hushem_5x_deit_small_adamax_001_fold5
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: 1.9978
- Accuracy: 0.7561
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: 0.001
- 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 |
|---|---|---|---|---|
| 1.4105 | 1.0 | 28 | 1.2401 | 0.4878 |
| 1.1305 | 2.0 | 56 | 1.2429 | 0.4634 |
| 0.8883 | 3.0 | 84 | 1.0090 | 0.5366 |
| 0.7137 | 4.0 | 112 | 0.7064 | 0.7317 |
| 0.4682 | 5.0 | 140 | 0.8445 | 0.7073 |
| 0.5286 | 6.0 | 168 | 1.1652 | 0.5854 |
| 0.3742 | 7.0 | 196 | 0.6789 | 0.8049 |
| 0.3573 | 8.0 | 224 | 0.7278 | 0.7317 |
| 0.272 | 9.0 | 252 | 0.8849 | 0.7561 |
| 0.1472 | 10.0 | 280 | 1.2627 | 0.6829 |
| 0.1837 | 11.0 | 308 | 1.1712 | 0.7317 |
| 0.0937 | 12.0 | 336 | 1.4720 | 0.7073 |
| 0.1467 | 13.0 | 364 | 1.7992 | 0.6585 |
| 0.1394 | 14.0 | 392 | 1.7959 | 0.5854 |
| 0.0985 | 15.0 | 420 | 1.4497 | 0.7317 |
| 0.0548 | 16.0 | 448 | 1.4327 | 0.7561 |
| 0.0354 | 17.0 | 476 | 1.5157 | 0.7317 |
| 0.0897 | 18.0 | 504 | 1.9967 | 0.7561 |
| 0.0783 | 19.0 | 532 | 1.8000 | 0.6829 |
| 0.0872 | 20.0 | 560 | 2.1630 | 0.7073 |
| 0.0467 | 21.0 | 588 | 1.7971 | 0.7073 |
| 0.0024 | 22.0 | 616 | 1.1519 | 0.8049 |
| 0.001 | 23.0 | 644 | 1.4688 | 0.7805 |
| 0.0101 | 24.0 | 672 | 1.1822 | 0.8293 |
| 0.005 | 25.0 | 700 | 1.2237 | 0.8293 |
| 0.0001 | 26.0 | 728 | 1.7234 | 0.7317 |
| 0.0025 | 27.0 | 756 | 1.4712 | 0.7561 |
| 0.0001 | 28.0 | 784 | 2.0676 | 0.7805 |
| 0.0003 | 29.0 | 812 | 2.0101 | 0.7317 |
| 0.0 | 30.0 | 840 | 2.0010 | 0.7561 |
| 0.0 | 31.0 | 868 | 1.9976 | 0.7561 |
| 0.0 | 32.0 | 896 | 1.9954 | 0.7561 |
| 0.0 | 33.0 | 924 | 1.9948 | 0.7561 |
| 0.0 | 34.0 | 952 | 1.9948 | 0.7561 |
| 0.0 | 35.0 | 980 | 1.9948 | 0.7561 |
| 0.0 | 36.0 | 1008 | 1.9942 | 0.7561 |
| 0.0 | 37.0 | 1036 | 1.9944 | 0.7561 |
| 0.0 | 38.0 | 1064 | 1.9949 | 0.7561 |
| 0.0 | 39.0 | 1092 | 1.9951 | 0.7561 |
| 0.0 | 40.0 | 1120 | 1.9957 | 0.7561 |
| 0.0 | 41.0 | 1148 | 1.9963 | 0.7561 |
| 0.0 | 42.0 | 1176 | 1.9964 | 0.7561 |
| 0.0 | 43.0 | 1204 | 1.9969 | 0.7561 |
| 0.0 | 44.0 | 1232 | 1.9973 | 0.7561 |
| 0.0 | 45.0 | 1260 | 1.9974 | 0.7561 |
| 0.0 | 46.0 | 1288 | 1.9976 | 0.7561 |
| 0.0 | 47.0 | 1316 | 1.9978 | 0.7561 |
| 0.0 | 48.0 | 1344 | 1.9978 | 0.7561 |
| 0.0 | 49.0 | 1372 | 1.9978 | 0.7561 |
| 0.0 | 50.0 | 1400 | 1.9978 | 0.7561 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0