hushem_1x_deit_tiny_rms_lr001_fold4
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: 1.1726
- Accuracy: 0.4524
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 | 5.8408 | 0.2619 |
| 4.7138 | 2.0 | 12 | 1.8632 | 0.2381 |
| 4.7138 | 3.0 | 18 | 1.9369 | 0.2619 |
| 1.8439 | 4.0 | 24 | 1.7584 | 0.2381 |
| 1.6449 | 5.0 | 30 | 1.4723 | 0.2619 |
| 1.6449 | 6.0 | 36 | 1.7187 | 0.2381 |
| 1.5171 | 7.0 | 42 | 1.4960 | 0.2381 |
| 1.5171 | 8.0 | 48 | 1.3962 | 0.2619 |
| 1.4701 | 9.0 | 54 | 1.4942 | 0.2619 |
| 1.4652 | 10.0 | 60 | 1.3642 | 0.2381 |
| 1.4652 | 11.0 | 66 | 1.4490 | 0.2619 |
| 1.4547 | 12.0 | 72 | 1.1912 | 0.4524 |
| 1.4547 | 13.0 | 78 | 1.4737 | 0.2857 |
| 1.3944 | 14.0 | 84 | 1.2170 | 0.4286 |
| 1.3536 | 15.0 | 90 | 1.3540 | 0.2381 |
| 1.3536 | 16.0 | 96 | 1.0819 | 0.6190 |
| 1.2835 | 17.0 | 102 | 1.1640 | 0.4286 |
| 1.2835 | 18.0 | 108 | 1.2309 | 0.3333 |
| 1.306 | 19.0 | 114 | 1.3288 | 0.2857 |
| 1.2522 | 20.0 | 120 | 1.4561 | 0.2857 |
| 1.2522 | 21.0 | 126 | 1.0774 | 0.4762 |
| 1.2491 | 22.0 | 132 | 1.1807 | 0.4286 |
| 1.2491 | 23.0 | 138 | 1.1668 | 0.3810 |
| 1.1882 | 24.0 | 144 | 1.2075 | 0.4286 |
| 1.2028 | 25.0 | 150 | 1.2635 | 0.3333 |
| 1.2028 | 26.0 | 156 | 1.1653 | 0.3810 |
| 1.1822 | 27.0 | 162 | 1.1741 | 0.4048 |
| 1.1822 | 28.0 | 168 | 1.4014 | 0.2619 |
| 1.1086 | 29.0 | 174 | 1.0259 | 0.5476 |
| 1.1111 | 30.0 | 180 | 1.1225 | 0.5238 |
| 1.1111 | 31.0 | 186 | 1.1813 | 0.5 |
| 1.0458 | 32.0 | 192 | 1.1678 | 0.4286 |
| 1.0458 | 33.0 | 198 | 1.1915 | 0.4048 |
| 1.1348 | 34.0 | 204 | 1.3148 | 0.5 |
| 0.9776 | 35.0 | 210 | 1.0082 | 0.5238 |
| 0.9776 | 36.0 | 216 | 0.9144 | 0.6190 |
| 0.9456 | 37.0 | 222 | 1.0677 | 0.4762 |
| 0.9456 | 38.0 | 228 | 1.0695 | 0.5238 |
| 0.8714 | 39.0 | 234 | 1.1982 | 0.4762 |
| 0.8643 | 40.0 | 240 | 1.1143 | 0.4048 |
| 0.8643 | 41.0 | 246 | 1.1270 | 0.4524 |
| 0.7971 | 42.0 | 252 | 1.1726 | 0.4524 |
| 0.7971 | 43.0 | 258 | 1.1726 | 0.4524 |
| 0.7662 | 44.0 | 264 | 1.1726 | 0.4524 |
| 0.7801 | 45.0 | 270 | 1.1726 | 0.4524 |
| 0.7801 | 46.0 | 276 | 1.1726 | 0.4524 |
| 0.7773 | 47.0 | 282 | 1.1726 | 0.4524 |
| 0.7773 | 48.0 | 288 | 1.1726 | 0.4524 |
| 0.7728 | 49.0 | 294 | 1.1726 | 0.4524 |
| 0.7828 | 50.0 | 300 | 1.1726 | 0.4524 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1