hushem_1x_deit_small_rms_0001_fold3
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: 1.6051
- Accuracy: 0.6279
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: 0.0001
- 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 | 1.6050 | 0.2326 |
| 1.8871 | 2.0 | 12 | 1.6526 | 0.2558 |
| 1.8871 | 3.0 | 18 | 1.5125 | 0.3488 |
| 1.4752 | 4.0 | 24 | 1.4964 | 0.2558 |
| 1.4078 | 5.0 | 30 | 1.4513 | 0.2558 |
| 1.4078 | 6.0 | 36 | 1.3471 | 0.2791 |
| 1.2466 | 7.0 | 42 | 1.9402 | 0.3023 |
| 1.2466 | 8.0 | 48 | 1.3364 | 0.3953 |
| 1.2272 | 9.0 | 54 | 1.3189 | 0.3721 |
| 1.0035 | 10.0 | 60 | 1.3183 | 0.4186 |
| 1.0035 | 11.0 | 66 | 1.1773 | 0.4419 |
| 0.7718 | 12.0 | 72 | 1.0313 | 0.5814 |
| 0.7718 | 13.0 | 78 | 1.0831 | 0.6279 |
| 0.4478 | 14.0 | 84 | 1.4838 | 0.5814 |
| 0.14 | 15.0 | 90 | 1.1904 | 0.6744 |
| 0.14 | 16.0 | 96 | 1.2473 | 0.6512 |
| 0.0804 | 17.0 | 102 | 1.4013 | 0.6977 |
| 0.0804 | 18.0 | 108 | 1.4032 | 0.6512 |
| 0.0101 | 19.0 | 114 | 1.4918 | 0.6977 |
| 0.0016 | 20.0 | 120 | 1.4874 | 0.6279 |
| 0.0016 | 21.0 | 126 | 1.4977 | 0.6279 |
| 0.0007 | 22.0 | 132 | 1.5076 | 0.6279 |
| 0.0007 | 23.0 | 138 | 1.5163 | 0.6512 |
| 0.0006 | 24.0 | 144 | 1.5254 | 0.6512 |
| 0.0005 | 25.0 | 150 | 1.5330 | 0.6512 |
| 0.0005 | 26.0 | 156 | 1.5401 | 0.6512 |
| 0.0004 | 27.0 | 162 | 1.5491 | 0.6279 |
| 0.0004 | 28.0 | 168 | 1.5572 | 0.6279 |
| 0.0004 | 29.0 | 174 | 1.5632 | 0.6279 |
| 0.0003 | 30.0 | 180 | 1.5688 | 0.6279 |
| 0.0003 | 31.0 | 186 | 1.5748 | 0.6279 |
| 0.0003 | 32.0 | 192 | 1.5796 | 0.6279 |
| 0.0003 | 33.0 | 198 | 1.5848 | 0.6279 |
| 0.0003 | 34.0 | 204 | 1.5896 | 0.6279 |
| 0.0003 | 35.0 | 210 | 1.5928 | 0.6279 |
| 0.0003 | 36.0 | 216 | 1.5963 | 0.6279 |
| 0.0003 | 37.0 | 222 | 1.5989 | 0.6279 |
| 0.0003 | 38.0 | 228 | 1.6012 | 0.6279 |
| 0.0003 | 39.0 | 234 | 1.6030 | 0.6279 |
| 0.0002 | 40.0 | 240 | 1.6043 | 0.6279 |
| 0.0002 | 41.0 | 246 | 1.6049 | 0.6279 |
| 0.0002 | 42.0 | 252 | 1.6051 | 0.6279 |
| 0.0002 | 43.0 | 258 | 1.6051 | 0.6279 |
| 0.0002 | 44.0 | 264 | 1.6051 | 0.6279 |
| 0.0002 | 45.0 | 270 | 1.6051 | 0.6279 |
| 0.0002 | 46.0 | 276 | 1.6051 | 0.6279 |
| 0.0002 | 47.0 | 282 | 1.6051 | 0.6279 |
| 0.0002 | 48.0 | 288 | 1.6051 | 0.6279 |
| 0.0002 | 49.0 | 294 | 1.6051 | 0.6279 |
| 0.0002 | 50.0 | 300 | 1.6051 | 0.6279 |
Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1