deit-rice-disease
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: 1.1873
- Accuracy: 0.9490
- F1 Score: 0.9528
- Recall: 0.9596
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: 50
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
|---|---|---|---|---|---|---|
| 2.7759 | 1.0 | 20 | 2.7706 | 0.3057 | 0.3020 | 0.3099 |
| 2.6344 | 2.0 | 40 | 2.6186 | 0.4777 | 0.4633 | 0.4620 |
| 2.3890 | 3.0 | 60 | 2.3909 | 0.6242 | 0.6270 | 0.6041 |
| 2.0561 | 4.0 | 80 | 2.0708 | 0.7452 | 0.7566 | 0.7454 |
| 1.6978 | 5.0 | 100 | 1.7234 | 0.8153 | 0.8236 | 0.8209 |
| 1.4285 | 6.0 | 120 | 1.5000 | 0.8408 | 0.8465 | 0.8518 |
| 1.2876 | 7.0 | 140 | 1.3515 | 0.8917 | 0.8952 | 0.9039 |
| 1.2463 | 8.0 | 160 | 1.3041 | 0.9108 | 0.9136 | 0.9222 |
| 1.1444 | 9.0 | 180 | 1.2831 | 0.9236 | 0.9256 | 0.9364 |
| 1.1086 | 10.0 | 200 | 1.2490 | 0.9299 | 0.9327 | 0.9425 |
| 1.0744 | 11.0 | 220 | 1.2496 | 0.9299 | 0.9327 | 0.9425 |
| 1.0598 | 12.0 | 240 | 1.2804 | 0.9299 | 0.9327 | 0.9425 |
| 1.0738 | 13.0 | 260 | 1.2100 | 0.9363 | 0.9398 | 0.9486 |
| 1.0744 | 14.0 | 280 | 1.2112 | 0.9299 | 0.9327 | 0.9405 |
| 1.0476 | 15.0 | 300 | 1.2396 | 0.9363 | 0.9398 | 0.9486 |
| 1.0307 | 16.0 | 320 | 1.1843 | 0.9363 | 0.9396 | 0.9461 |
| 1.0167 | 17.0 | 340 | 1.1528 | 0.9490 | 0.9513 | 0.9559 |
| 1.0089 | 18.0 | 360 | 1.1433 | 0.9554 | 0.9586 | 0.9620 |
| 1.0003 | 19.0 | 380 | 1.1530 | 0.9427 | 0.9442 | 0.9498 |
| 1.0012 | 20.0 | 400 | 1.1984 | 0.9299 | 0.9327 | 0.9400 |
| 0.9774 | 21.0 | 420 | 1.1554 | 0.9554 | 0.9584 | 0.9620 |
| 0.9777 | 22.0 | 440 | 1.1511 | 0.9490 | 0.9527 | 0.9571 |
| 0.9818 | 23.0 | 460 | 1.1955 | 0.9490 | 0.9528 | 0.9596 |
| 0.9811 | 24.0 | 480 | 1.2225 | 0.9427 | 0.9458 | 0.9515 |
| 0.9757 | 25.0 | 500 | 1.1872 | 0.9427 | 0.9457 | 0.9535 |
| 0.9845 | 26.0 | 520 | 1.2139 | 0.9363 | 0.9410 | 0.9498 |
| 0.9657 | 27.0 | 540 | 1.1985 | 0.9427 | 0.9469 | 0.9547 |
| 0.9737 | 28.0 | 560 | 1.1873 | 0.9490 | 0.9528 | 0.9596 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2