smids_5x_deit_small_rms_00001_fold3
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0303
- Accuracy: 0.8967
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 |
|---|---|---|---|---|
| 0.2375 | 1.0 | 375 | 0.2542 | 0.9083 |
| 0.1409 | 2.0 | 750 | 0.2770 | 0.91 |
| 0.0609 | 3.0 | 1125 | 0.3094 | 0.8983 |
| 0.0237 | 4.0 | 1500 | 0.3996 | 0.8983 |
| 0.0338 | 5.0 | 1875 | 0.5715 | 0.8883 |
| 0.0534 | 6.0 | 2250 | 0.6112 | 0.8933 |
| 0.0052 | 7.0 | 2625 | 0.6878 | 0.8933 |
| 0.0006 | 8.0 | 3000 | 0.6908 | 0.9 |
| 0.0283 | 9.0 | 3375 | 0.6671 | 0.8983 |
| 0.0065 | 10.0 | 3750 | 0.7326 | 0.9067 |
| 0.0065 | 11.0 | 4125 | 0.6436 | 0.915 |
| 0.0 | 12.0 | 4500 | 0.7498 | 0.9 |
| 0.0016 | 13.0 | 4875 | 0.8235 | 0.8933 |
| 0.0003 | 14.0 | 5250 | 0.8655 | 0.895 |
| 0.0082 | 15.0 | 5625 | 0.7486 | 0.9067 |
| 0.0001 | 16.0 | 6000 | 0.8142 | 0.9017 |
| 0.0001 | 17.0 | 6375 | 0.7387 | 0.8917 |
| 0.0001 | 18.0 | 6750 | 0.8424 | 0.9067 |
| 0.0 | 19.0 | 7125 | 0.7973 | 0.9017 |
| 0.0378 | 20.0 | 7500 | 0.7948 | 0.8967 |
| 0.0 | 21.0 | 7875 | 0.8629 | 0.8917 |
| 0.0 | 22.0 | 8250 | 0.7939 | 0.9017 |
| 0.0063 | 23.0 | 8625 | 0.8369 | 0.89 |
| 0.0 | 24.0 | 9000 | 0.8848 | 0.9 |
| 0.0 | 25.0 | 9375 | 0.8284 | 0.91 |
| 0.0 | 26.0 | 9750 | 0.9412 | 0.9 |
| 0.0 | 27.0 | 10125 | 0.8363 | 0.905 |
| 0.0 | 28.0 | 10500 | 0.9351 | 0.8917 |
| 0.0 | 29.0 | 10875 | 0.8734 | 0.9033 |
| 0.0037 | 30.0 | 11250 | 0.9770 | 0.9067 |
| 0.0 | 31.0 | 11625 | 0.8887 | 0.905 |
| 0.0 | 32.0 | 12000 | 0.9455 | 0.9 |
| 0.0 | 33.0 | 12375 | 0.9432 | 0.9033 |
| 0.0 | 34.0 | 12750 | 0.9703 | 0.8983 |
| 0.0 | 35.0 | 13125 | 0.9495 | 0.9067 |
| 0.0 | 36.0 | 13500 | 0.9886 | 0.8983 |
| 0.0 | 37.0 | 13875 | 0.9999 | 0.9 |
| 0.0 | 38.0 | 14250 | 1.0388 | 0.8983 |
| 0.0 | 39.0 | 14625 | 1.0645 | 0.8917 |
| 0.0038 | 40.0 | 15000 | 0.9923 | 0.9017 |
| 0.0 | 41.0 | 15375 | 1.0132 | 0.8983 |
| 0.0 | 42.0 | 15750 | 1.0058 | 0.9017 |
| 0.0 | 43.0 | 16125 | 1.0185 | 0.8983 |
| 0.0 | 44.0 | 16500 | 1.0211 | 0.8967 |
| 0.0 | 45.0 | 16875 | 1.0174 | 0.8967 |
| 0.0 | 46.0 | 17250 | 1.0256 | 0.8967 |
| 0.0 | 47.0 | 17625 | 1.0251 | 0.8967 |
| 0.0 | 48.0 | 18000 | 1.0297 | 0.8967 |
| 0.0 | 49.0 | 18375 | 1.0318 | 0.8967 |
| 0.0 | 50.0 | 18750 | 1.0303 | 0.8967 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2