
<!-- 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. -->

# dinov2-base_rice-leaf-disease-augmented-v4_v5_fft

This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2683
- Accuracy: 0.9430

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3383        | 0.5   | 64   | 0.4827          | 0.8356   |
| 0.2775        | 1.0   | 128  | 0.3518          | 0.8658   |
| 0.1456        | 1.5   | 192  | 0.5239          | 0.8490   |
| 0.1629        | 2.0   | 256  | 0.2593          | 0.9295   |
| 0.1255        | 2.5   | 320  | 0.3740          | 0.8993   |
| 0.1142        | 3.0   | 384  | 0.3753          | 0.9128   |
| 0.06          | 3.5   | 448  | 0.3722          | 0.9295   |
| 0.0587        | 4.0   | 512  | 0.4174          | 0.9228   |
| 0.0157        | 4.5   | 576  | 0.3364          | 0.9329   |
| 0.0062        | 5.0   | 640  | 0.2237          | 0.9396   |
| 0.0012        | 5.5   | 704  | 0.2186          | 0.9530   |
| 0.0001        | 6.0   | 768  | 0.2342          | 0.9430   |
| 0.0           | 6.5   | 832  | 0.2343          | 0.9430   |
| 0.0           | 7.0   | 896  | 0.2563          | 0.9430   |
| 0.0           | 7.5   | 960  | 0.2597          | 0.9430   |
| 0.0           | 8.0   | 1024 | 0.2546          | 0.9430   |
| 0.0           | 8.5   | 1088 | 0.2553          | 0.9430   |
| 0.0           | 9.0   | 1152 | 0.2562          | 0.9430   |
| 0.0           | 9.5   | 1216 | 0.2570          | 0.9430   |
| 0.0           | 10.0  | 1280 | 0.2564          | 0.9430   |
| 0.0           | 10.5  | 1344 | 0.2566          | 0.9430   |
| 0.0           | 11.0  | 1408 | 0.2565          | 0.9430   |
| 0.0           | 11.5  | 1472 | 0.2578          | 0.9430   |
| 0.0           | 12.0  | 1536 | 0.2580          | 0.9430   |
| 0.0           | 12.5  | 1600 | 0.2571          | 0.9430   |
| 0.0           | 13.0  | 1664 | 0.2590          | 0.9430   |
| 0.0           | 13.5  | 1728 | 0.2599          | 0.9430   |
| 0.0           | 14.0  | 1792 | 0.2595          | 0.9430   |
| 0.0           | 14.5  | 1856 | 0.2594          | 0.9430   |
| 0.0           | 15.0  | 1920 | 0.2597          | 0.9430   |
| 0.0           | 15.5  | 1984 | 0.2596          | 0.9430   |
| 0.0           | 16.0  | 2048 | 0.2597          | 0.9430   |
| 0.0           | 16.5  | 2112 | 0.2605          | 0.9430   |
| 0.0           | 17.0  | 2176 | 0.2602          | 0.9430   |
| 0.0           | 17.5  | 2240 | 0.2608          | 0.9430   |
| 0.0           | 18.0  | 2304 | 0.2617          | 0.9430   |
| 0.0           | 18.5  | 2368 | 0.2628          | 0.9430   |
| 0.0           | 19.0  | 2432 | 0.2621          | 0.9430   |
| 0.0           | 19.5  | 2496 | 0.2621          | 0.9430   |
| 0.0           | 20.0  | 2560 | 0.2621          | 0.9430   |
| 0.0           | 20.5  | 2624 | 0.2621          | 0.9430   |
| 0.0           | 21.0  | 2688 | 0.2625          | 0.9430   |
| 0.0           | 21.5  | 2752 | 0.2625          | 0.9430   |
| 0.0           | 22.0  | 2816 | 0.2638          | 0.9430   |
| 0.0           | 22.5  | 2880 | 0.2648          | 0.9430   |
| 0.0           | 23.0  | 2944 | 0.2648          | 0.9430   |
| 0.0           | 23.5  | 3008 | 0.2645          | 0.9430   |
| 0.0           | 24.0  | 3072 | 0.2652          | 0.9430   |
| 0.0           | 24.5  | 3136 | 0.2654          | 0.9430   |
| 0.0           | 25.0  | 3200 | 0.2654          | 0.9430   |
| 0.0           | 25.5  | 3264 | 0.2654          | 0.9430   |
| 0.0           | 26.0  | 3328 | 0.2658          | 0.9430   |
| 0.0           | 26.5  | 3392 | 0.2657          | 0.9430   |
| 0.0           | 27.0  | 3456 | 0.2670          | 0.9430   |
| 0.0           | 27.5  | 3520 | 0.2680          | 0.9430   |
| 0.0           | 28.0  | 3584 | 0.2681          | 0.9430   |
| 0.0           | 28.5  | 3648 | 0.2687          | 0.9430   |
| 0.0           | 29.0  | 3712 | 0.2684          | 0.9430   |
| 0.0           | 29.5  | 3776 | 0.2683          | 0.9430   |
| 0.0           | 30.0  | 3840 | 0.2683          | 0.9430   |


### 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