deit_fold_5
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: 1.1404
- Accuracy: 0.9619
- F1 Score: 0.9640
- Recall: 0.9646
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- num_epochs: 100
- label_smoothing_factor: 0.15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
|---|---|---|---|---|---|---|
| 2.8530 | 1.0 | 19 | 2.8472 | 0.2457 | 0.1618 | 0.2585 |
| 2.7569 | 2.0 | 38 | 2.6803 | 0.3460 | 0.2744 | 0.3182 |
| 2.5095 | 3.0 | 57 | 2.4824 | 0.5329 | 0.4757 | 0.4751 |
| 2.2538 | 4.0 | 76 | 2.2623 | 0.6436 | 0.6377 | 0.6053 |
| 1.8808 | 5.0 | 95 | 1.9165 | 0.7855 | 0.7984 | 0.7868 |
| 1.5094 | 6.0 | 114 | 1.5722 | 0.8720 | 0.8806 | 0.8797 |
| 1.2994 | 7.0 | 133 | 1.3893 | 0.8927 | 0.8983 | 0.8981 |
| 1.1850 | 8.0 | 152 | 1.3009 | 0.9135 | 0.9173 | 0.9189 |
| 1.0969 | 9.0 | 171 | 1.2608 | 0.9239 | 0.9300 | 0.9365 |
| 1.0729 | 10.0 | 190 | 1.2305 | 0.9377 | 0.9417 | 0.9437 |
| 1.0772 | 11.0 | 209 | 1.2142 | 0.9412 | 0.9445 | 0.9474 |
| 1.0385 | 12.0 | 228 | 1.1961 | 0.9481 | 0.9508 | 0.9524 |
| 1.0451 | 13.0 | 247 | 1.1773 | 0.9481 | 0.9514 | 0.9524 |
| 1.0382 | 14.0 | 266 | 1.1736 | 0.9516 | 0.9550 | 0.9561 |
| 1.0027 | 15.0 | 285 | 1.1668 | 0.9446 | 0.9486 | 0.9512 |
| 1.0166 | 16.0 | 304 | 1.1624 | 0.9446 | 0.9489 | 0.9525 |
| 1.0117 | 17.0 | 323 | 1.1576 | 0.9481 | 0.9506 | 0.9511 |
| 0.9923 | 18.0 | 342 | 1.1607 | 0.9446 | 0.9481 | 0.9486 |
| 0.9858 | 19.0 | 361 | 1.1551 | 0.9446 | 0.9489 | 0.9499 |
| 0.9924 | 20.0 | 380 | 1.1926 | 0.9377 | 0.9414 | 0.9384 |
| 0.9967 | 21.0 | 399 | 1.1682 | 0.9481 | 0.9507 | 0.9496 |
| 0.9727 | 22.0 | 418 | 1.1736 | 0.9446 | 0.9482 | 0.9461 |
| 0.9704 | 23.0 | 437 | 1.1639 | 0.9516 | 0.9544 | 0.9522 |
| 0.9733 | 24.0 | 456 | 1.1616 | 0.9481 | 0.9516 | 0.9536 |
| 0.9721 | 25.0 | 475 | 1.1662 | 0.9446 | 0.9482 | 0.9461 |
| 0.9680 | 26.0 | 494 | 1.1546 | 0.9481 | 0.9516 | 0.9511 |
| 0.9649 | 27.0 | 513 | 1.1476 | 0.9550 | 0.9582 | 0.9571 |
| 0.9737 | 28.0 | 532 | 1.1391 | 0.9550 | 0.9584 | 0.9584 |
| 0.9751 | 29.0 | 551 | 1.1496 | 0.9550 | 0.9580 | 0.9558 |
| 0.9702 | 30.0 | 570 | 1.1402 | 0.9550 | 0.9581 | 0.9597 |
| 0.9657 | 31.0 | 589 | 1.1368 | 0.9585 | 0.9610 | 0.9621 |
| 0.9674 | 32.0 | 608 | 1.1334 | 0.9550 | 0.9581 | 0.9597 |
| 0.9623 | 33.0 | 627 | 1.1573 | 0.9550 | 0.9574 | 0.9558 |
| 0.9718 | 34.0 | 646 | 1.1462 | 0.9585 | 0.9606 | 0.9595 |
| 0.9699 | 35.0 | 665 | 1.1404 | 0.9619 | 0.9640 | 0.9646 |
| 0.9699 | 36.0 | 684 | 1.1588 | 0.9516 | 0.9551 | 0.9572 |
| 0.9673 | 37.0 | 703 | 1.1423 | 0.9585 | 0.9615 | 0.9621 |
| 0.9611 | 38.0 | 722 | 1.1642 | 0.9481 | 0.9518 | 0.9485 |
| 0.9566 | 39.0 | 741 | 1.1492 | 0.9550 | 0.9580 | 0.9585 |
| 0.9627 | 40.0 | 760 | 1.1508 | 0.9585 | 0.9610 | 0.9621 |
| 0.9546 | 41.0 | 779 | 1.1408 | 0.9619 | 0.9640 | 0.9646 |
| 0.9604 | 42.0 | 798 | 1.1421 | 0.9619 | 0.9640 | 0.9646 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2