smids_3x_deit_small_adamax_00001_fold5
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: 0.8664
- Accuracy: 0.8867
Model description
More information needed
Intended uses & 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 |
|---|---|---|---|---|
| 0.3734 | 1.0 | 225 | 0.3687 | 0.8433 |
| 0.2659 | 2.0 | 450 | 0.3135 | 0.8733 |
| 0.1931 | 3.0 | 675 | 0.3034 | 0.8767 |
| 0.143 | 4.0 | 900 | 0.2889 | 0.885 |
| 0.131 | 5.0 | 1125 | 0.3167 | 0.8833 |
| 0.0729 | 6.0 | 1350 | 0.3187 | 0.8867 |
| 0.0909 | 7.0 | 1575 | 0.3427 | 0.8833 |
| 0.081 | 8.0 | 1800 | 0.3465 | 0.885 |
| 0.0722 | 9.0 | 2025 | 0.4276 | 0.8833 |
| 0.0153 | 10.0 | 2250 | 0.5104 | 0.8767 |
| 0.0073 | 11.0 | 2475 | 0.5571 | 0.8817 |
| 0.0388 | 12.0 | 2700 | 0.5705 | 0.895 |
| 0.0128 | 13.0 | 2925 | 0.6043 | 0.8767 |
| 0.0017 | 14.0 | 3150 | 0.6203 | 0.8867 |
| 0.0016 | 15.0 | 3375 | 0.6463 | 0.8883 |
| 0.0003 | 16.0 | 3600 | 0.6591 | 0.8867 |
| 0.0001 | 17.0 | 3825 | 0.6914 | 0.8867 |
| 0.0002 | 18.0 | 4050 | 0.7053 | 0.885 |
| 0.0391 | 19.0 | 4275 | 0.7283 | 0.8867 |
| 0.0001 | 20.0 | 4500 | 0.7538 | 0.885 |
| 0.0001 | 21.0 | 4725 | 0.7610 | 0.8833 |
| 0.0001 | 22.0 | 4950 | 0.7539 | 0.885 |
| 0.0001 | 23.0 | 5175 | 0.7650 | 0.8883 |
| 0.0242 | 24.0 | 5400 | 0.7853 | 0.8817 |
| 0.0001 | 25.0 | 5625 | 0.7908 | 0.8833 |
| 0.0001 | 26.0 | 5850 | 0.7914 | 0.885 |
| 0.0001 | 27.0 | 6075 | 0.8136 | 0.8817 |
| 0.0 | 28.0 | 6300 | 0.8276 | 0.8817 |
| 0.0067 | 29.0 | 6525 | 0.8343 | 0.8817 |
| 0.0 | 30.0 | 6750 | 0.8302 | 0.885 |
| 0.0 | 31.0 | 6975 | 0.8207 | 0.885 |
| 0.0 | 32.0 | 7200 | 0.8405 | 0.8833 |
| 0.0056 | 33.0 | 7425 | 0.8445 | 0.8833 |
| 0.0 | 34.0 | 7650 | 0.8188 | 0.885 |
| 0.0026 | 35.0 | 7875 | 0.8428 | 0.885 |
| 0.0 | 36.0 | 8100 | 0.8431 | 0.8867 |
| 0.0072 | 37.0 | 8325 | 0.8465 | 0.8867 |
| 0.0 | 38.0 | 8550 | 0.8730 | 0.885 |
| 0.0 | 39.0 | 8775 | 0.8549 | 0.8867 |
| 0.0 | 40.0 | 9000 | 0.8617 | 0.8833 |
| 0.0 | 41.0 | 9225 | 0.8684 | 0.885 |
| 0.0 | 42.0 | 9450 | 0.8688 | 0.885 |
| 0.0038 | 43.0 | 9675 | 0.8699 | 0.885 |
| 0.0 | 44.0 | 9900 | 0.8640 | 0.8867 |
| 0.0 | 45.0 | 10125 | 0.8568 | 0.8867 |
| 0.0 | 46.0 | 10350 | 0.8661 | 0.885 |
| 0.0 | 47.0 | 10575 | 0.8676 | 0.8867 |
| 0.0 | 48.0 | 10800 | 0.8675 | 0.8867 |
| 0.0 | 49.0 | 11025 | 0.8664 | 0.8867 |
| 0.0 | 50.0 | 11250 | 0.8664 | 0.8867 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2