smids_5x_deit_tiny_rms_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: 1.4357
- 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: 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 |
|---|---|---|---|---|
| 0.3287 | 1.0 | 375 | 0.3863 | 0.85 |
| 0.2455 | 2.0 | 750 | 0.3649 | 0.8717 |
| 0.1213 | 3.0 | 1125 | 0.4642 | 0.8583 |
| 0.1727 | 4.0 | 1500 | 0.5805 | 0.8617 |
| 0.1128 | 5.0 | 1875 | 0.6371 | 0.8483 |
| 0.0689 | 6.0 | 2250 | 0.6331 | 0.8683 |
| 0.0983 | 7.0 | 2625 | 0.6829 | 0.865 |
| 0.1105 | 8.0 | 3000 | 0.6645 | 0.8617 |
| 0.0716 | 9.0 | 3375 | 0.9136 | 0.8583 |
| 0.0639 | 10.0 | 3750 | 0.7869 | 0.8867 |
| 0.0325 | 11.0 | 4125 | 0.8744 | 0.8733 |
| 0.0627 | 12.0 | 4500 | 0.9757 | 0.8567 |
| 0.0409 | 13.0 | 4875 | 0.9654 | 0.8633 |
| 0.0848 | 14.0 | 5250 | 0.8074 | 0.8667 |
| 0.0374 | 15.0 | 5625 | 0.9236 | 0.8667 |
| 0.037 | 16.0 | 6000 | 1.0898 | 0.8617 |
| 0.0497 | 17.0 | 6375 | 1.1236 | 0.8583 |
| 0.0095 | 18.0 | 6750 | 1.0183 | 0.87 |
| 0.0289 | 19.0 | 7125 | 1.0208 | 0.8783 |
| 0.0255 | 20.0 | 7500 | 1.1375 | 0.8667 |
| 0.0016 | 21.0 | 7875 | 1.1251 | 0.8617 |
| 0.0005 | 22.0 | 8250 | 1.0252 | 0.8717 |
| 0.015 | 23.0 | 8625 | 1.1223 | 0.865 |
| 0.0375 | 24.0 | 9000 | 1.0372 | 0.8733 |
| 0.0379 | 25.0 | 9375 | 0.9869 | 0.8667 |
| 0.0001 | 26.0 | 9750 | 1.0331 | 0.8733 |
| 0.0134 | 27.0 | 10125 | 0.9754 | 0.885 |
| 0.0 | 28.0 | 10500 | 1.0742 | 0.8583 |
| 0.0001 | 29.0 | 10875 | 1.0378 | 0.88 |
| 0.0 | 30.0 | 11250 | 1.1203 | 0.875 |
| 0.0077 | 31.0 | 11625 | 1.1471 | 0.8783 |
| 0.0003 | 32.0 | 12000 | 1.1437 | 0.8783 |
| 0.0 | 33.0 | 12375 | 1.1521 | 0.875 |
| 0.0003 | 34.0 | 12750 | 1.2362 | 0.865 |
| 0.0 | 35.0 | 13125 | 1.2535 | 0.8567 |
| 0.0 | 36.0 | 13500 | 1.2428 | 0.865 |
| 0.0002 | 37.0 | 13875 | 1.3504 | 0.8583 |
| 0.0191 | 38.0 | 14250 | 1.2705 | 0.87 |
| 0.0 | 39.0 | 14625 | 1.3466 | 0.8667 |
| 0.0 | 40.0 | 15000 | 1.3575 | 0.8617 |
| 0.0 | 41.0 | 15375 | 1.3681 | 0.8667 |
| 0.0 | 42.0 | 15750 | 1.3681 | 0.87 |
| 0.0 | 43.0 | 16125 | 1.3799 | 0.865 |
| 0.0 | 44.0 | 16500 | 1.3559 | 0.8667 |
| 0.0 | 45.0 | 16875 | 1.3770 | 0.865 |
| 0.0 | 46.0 | 17250 | 1.4044 | 0.8667 |
| 0.0 | 47.0 | 17625 | 1.4188 | 0.8683 |
| 0.0 | 48.0 | 18000 | 1.4286 | 0.8667 |
| 0.0 | 49.0 | 18375 | 1.4343 | 0.8667 |
| 0.0 | 50.0 | 18750 | 1.4357 | 0.8667 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2