hushem_1x_deit_base_rms_001_fold2
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0357
- Accuracy: 0.6444
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 | 5.4375 | 0.2667 |
| 5.152 | 2.0 | 12 | 2.4176 | 0.2444 |
| 5.152 | 3.0 | 18 | 1.6834 | 0.2444 |
| 2.2029 | 4.0 | 24 | 1.5993 | 0.2667 |
| 1.6431 | 5.0 | 30 | 1.5694 | 0.2444 |
| 1.6431 | 6.0 | 36 | 1.6003 | 0.2444 |
| 1.5667 | 7.0 | 42 | 1.5690 | 0.2444 |
| 1.5667 | 8.0 | 48 | 1.5571 | 0.2444 |
| 1.5065 | 9.0 | 54 | 1.4670 | 0.2444 |
| 1.4556 | 10.0 | 60 | 1.4809 | 0.2444 |
| 1.4556 | 11.0 | 66 | 1.4913 | 0.2667 |
| 1.4366 | 12.0 | 72 | 1.4381 | 0.2444 |
| 1.4366 | 13.0 | 78 | 1.5011 | 0.2667 |
| 1.45 | 14.0 | 84 | 1.6192 | 0.2667 |
| 1.6105 | 15.0 | 90 | 1.3933 | 0.2667 |
| 1.6105 | 16.0 | 96 | 1.3754 | 0.3556 |
| 1.463 | 17.0 | 102 | 1.6119 | 0.2444 |
| 1.463 | 18.0 | 108 | 1.4972 | 0.2444 |
| 1.4133 | 19.0 | 114 | 1.2907 | 0.3111 |
| 1.3552 | 20.0 | 120 | 1.3783 | 0.2667 |
| 1.3552 | 21.0 | 126 | 1.2531 | 0.4 |
| 1.2635 | 22.0 | 132 | 1.2107 | 0.4222 |
| 1.2635 | 23.0 | 138 | 1.2781 | 0.3778 |
| 1.2442 | 24.0 | 144 | 1.1028 | 0.4222 |
| 1.1223 | 25.0 | 150 | 1.1738 | 0.4444 |
| 1.1223 | 26.0 | 156 | 1.1566 | 0.5111 |
| 1.0131 | 27.0 | 162 | 1.0937 | 0.5111 |
| 1.0131 | 28.0 | 168 | 1.0849 | 0.5556 |
| 0.9912 | 29.0 | 174 | 1.3429 | 0.5111 |
| 0.853 | 30.0 | 180 | 0.9919 | 0.6222 |
| 0.853 | 31.0 | 186 | 1.0799 | 0.5556 |
| 0.6912 | 32.0 | 192 | 1.1042 | 0.5333 |
| 0.6912 | 33.0 | 198 | 1.0782 | 0.5556 |
| 0.6669 | 34.0 | 204 | 0.9785 | 0.5333 |
| 0.5453 | 35.0 | 210 | 1.1312 | 0.6444 |
| 0.5453 | 36.0 | 216 | 1.0910 | 0.5556 |
| 0.5668 | 37.0 | 222 | 1.1103 | 0.6 |
| 0.5668 | 38.0 | 228 | 1.1358 | 0.5778 |
| 0.4266 | 39.0 | 234 | 1.0340 | 0.6222 |
| 0.471 | 40.0 | 240 | 1.0428 | 0.6222 |
| 0.471 | 41.0 | 246 | 1.0358 | 0.6444 |
| 0.4178 | 42.0 | 252 | 1.0357 | 0.6444 |
| 0.4178 | 43.0 | 258 | 1.0357 | 0.6444 |
| 0.3636 | 44.0 | 264 | 1.0357 | 0.6444 |
| 0.3974 | 45.0 | 270 | 1.0357 | 0.6444 |
| 0.3974 | 46.0 | 276 | 1.0357 | 0.6444 |
| 0.3949 | 47.0 | 282 | 1.0357 | 0.6444 |
| 0.3949 | 48.0 | 288 | 1.0357 | 0.6444 |
| 0.3754 | 49.0 | 294 | 1.0357 | 0.6444 |
| 0.3739 | 50.0 | 300 | 1.0357 | 0.6444 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0