---
library_name: transformers
license: apache-2.0
inference: true
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: plant_disease_detection(vriskharakshak)
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9811046511627907
---

<!-- 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. -->

# plant_disease_detection(vriksharakshak)

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0880
- Accuracy: 0.9811

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

# use this model
```python
from transformers import pipeline
from PIL import Image
import requests

# Load the image classification pipeline with a specific model
pipe = pipeline("image-classification", "ozair23/swin-tiny-patch4-window7-224-finetuned-plantdisease")

# Load the image from a URL
url = 'https://huggingface.co/nielsr/convnext-tiny-finetuned-eurostat/resolve/main/forest.png'
image = Image.open(requests.get(url, stream=True).raw)

# Classify the image
results = pipe(image)

# Display the results
print("Predictions:")
for result in results:
    print(f"Label: {result['label']}, Score: {result['score']:.4f}")
```
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1968        | 0.9983 | 145  | 0.0880          | 0.9811   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1