hushem_5x_deit_base_sgd_001_fold1
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: 1.2935
- Accuracy: 0.4222
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 |
|---|---|---|---|---|
| 1.3985 | 1.0 | 27 | 1.4731 | 0.2444 |
| 1.3797 | 2.0 | 54 | 1.4540 | 0.2667 |
| 1.3671 | 3.0 | 81 | 1.4399 | 0.3333 |
| 1.3529 | 4.0 | 108 | 1.4301 | 0.3333 |
| 1.3075 | 5.0 | 135 | 1.4206 | 0.3778 |
| 1.3006 | 6.0 | 162 | 1.4113 | 0.3778 |
| 1.2955 | 7.0 | 189 | 1.4036 | 0.3778 |
| 1.2684 | 8.0 | 216 | 1.3964 | 0.4 |
| 1.2547 | 9.0 | 243 | 1.3899 | 0.4 |
| 1.2309 | 10.0 | 270 | 1.3835 | 0.4 |
| 1.2188 | 11.0 | 297 | 1.3776 | 0.3778 |
| 1.1974 | 12.0 | 324 | 1.3722 | 0.3778 |
| 1.1972 | 13.0 | 351 | 1.3669 | 0.4 |
| 1.1775 | 14.0 | 378 | 1.3615 | 0.3778 |
| 1.1771 | 15.0 | 405 | 1.3571 | 0.3778 |
| 1.1595 | 16.0 | 432 | 1.3529 | 0.3778 |
| 1.11 | 17.0 | 459 | 1.3491 | 0.4 |
| 1.116 | 18.0 | 486 | 1.3456 | 0.4 |
| 1.0955 | 19.0 | 513 | 1.3420 | 0.4 |
| 1.0866 | 20.0 | 540 | 1.3386 | 0.4 |
| 1.0678 | 21.0 | 567 | 1.3355 | 0.4 |
| 1.0655 | 22.0 | 594 | 1.3327 | 0.4 |
| 1.0356 | 23.0 | 621 | 1.3298 | 0.4 |
| 1.0185 | 24.0 | 648 | 1.3265 | 0.3778 |
| 1.0437 | 25.0 | 675 | 1.3237 | 0.4 |
| 1.0442 | 26.0 | 702 | 1.3211 | 0.3778 |
| 1.028 | 27.0 | 729 | 1.3185 | 0.3778 |
| 1.0044 | 28.0 | 756 | 1.3165 | 0.3778 |
| 1.002 | 29.0 | 783 | 1.3148 | 0.4 |
| 0.9934 | 30.0 | 810 | 1.3131 | 0.4 |
| 0.9758 | 31.0 | 837 | 1.3109 | 0.4 |
| 0.9861 | 32.0 | 864 | 1.3087 | 0.4 |
| 0.9889 | 33.0 | 891 | 1.3069 | 0.4 |
| 0.9637 | 34.0 | 918 | 1.3052 | 0.4 |
| 0.9733 | 35.0 | 945 | 1.3034 | 0.4 |
| 0.9304 | 36.0 | 972 | 1.3021 | 0.4222 |
| 0.9586 | 37.0 | 999 | 1.3007 | 0.4222 |
| 0.9329 | 38.0 | 1026 | 1.2994 | 0.4222 |
| 0.918 | 39.0 | 1053 | 1.2983 | 0.4222 |
| 0.9142 | 40.0 | 1080 | 1.2972 | 0.4222 |
| 0.9236 | 41.0 | 1107 | 1.2963 | 0.4222 |
| 0.929 | 42.0 | 1134 | 1.2957 | 0.4222 |
| 0.9525 | 43.0 | 1161 | 1.2951 | 0.4222 |
| 0.8934 | 44.0 | 1188 | 1.2944 | 0.4222 |
| 0.9348 | 45.0 | 1215 | 1.2941 | 0.4222 |
| 0.9068 | 46.0 | 1242 | 1.2937 | 0.4222 |
| 0.9064 | 47.0 | 1269 | 1.2936 | 0.4222 |
| 0.9044 | 48.0 | 1296 | 1.2934 | 0.4222 |
| 0.9396 | 49.0 | 1323 | 1.2935 | 0.4222 |
| 0.894 | 50.0 | 1350 | 1.2935 | 0.4222 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0