hushem_1x_deit_sgd_adamax_lr0001_fold3
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.4906
- Accuracy: 0.3023
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 | 1.5410 | 0.3023 |
| 1.6256 | 2.0 | 12 | 1.5383 | 0.3023 |
| 1.6256 | 3.0 | 18 | 1.5355 | 0.3023 |
| 1.6209 | 4.0 | 24 | 1.5329 | 0.3023 |
| 1.6213 | 5.0 | 30 | 1.5304 | 0.3023 |
| 1.6213 | 6.0 | 36 | 1.5279 | 0.3023 |
| 1.6054 | 7.0 | 42 | 1.5256 | 0.3023 |
| 1.6054 | 8.0 | 48 | 1.5235 | 0.3023 |
| 1.622 | 9.0 | 54 | 1.5216 | 0.3023 |
| 1.5637 | 10.0 | 60 | 1.5195 | 0.3023 |
| 1.5637 | 11.0 | 66 | 1.5177 | 0.3023 |
| 1.5868 | 12.0 | 72 | 1.5160 | 0.3023 |
| 1.5868 | 13.0 | 78 | 1.5142 | 0.3023 |
| 1.5951 | 14.0 | 84 | 1.5125 | 0.3023 |
| 1.5787 | 15.0 | 90 | 1.5108 | 0.3023 |
| 1.5787 | 16.0 | 96 | 1.5091 | 0.3023 |
| 1.5727 | 17.0 | 102 | 1.5077 | 0.3023 |
| 1.5727 | 18.0 | 108 | 1.5063 | 0.3023 |
| 1.5858 | 19.0 | 114 | 1.5049 | 0.3023 |
| 1.5652 | 20.0 | 120 | 1.5036 | 0.3023 |
| 1.5652 | 21.0 | 126 | 1.5024 | 0.3023 |
| 1.5577 | 22.0 | 132 | 1.5012 | 0.3023 |
| 1.5577 | 23.0 | 138 | 1.5001 | 0.3023 |
| 1.5855 | 24.0 | 144 | 1.4991 | 0.3023 |
| 1.5594 | 25.0 | 150 | 1.4981 | 0.3023 |
| 1.5594 | 26.0 | 156 | 1.4972 | 0.3023 |
| 1.5496 | 27.0 | 162 | 1.4964 | 0.3023 |
| 1.5496 | 28.0 | 168 | 1.4956 | 0.3023 |
| 1.5543 | 29.0 | 174 | 1.4949 | 0.3023 |
| 1.5415 | 30.0 | 180 | 1.4943 | 0.3023 |
| 1.5415 | 31.0 | 186 | 1.4938 | 0.3023 |
| 1.5408 | 32.0 | 192 | 1.4932 | 0.3023 |
| 1.5408 | 33.0 | 198 | 1.4926 | 0.3023 |
| 1.5602 | 34.0 | 204 | 1.4922 | 0.3023 |
| 1.5429 | 35.0 | 210 | 1.4918 | 0.3023 |
| 1.5429 | 36.0 | 216 | 1.4914 | 0.3023 |
| 1.5494 | 37.0 | 222 | 1.4912 | 0.3023 |
| 1.5494 | 38.0 | 228 | 1.4909 | 0.3023 |
| 1.5361 | 39.0 | 234 | 1.4908 | 0.3023 |
| 1.5628 | 40.0 | 240 | 1.4906 | 0.3023 |
| 1.5628 | 41.0 | 246 | 1.4906 | 0.3023 |
| 1.5458 | 42.0 | 252 | 1.4906 | 0.3023 |
| 1.5458 | 43.0 | 258 | 1.4906 | 0.3023 |
| 1.5716 | 44.0 | 264 | 1.4906 | 0.3023 |
| 1.5384 | 45.0 | 270 | 1.4906 | 0.3023 |
| 1.5384 | 46.0 | 276 | 1.4906 | 0.3023 |
| 1.5475 | 47.0 | 282 | 1.4906 | 0.3023 |
| 1.5475 | 48.0 | 288 | 1.4906 | 0.3023 |
| 1.5338 | 49.0 | 294 | 1.4906 | 0.3023 |
| 1.5337 | 50.0 | 300 | 1.4906 | 0.3023 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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