3870 | 2.1669 | 0.7778 |
| 0.0001 | 19.0 | 4085 | 1.9508 | 0.8 |
| 0.0 | 20.0 | 4300 | 3.0396 | 0.6889 |
| 0.0 | 21.0 | 4515 | 1.8449 | 0.7333 |
| 0.0 | 22.0 | 4730 | 1.8614 | 0.7333 |
| 0.0 | 23.0 | 4945 | 1.8711 | 0.7333 |
| 0.0 | 24.0 | 5160 | 1.8758 | 0.7333 |
| 0.0 | 25.0 | 5375 | 1.8839 | 0.7333 |
| 0.0 | 26.0 | 5590 | 1.8890 | 0.7111 |
| 0.0 | 27.0 | 5805 | 1.8959 | 0.7111 |
| 0.0 | 28.0 | 6020 | 1.9021 | 0.7111 |
| 0.0 | 29.0 | 6235 | 1.9100 | 0.7111 |
| 0.0 | 30.0 | 6450 | 1.9180 | 0.7111 |
| 0.0 | 31.0 | 6665 | 1.9279 | 0.7111 |
| 0.0 | 32.0 | 6880 | 1.9382 | 0.7333 |
| 0.0 | 33.0 | 7095 | 1.9497 | 0.7333 |
| 0.0 | 34.0 | 7310 | 1.9619 | 0.7333 |
| 0.0 | 35.0 | 7525 | 1.9743 | 0.7333 |
| 0.0 | 36.0 | 7740 | 1.9878 | 0.7333 |
| 0.0 | 37.0 | 7955 | 2.0026 | 0.7333 |
| 0.0 | 38.0 | 8170 | 2.0159 | 0.7333 |
| 0.0 | 39.0 | 8385 | 2.0312 | 0.7333 |
| 0.0 | 40.0 | 8600 | 2.0457 | 0.7333 |
| 0.0 | 41.0 | 8815 | 2.0615 | 0.7333 |
| 0.0 | 42.0 | 9030 | 2.0758 | 0.7333 |
| 0.0 | 43.0 | 9245 | 2.0899 | 0.7333 |
| 0.0 | 44.0 | 9460 | 2.1029 | 0.7333 |
| 0.0 | 45.0 | 9675 | 2.1161 | 0.7333 |
| 0.0 | 46.0 | 9890 | 2.1279 | 0.7556 |
| 0.0 | 47.0 | 10105 | 2.1385 | 0.7556 |
| 0.0 | 48.0 | 10320 | 2.1469 | 0.7556 |
| 0.0 | 49.0 | 10535 | 2.1525 | 0.7556 |
| 0.0 | 50.0 | 10750 | 2.1541 | 0.7556 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 1.1237
- 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4318 | 1.0 | 220 | 1.6149 | 0.1707 |
| 1.3729 | 2.0 | 440 | 1.5770 | 0.1951 |
| 1.3561 | 3.0 | 660 | 1.5432 | 0.1707 |
| 1.3096 | 4.0 | 880 | 1.5177 | 0.1463 |
| 1.2756 | 5.0 | 1100 | 1.4946 | 0.1951 |
| 1.2485 | 6.0 | 1320 | 1.4738 | 0.2195 |
| 1.1719 | 7.0 | 1540 | 1.4569 | 0.2195 |
| 1.1324 | 8.0 | 1760 | 1.4401 | 0.2439 |
| 1.1522 | 9.0 | 1980 | 1.4251 | 0.2683 |
| 1.1548 | 10.0 | 2200 | 1.4097 | 0.2439 |
| 1.1099 | 11.0 | 2420 | 1.3960 | 0.2683 |
| 1.0799 | 12.0 | 2640 | 1.3821 | 0.2683 |
| 1.072 | 13.0 | 2860 | 1.3689 | 0.2927 |
| 1.0381 | 14.0 | 3080 | 1.3552 | 0.3171 |
| 1.0533 | 15.0 | 3300 | 1.3423 | 0.2927 |
| 1.0294 | 16.0 | 3520 | 1.3293 | 0.3171 |
| 1.004 | 17.0 | 3740 | 1.3169 | 0.3171 |
| 1.0138 | 18.0 | 3960 | 1.3048 | 0.3171 |
| 0.9902 | 19.0 | 4180 | 1.2935 | 0.3171 |
| 0.9047 | 20.0 | 4400 | 1.2817 | 0.3171 |
| 0.9213 | 21.0 | 4620 | 1.2707 | 0.3415 |
| 0.9555 | 22.0 | 4840 | 1.2595 | 0.3415 |
| 0.9607 | 23.0 | 5060 | 1.2491 | 0.3415 |
