hushem_5x_deit_small_adamax_001_fold4
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: 2.6296
- Accuracy: 0.7143
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 |
|---|---|---|---|---|
| 1.4114 | 1.0 | 28 | 1.1807 | 0.5238 |
| 1.2764 | 2.0 | 56 | 0.9991 | 0.5 |
| 0.9708 | 3.0 | 84 | 0.8093 | 0.5952 |
| 0.8975 | 4.0 | 112 | 0.7127 | 0.7143 |
| 0.8745 | 5.0 | 140 | 0.6175 | 0.7857 |
| 0.8263 | 6.0 | 168 | 0.5662 | 0.7857 |
| 0.7023 | 7.0 | 196 | 0.9422 | 0.4762 |
| 0.739 | 8.0 | 224 | 1.0785 | 0.6667 |
| 0.6219 | 9.0 | 252 | 0.7234 | 0.7381 |
| 0.5557 | 10.0 | 280 | 0.9010 | 0.6429 |
| 0.5536 | 11.0 | 308 | 0.8428 | 0.6905 |
| 0.4341 | 12.0 | 336 | 1.4219 | 0.5952 |
| 0.4008 | 13.0 | 364 | 1.0252 | 0.6190 |
| 0.3661 | 14.0 | 392 | 0.9782 | 0.6667 |
| 0.2255 | 15.0 | 420 | 1.0569 | 0.6905 |
| 0.1906 | 16.0 | 448 | 1.2300 | 0.7381 |
| 0.1975 | 17.0 | 476 | 1.3396 | 0.6905 |
| 0.1887 | 18.0 | 504 | 1.2504 | 0.6905 |
| 0.1207 | 19.0 | 532 | 1.3305 | 0.6905 |
| 0.0442 | 20.0 | 560 | 1.3516 | 0.7143 |
| 0.0689 | 21.0 | 588 | 1.7387 | 0.7143 |
| 0.0525 | 22.0 | 616 | 1.7134 | 0.6667 |
| 0.0339 | 23.0 | 644 | 1.8502 | 0.7381 |
| 0.0226 | 24.0 | 672 | 2.0846 | 0.7143 |
| 0.0026 | 25.0 | 700 | 2.2517 | 0.6905 |
| 0.0003 | 26.0 | 728 | 2.4690 | 0.6667 |
| 0.0231 | 27.0 | 756 | 2.7345 | 0.7143 |
| 0.0001 | 28.0 | 784 | 2.7167 | 0.7381 |
| 0.0001 | 29.0 | 812 | 2.5959 | 0.7143 |
| 0.0033 | 30.0 | 840 | 2.4481 | 0.7143 |
| 0.0096 | 31.0 | 868 | 2.5344 | 0.6905 |
| 0.0001 | 32.0 | 896 | 2.5507 | 0.7143 |
| 0.0 | 33.0 | 924 | 2.5703 | 0.7143 |
| 0.0 | 34.0 | 952 | 2.5803 | 0.7143 |
| 0.0 | 35.0 | 980 | 2.5893 | 0.7143 |
| 0.0 | 36.0 | 1008 | 2.5965 | 0.7143 |
| 0.0 | 37.0 | 1036 | 2.6022 | 0.7143 |
| 0.0 | 38.0 | 1064 | 2.6075 | 0.7143 |
| 0.0 | 39.0 | 1092 | 2.6116 | 0.7143 |
| 0.0 | 40.0 | 1120 | 2.6157 | 0.7143 |
| 0.0 | 41.0 | 1148 | 2.6188 | 0.7143 |
| 0.0 | 42.0 | 1176 | 2.6214 | 0.7143 |
| 0.0 | 43.0 | 1204 | 2.6241 | 0.7143 |
| 0.0 | 44.0 | 1232 | 2.6260 | 0.7143 |
| 0.0 | 45.0 | 1260 | 2.6276 | 0.7143 |
| 0.0 | 46.0 | 1288 | 2.6284 | 0.7143 |
| 0.0 | 47.0 | 1316 | 2.6292 | 0.7143 |
| 0.0 | 48.0 | 1344 | 2.6296 | 0.7143 |
| 0.0 | 49.0 | 1372 | 2.6296 | 0.7143 |
| 0.0 | 50.0 | 1400 | 2.6296 | 0.7143 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0