hushem_1x_deit_small_sgd_00001_fold4
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: 1.4772
- Accuracy: 0.2857
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 | 1.4838 | 0.2619 |
| 1.5238 | 2.0 | 12 | 1.4835 | 0.2619 |
| 1.5238 | 3.0 | 18 | 1.4831 | 0.2619 |
| 1.5534 | 4.0 | 24 | 1.4828 | 0.2619 |
| 1.5381 | 5.0 | 30 | 1.4825 | 0.2619 |
| 1.5381 | 6.0 | 36 | 1.4822 | 0.2619 |
| 1.5402 | 7.0 | 42 | 1.4819 | 0.2619 |
| 1.5402 | 8.0 | 48 | 1.4816 | 0.2619 |
| 1.5343 | 9.0 | 54 | 1.4813 | 0.2619 |
| 1.5296 | 10.0 | 60 | 1.4811 | 0.2619 |
| 1.5296 | 11.0 | 66 | 1.4808 | 0.2619 |
| 1.5185 | 12.0 | 72 | 1.4805 | 0.2619 |
| 1.5185 | 13.0 | 78 | 1.4803 | 0.2619 |
| 1.5511 | 14.0 | 84 | 1.4801 | 0.2619 |
| 1.5137 | 15.0 | 90 | 1.4798 | 0.2619 |
| 1.5137 | 16.0 | 96 | 1.4796 | 0.2619 |
| 1.5299 | 17.0 | 102 | 1.4794 | 0.2619 |
| 1.5299 | 18.0 | 108 | 1.4792 | 0.2857 |
| 1.4899 | 19.0 | 114 | 1.4790 | 0.2857 |
| 1.5822 | 20.0 | 120 | 1.4789 | 0.2857 |
| 1.5822 | 21.0 | 126 | 1.4787 | 0.2857 |
| 1.5002 | 22.0 | 132 | 1.4786 | 0.2857 |
| 1.5002 | 23.0 | 138 | 1.4784 | 0.2857 |
| 1.5297 | 24.0 | 144 | 1.4783 | 0.2857 |
| 1.5406 | 25.0 | 150 | 1.4781 | 0.2857 |
| 1.5406 | 26.0 | 156 | 1.4780 | 0.2857 |
| 1.5241 | 27.0 | 162 | 1.4779 | 0.2857 |
| 1.5241 | 28.0 | 168 | 1.4778 | 0.2857 |
| 1.5379 | 29.0 | 174 | 1.4777 | 0.2857 |
| 1.5253 | 30.0 | 180 | 1.4776 | 0.2857 |
| 1.5253 | 31.0 | 186 | 1.4775 | 0.2857 |
| 1.549 | 32.0 | 192 | 1.4775 | 0.2857 |
| 1.549 | 33.0 | 198 | 1.4774 | 0.2857 |
| 1.5016 | 34.0 | 204 | 1.4774 | 0.2857 |
| 1.4996 | 35.0 | 210 | 1.4773 | 0.2857 |
| 1.4996 | 36.0 | 216 | 1.4773 | 0.2857 |
| 1.533 | 37.0 | 222 | 1.4772 | 0.2857 |
| 1.533 | 38.0 | 228 | 1.4772 | 0.2857 |
| 1.5136 | 39.0 | 234 | 1.4772 | 0.2857 |
| 1.5288 | 40.0 | 240 | 1.4772 | 0.2857 |
| 1.5288 | 41.0 | 246 | 1.4772 | 0.2857 |
| 1.5195 | 42.0 | 252 | 1.4772 | 0.2857 |
| 1.5195 | 43.0 | 258 | 1.4772 | 0.2857 |
| 1.5432 | 44.0 | 264 | 1.4772 | 0.2857 |
| 1.5238 | 45.0 | 270 | 1.4772 | 0.2857 |
| 1.5238 | 46.0 | 276 | 1.4772 | 0.2857 |
| 1.544 | 47.0 | 282 | 1.4772 | 0.2857 |
| 1.544 | 48.0 | 288 | 1.4772 | 0.2857 |
| 1.5337 | 49.0 | 294 | 1.4772 | 0.2857 |
| 1.5345 | 50.0 | 300 | 1.4772 | 0.2857 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1