| 0.9344 | 24.0 | 5280 | 1.2391 | 0.3415 |
| 0.8688 | 25.0 | 5500 | 1.2295 | 0.3902 |
| 0.9175 | 26.0 | 5720 | 1.2208 | 0.4146 |
| 0.887 | 27.0 | 5940 | 1.2120 | 0.4390 |
| 0.905 | 28.0 | 6160 | 1.2036 | 0.4634 |
| 0.8477 | 29.0 | 6380 | 1.1957 | 0.4878 |
| 0.8486 | 30.0 | 6600 | 1.1887 | 0.4878 |
| 0.9203 | 31.0 | 6820 | 1.1822 | 0.4878 |
| 0.8893 | 32.0 | 7040 | 1.1760 | 0.4878 |
| 0.8469 | 33.0 | 7260 | 1.1702 | 0.4878 |
| 0.7935 | 34.0 | 7480 | 1.1645 | 0.4878 |
| 0.7904 | 35.0 | 7700 | 1.1593 | 0.4878 |
| 0.7994 | 36.0 | 7920 | 1.1544 | 0.5122 |
| 0.8205 | 37.0 | 8140 | 1.1499 | 0.5122 |
| 0.8696 | 38.0 | 8360 | 1.1458 | 0.5122 |
| 0.8262 | 39.0 | 8580 | 1.1421 | 0.5122 |
| 0.7584 | 40.0 | 8800 | 1.1388 | 0.5122 |
| 0.8457 | 41.0 | 9020 | 1.1358 | 0.5122 |
| 0.8307 | 42.0 | 9240 | 1.1331 | 0.5122 |
| 0.8183 | 43.0 | 9460 | 1.1307 | 0.5122 |
| 0.7718 | 44.0 | 9680 | 1.1287 | 0.5122 |
| 0.7855 | 45.0 | 9900 | 1.1271 | 0.5122 |
| 0.7875 | 46.0 | 10120 | 1.1258 | 0.5122 |
| 0.8109 | 47.0 | 10340 | 1.1248 | 0.5122 |
| 0.7297 | 48.0 | 10560 | 1.1241 | 0.5122 |
| 0.7352 | 49.0 | 10780 | 1.1238 | 0.5122 |
| 0.7935 | 50.0 | 11000 | 1.1237 | 0.5122 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 3.2920
- Accuracy: 0.7333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1775 | 1.0 | 215 | 1.6855 | 0.7111 |
| 0.1537 | 2.0 | 430 | 1.3524 | 0.7111 |
| 0.0687 | 3.0 | 645 | 2.1272 | 0.7333 |
| 0.0127 | 4.0 | 860 | 1.6443 | 0.7778 |
| 0.1338 | 5.0 | 1075 | 1.6931 | 0.7111 |
| 0.0106 | 6.0 | 1290 | 2.4757 | 0.6667 |
| 0.049 | 7.0 | 1505 | 2.6204 | 0.6889 |
| 0.0012 | 8.0 | 1720 | 1.8192 | 0.7333 |
| 0.0005 | 9.0 | 1935 | 1.7811 | 0.7556 |
| 0.0005 | 10.0 | 2150 | 2.2694 | 0.6889 |
| 0.0153 | 11.0 | 2365 | 1.6459 | 0.7333 |
| 0.0005 | 12.0 | 2580 | 1.8151 | 0.7778 |
| 0.0072 | 13.0 | 2795 | 1.9954 | 0.7556 |
| 0.0 | 14.0 | 3010 | 2.3490 | 0.7778 |
| 0.0073 | 15.0 | 3225 | 2.3310 | 0.7556 |
| 0.0002 | 16.0 | 3440 | 2.4489 | 0.6667 |
| 0.0001 | 17.0 | 3655 | 2.8003 | 0.6222 |
| 0.0 | 18.0 | 3870 | 2.6717 | 0.7333 |
| 0.0 | 19.0 | 4085 | 2.6848 | 0.7333 |
| 0.0 | 20.0 | 4300 | 2.6999 | 0.7333 |
| 0.0 | 21.0 | 4515 | 2.7166 | 0.7333 |
| 0.0 | 22.0 | 4730 | 2.7339 | 0.7333 |
| 0.0 | 23.0 | 4945 | 2.7519 | 0.7333 |
| 0.0 | 24.0 | 5160 | 2.7709 | 0.7333 |
| 0.0 | 25.0 | 5375 | 2.7907 | 0.7333 |
| 0.0 | 26.0 | 5590 | 2.8115 | 0.7333 |
