
<!-- 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. -->

# tomato-disease-detection_V2

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0483
- Accuracy: 0.9887

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1585        | 1.0   | 949  | 0.1547          | 0.9663   |
| 0.1129        | 2.0   | 1898 | 0.0773          | 0.9826   |
| 0.0223        | 3.0   | 2847 | 0.0483          | 0.9887   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

tomato___bacterial_spot tomato___early_blight healthy tomato___late_blight tomato___leaf_mold tomato___septoria_leaf_spot tomato___spider_mites two-spotted_spider_mite tomato___target_spot tomato___tomato_yellow_leaf_curl_virus tomato___tomato_mosaic_virus affected