hushem_1x_deit_small_rms_00001_fold2
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: 1.5704
- Accuracy: 0.6889
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 |
|---|---|---|---|---|
| No log | 1.0 | 6 | 1.2845 | 0.4667 |
| 1.3044 | 2.0 | 12 | 1.1326 | 0.6 |
| 1.3044 | 3.0 | 18 | 1.1536 | 0.5778 |
| 0.5139 | 4.0 | 24 | 1.0519 | 0.6444 |
| 0.1664 | 5.0 | 30 | 1.2441 | 0.6222 |
| 0.1664 | 6.0 | 36 | 1.1538 | 0.6667 |
| 0.0485 | 7.0 | 42 | 1.2477 | 0.6444 |
| 0.0485 | 8.0 | 48 | 1.2419 | 0.6889 |
| 0.0142 | 9.0 | 54 | 1.3079 | 0.6889 |
| 0.0058 | 10.0 | 60 | 1.3704 | 0.6667 |
| 0.0058 | 11.0 | 66 | 1.3838 | 0.6667 |
| 0.0035 | 12.0 | 72 | 1.3979 | 0.6667 |
| 0.0035 | 13.0 | 78 | 1.4038 | 0.6889 |
| 0.0026 | 14.0 | 84 | 1.4193 | 0.6889 |
| 0.0021 | 15.0 | 90 | 1.4242 | 0.6889 |
| 0.0021 | 16.0 | 96 | 1.4453 | 0.6667 |
| 0.0018 | 17.0 | 102 | 1.4655 | 0.6667 |
| 0.0018 | 18.0 | 108 | 1.4682 | 0.6667 |
| 0.0016 | 19.0 | 114 | 1.4744 | 0.6889 |
| 0.0013 | 20.0 | 120 | 1.4877 | 0.6667 |
| 0.0013 | 21.0 | 126 | 1.4940 | 0.6889 |
| 0.0012 | 22.0 | 132 | 1.5021 | 0.6889 |
| 0.0012 | 23.0 | 138 | 1.5107 | 0.6889 |
| 0.0011 | 24.0 | 144 | 1.5166 | 0.6889 |
| 0.001 | 25.0 | 150 | 1.5213 | 0.6889 |
| 0.001 | 26.0 | 156 | 1.5258 | 0.6889 |
| 0.0009 | 27.0 | 162 | 1.5322 | 0.6889 |
| 0.0009 | 28.0 | 168 | 1.5366 | 0.6889 |
| 0.0008 | 29.0 | 174 | 1.5426 | 0.6889 |
| 0.0008 | 30.0 | 180 | 1.5478 | 0.6889 |
| 0.0008 | 31.0 | 186 | 1.5526 | 0.6889 |
| 0.0008 | 32.0 | 192 | 1.5537 | 0.6889 |
| 0.0008 | 33.0 | 198 | 1.5567 | 0.6889 |
| 0.0008 | 34.0 | 204 | 1.5600 | 0.6889 |
| 0.0007 | 35.0 | 210 | 1.5606 | 0.6889 |
| 0.0007 | 36.0 | 216 | 1.5623 | 0.6889 |
| 0.0007 | 37.0 | 222 | 1.5647 | 0.6889 |
| 0.0007 | 38.0 | 228 | 1.5675 | 0.6889 |
| 0.0007 | 39.0 | 234 | 1.5687 | 0.6889 |
| 0.0007 | 40.0 | 240 | 1.5698 | 0.6889 |
| 0.0007 | 41.0 | 246 | 1.5703 | 0.6889 |
| 0.0007 | 42.0 | 252 | 1.5704 | 0.6889 |
| 0.0007 | 43.0 | 258 | 1.5704 | 0.6889 |
| 0.0006 | 44.0 | 264 | 1.5704 | 0.6889 |
| 0.0007 | 45.0 | 270 | 1.5704 | 0.6889 |
| 0.0007 | 46.0 | 276 | 1.5704 | 0.6889 |
| 0.0006 | 47.0 | 282 | 1.5704 | 0.6889 |
| 0.0006 | 48.0 | 288 | 1.5704 | 0.6889 |
| 0.0007 | 49.0 | 294 | 1.5704 | 0.6889 |
| 0.0006 | 50.0 | 300 | 1.5704 | 0.6889 |
Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1