
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-hybrid-base-bit-384_rice-leaf-disease-augmented-v4_v5_pft

This model is a fine-tuned version of [google/vit-hybrid-base-bit-384](https://huggingface.co/google/vit-hybrid-base-bit-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2706
- Accuracy: 0.9195

## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 256
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2177        | 0.5   | 64   | 2.0234          | 0.2248   |
| 1.7719        | 1.0   | 128  | 1.4579          | 0.5872   |
| 1.258         | 1.5   | 192  | 1.0302          | 0.7081   |
| 0.9323        | 2.0   | 256  | 0.7450          | 0.8154   |
| 0.7252        | 2.5   | 320  | 0.6527          | 0.7953   |
| 0.6134        | 3.0   | 384  | 0.5488          | 0.8154   |
| 0.5375        | 3.5   | 448  | 0.5004          | 0.8221   |
| 0.5028        | 4.0   | 512  | 0.4624          | 0.8557   |
| 0.471         | 4.5   | 576  | 0.4532          | 0.8557   |
| 0.44          | 5.0   | 640  | 0.4378          | 0.8557   |
| 0.4302        | 5.5   | 704  | 0.4446          | 0.8423   |
| 0.4267        | 6.0   | 768  | 0.4300          | 0.8591   |
| 0.4341        | 6.5   | 832  | 0.4302          | 0.8591   |
| 0.406         | 7.0   | 896  | 0.4174          | 0.8658   |
| 0.4077        | 7.5   | 960  | 0.3973          | 0.8523   |
| 0.3639        | 8.0   | 1024 | 0.3747          | 0.8792   |
| 0.3463        | 8.5   | 1088 | 0.3701          | 0.8859   |
| 0.343         | 9.0   | 1152 | 0.3682          | 0.8859   |
| 0.322         | 9.5   | 1216 | 0.3567          | 0.8792   |
| 0.3224        | 10.0  | 1280 | 0.3555          | 0.8859   |
| 0.3103        | 10.5  | 1344 | 0.3529          | 0.8859   |
| 0.314         | 11.0  | 1408 | 0.3531          | 0.8859   |
| 0.3153        | 11.5  | 1472 | 0.3546          | 0.8859   |
| 0.3033        | 12.0  | 1536 | 0.3434          | 0.8792   |
| 0.2905        | 12.5  | 1600 | 0.3326          | 0.8859   |
| 0.2857        | 13.0  | 1664 | 0.3323          | 0.8893   |
| 0.2693        | 13.5  | 1728 | 0.3238          | 0.8893   |
| 0.2683        | 14.0  | 1792 | 0.3273          | 0.9027   |
| 0.2582        | 14.5  | 1856 | 0.3243          | 0.9060   |
| 0.2544        | 15.0  | 1920 | 0.3181          | 0.8993   |
| 0.2478        | 15.5  | 1984 | 0.3167          | 0.8993   |
| 0.255         | 16.0  | 2048 | 0.3166          | 0.8993   |
| 0.2586        | 16.5  | 2112 | 0.3087          | 0.8993   |
| 0.24          | 17.0  | 2176 | 0.3126          | 0.9060   |
| 0.2351        | 17.5  | 2240 | 0.3032          | 0.9027   |
| 0.2302        | 18.0  | 2304 | 0.3005          | 0.9094   |
| 0.2229        | 18.5  | 2368 | 0.2993          | 0.9128   |
| 0.2185        | 19.0  | 2432 | 0.2982          | 0.9027   |
| 0.2138        | 19.5  | 2496 | 0.2968          | 0.9027   |
| 0.2128        | 20.0  | 2560 | 0.2952          | 0.9027   |
| 0.2134        | 20.5  | 2624 | 0.2946          | 0.9027   |
| 0.2107        | 21.0  | 2688 | 0.3014          | 0.8993   |
| 0.2077        | 21.5  | 2752 | 0.2885          | 0.9060   |
| 0.2073        | 22.0  | 2816 | 0.2911          | 0.9094   |
| 0.1943        | 22.5  | 2880 | 0.2853          | 0.9128   |
| 0.1979        | 23.0  | 2944 | 0.2806          | 0.9094   |
| 0.1907        | 23.5  | 3008 | 0.2793          | 0.9161   |
| 0.1848        | 24.0  | 3072 | 0.2794          | 0.9094   |
| 0.1884        | 24.5  | 3136 | 0.2780          | 0.9094   |
| 0.179         | 25.0  | 3200 | 0.2778          | 0.9094   |
| 0.1872        | 25.5  | 3264 | 0.2828          | 0.9128   |
| 0.181         | 26.0  | 3328 | 0.2749          | 0.9128   |
| 0.1779        | 26.5  | 3392 | 0.2752          | 0.9094   |
| 0.1783        | 27.0  | 3456 | 0.2730          | 0.9128   |
| 0.1777        | 27.5  | 3520 | 0.2720          | 0.9195   |
| 0.162         | 28.0  | 3584 | 0.2717          | 0.9128   |
| 0.1682        | 28.5  | 3648 | 0.2679          | 0.9128   |
| 0.1599        | 29.0  | 3712 | 0.2709          | 0.9128   |
| 0.1587        | 29.5  | 3776 | 0.2711          | 0.9161   |
| 0.164         | 30.0  | 3840 | 0.2706          | 0.9195   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.1

bacterial leaf blight brown spot healthy rice leaf leaf blast leaf scald narrow brown leaf spot rice hispa sheath blight