| 0.0 | 27.0 | 5805 | 2.8327 | 0.7333 |
| 0.0 | 28.0 | 6020 | 2.8548 | 0.7333 |
| 0.0 | 29.0 | 6235 | 2.8773 | 0.7333 |
| 0.0 | 30.0 | 6450 | 2.9001 | 0.7333 |
| 0.0 | 31.0 | 6665 | 2.9234 | 0.7333 |
| 0.0 | 32.0 | 6880 | 2.9473 | 0.7333 |
| 0.0 | 33.0 | 7095 | 2.9712 | 0.7333 |
| 0.0 | 34.0 | 7310 | 2.9955 | 0.7333 |
| 0.0 | 35.0 | 7525 | 3.0198 | 0.7333 |
| 0.0 | 36.0 | 7740 | 3.0443 | 0.7333 |
| 0.0 | 37.0 | 7955 | 3.0682 | 0.7333 |
| 0.0 | 38.0 | 8170 | 3.0917 | 0.7333 |
| 0.0 | 39.0 | 8385 | 3.1162 | 0.7333 |
| 0.0 | 40.0 | 8600 | 3.1397 | 0.7333 |
| 0.0 | 41.0 | 8815 | 3.1619 | 0.7333 |
| 0.0 | 42.0 | 9030 | 3.1849 | 0.7333 |
| 0.0 | 43.0 | 9245 | 3.2057 | 0.7333 |
| 0.0 | 44.0 | 9460 | 3.2253 | 0.7333 |
| 0.0 | 45.0 | 9675 | 3.2434 | 0.7333 |
| 0.0 | 46.0 | 9890 | 3.2592 | 0.7333 |
| 0.0 | 47.0 | 10105 | 3.2727 | 0.7333 |
| 0.0 | 48.0 | 10320 | 3.2833 | 0.7333 |
| 0.0 | 49.0 | 10535 | 3.2902 | 0.7333 |
| 0.0 | 50.0 | 10750 | 3.2920 | 0.7333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
SaladSlayer00/new_model
|
<!-- 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. -->
# SaladSlayer00/new_model
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:
- Train Loss: 1.2935
- Validation Loss: 1.6986
- Validation Accuracy: 0.5619
- Epoch: 11
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:---------------:|:-------------------:|:-----:|
| 7.0613 | 4.8451 | 0.0134 | 0 |
| 4.6529 | 4.5201 | 0.0658 | 1 |
| 4.3215 | 4.1158 | 0.0992 | 2 |
| 3.8808 | 3.6981 | 0.1806 | 3 |
| 3.4497 | 3.2741 | 0.2553 | 4 |
| 3.0361 | 2.9681 | 0.3177 | 5 |
| 2.6734 | 2.6529 | 0.3690 | 6 |
| 2.3306 | 2.3803 | 0.4091 | 7 |
| 2.0284 | 2.1731 | 0.4738 | 8 |
| 1.7542 | 1.9839 | 0.4883 | 9 |
| 1.5084 | 1.8335 | 0.5284 | 10 |
| 1.2935 | 1.6986 | 0.5619 | 11 |
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
[
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"ben_affleck",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
"tom_ellis",
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"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"jason_momoa",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"johnny_depp",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
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"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"lionel_messi",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata",
