hushem_1x_deit_tiny_sgd_lr001_fold2
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: 1.4182
- Accuracy: 0.3556
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 6 | 1.6125 | 0.1333 |
| 1.5852 | 2.0 | 12 | 1.5871 | 0.1333 |
| 1.5852 | 3.0 | 18 | 1.5653 | 0.1556 |
| 1.5028 | 4.0 | 24 | 1.5474 | 0.1556 |
| 1.4795 | 5.0 | 30 | 1.5322 | 0.1556 |
| 1.4795 | 6.0 | 36 | 1.5188 | 0.1556 |
| 1.4252 | 7.0 | 42 | 1.5071 | 0.1556 |
| 1.4252 | 8.0 | 48 | 1.4989 | 0.1556 |
| 1.3707 | 9.0 | 54 | 1.4901 | 0.1778 |
| 1.365 | 10.0 | 60 | 1.4824 | 0.2 |
| 1.365 | 11.0 | 66 | 1.4748 | 0.2 |
| 1.3235 | 12.0 | 72 | 1.4694 | 0.2444 |
| 1.3235 | 13.0 | 78 | 1.4635 | 0.2444 |
| 1.3233 | 14.0 | 84 | 1.4596 | 0.2444 |
| 1.2774 | 15.0 | 90 | 1.4554 | 0.2444 |
| 1.2774 | 16.0 | 96 | 1.4518 | 0.2444 |
| 1.2584 | 17.0 | 102 | 1.4482 | 0.2667 |
| 1.2584 | 18.0 | 108 | 1.4450 | 0.2667 |
| 1.2788 | 19.0 | 114 | 1.4423 | 0.2667 |
| 1.2388 | 20.0 | 120 | 1.4398 | 0.2667 |
| 1.2388 | 21.0 | 126 | 1.4370 | 0.2889 |
| 1.2317 | 22.0 | 132 | 1.4351 | 0.2667 |
| 1.2317 | 23.0 | 138 | 1.4327 | 0.2889 |
| 1.2286 | 24.0 | 144 | 1.4312 | 0.2889 |
| 1.2033 | 25.0 | 150 | 1.4298 | 0.2889 |
| 1.2033 | 26.0 | 156 | 1.4283 | 0.3111 |
| 1.1965 | 27.0 | 162 | 1.4267 | 0.3111 |
| 1.1965 | 28.0 | 168 | 1.4258 | 0.3111 |
| 1.1963 | 29.0 | 174 | 1.4246 | 0.3111 |
| 1.1946 | 30.0 | 180 | 1.4236 | 0.3111 |
| 1.1946 | 31.0 | 186 | 1.4227 | 0.3333 |
| 1.1805 | 32.0 | 192 | 1.4218 | 0.3556 |
| 1.1805 | 33.0 | 198 | 1.4211 | 0.3556 |
| 1.1439 | 34.0 | 204 | 1.4203 | 0.3556 |
| 1.1699 | 35.0 | 210 | 1.4197 | 0.3556 |
| 1.1699 | 36.0 | 216 | 1.4193 | 0.3556 |
| 1.156 | 37.0 | 222 | 1.4190 | 0.3556 |
| 1.156 | 38.0 | 228 | 1.4187 | 0.3556 |
| 1.1475 | 39.0 | 234 | 1.4185 | 0.3556 |
| 1.1517 | 40.0 | 240 | 1.4183 | 0.3556 |
| 1.1517 | 41.0 | 246 | 1.4182 | 0.3556 |
| 1.1468 | 42.0 | 252 | 1.4182 | 0.3556 |
| 1.1468 | 43.0 | 258 | 1.4182 | 0.3556 |
| 1.1597 | 44.0 | 264 | 1.4182 | 0.3556 |
| 1.1542 | 45.0 | 270 | 1.4182 | 0.3556 |
| 1.1542 | 46.0 | 276 | 1.4182 | 0.3556 |
| 1.1604 | 47.0 | 282 | 1.4182 | 0.3556 |
| 1.1604 | 48.0 | 288 | 1.4182 | 0.3556 |
| 1.1576 | 49.0 | 294 | 1.4182 | 0.3556 |
| 1.143 | 50.0 | 300 | 1.4182 | 0.3556 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1