hushem_1x_deit_tiny_rms_lr001_fold1
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: 1.6888
- Accuracy: 0.3778
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 | 5.6136 | 0.2667 |
| 3.3911 | 2.0 | 12 | 1.9975 | 0.2444 |
| 3.3911 | 3.0 | 18 | 1.6108 | 0.2444 |
| 1.8768 | 4.0 | 24 | 1.5122 | 0.2667 |
| 1.5956 | 5.0 | 30 | 1.5150 | 0.2444 |
| 1.5956 | 6.0 | 36 | 1.8016 | 0.2444 |
| 1.4916 | 7.0 | 42 | 1.5690 | 0.4444 |
| 1.4916 | 8.0 | 48 | 1.4991 | 0.2667 |
| 1.4756 | 9.0 | 54 | 1.4574 | 0.2444 |
| 1.4478 | 10.0 | 60 | 1.4022 | 0.2444 |
| 1.4478 | 11.0 | 66 | 1.4406 | 0.2667 |
| 1.421 | 12.0 | 72 | 1.3666 | 0.2444 |
| 1.421 | 13.0 | 78 | 1.3200 | 0.2667 |
| 1.4101 | 14.0 | 84 | 1.4311 | 0.2444 |
| 1.366 | 15.0 | 90 | 1.5240 | 0.4 |
| 1.366 | 16.0 | 96 | 1.1533 | 0.5333 |
| 1.3311 | 17.0 | 102 | 1.1480 | 0.4667 |
| 1.3311 | 18.0 | 108 | 1.5207 | 0.2444 |
| 1.1912 | 19.0 | 114 | 1.6588 | 0.2889 |
| 1.1923 | 20.0 | 120 | 1.4947 | 0.4667 |
| 1.1923 | 21.0 | 126 | 1.3281 | 0.2444 |
| 1.1796 | 22.0 | 132 | 1.3569 | 0.3333 |
| 1.1796 | 23.0 | 138 | 1.7298 | 0.2444 |
| 1.1031 | 24.0 | 144 | 1.6401 | 0.3556 |
| 1.2056 | 25.0 | 150 | 1.3732 | 0.2889 |
| 1.2056 | 26.0 | 156 | 1.8651 | 0.3333 |
| 1.1039 | 27.0 | 162 | 1.2494 | 0.4667 |
| 1.1039 | 28.0 | 168 | 1.4459 | 0.2889 |
| 1.0659 | 29.0 | 174 | 1.4875 | 0.3333 |
| 1.0534 | 30.0 | 180 | 1.4599 | 0.3556 |
| 1.0534 | 31.0 | 186 | 1.3781 | 0.3556 |
| 1.0466 | 32.0 | 192 | 1.7266 | 0.4 |
| 1.0466 | 33.0 | 198 | 1.5340 | 0.4222 |
| 0.973 | 34.0 | 204 | 1.5429 | 0.3111 |
| 1.0226 | 35.0 | 210 | 1.6233 | 0.3111 |
| 1.0226 | 36.0 | 216 | 1.7204 | 0.3111 |
| 0.9676 | 37.0 | 222 | 1.7918 | 0.4 |
| 0.9676 | 38.0 | 228 | 1.6933 | 0.3333 |
| 0.8379 | 39.0 | 234 | 1.6708 | 0.4222 |
| 0.8938 | 40.0 | 240 | 1.6748 | 0.4 |
| 0.8938 | 41.0 | 246 | 1.6963 | 0.3778 |
| 0.8462 | 42.0 | 252 | 1.6888 | 0.3778 |
| 0.8462 | 43.0 | 258 | 1.6888 | 0.3778 |
| 0.8764 | 44.0 | 264 | 1.6888 | 0.3778 |
| 0.8676 | 45.0 | 270 | 1.6888 | 0.3778 |
| 0.8676 | 46.0 | 276 | 1.6888 | 0.3778 |
| 0.8418 | 47.0 | 282 | 1.6888 | 0.3778 |
| 0.8418 | 48.0 | 288 | 1.6888 | 0.3778 |
| 0.8647 | 49.0 | 294 | 1.6888 | 0.3778 |
| 0.872 | 50.0 | 300 | 1.6888 | 0.3778 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1