hushem_40x_deit_tiny_rms_0001_fold3
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.8398
- 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: 0.0001
- 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 |
|---|---|---|---|---|
| 0.1282 | 1.0 | 217 | 0.8975 | 0.8372 |
| 0.0209 | 2.0 | 434 | 0.8245 | 0.7674 |
| 0.0218 | 3.0 | 651 | 0.3670 | 0.9302 |
| 0.0205 | 4.0 | 868 | 1.2586 | 0.8372 |
| 0.0008 | 5.0 | 1085 | 0.8797 | 0.7907 |
| 0.0606 | 6.0 | 1302 | 1.1984 | 0.8605 |
| 0.1313 | 7.0 | 1519 | 1.3827 | 0.8372 |
| 0.0283 | 8.0 | 1736 | 0.8068 | 0.8605 |
| 0.0037 | 9.0 | 1953 | 1.0055 | 0.8837 |
| 0.0058 | 10.0 | 2170 | 1.7904 | 0.8140 |
| 0.0074 | 11.0 | 2387 | 1.3591 | 0.8140 |
| 0.0197 | 12.0 | 2604 | 1.3843 | 0.8605 |
| 0.0 | 13.0 | 2821 | 1.1075 | 0.8837 |
| 0.0155 | 14.0 | 3038 | 1.0442 | 0.8837 |
| 0.0002 | 15.0 | 3255 | 1.5088 | 0.8605 |
| 0.0288 | 16.0 | 3472 | 0.6806 | 0.8605 |
| 0.0057 | 17.0 | 3689 | 0.9450 | 0.8837 |
| 0.0 | 18.0 | 3906 | 1.1935 | 0.8372 |
| 0.0 | 19.0 | 4123 | 1.2605 | 0.8605 |
| 0.0 | 20.0 | 4340 | 1.0286 | 0.8140 |
| 0.0001 | 21.0 | 4557 | 0.9245 | 0.8605 |
| 0.0039 | 22.0 | 4774 | 1.3627 | 0.8372 |
| 0.0 | 23.0 | 4991 | 1.4994 | 0.8605 |
| 0.0001 | 24.0 | 5208 | 1.2134 | 0.7907 |
| 0.0001 | 25.0 | 5425 | 1.0301 | 0.8372 |
| 0.0 | 26.0 | 5642 | 1.0457 | 0.8837 |
| 0.0 | 27.0 | 5859 | 1.2728 | 0.8140 |
| 0.0 | 28.0 | 6076 | 1.0821 | 0.8837 |
| 0.0 | 29.0 | 6293 | 1.1243 | 0.8837 |
| 0.0 | 30.0 | 6510 | 1.1728 | 0.8837 |
| 0.0 | 31.0 | 6727 | 1.2386 | 0.8605 |
| 0.0 | 32.0 | 6944 | 1.3089 | 0.8605 |
| 0.0 | 33.0 | 7161 | 1.3713 | 0.8605 |
| 0.0 | 34.0 | 7378 | 1.4458 | 0.8605 |
| 0.0 | 35.0 | 7595 | 1.5096 | 0.8605 |
| 0.0 | 36.0 | 7812 | 1.5439 | 0.8605 |
| 0.0 | 37.0 | 8029 | 1.5992 | 0.8605 |
| 0.0 | 38.0 | 8246 | 1.6228 | 0.8605 |
| 0.0 | 39.0 | 8463 | 1.6686 | 0.8372 |
| 0.0 | 40.0 | 8680 | 1.7133 | 0.8372 |
| 0.0 | 41.0 | 8897 | 1.7502 | 0.8372 |
| 0.0 | 42.0 | 9114 | 1.7750 | 0.8372 |
| 0.0 | 43.0 | 9331 | 1.7947 | 0.8372 |
| 0.0 | 44.0 | 9548 | 1.8093 | 0.8372 |
| 0.0 | 45.0 | 9765 | 1.8201 | 0.8372 |
| 0.0 | 46.0 | 9982 | 1.8280 | 0.8372 |
| 0.0 | 47.0 | 10199 | 1.8337 | 0.8372 |
| 0.0 | 48.0 | 10416 | 1.8373 | 0.8372 |
| 0.0 | 49.0 | 10633 | 1.8394 | 0.8372 |
| 0.0 | 50.0 | 10850 | 1.8398 | 0.8140 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2