hushem_40x_deit_small_sgd_00001_fold1
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: 1.4582
- Accuracy: 0.2444
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 |
|---|---|---|---|---|
| 2.0145 | 1.0 | 215 | 1.6167 | 0.2444 |
| 1.9528 | 2.0 | 430 | 1.6051 | 0.2444 |
| 1.8549 | 3.0 | 645 | 1.5941 | 0.2444 |
| 1.9735 | 4.0 | 860 | 1.5837 | 0.2444 |
| 1.8613 | 5.0 | 1075 | 1.5740 | 0.2444 |
| 1.8998 | 6.0 | 1290 | 1.5649 | 0.2667 |
| 1.9805 | 7.0 | 1505 | 1.5563 | 0.2667 |
| 1.7761 | 8.0 | 1720 | 1.5483 | 0.2444 |
| 1.8452 | 9.0 | 1935 | 1.5408 | 0.2222 |
| 1.7743 | 10.0 | 2150 | 1.5338 | 0.2222 |
| 1.7542 | 11.0 | 2365 | 1.5272 | 0.2222 |
| 1.7225 | 12.0 | 2580 | 1.5212 | 0.2222 |
| 1.7844 | 13.0 | 2795 | 1.5156 | 0.2222 |
| 1.7312 | 14.0 | 3010 | 1.5104 | 0.2444 |
| 1.766 | 15.0 | 3225 | 1.5056 | 0.2222 |
| 1.6922 | 16.0 | 3440 | 1.5012 | 0.2222 |
| 1.6889 | 17.0 | 3655 | 1.4971 | 0.2222 |
| 1.6643 | 18.0 | 3870 | 1.4934 | 0.2 |
| 1.6328 | 19.0 | 4085 | 1.4899 | 0.2222 |
| 1.6408 | 20.0 | 4300 | 1.4868 | 0.2222 |
| 1.5927 | 21.0 | 4515 | 1.4839 | 0.2222 |
| 1.6137 | 22.0 | 4730 | 1.4813 | 0.2 |
| 1.623 | 23.0 | 4945 | 1.4789 | 0.1778 |
| 1.5692 | 24.0 | 5160 | 1.4767 | 0.1778 |
| 1.5514 | 25.0 | 5375 | 1.4747 | 0.1778 |
| 1.5483 | 26.0 | 5590 | 1.4729 | 0.2222 |
| 1.571 | 27.0 | 5805 | 1.4713 | 0.2222 |
| 1.5882 | 28.0 | 6020 | 1.4698 | 0.2222 |
| 1.524 | 29.0 | 6235 | 1.4684 | 0.2222 |
| 1.5611 | 30.0 | 6450 | 1.4672 | 0.2222 |
| 1.5511 | 31.0 | 6665 | 1.4661 | 0.2222 |
| 1.5655 | 32.0 | 6880 | 1.4650 | 0.2222 |
| 1.5736 | 33.0 | 7095 | 1.4641 | 0.2222 |
| 1.5317 | 34.0 | 7310 | 1.4633 | 0.2222 |
| 1.5555 | 35.0 | 7525 | 1.4625 | 0.2222 |
| 1.5608 | 36.0 | 7740 | 1.4619 | 0.2222 |
| 1.5011 | 37.0 | 7955 | 1.4612 | 0.2222 |
| 1.5571 | 38.0 | 8170 | 1.4607 | 0.2444 |
| 1.4975 | 39.0 | 8385 | 1.4602 | 0.2444 |
| 1.4908 | 40.0 | 8600 | 1.4598 | 0.2444 |
| 1.5291 | 41.0 | 8815 | 1.4595 | 0.2444 |
| 1.52 | 42.0 | 9030 | 1.4592 | 0.2444 |
| 1.5041 | 43.0 | 9245 | 1.4589 | 0.2444 |
| 1.5102 | 44.0 | 9460 | 1.4587 | 0.2444 |
| 1.5245 | 45.0 | 9675 | 1.4585 | 0.2444 |
| 1.4992 | 46.0 | 9890 | 1.4584 | 0.2444 |
| 1.4976 | 47.0 | 10105 | 1.4583 | 0.2444 |
| 1.5255 | 48.0 | 10320 | 1.4582 | 0.2444 |
| 1.4826 | 49.0 | 10535 | 1.4582 | 0.2444 |
| 1.5224 | 50.0 | 10750 | 1.4582 | 0.2444 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2