hushem_1x_deit_small_sgd_0001_fold5
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: 1.4475
- Accuracy: 0.2683
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 | 1.5161 | 0.2439 |
| 1.5359 | 2.0 | 12 | 1.5118 | 0.2439 |
| 1.5359 | 3.0 | 18 | 1.5076 | 0.2439 |
| 1.5171 | 4.0 | 24 | 1.5040 | 0.2439 |
| 1.5208 | 5.0 | 30 | 1.5002 | 0.2683 |
| 1.5208 | 6.0 | 36 | 1.4969 | 0.2683 |
| 1.5066 | 7.0 | 42 | 1.4937 | 0.2683 |
| 1.5066 | 8.0 | 48 | 1.4908 | 0.2683 |
| 1.4941 | 9.0 | 54 | 1.4878 | 0.2683 |
| 1.4953 | 10.0 | 60 | 1.4851 | 0.2683 |
| 1.4953 | 11.0 | 66 | 1.4825 | 0.2683 |
| 1.498 | 12.0 | 72 | 1.4798 | 0.2683 |
| 1.498 | 13.0 | 78 | 1.4774 | 0.2683 |
| 1.465 | 14.0 | 84 | 1.4753 | 0.2683 |
| 1.4811 | 15.0 | 90 | 1.4730 | 0.2683 |
| 1.4811 | 16.0 | 96 | 1.4709 | 0.2683 |
| 1.476 | 17.0 | 102 | 1.4689 | 0.2683 |
| 1.476 | 18.0 | 108 | 1.4672 | 0.2683 |
| 1.4977 | 19.0 | 114 | 1.4656 | 0.2683 |
| 1.4745 | 20.0 | 120 | 1.4639 | 0.2683 |
| 1.4745 | 21.0 | 126 | 1.4624 | 0.2683 |
| 1.4662 | 22.0 | 132 | 1.4609 | 0.2683 |
| 1.4662 | 23.0 | 138 | 1.4594 | 0.2683 |
| 1.4905 | 24.0 | 144 | 1.4581 | 0.2683 |
| 1.465 | 25.0 | 150 | 1.4568 | 0.2683 |
| 1.465 | 26.0 | 156 | 1.4556 | 0.2683 |
| 1.4499 | 27.0 | 162 | 1.4545 | 0.2683 |
| 1.4499 | 28.0 | 168 | 1.4535 | 0.2683 |
| 1.473 | 29.0 | 174 | 1.4527 | 0.2683 |
| 1.4704 | 30.0 | 180 | 1.4520 | 0.2683 |
| 1.4704 | 31.0 | 186 | 1.4512 | 0.2683 |
| 1.4654 | 32.0 | 192 | 1.4506 | 0.2683 |
| 1.4654 | 33.0 | 198 | 1.4500 | 0.2683 |
| 1.4322 | 34.0 | 204 | 1.4494 | 0.2683 |
| 1.459 | 35.0 | 210 | 1.4490 | 0.2683 |
| 1.459 | 36.0 | 216 | 1.4486 | 0.2683 |
| 1.4499 | 37.0 | 222 | 1.4482 | 0.2683 |
| 1.4499 | 38.0 | 228 | 1.4480 | 0.2683 |
| 1.4314 | 39.0 | 234 | 1.4477 | 0.2683 |
| 1.4745 | 40.0 | 240 | 1.4476 | 0.2683 |
| 1.4745 | 41.0 | 246 | 1.4476 | 0.2683 |
| 1.4482 | 42.0 | 252 | 1.4475 | 0.2683 |
| 1.4482 | 43.0 | 258 | 1.4475 | 0.2683 |
| 1.4526 | 44.0 | 264 | 1.4475 | 0.2683 |
| 1.4693 | 45.0 | 270 | 1.4475 | 0.2683 |
| 1.4693 | 46.0 | 276 | 1.4475 | 0.2683 |
| 1.4506 | 47.0 | 282 | 1.4475 | 0.2683 |
| 1.4506 | 48.0 | 288 | 1.4475 | 0.2683 |
| 1.4529 | 49.0 | 294 | 1.4475 | 0.2683 |
| 1.4667 | 50.0 | 300 | 1.4475 | 0.2683 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1