smids_1x_deit_small_rms_00001_fold4
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2283
- Accuracy: 0.86
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: 1e-05
- 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 |
|---|---|---|---|---|
| 0.3693 | 1.0 | 75 | 0.4169 | 0.8367 |
| 0.25 | 2.0 | 150 | 0.3480 | 0.86 |
| 0.1826 | 3.0 | 225 | 0.3907 | 0.8517 |
| 0.103 | 4.0 | 300 | 0.4268 | 0.8533 |
| 0.0588 | 5.0 | 375 | 0.4745 | 0.8517 |
| 0.0211 | 6.0 | 450 | 0.5873 | 0.86 |
| 0.0762 | 7.0 | 525 | 0.6785 | 0.8567 |
| 0.0033 | 8.0 | 600 | 0.6768 | 0.8533 |
| 0.0377 | 9.0 | 675 | 0.7784 | 0.855 |
| 0.0107 | 10.0 | 750 | 0.8289 | 0.8467 |
| 0.0009 | 11.0 | 825 | 0.8979 | 0.845 |
| 0.0002 | 12.0 | 900 | 0.8647 | 0.8617 |
| 0.0003 | 13.0 | 975 | 0.8591 | 0.8583 |
| 0.0077 | 14.0 | 1050 | 0.9903 | 0.8483 |
| 0.0002 | 15.0 | 1125 | 0.9262 | 0.86 |
| 0.0075 | 16.0 | 1200 | 1.1297 | 0.8283 |
| 0.0005 | 17.0 | 1275 | 0.9421 | 0.86 |
| 0.0146 | 18.0 | 1350 | 0.8922 | 0.86 |
| 0.0001 | 19.0 | 1425 | 0.9244 | 0.8683 |
| 0.0001 | 20.0 | 1500 | 0.9926 | 0.8683 |
| 0.003 | 21.0 | 1575 | 0.9538 | 0.8633 |
| 0.0001 | 22.0 | 1650 | 0.9796 | 0.8633 |
| 0.0 | 23.0 | 1725 | 0.9957 | 0.865 |
| 0.0079 | 24.0 | 1800 | 0.9969 | 0.8667 |
| 0.0074 | 25.0 | 1875 | 1.0816 | 0.86 |
| 0.0 | 26.0 | 1950 | 1.1025 | 0.8617 |
| 0.0 | 27.0 | 2025 | 1.1525 | 0.8467 |
| 0.0057 | 28.0 | 2100 | 1.1210 | 0.855 |
| 0.0181 | 29.0 | 2175 | 1.1276 | 0.86 |
| 0.0 | 30.0 | 2250 | 1.1208 | 0.8617 |
| 0.0 | 31.0 | 2325 | 1.1193 | 0.865 |
| 0.0 | 32.0 | 2400 | 1.1408 | 0.8617 |
| 0.0 | 33.0 | 2475 | 1.1431 | 0.8633 |
| 0.0 | 34.0 | 2550 | 1.1491 | 0.86 |
| 0.0 | 35.0 | 2625 | 1.1589 | 0.8617 |
| 0.0 | 36.0 | 2700 | 1.1620 | 0.8617 |
| 0.0031 | 37.0 | 2775 | 1.1838 | 0.8633 |
| 0.0 | 38.0 | 2850 | 1.1840 | 0.8633 |
| 0.0 | 39.0 | 2925 | 1.1861 | 0.8617 |
| 0.0 | 40.0 | 3000 | 1.2058 | 0.8633 |
| 0.0028 | 41.0 | 3075 | 1.1981 | 0.865 |
| 0.0 | 42.0 | 3150 | 1.2026 | 0.8617 |
| 0.0 | 43.0 | 3225 | 1.2159 | 0.86 |
| 0.0 | 44.0 | 3300 | 1.2159 | 0.86 |
| 0.0 | 45.0 | 3375 | 1.2189 | 0.86 |
| 0.0 | 46.0 | 3450 | 1.2225 | 0.86 |
| 0.0 | 47.0 | 3525 | 1.2244 | 0.86 |
| 0.0 | 48.0 | 3600 | 1.2263 | 0.86 |
| 0.0 | 49.0 | 3675 | 1.2278 | 0.86 |
| 0.0 | 50.0 | 3750 | 1.2283 | 0.86 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0