"beatrice_insalata"
] |
hkivancoral/hushem_40x_deit_base_rms_00001_fold2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_40x_deit_base_rms_00001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2145
- Accuracy: 0.7778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0124 | 1.0 | 215 | 0.9769 | 0.7556 |
| 0.0003 | 2.0 | 430 | 1.1164 | 0.7556 |
| 0.0001 | 3.0 | 645 | 1.2999 | 0.7556 |
| 0.0 | 4.0 | 860 | 1.4171 | 0.7556 |
| 0.0 | 5.0 | 1075 | 1.5668 | 0.7778 |
| 0.0 | 6.0 | 1290 | 1.6850 | 0.7778 |
| 0.0 | 7.0 | 1505 | 1.8146 | 0.7778 |
| 0.0 | 8.0 | 1720 | 1.9589 | 0.7778 |
| 0.0 | 9.0 | 1935 | 2.1064 | 0.8 |
| 0.0 | 10.0 | 2150 | 2.2093 | 0.8 |
| 0.0 | 11.0 | 2365 | 2.2933 | 0.8 |
| 0.0 | 12.0 | 2580 | 2.3766 | 0.8 |
| 0.0 | 13.0 | 2795 | 2.4083 | 0.7778 |
| 0.0 | 14.0 | 3010 | 2.4352 | 0.7778 |
| 0.0 | 15.0 | 3225 | 2.4429 | 0.7778 |
| 0.0 | 16.0 | 3440 | 2.4405 | 0.7778 |
| 0.0 | 17.0 | 3655 | 2.4464 | 0.7778 |
| 0.0 | 18.0 | 3870 | 2.4337 | 0.7778 |
| 0.0 | 19.0 | 4085 | 2.4439 | 0.7778 |
| 0.0 | 20.0 | 4300 | 2.4205 | 0.7778 |
| 0.0 | 21.0 | 4515 | 2.4211 | 0.7778 |
| 0.0 | 22.0 | 4730 | 2.4042 | 0.7778 |
| 0.0 | 23.0 | 4945 | 2.3825 | 0.7778 |
| 0.0 | 24.0 | 5160 | 2.3776 | 0.7778 |
| 0.0 | 25.0 | 5375 | 2.3705 | 0.7778 |
| 0.0 | 26.0 | 5590 | 2.3563 | 0.7778 |
| 0.0 | 27.0 | 5805 | 2.3321 | 0.7778 |
| 0.0 | 28.0 | 6020 | 2.3284 | 0.7778 |
| 0.0 | 29.0 | 6235 | 2.3256 | 0.7778 |
| 0.0 | 30.0 | 6450 | 2.3054 | 0.7778 |
| 0.0 | 31.0 | 6665 | 2.2910 | 0.7778 |
| 0.0 | 32.0 | 6880 | 2.2963 | 0.7778 |
| 0.0 | 33.0 | 7095 | 2.2902 | 0.7778 |
| 0.0 | 34.0 | 7310 | 2.2745 | 0.7778 |
| 0.0 | 35.0 | 7525 | 2.2617 | 0.7778 |
| 0.0 | 36.0 | 7740 | 2.2546 | 0.7778 |
| 0.0 | 37.0 | 7955 | 2.2630 | 0.7778 |
| 0.0 | 38.0 | 8170 | 2.2430 | 0.7778 |
| 0.0 | 39.0 | 8385 | 2.2389 | 0.7778 |
| 0.0 | 40.0 | 8600 | 2.2433 | 0.7778 |
| 0.0 | 41.0 | 8815 | 2.2306 | 0.7778 |
| 0.0 | 42.0 | 9030 | 2.2253 | 0.7778 |
| 0.0 | 43.0 | 9245 | 2.2215 | 0.7778 |
| 0.0 | 44.0 | 9460 | 2.2183 | 0.7778 |
| 0.0 | 45.0 | 9675 | 2.2187 | 0.7778 |
| 0.0 | 46.0 | 9890 | 2.2190 | 0.7778 |
| 0.0 | 47.0 | 10105 | 2.2156 | 0.7778 |
| 0.0 | 48.0 | 10320 | 2.2160 | 0.7778 |
| 0.0 | 49.0 | 10535 | 2.2147 | 0.7778 |
| 0.0 | 50.0 | 10750 | 2.2145 | 0.7778 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
