hushem_5x_deit_small_sgd_0001_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: 1.3702
- Accuracy: 0.2683
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: 0.0001
- 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 |
|---|---|---|---|---|
| 1.5914 | 1.0 | 28 | 1.5102 | 0.2195 |
| 1.502 | 2.0 | 56 | 1.4998 | 0.2439 |
| 1.5359 | 3.0 | 84 | 1.4897 | 0.2439 |
| 1.4953 | 4.0 | 112 | 1.4806 | 0.2195 |
| 1.505 | 5.0 | 140 | 1.4721 | 0.2439 |
| 1.5366 | 6.0 | 168 | 1.4645 | 0.2439 |
| 1.5251 | 7.0 | 196 | 1.4572 | 0.2439 |
| 1.4698 | 8.0 | 224 | 1.4506 | 0.2439 |
| 1.4915 | 9.0 | 252 | 1.4443 | 0.2439 |
| 1.4618 | 10.0 | 280 | 1.4384 | 0.2439 |
| 1.4473 | 11.0 | 308 | 1.4329 | 0.2439 |
| 1.4682 | 12.0 | 336 | 1.4279 | 0.2439 |
| 1.4426 | 13.0 | 364 | 1.4233 | 0.2439 |
| 1.4128 | 14.0 | 392 | 1.4190 | 0.2683 |
| 1.4363 | 15.0 | 420 | 1.4150 | 0.2683 |
| 1.4383 | 16.0 | 448 | 1.4113 | 0.2683 |
| 1.4168 | 17.0 | 476 | 1.4079 | 0.2683 |
| 1.4317 | 18.0 | 504 | 1.4047 | 0.2683 |
| 1.4208 | 19.0 | 532 | 1.4016 | 0.2927 |
| 1.4021 | 20.0 | 560 | 1.3989 | 0.2927 |
| 1.4325 | 21.0 | 588 | 1.3963 | 0.2927 |
| 1.4072 | 22.0 | 616 | 1.3940 | 0.2927 |
| 1.3729 | 23.0 | 644 | 1.3918 | 0.2927 |
| 1.3955 | 24.0 | 672 | 1.3898 | 0.2927 |
| 1.3868 | 25.0 | 700 | 1.3879 | 0.2927 |
| 1.3985 | 26.0 | 728 | 1.3861 | 0.2683 |
| 1.3854 | 27.0 | 756 | 1.3845 | 0.2683 |
| 1.3968 | 28.0 | 784 | 1.3830 | 0.2683 |
| 1.3689 | 29.0 | 812 | 1.3816 | 0.2683 |
| 1.4069 | 30.0 | 840 | 1.3803 | 0.2683 |
| 1.387 | 31.0 | 868 | 1.3791 | 0.2683 |
| 1.3786 | 32.0 | 896 | 1.3780 | 0.2683 |
| 1.3773 | 33.0 | 924 | 1.3769 | 0.2683 |
| 1.3779 | 34.0 | 952 | 1.3760 | 0.2683 |
| 1.3797 | 35.0 | 980 | 1.3751 | 0.2683 |
| 1.3671 | 36.0 | 1008 | 1.3744 | 0.2683 |
| 1.3638 | 37.0 | 1036 | 1.3737 | 0.2683 |
| 1.3614 | 38.0 | 1064 | 1.3731 | 0.2683 |
| 1.3646 | 39.0 | 1092 | 1.3725 | 0.2683 |
| 1.3609 | 40.0 | 1120 | 1.3720 | 0.2683 |
| 1.3899 | 41.0 | 1148 | 1.3716 | 0.2683 |
| 1.3896 | 42.0 | 1176 | 1.3712 | 0.2683 |
| 1.3725 | 43.0 | 1204 | 1.3709 | 0.2683 |
| 1.3896 | 44.0 | 1232 | 1.3706 | 0.2683 |
| 1.3695 | 45.0 | 1260 | 1.3704 | 0.2683 |
| 1.3698 | 46.0 | 1288 | 1.3703 | 0.2683 |
| 1.3813 | 47.0 | 1316 | 1.3702 | 0.2683 |
| 1.3636 | 48.0 | 1344 | 1.3702 | 0.2683 |
| 1.3528 | 49.0 | 1372 | 1.3702 | 0.2683 |
| 1.3747 | 50.0 | 1400 | 1.3702 | 0.2683 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0