hushem_1x_deit_small_rms_00001_fold1
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: 0.8886
- 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 | 1.2079 | 0.6 |
| 1.2767 | 2.0 | 12 | 1.0689 | 0.5778 |
| 1.2767 | 3.0 | 18 | 0.8710 | 0.7111 |
| 0.496 | 4.0 | 24 | 0.7898 | 0.7111 |
| 0.172 | 5.0 | 30 | 0.8332 | 0.6889 |
| 0.172 | 6.0 | 36 | 1.0763 | 0.5556 |
| 0.0549 | 7.0 | 42 | 0.6935 | 0.7556 |
| 0.0549 | 8.0 | 48 | 0.7853 | 0.6667 |
| 0.0174 | 9.0 | 54 | 0.7910 | 0.6889 |
| 0.0066 | 10.0 | 60 | 0.7721 | 0.7111 |
| 0.0066 | 11.0 | 66 | 0.7433 | 0.6889 |
| 0.0038 | 12.0 | 72 | 0.7425 | 0.6889 |
| 0.0038 | 13.0 | 78 | 0.7897 | 0.6667 |
| 0.003 | 14.0 | 84 | 0.8070 | 0.6889 |
| 0.0022 | 15.0 | 90 | 0.8283 | 0.6889 |
| 0.0022 | 16.0 | 96 | 0.8010 | 0.6889 |
| 0.0019 | 17.0 | 102 | 0.8154 | 0.6889 |
| 0.0019 | 18.0 | 108 | 0.8316 | 0.6889 |
| 0.0016 | 19.0 | 114 | 0.8425 | 0.6889 |
| 0.0014 | 20.0 | 120 | 0.8372 | 0.6889 |
| 0.0014 | 21.0 | 126 | 0.8392 | 0.6889 |
| 0.0013 | 22.0 | 132 | 0.8471 | 0.6889 |
| 0.0013 | 23.0 | 138 | 0.8542 | 0.7111 |
| 0.0012 | 24.0 | 144 | 0.8532 | 0.6889 |
| 0.001 | 25.0 | 150 | 0.8592 | 0.6889 |
| 0.001 | 26.0 | 156 | 0.8620 | 0.6889 |
| 0.001 | 27.0 | 162 | 0.8623 | 0.6889 |
| 0.001 | 28.0 | 168 | 0.8684 | 0.6889 |
| 0.0009 | 29.0 | 174 | 0.8656 | 0.6667 |
| 0.0009 | 30.0 | 180 | 0.8670 | 0.6889 |
| 0.0009 | 31.0 | 186 | 0.8710 | 0.6889 |
| 0.0008 | 32.0 | 192 | 0.8750 | 0.6889 |
| 0.0008 | 33.0 | 198 | 0.8785 | 0.6667 |
| 0.0008 | 34.0 | 204 | 0.8810 | 0.6667 |
| 0.0008 | 35.0 | 210 | 0.8827 | 0.6667 |
| 0.0008 | 36.0 | 216 | 0.8847 | 0.6889 |
| 0.0007 | 37.0 | 222 | 0.8870 | 0.6889 |
| 0.0007 | 38.0 | 228 | 0.8890 | 0.6889 |
| 0.0007 | 39.0 | 234 | 0.8882 | 0.6889 |
| 0.0007 | 40.0 | 240 | 0.8885 | 0.6889 |
| 0.0007 | 41.0 | 246 | 0.8887 | 0.6889 |
| 0.0007 | 42.0 | 252 | 0.8886 | 0.6889 |
| 0.0007 | 43.0 | 258 | 0.8886 | 0.6889 |
| 0.0007 | 44.0 | 264 | 0.8886 | 0.6889 |
| 0.0007 | 45.0 | 270 | 0.8886 | 0.6889 |
| 0.0007 | 46.0 | 276 | 0.8886 | 0.6889 |
| 0.0007 | 47.0 | 282 | 0.8886 | 0.6889 |
| 0.0007 | 48.0 | 288 | 0.8886 | 0.6889 |
| 0.0007 | 49.0 | 294 | 0.8886 | 0.6889 |
| 0.0007 | 50.0 | 300 | 0.8886 | 0.6889 |
Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1