smids_3x_deit_base_rms_0001_fold4
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2420
- Accuracy: 0.8867
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 |
|---|---|---|---|---|
| 0.3339 | 1.0 | 225 | 0.3477 | 0.8767 |
| 0.1864 | 2.0 | 450 | 0.4551 | 0.875 |
| 0.1083 | 3.0 | 675 | 0.7108 | 0.8417 |
| 0.1229 | 4.0 | 900 | 0.4781 | 0.8883 |
| 0.0535 | 5.0 | 1125 | 0.6232 | 0.8733 |
| 0.0735 | 6.0 | 1350 | 0.5812 | 0.88 |
| 0.0369 | 7.0 | 1575 | 0.6859 | 0.8667 |
| 0.055 | 8.0 | 1800 | 0.7382 | 0.8417 |
| 0.0533 | 9.0 | 2025 | 0.7001 | 0.8833 |
| 0.0337 | 10.0 | 2250 | 0.7953 | 0.855 |
| 0.0039 | 11.0 | 2475 | 0.6932 | 0.8783 |
| 0.0367 | 12.0 | 2700 | 0.7006 | 0.8733 |
| 0.0008 | 13.0 | 2925 | 0.6228 | 0.895 |
| 0.0352 | 14.0 | 3150 | 0.6483 | 0.88 |
| 0.0536 | 15.0 | 3375 | 0.7338 | 0.8733 |
| 0.0282 | 16.0 | 3600 | 0.7455 | 0.8667 |
| 0.0418 | 17.0 | 3825 | 0.6314 | 0.8833 |
| 0.0045 | 18.0 | 4050 | 0.8581 | 0.8833 |
| 0.0013 | 19.0 | 4275 | 0.8791 | 0.8717 |
| 0.0236 | 20.0 | 4500 | 0.8293 | 0.8733 |
| 0.0003 | 21.0 | 4725 | 0.9177 | 0.87 |
| 0.0214 | 22.0 | 4950 | 0.9227 | 0.8633 |
| 0.0034 | 23.0 | 5175 | 0.7968 | 0.8867 |
| 0.0319 | 24.0 | 5400 | 0.8643 | 0.885 |
| 0.0068 | 25.0 | 5625 | 0.8481 | 0.88 |
| 0.0008 | 26.0 | 5850 | 1.0685 | 0.8733 |
| 0.0 | 27.0 | 6075 | 0.9795 | 0.88 |
| 0.016 | 28.0 | 6300 | 0.9377 | 0.87 |
| 0.0001 | 29.0 | 6525 | 1.0537 | 0.855 |
| 0.0 | 30.0 | 6750 | 1.0268 | 0.865 |
| 0.0184 | 31.0 | 6975 | 0.8036 | 0.8733 |
| 0.0 | 32.0 | 7200 | 0.9837 | 0.88 |
| 0.0 | 33.0 | 7425 | 0.9530 | 0.8917 |
| 0.0 | 34.0 | 7650 | 1.0294 | 0.8883 |
| 0.0 | 35.0 | 7875 | 1.0787 | 0.8817 |
| 0.0 | 36.0 | 8100 | 1.0515 | 0.885 |
| 0.0 | 37.0 | 8325 | 1.0565 | 0.8867 |
| 0.0 | 38.0 | 8550 | 1.0716 | 0.89 |
| 0.0 | 39.0 | 8775 | 1.0979 | 0.885 |
| 0.0 | 40.0 | 9000 | 1.1315 | 0.8867 |
| 0.0 | 41.0 | 9225 | 1.1955 | 0.8867 |
| 0.0034 | 42.0 | 9450 | 1.1923 | 0.8933 |
| 0.0 | 43.0 | 9675 | 1.1858 | 0.8917 |
| 0.0029 | 44.0 | 9900 | 1.2299 | 0.89 |
| 0.0 | 45.0 | 10125 | 1.2340 | 0.89 |
| 0.0 | 46.0 | 10350 | 1.2528 | 0.885 |
| 0.0 | 47.0 | 10575 | 1.2511 | 0.8867 |
| 0.0 | 48.0 | 10800 | 1.2410 | 0.8867 |
| 0.0 | 49.0 | 11025 | 1.2401 | 0.8867 |
| 0.0 | 50.0 | 11250 | 1.2420 | 0.8867 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2