smids_3x_deit_small_adamax_00001_fold4
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0772
- Accuracy: 0.87
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 |
|---|---|---|---|---|
| 0.3797 | 1.0 | 225 | 0.3792 | 0.8467 |
| 0.1834 | 2.0 | 450 | 0.3415 | 0.8583 |
| 0.1521 | 3.0 | 675 | 0.3364 | 0.8767 |
| 0.1673 | 4.0 | 900 | 0.3475 | 0.8733 |
| 0.1145 | 5.0 | 1125 | 0.3681 | 0.8733 |
| 0.0638 | 6.0 | 1350 | 0.4237 | 0.8683 |
| 0.0638 | 7.0 | 1575 | 0.4385 | 0.875 |
| 0.1043 | 8.0 | 1800 | 0.4695 | 0.88 |
| 0.0499 | 9.0 | 2025 | 0.5316 | 0.8783 |
| 0.0307 | 10.0 | 2250 | 0.6026 | 0.87 |
| 0.0636 | 11.0 | 2475 | 0.6654 | 0.87 |
| 0.0182 | 12.0 | 2700 | 0.6645 | 0.8767 |
| 0.0011 | 13.0 | 2925 | 0.7026 | 0.8717 |
| 0.0033 | 14.0 | 3150 | 0.7540 | 0.8717 |
| 0.0005 | 15.0 | 3375 | 0.7821 | 0.87 |
| 0.0003 | 16.0 | 3600 | 0.7783 | 0.8767 |
| 0.0002 | 17.0 | 3825 | 0.8172 | 0.8717 |
| 0.0057 | 18.0 | 4050 | 0.8281 | 0.8717 |
| 0.0001 | 19.0 | 4275 | 0.8404 | 0.8717 |
| 0.0001 | 20.0 | 4500 | 0.8657 | 0.8733 |
| 0.0001 | 21.0 | 4725 | 0.8953 | 0.875 |
| 0.0001 | 22.0 | 4950 | 0.9021 | 0.87 |
| 0.0001 | 23.0 | 5175 | 0.9104 | 0.8733 |
| 0.0001 | 24.0 | 5400 | 0.9154 | 0.8733 |
| 0.0001 | 25.0 | 5625 | 0.9415 | 0.87 |
| 0.0 | 26.0 | 5850 | 0.9539 | 0.8733 |
| 0.0 | 27.0 | 6075 | 0.9672 | 0.87 |
| 0.0001 | 28.0 | 6300 | 0.9843 | 0.8683 |
| 0.0 | 29.0 | 6525 | 0.9786 | 0.87 |
| 0.0 | 30.0 | 6750 | 0.9994 | 0.8683 |
| 0.0 | 31.0 | 6975 | 0.9988 | 0.8733 |
| 0.0 | 32.0 | 7200 | 1.0032 | 0.8717 |
| 0.0 | 33.0 | 7425 | 1.0174 | 0.87 |
| 0.0 | 34.0 | 7650 | 1.0208 | 0.87 |
| 0.0 | 35.0 | 7875 | 1.0273 | 0.8717 |
| 0.0 | 36.0 | 8100 | 1.0325 | 0.8717 |
| 0.0 | 37.0 | 8325 | 1.0347 | 0.87 |
| 0.0 | 38.0 | 8550 | 1.0403 | 0.87 |
| 0.0 | 39.0 | 8775 | 1.0555 | 0.87 |
| 0.0 | 40.0 | 9000 | 1.0534 | 0.87 |
| 0.0 | 41.0 | 9225 | 1.0588 | 0.87 |
| 0.0084 | 42.0 | 9450 | 1.0654 | 0.87 |
| 0.0 | 43.0 | 9675 | 1.0680 | 0.87 |
| 0.0079 | 44.0 | 9900 | 1.0688 | 0.8683 |
| 0.0 | 45.0 | 10125 | 1.0727 | 0.87 |
| 0.0 | 46.0 | 10350 | 1.0741 | 0.87 |
| 0.0 | 47.0 | 10575 | 1.0751 | 0.87 |
| 0.0 | 48.0 | 10800 | 1.0765 | 0.87 |
| 0.0 | 49.0 | 11025 | 1.0763 | 0.8667 |
| 0.0 | 50.0 | 11250 | 1.0772 | 0.87 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2