smids_3x_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: 1.4399
- Accuracy: 0.86
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 |
|---|---|---|---|---|
| 0.3979 | 1.0 | 225 | 0.5683 | 0.7933 |
| 0.2606 | 2.0 | 450 | 0.4043 | 0.84 |
| 0.2162 | 3.0 | 675 | 0.5101 | 0.82 |
| 0.2443 | 4.0 | 900 | 0.4744 | 0.84 |
| 0.1743 | 5.0 | 1125 | 0.4964 | 0.8567 |
| 0.1364 | 6.0 | 1350 | 0.5063 | 0.8433 |
| 0.1949 | 7.0 | 1575 | 0.5298 | 0.855 |
| 0.116 | 8.0 | 1800 | 0.6753 | 0.8617 |
| 0.1838 | 9.0 | 2025 | 0.6150 | 0.84 |
| 0.0967 | 10.0 | 2250 | 0.7712 | 0.8433 |
| 0.0509 | 11.0 | 2475 | 0.7887 | 0.8417 |
| 0.0235 | 12.0 | 2700 | 0.6823 | 0.8633 |
| 0.0623 | 13.0 | 2925 | 0.6980 | 0.8533 |
| 0.0443 | 14.0 | 3150 | 0.6897 | 0.87 |
| 0.0341 | 15.0 | 3375 | 0.8718 | 0.8417 |
| 0.037 | 16.0 | 3600 | 0.8120 | 0.865 |
| 0.0314 | 17.0 | 3825 | 0.8372 | 0.8467 |
| 0.0477 | 18.0 | 4050 | 0.7518 | 0.85 |
| 0.0002 | 19.0 | 4275 | 1.0178 | 0.8517 |
| 0.0091 | 20.0 | 4500 | 1.1728 | 0.8333 |
| 0.0291 | 21.0 | 4725 | 0.9139 | 0.85 |
| 0.0076 | 22.0 | 4950 | 1.0132 | 0.8533 |
| 0.0005 | 23.0 | 5175 | 1.0336 | 0.8567 |
| 0.0002 | 24.0 | 5400 | 1.1694 | 0.85 |
| 0.0032 | 25.0 | 5625 | 1.1362 | 0.86 |
| 0.0002 | 26.0 | 5850 | 1.2122 | 0.85 |
| 0.011 | 27.0 | 6075 | 1.2712 | 0.8567 |
| 0.0237 | 28.0 | 6300 | 1.3743 | 0.85 |
| 0.0 | 29.0 | 6525 | 1.2063 | 0.86 |
| 0.0 | 30.0 | 6750 | 1.3085 | 0.86 |
| 0.0 | 31.0 | 6975 | 1.3297 | 0.8567 |
| 0.0 | 32.0 | 7200 | 1.2473 | 0.855 |
| 0.0 | 33.0 | 7425 | 1.1982 | 0.8617 |
| 0.0 | 34.0 | 7650 | 1.2288 | 0.8617 |
| 0.0 | 35.0 | 7875 | 1.2397 | 0.8617 |
| 0.0 | 36.0 | 8100 | 1.2697 | 0.8617 |
| 0.0 | 37.0 | 8325 | 1.2895 | 0.8583 |
| 0.0 | 38.0 | 8550 | 1.3064 | 0.8583 |
| 0.0 | 39.0 | 8775 | 1.3040 | 0.8583 |
| 0.0 | 40.0 | 9000 | 1.3224 | 0.8617 |
| 0.0 | 41.0 | 9225 | 1.3481 | 0.8567 |
| 0.0032 | 42.0 | 9450 | 1.3550 | 0.8583 |
| 0.0 | 43.0 | 9675 | 1.3631 | 0.86 |
| 0.0029 | 44.0 | 9900 | 1.3898 | 0.8567 |
| 0.0 | 45.0 | 10125 | 1.3948 | 0.86 |
| 0.0 | 46.0 | 10350 | 1.4079 | 0.86 |
| 0.0 | 47.0 | 10575 | 1.4219 | 0.86 |
| 0.0 | 48.0 | 10800 | 1.4319 | 0.8583 |
| 0.0 | 49.0 | 11025 | 1.4436 | 0.8583 |
| 0.0 | 50.0 | 11250 | 1.4399 | 0.86 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2