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vit-base-patch16-224-finetuned-main-gpu-30e-final
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0231
- Accuracy: 0.9940
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5113 | 1.0 | 551 | 0.4745 | 0.7971 |
| 0.3409 | 2.0 | 1102 | 0.2697 | 0.8961 |
| 0.2675 | 3.0 | 1653 | 0.1611 | 0.9381 |
| 0.2092 | 4.0 | 2204 | 0.1176 | 0.9548 |
| 0.2008 | 5.0 | 2755 | 0.0889 | 0.9656 |
| 0.1555 | 6.0 | 3306 | 0.0666 | 0.9759 |
| 0.1614 | 7.0 | 3857 | 0.0576 | 0.9778 |
| 0.1518 | 8.0 | 4408 | 0.0517 | 0.9814 |
| 0.1231 | 9.0 | 4959 | 0.0528 | 0.9812 |
| 0.1076 | 10.0 | 5510 | 0.0426 | 0.9850 |
| 0.0953 | 11.0 | 6061 | 0.0634 | 0.9795 |
| 0.1097 | 12.0 | 6612 | 0.0398 | 0.9860 |
| 0.0763 | 13.0 | 7163 | 0.0348 | 0.9866 |
| 0.0895 | 14.0 | 7714 | 0.0341 | 0.9884 |
| 0.06 | 15.0 | 8265 | 0.0381 | 0.9883 |
| 0.0767 | 16.0 | 8816 | 0.0382 | 0.9875 |
| 0.0868 | 17.0 | 9367 | 0.0309 | 0.9898 |
| 0.091 | 18.0 | 9918 | 0.0339 | 0.9885 |
| 0.0817 | 19.0 | 10469 | 0.0243 | 0.9913 |
| 0.0641 | 20.0 | 11020 | 0.0286 | 0.9906 |
| 0.0703 | 21.0 | 11571 | 0.0314 | 0.9906 |
| 0.0642 | 22.0 | 12122 | 0.0261 | 0.9913 |
| 0.0695 | 23.0 | 12673 | 0.0260 | 0.9920 |
| 0.0664 | 24.0 | 13224 | 0.0241 | 0.9928 |
| 0.0552 | 25.0 | 13775 | 0.0258 | 0.9928 |
| 0.056 | 26.0 | 14326 | 0.0230 | 0.9939 |
| 0.0488 | 27.0 | 14877 | 0.0221 | 0.9936 |
| 0.0389 | 28.0 | 15428 | 0.0225 | 0.9930 |
| 0.0402 | 29.0 | 15979 | 0.0231 | 0.9940 |
| 0.0424 | 30.0 | 16530 | 0.0211 | 0.9939 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
