smids_3x_deit_tiny_adamax_0001_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: 1.3554
- Accuracy: 0.875
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 |
|---|---|---|---|---|
| 0.3155 | 1.0 | 225 | 0.3767 | 0.855 |
| 0.1407 | 2.0 | 450 | 0.3968 | 0.87 |
| 0.0909 | 3.0 | 675 | 0.5151 | 0.8617 |
| 0.1097 | 4.0 | 900 | 0.4932 | 0.8633 |
| 0.0371 | 5.0 | 1125 | 0.6275 | 0.8683 |
| 0.0101 | 6.0 | 1350 | 0.7429 | 0.8633 |
| 0.0391 | 7.0 | 1575 | 0.8418 | 0.865 |
| 0.0077 | 8.0 | 1800 | 0.8293 | 0.8767 |
| 0.0687 | 9.0 | 2025 | 0.9529 | 0.8667 |
| 0.0191 | 10.0 | 2250 | 1.0880 | 0.8633 |
| 0.0325 | 11.0 | 2475 | 1.1276 | 0.8583 |
| 0.0003 | 12.0 | 2700 | 1.0880 | 0.8583 |
| 0.0043 | 13.0 | 2925 | 1.1462 | 0.8717 |
| 0.0003 | 14.0 | 3150 | 1.2942 | 0.8467 |
| 0.04 | 15.0 | 3375 | 1.1259 | 0.86 |
| 0.0007 | 16.0 | 3600 | 1.1878 | 0.86 |
| 0.0001 | 17.0 | 3825 | 1.2005 | 0.8683 |
| 0.0003 | 18.0 | 4050 | 1.1961 | 0.8667 |
| 0.0 | 19.0 | 4275 | 1.1734 | 0.8717 |
| 0.0002 | 20.0 | 4500 | 1.2441 | 0.865 |
| 0.0 | 21.0 | 4725 | 1.2631 | 0.87 |
| 0.0 | 22.0 | 4950 | 1.1604 | 0.8733 |
| 0.0 | 23.0 | 5175 | 1.1940 | 0.8683 |
| 0.0 | 24.0 | 5400 | 1.2754 | 0.8683 |
| 0.0 | 25.0 | 5625 | 1.2790 | 0.8617 |
| 0.0 | 26.0 | 5850 | 1.2664 | 0.8733 |
| 0.0 | 27.0 | 6075 | 1.2877 | 0.875 |
| 0.0 | 28.0 | 6300 | 1.2788 | 0.88 |
| 0.0 | 29.0 | 6525 | 1.2589 | 0.8783 |
| 0.0 | 30.0 | 6750 | 1.2671 | 0.8783 |
| 0.0 | 31.0 | 6975 | 1.2750 | 0.8783 |
| 0.0 | 32.0 | 7200 | 1.2783 | 0.875 |
| 0.0 | 33.0 | 7425 | 1.2973 | 0.8733 |
| 0.0 | 34.0 | 7650 | 1.3104 | 0.8717 |
| 0.0 | 35.0 | 7875 | 1.3108 | 0.8717 |
| 0.0 | 36.0 | 8100 | 1.3246 | 0.8717 |
| 0.0 | 37.0 | 8325 | 1.3217 | 0.87 |
| 0.0 | 38.0 | 8550 | 1.3349 | 0.8733 |
| 0.0 | 39.0 | 8775 | 1.3355 | 0.8733 |
| 0.0 | 40.0 | 9000 | 1.3337 | 0.8733 |
| 0.0 | 41.0 | 9225 | 1.3326 | 0.875 |
| 0.0036 | 42.0 | 9450 | 1.3380 | 0.8733 |
| 0.0 | 43.0 | 9675 | 1.3430 | 0.875 |
| 0.0032 | 44.0 | 9900 | 1.3445 | 0.8733 |
| 0.0 | 45.0 | 10125 | 1.3492 | 0.875 |
| 0.0 | 46.0 | 10350 | 1.3514 | 0.875 |
| 0.0 | 47.0 | 10575 | 1.3535 | 0.875 |
| 0.0 | 48.0 | 10800 | 1.3548 | 0.875 |
| 0.0 | 49.0 | 11025 | 1.3543 | 0.875 |
| 0.0 | 50.0 | 11250 | 1.3554 | 0.875 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2