hushem_1x_beit_base_rms_001_fold5
This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0249
- Accuracy: 0.5610
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: 0.001
- 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 | 4.0765 | 0.2683 |
| 4.3424 | 2.0 | 12 | 1.4584 | 0.2439 |
| 4.3424 | 3.0 | 18 | 1.4177 | 0.2439 |
| 1.6981 | 4.0 | 24 | 1.4396 | 0.2439 |
| 1.439 | 5.0 | 30 | 1.4302 | 0.2439 |
| 1.439 | 6.0 | 36 | 1.4113 | 0.2683 |
| 1.4514 | 7.0 | 42 | 1.4298 | 0.2439 |
| 1.4514 | 8.0 | 48 | 1.4142 | 0.2683 |
| 1.4037 | 9.0 | 54 | 1.3909 | 0.2683 |
| 1.4226 | 10.0 | 60 | 1.3819 | 0.2683 |
| 1.4226 | 11.0 | 66 | 1.3922 | 0.2683 |
| 1.3954 | 12.0 | 72 | 1.3475 | 0.2195 |
| 1.3954 | 13.0 | 78 | 1.3669 | 0.2439 |
| 1.4193 | 14.0 | 84 | 1.3582 | 0.2683 |
| 1.3817 | 15.0 | 90 | 1.3869 | 0.2439 |
| 1.3817 | 16.0 | 96 | 1.6362 | 0.2439 |
| 1.3794 | 17.0 | 102 | 1.4473 | 0.2439 |
| 1.3794 | 18.0 | 108 | 1.3118 | 0.4146 |
| 1.3773 | 19.0 | 114 | 1.3101 | 0.3415 |
| 1.3081 | 20.0 | 120 | 1.4119 | 0.2439 |
| 1.3081 | 21.0 | 126 | 1.2040 | 0.4634 |
| 1.2767 | 22.0 | 132 | 2.0544 | 0.2439 |
| 1.2767 | 23.0 | 138 | 1.2316 | 0.3415 |
| 1.3145 | 24.0 | 144 | 1.3728 | 0.2683 |
| 1.2519 | 25.0 | 150 | 1.3114 | 0.2927 |
| 1.2519 | 26.0 | 156 | 1.1523 | 0.5122 |
| 1.2177 | 27.0 | 162 | 1.1097 | 0.4634 |
| 1.2177 | 28.0 | 168 | 1.2516 | 0.3902 |
| 1.1299 | 29.0 | 174 | 1.1372 | 0.4390 |
| 1.1588 | 30.0 | 180 | 1.1704 | 0.4146 |
| 1.1588 | 31.0 | 186 | 1.0311 | 0.5610 |
| 1.1686 | 32.0 | 192 | 1.0730 | 0.4634 |
| 1.1686 | 33.0 | 198 | 1.0832 | 0.4634 |
| 1.038 | 34.0 | 204 | 1.1414 | 0.4878 |
| 1.0117 | 35.0 | 210 | 0.9564 | 0.6585 |
| 1.0117 | 36.0 | 216 | 1.1782 | 0.4146 |
| 1.0097 | 37.0 | 222 | 1.0629 | 0.5122 |
| 1.0097 | 38.0 | 228 | 1.0278 | 0.4634 |
| 0.9459 | 39.0 | 234 | 1.0014 | 0.5610 |
| 0.8786 | 40.0 | 240 | 0.9935 | 0.5854 |
| 0.8786 | 41.0 | 246 | 1.0190 | 0.5610 |
| 0.8792 | 42.0 | 252 | 1.0249 | 0.5610 |
| 0.8792 | 43.0 | 258 | 1.0249 | 0.5610 |
| 0.7834 | 44.0 | 264 | 1.0249 | 0.5610 |
| 0.8444 | 45.0 | 270 | 1.0249 | 0.5610 |
| 0.8444 | 46.0 | 276 | 1.0249 | 0.5610 |
| 0.8306 | 47.0 | 282 | 1.0249 | 0.5610 |
| 0.8306 | 48.0 | 288 | 1.0249 | 0.5610 |
| 0.8546 | 49.0 | 294 | 1.0249 | 0.5610 |
| 0.8485 | 50.0 | 300 | 1.0249 | 0.5610 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0