smids_3x_deit_tiny_adamax_00001_fold4
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: 1.1449
- Accuracy: 0.8667
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 |
|---|---|---|---|---|
| 0.4417 | 1.0 | 225 | 0.4569 | 0.8083 |
| 0.2331 | 2.0 | 450 | 0.3996 | 0.8383 |
| 0.2011 | 3.0 | 675 | 0.3738 | 0.8467 |
| 0.2185 | 4.0 | 900 | 0.3745 | 0.8633 |
| 0.1818 | 5.0 | 1125 | 0.3725 | 0.875 |
| 0.1063 | 6.0 | 1350 | 0.4112 | 0.8717 |
| 0.1356 | 7.0 | 1575 | 0.4194 | 0.875 |
| 0.1607 | 8.0 | 1800 | 0.4543 | 0.8533 |
| 0.1116 | 9.0 | 2025 | 0.4653 | 0.8683 |
| 0.0765 | 10.0 | 2250 | 0.5277 | 0.8733 |
| 0.0939 | 11.0 | 2475 | 0.5314 | 0.875 |
| 0.0447 | 12.0 | 2700 | 0.6029 | 0.87 |
| 0.0241 | 13.0 | 2925 | 0.6080 | 0.88 |
| 0.0181 | 14.0 | 3150 | 0.6484 | 0.8783 |
| 0.0217 | 15.0 | 3375 | 0.7384 | 0.8733 |
| 0.0012 | 16.0 | 3600 | 0.7227 | 0.8717 |
| 0.0028 | 17.0 | 3825 | 0.7801 | 0.8683 |
| 0.0175 | 18.0 | 4050 | 0.7970 | 0.8667 |
| 0.0003 | 19.0 | 4275 | 0.8483 | 0.8683 |
| 0.0027 | 20.0 | 4500 | 0.8736 | 0.875 |
| 0.0021 | 21.0 | 4725 | 0.8846 | 0.8717 |
| 0.0002 | 22.0 | 4950 | 0.8988 | 0.875 |
| 0.0002 | 23.0 | 5175 | 0.9264 | 0.8683 |
| 0.0001 | 24.0 | 5400 | 0.9315 | 0.8717 |
| 0.0001 | 25.0 | 5625 | 0.9598 | 0.87 |
| 0.0001 | 26.0 | 5850 | 0.9683 | 0.8667 |
| 0.0001 | 27.0 | 6075 | 0.9914 | 0.8667 |
| 0.0 | 28.0 | 6300 | 1.0058 | 0.8683 |
| 0.0001 | 29.0 | 6525 | 1.0095 | 0.8683 |
| 0.0 | 30.0 | 6750 | 1.0176 | 0.865 |
| 0.0 | 31.0 | 6975 | 1.0268 | 0.865 |
| 0.0 | 32.0 | 7200 | 1.0316 | 0.8667 |
| 0.0 | 33.0 | 7425 | 1.0500 | 0.8667 |
| 0.0 | 34.0 | 7650 | 1.0562 | 0.87 |
| 0.0 | 35.0 | 7875 | 1.0705 | 0.8667 |
| 0.0 | 36.0 | 8100 | 1.0794 | 0.8683 |
| 0.0 | 37.0 | 8325 | 1.0868 | 0.8667 |
| 0.0 | 38.0 | 8550 | 1.0946 | 0.865 |
| 0.0 | 39.0 | 8775 | 1.1049 | 0.8667 |
| 0.0 | 40.0 | 9000 | 1.1087 | 0.8667 |
| 0.0 | 41.0 | 9225 | 1.1122 | 0.8667 |
| 0.0091 | 42.0 | 9450 | 1.1194 | 0.8667 |
| 0.0 | 43.0 | 9675 | 1.1272 | 0.8667 |
| 0.0077 | 44.0 | 9900 | 1.1280 | 0.8667 |
| 0.0 | 45.0 | 10125 | 1.1353 | 0.8667 |
| 0.0 | 46.0 | 10350 | 1.1386 | 0.8667 |
| 0.0 | 47.0 | 10575 | 1.1418 | 0.8683 |
| 0.0 | 48.0 | 10800 | 1.1440 | 0.8667 |
| 0.0 | 49.0 | 11025 | 1.1434 | 0.8667 |
| 0.0 | 50.0 | 11250 | 1.1449 | 0.8667 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2