deit-base-distilled-patch16-224-hasta-65-fold5
This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0163
- Accuracy: 0.5556
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.5714 | 1 | 1.1751 | 0.3889 |
| No log | 1.7143 | 3 | 1.0341 | 0.4722 |
| No log | 2.8571 | 5 | 1.3059 | 0.2778 |
| No log | 4.0 | 7 | 1.3255 | 0.2778 |
| No log | 4.5714 | 8 | 1.1834 | 0.2778 |
| 1.1839 | 5.7143 | 10 | 1.0357 | 0.5278 |
| 1.1839 | 6.8571 | 12 | 1.0608 | 0.3889 |
| 1.1839 | 8.0 | 14 | 1.2060 | 0.3333 |
| 1.1839 | 8.5714 | 15 | 1.1938 | 0.3889 |
| 1.1839 | 9.7143 | 17 | 1.0825 | 0.5 |
| 1.1839 | 10.8571 | 19 | 1.1488 | 0.3889 |
| 0.9707 | 12.0 | 21 | 1.1268 | 0.3889 |
| 0.9707 | 12.5714 | 22 | 1.0563 | 0.5 |
| 0.9707 | 13.7143 | 24 | 1.0570 | 0.5278 |
| 0.9707 | 14.8571 | 26 | 1.1166 | 0.4167 |
| 0.9707 | 16.0 | 28 | 1.0609 | 0.4444 |
| 0.9707 | 16.5714 | 29 | 1.0379 | 0.4722 |
| 0.8668 | 17.7143 | 31 | 1.0610 | 0.4444 |
| 0.8668 | 18.8571 | 33 | 1.1811 | 0.4167 |
| 0.8668 | 20.0 | 35 | 1.1028 | 0.4444 |
| 0.8668 | 20.5714 | 36 | 1.0950 | 0.4444 |
| 0.8668 | 21.7143 | 38 | 1.1424 | 0.4722 |
| 0.6889 | 22.8571 | 40 | 1.3027 | 0.4167 |
| 0.6889 | 24.0 | 42 | 1.2030 | 0.4167 |
| 0.6889 | 24.5714 | 43 | 1.2148 | 0.4167 |
| 0.6889 | 25.7143 | 45 | 1.3066 | 0.4167 |
| 0.6889 | 26.8571 | 47 | 1.3881 | 0.3611 |
| 0.6889 | 28.0 | 49 | 1.2566 | 0.4444 |
| 0.576 | 28.5714 | 50 | 1.1891 | 0.4444 |
| 0.576 | 29.7143 | 52 | 1.1638 | 0.4167 |
| 0.576 | 30.8571 | 54 | 1.2530 | 0.4167 |
| 0.576 | 32.0 | 56 | 1.1383 | 0.5 |
| 0.576 | 32.5714 | 57 | 1.0968 | 0.5 |
| 0.576 | 33.7143 | 59 | 1.0163 | 0.5556 |
| 0.4773 | 34.8571 | 61 | 1.1107 | 0.5 |
| 0.4773 | 36.0 | 63 | 1.1341 | 0.5 |
| 0.4773 | 36.5714 | 64 | 1.1152 | 0.5278 |
| 0.4773 | 37.7143 | 66 | 1.1158 | 0.5556 |
| 0.4773 | 38.8571 | 68 | 1.1628 | 0.4722 |
| 0.4186 | 40.0 | 70 | 1.2305 | 0.4444 |
| 0.4186 | 40.5714 | 71 | 1.2181 | 0.4722 |
| 0.4186 | 41.7143 | 73 | 1.2164 | 0.5 |
| 0.4186 | 42.8571 | 75 | 1.2225 | 0.5 |
| 0.4186 | 44.0 | 77 | 1.2298 | 0.5 |
| 0.4186 | 44.5714 | 78 | 1.2651 | 0.4722 |
| 0.3318 | 45.7143 | 80 | 1.3628 | 0.4167 |
| 0.3318 | 46.8571 | 82 | 1.3817 | 0.4167 |
| 0.3318 | 48.0 | 84 | 1.3594 | 0.4167 |
| 0.3318 | 48.5714 | 85 | 1.3553 | 0.4444 |
| 0.3318 | 49.7143 | 87 | 1.3548 | 0.4167 |
| 0.3318 | 50.8571 | 89 | 1.4113 | 0.4167 |
| 0.344 | 52.0 | 91 | 1.4433 | 0.4167 |
| 0.344 | 52.5714 | 92 | 1.4449 | 0.4167 |
| 0.344 | 53.7143 | 94 | 1.4514 | 0.4167 |
| 0.344 | 54.8571 | 96 | 1.4685 | 0.4167 |
| 0.344 | 56.0 | 98 | 1.4734 | 0.4167 |
| 0.344 | 56.5714 | 99 | 1.4747 | 0.4167 |
| 0.3305 | 57.1429 | 100 | 1.4732 | 0.4167 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1