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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# Fine-Tuned ViT for Beans Leaf Disease Classification
## Model Information
* **Model Name**: VIT_Beans_Leaf_Disease_Classifier
* **Base Model**: Google/ViT-base-patch16-224-in21k
* **Task**: Image Classification (Beans Leaf Disease Classification)
* **Dataset**: Beans leaf dataset with images of diseased and healthy leaves.
## Problem Statement
The goal of this model is to classify leaf images into three categories:
```
{
"angular_leaf_spot": 0,
"bean_rust": 1,
"healthy": 2,
}
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1495 | 1.54 | 100 | 0.0910 | 0.9774 |
| 0.0121 | 3.08 | 200 | 0.0155 | 1.0 |
## Framework versions
+ Transformers 4.33.2
+ Pytorch 2.0.1+cu118
+ Datasets 2.14.5
+ Tokenizers 0.13.3
## Get Started With The Model:
```
! pip -q install datasets transformers[torch]
```
```python
from transformers import pipeline
from PIL import Image
# Use a pipeline as a high-level helper
pipe = pipeline("image-classification", model="ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier")
# Load the image
image_path = "Your image_path "
image = Image.open(image_path)
# Run inference using the pipeline
result = pipe(image)
# The result contains the predicted label and the corresponding score
predicted_label = result[0]['label']
confidence_score = result[0]['score']
print(f"Predicted Label: {predicted_label}")
print(f"Confidence Score: {confidence_score}")
```
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