hkivancoral/hushem_40x_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_40x_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: 2.0018
- Accuracy: 0.8140
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2113 | 1.0 | 217 | 1.0639 | 0.7209 |
| 0.3075 | 2.0 | 434 | 0.6999 | 0.7442 |
| 0.0797 | 3.0 | 651 | 1.4112 | 0.7209 |
| 0.0613 | 4.0 | 868 | 0.8895 | 0.8605 |
| 0.0448 | 5.0 | 1085 | 0.8165 | 0.8140 |
| 0.0133 | 6.0 | 1302 | 1.2281 | 0.7907 |
| 0.0099 | 7.0 | 1519 | 1.6935 | 0.7907 |
| 0.0195 | 8.0 | 1736 | 0.9261 | 0.8837 |
| 0.0441 | 9.0 | 1953 | 0.6136 | 0.8605 |
| 0.0408 | 10.0 | 2170 | 1.0937 | 0.8605 |
| 0.0001 | 11.0 | 2387 | 1.3536 | 0.8372 |
| 0.0014 | 12.0 | 2604 | 1.5056 | 0.8372 |
| 0.0152 | 13.0 | 2821 | 1.3542 | 0.8140 |
| 0.0011 | 14.0 | 3038 | 1.1435 | 0.8140 |
| 0.0006 | 15.0 | 3255 | 1.7874 | 0.7907 |
| 0.0244 | 16.0 | 3472 | 1.5609 | 0.8140 |
| 0.0 | 17.0 | 3689 | 0.9143 | 0.9070 |
| 0.0 | 18.0 | 3906 | 1.3119 | 0.8140 |
| 0.0 | 19.0 | 4123 | 1.5264 | 0.8372 |
| 0.0024 | 20.0 | 4340 | 1.6055 | 0.8140 |
| 0.0 | 21.0 | 4557 | 1.7071 | 0.8140 |
| 0.0 | 22.0 | 4774 | 1.6943 | 0.8140 |
| 0.0 | 23.0 | 4991 | 1.6871 | 0.8140 |
| 0.0 | 24.0 | 5208 | 1.6854 | 0.8140 |
| 0.0 | 25.0 | 5425 | 1.6881 | 0.8140 |
| 0.0 | 26.0 | 5642 | 1.6930 | 0.8140 |
| 0.0 | 27.0 | 5859 | 1.6999 | 0.8140 |
| 0.0 | 28.0 | 6076 | 1.7095 | 0.8140 |
| 0.0 | 29.0 | 6293 | 1.7201 | 0.8140 |
| 0.0 | 30.0 | 6510 | 1.7321 | 0.8140 |
| 0.0 | 31.0 | 6727 | 1.7453 | 0.8140 |
| 0.0 | 32.0 | 6944 | 1.7591 | 0.8140 |
| 0.0 | 33.0 | 7161 | 1.7739 | 0.8140 |
| 0.0 | 34.0 | 7378 | 1.7893 | 0.8140 |
| 0.0 | 35.0 | 7595 | 1.8052 | 0.8140 |
| 0.0 | 36.0 | 7812 | 1.8215 | 0.8140 |
| 0.0 | 37.0 | 8029 | 1.8380 | 0.8140 |
| 0.0 | 38.0 | 8246 | 1.8542 | 0.8140 |
| 0.0 | 39.0 | 8463 | 1.8709 | 0.8140 |
| 0.0 | 40.0 | 8680 | 1.8874 | 0.8140 |
| 0.0 | 41.0 | 8897 | 1.9038 | 0.8140 |
| 0.0 | 42.0 | 9114 | 1.9194 | 0.8140 |
| 0.0 | 43.0 | 9331 | 1.9350 | 0.8140 |
| 0.0 | 44.0 | 9548 | 1.9494 | 0.8140 |
| 0.0 | 45.0 | 9765 | 1.9631 | 0.8140 |
| 0.0 | 46.0 | 9982 | 1.9753 | 0.8140 |
| 0.0 | 47.0 | 10199 | 1.9864 | 0.8140 |
| 0.0 | 48.0 | 10416 | 1.9949 | 0.8140 |
| 0.0 | 49.0 | 10633 | 2.0003 | 0.8140 |
| 0.0 | 50.0 | 10850 | 2.0018 | 0.8140 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
"01_normal",
"02_tapered",
"03_pyriform",
"04_amorphous"
] |
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