hushem_40x_deit_tiny_adamax_001_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: 1.2217
- Accuracy: 0.8837
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 |
|---|---|---|---|---|
| 0.3354 | 1.0 | 217 | 0.9126 | 0.6744 |
| 0.1561 | 2.0 | 434 | 1.0121 | 0.6977 |
| 0.0656 | 3.0 | 651 | 1.0046 | 0.7442 |
| 0.07 | 4.0 | 868 | 0.8401 | 0.7674 |
| 0.0448 | 5.0 | 1085 | 1.1419 | 0.7907 |
| 0.0667 | 6.0 | 1302 | 1.2686 | 0.8372 |
| 0.0198 | 7.0 | 1519 | 1.1351 | 0.8372 |
| 0.009 | 8.0 | 1736 | 1.6617 | 0.8140 |
| 0.0113 | 9.0 | 1953 | 1.1646 | 0.8372 |
| 0.0284 | 10.0 | 2170 | 0.8325 | 0.9302 |
| 0.0093 | 11.0 | 2387 | 1.2543 | 0.8372 |
| 0.0217 | 12.0 | 2604 | 1.5838 | 0.8140 |
| 0.0059 | 13.0 | 2821 | 1.3486 | 0.8605 |
| 0.0271 | 14.0 | 3038 | 0.9639 | 0.8837 |
| 0.0039 | 15.0 | 3255 | 0.9754 | 0.9070 |
| 0.0 | 16.0 | 3472 | 1.4140 | 0.8140 |
| 0.0 | 17.0 | 3689 | 1.2390 | 0.8837 |
| 0.0011 | 18.0 | 3906 | 1.3250 | 0.8605 |
| 0.0004 | 19.0 | 4123 | 1.2826 | 0.8372 |
| 0.0002 | 20.0 | 4340 | 1.1795 | 0.8605 |
| 0.0 | 21.0 | 4557 | 0.9932 | 0.8837 |
| 0.0 | 22.0 | 4774 | 0.9999 | 0.8837 |
| 0.0 | 23.0 | 4991 | 1.0055 | 0.8837 |
| 0.0 | 24.0 | 5208 | 1.0099 | 0.8837 |
| 0.0 | 25.0 | 5425 | 1.0179 | 0.8837 |
| 0.0 | 26.0 | 5642 | 1.0241 | 0.8837 |
| 0.0 | 27.0 | 5859 | 1.0302 | 0.8837 |
| 0.0 | 28.0 | 6076 | 1.0378 | 0.8837 |
| 0.0 | 29.0 | 6293 | 1.0445 | 0.8837 |
| 0.0 | 30.0 | 6510 | 1.0519 | 0.8837 |
| 0.0 | 31.0 | 6727 | 1.0608 | 0.8837 |
| 0.0 | 32.0 | 6944 | 1.0697 | 0.8837 |
| 0.0 | 33.0 | 7161 | 1.0787 | 0.8837 |
| 0.0 | 34.0 | 7378 | 1.0879 | 0.8837 |
| 0.0 | 35.0 | 7595 | 1.0974 | 0.8837 |
| 0.0 | 36.0 | 7812 | 1.1074 | 0.8837 |
| 0.0 | 37.0 | 8029 | 1.1171 | 0.8837 |
| 0.0 | 38.0 | 8246 | 1.1279 | 0.8837 |
| 0.0 | 39.0 | 8463 | 1.1383 | 0.8837 |
| 0.0 | 40.0 | 8680 | 1.1481 | 0.8837 |
| 0.0 | 41.0 | 8897 | 1.1586 | 0.8837 |
| 0.0 | 42.0 | 9114 | 1.1687 | 0.8837 |
| 0.0 | 43.0 | 9331 | 1.1785 | 0.8837 |
| 0.0 | 44.0 | 9548 | 1.1883 | 0.8837 |
| 0.0 | 45.0 | 9765 | 1.1969 | 0.8837 |
| 0.0 | 46.0 | 9982 | 1.2048 | 0.8837 |
| 0.0 | 47.0 | 10199 | 1.2119 | 0.8837 |
| 0.0 | 48.0 | 10416 | 1.2174 | 0.8837 |
| 0.0 | 49.0 | 10633 | 1.2210 | 0.8837 |
| 0.0 | 50.0 | 10850 | 1.2217 | 0.8837 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2