smids_3x_deit_tiny_adamax_0001_fold5
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: 0.8810
- Accuracy: 0.8933
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: 0.0001
- 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 |
|---|---|---|---|---|
| 0.3187 | 1.0 | 225 | 0.2926 | 0.8683 |
| 0.1675 | 2.0 | 450 | 0.2434 | 0.9083 |
| 0.148 | 3.0 | 675 | 0.3249 | 0.8883 |
| 0.0849 | 4.0 | 900 | 0.2613 | 0.91 |
| 0.0858 | 5.0 | 1125 | 0.4160 | 0.895 |
| 0.0448 | 6.0 | 1350 | 0.5146 | 0.8833 |
| 0.0473 | 7.0 | 1575 | 0.6012 | 0.8833 |
| 0.0246 | 8.0 | 1800 | 0.5599 | 0.89 |
| 0.0437 | 9.0 | 2025 | 0.6206 | 0.8933 |
| 0.0184 | 10.0 | 2250 | 0.6714 | 0.9017 |
| 0.0009 | 11.0 | 2475 | 0.6631 | 0.9083 |
| 0.0431 | 12.0 | 2700 | 0.7764 | 0.8983 |
| 0.0005 | 13.0 | 2925 | 0.6164 | 0.9033 |
| 0.0002 | 14.0 | 3150 | 0.6308 | 0.9017 |
| 0.0025 | 15.0 | 3375 | 0.7289 | 0.8983 |
| 0.0001 | 16.0 | 3600 | 0.6634 | 0.905 |
| 0.0 | 17.0 | 3825 | 0.7636 | 0.9033 |
| 0.0233 | 18.0 | 4050 | 0.7494 | 0.905 |
| 0.027 | 19.0 | 4275 | 0.8179 | 0.8917 |
| 0.0 | 20.0 | 4500 | 0.8300 | 0.895 |
| 0.0 | 21.0 | 4725 | 0.8262 | 0.8967 |
| 0.0 | 22.0 | 4950 | 0.8472 | 0.8917 |
| 0.0 | 23.0 | 5175 | 0.7368 | 0.9067 |
| 0.0091 | 24.0 | 5400 | 0.7922 | 0.8983 |
| 0.0 | 25.0 | 5625 | 0.8707 | 0.9 |
| 0.0 | 26.0 | 5850 | 0.7645 | 0.9017 |
| 0.0 | 27.0 | 6075 | 0.8363 | 0.8983 |
| 0.0 | 28.0 | 6300 | 0.8125 | 0.905 |
| 0.0076 | 29.0 | 6525 | 0.7853 | 0.9067 |
| 0.0 | 30.0 | 6750 | 0.8267 | 0.8967 |
| 0.0 | 31.0 | 6975 | 0.8018 | 0.905 |
| 0.0 | 32.0 | 7200 | 0.8256 | 0.9 |
| 0.0059 | 33.0 | 7425 | 0.8776 | 0.8967 |
| 0.0 | 34.0 | 7650 | 0.8060 | 0.9 |
| 0.0032 | 35.0 | 7875 | 0.8635 | 0.9 |
| 0.0 | 36.0 | 8100 | 0.8389 | 0.895 |
| 0.005 | 37.0 | 8325 | 0.8643 | 0.895 |
| 0.0 | 38.0 | 8550 | 0.8458 | 0.8983 |
| 0.0 | 39.0 | 8775 | 0.8735 | 0.895 |
| 0.0 | 40.0 | 9000 | 0.8584 | 0.895 |
| 0.0 | 41.0 | 9225 | 0.8812 | 0.895 |
| 0.0 | 42.0 | 9450 | 0.8710 | 0.895 |
| 0.0032 | 43.0 | 9675 | 0.8774 | 0.895 |
| 0.0 | 44.0 | 9900 | 0.8731 | 0.895 |
| 0.0 | 45.0 | 10125 | 0.8725 | 0.8967 |
| 0.0 | 46.0 | 10350 | 0.8786 | 0.895 |
| 0.0 | 47.0 | 10575 | 0.8795 | 0.895 |
| 0.0 | 48.0 | 10800 | 0.8801 | 0.895 |
| 0.0 | 49.0 | 11025 | 0.8803 | 0.8933 |
| 0.0 | 50.0 | 11250 | 0.8810 | 0.8933 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2