vit_4090_1_9
This model is a fine-tuned version of google/vit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1320
- Accuracy: 0.9724
- Precision: 0.9730
- Recall: 0.9724
- F1: 0.9713
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: 24
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.5464 | 1.0 | 177 | 0.2534 | 0.9257 | 0.9311 | 0.9257 | 0.9243 |
| 0.2416 | 2.0 | 354 | 0.1512 | 0.9575 | 0.9579 | 0.9575 | 0.9555 |
| 0.1362 | 3.0 | 531 | 0.1294 | 0.9745 | 0.9749 | 0.9745 | 0.9740 |
| 0.0913 | 4.0 | 708 | 0.1155 | 0.9660 | 0.9664 | 0.9660 | 0.9636 |
| 0.0649 | 5.0 | 885 | 0.1336 | 0.9724 | 0.9729 | 0.9724 | 0.9714 |
| 0.0482 | 6.0 | 1062 | 0.1733 | 0.9660 | 0.9674 | 0.9660 | 0.9652 |
| 0.0307 | 7.0 | 1239 | 0.1847 | 0.9745 | 0.9753 | 0.9745 | 0.9736 |
| 0.0356 | 8.0 | 1416 | 0.1455 | 0.9703 | 0.9708 | 0.9703 | 0.9690 |
| 0.015 | 9.0 | 1593 | 0.1237 | 0.9745 | 0.9779 | 0.9745 | 0.9743 |
| 0.0079 | 10.0 | 1770 | 0.1545 | 0.9724 | 0.9739 | 0.9724 | 0.9722 |
| 0.0086 | 11.0 | 1947 | 0.1669 | 0.9745 | 0.9755 | 0.9745 | 0.9735 |
| 0.0059 | 12.0 | 2124 | 0.1449 | 0.9682 | 0.9697 | 0.9682 | 0.9683 |
| 0.0067 | 13.0 | 2301 | 0.1734 | 0.9724 | 0.9735 | 0.9724 | 0.9718 |
| 0.0009 | 14.0 | 2478 | 0.1419 | 0.9745 | 0.9749 | 0.9745 | 0.9739 |
| 0.0004 | 15.0 | 2655 | 0.1510 | 0.9745 | 0.9750 | 0.9745 | 0.9737 |
| 0.0007 | 16.0 | 2832 | 0.1676 | 0.9724 | 0.9730 | 0.9724 | 0.9713 |
| 0.0011 | 17.0 | 3009 | 0.1225 | 0.9724 | 0.9730 | 0.9724 | 0.9713 |
| 0.0015 | 18.0 | 3186 | 0.1345 | 0.9682 | 0.9691 | 0.9682 | 0.9675 |
| 0.0001 | 19.0 | 3363 | 0.1263 | 0.9724 | 0.9730 | 0.9724 | 0.9713 |
| 0.0001 | 20.0 | 3540 | 0.1320 | 0.9724 | 0.9730 | 0.9724 | 0.9713 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.1