---
library_name: transformers
tags:
- image-classification
- vit
- pytorch
license: apache-2.0
language:
- en
metrics:
- accuracy
- f1
datasets:
- AI-Lab-Makerere/beans
---

# Umsakwa/Uddayvit-image-classification-model

This Vision Transformer (ViT) model has been fine-tuned for image classification tasks on the [Beans Dataset](https://huggingface.co/datasets/beans), which consists of images of beans categorized into three classes:

- **Angular Leaf Spot**
- **Bean Rust**
- **Healthy**

## Model Details

- **Architecture**: Vision Transformer (ViT)
- **Base Model**: `google/vit-base-patch16-224-in21k`
- **Framework**: PyTorch
- **Task**: Image Classification
- **Labels**: 3 (angular_leaf_spot, bean_rust, healthy)
- **Input Shape**: 224x224 RGB images
- **Training Dataset**: [Beans Dataset](https://huggingface.co/datasets/beans)
- **Fine-Tuning**: The model was fine-tuned on the Beans dataset to classify plant diseases in beans.

### Model Description

The model uses the ViT architecture, which processes image patches using a transformer-based approach. It has been trained to classify bean diseases with high accuracy. This makes it particularly useful for agricultural applications, such as early disease detection and plant health monitoring.

- **Developed by**: Udday (Umsakwa)
- **Language(s)**: N/A (Image-based)
- **License**: Apache-2.0
- **Finetuned from**: `google/vit-base-patch16-224-in21k`

### Model Sources

- **Repository**: [Umsakwa/Uddayvit-image-classification-model](https://huggingface.co/Umsakwa/Uddayvit-image-classification-model)

## Uses

### Direct Use

This model can be directly used for classifying bean leaf images into one of three categories: angular leaf spot, bean rust, or healthy.

### Downstream Use

The model may also be fine-tuned further for similar agricultural image classification tasks or integrated into larger plant health monitoring systems.

### Out-of-Scope Use

- The model is not suitable for non-agricultural image classification tasks without further fine-tuning.
- Not robust to extreme distortions, occlusions, or very low-resolution images.

## Bias, Risks, and Limitations

- **Bias**: The dataset may contain biases due to specific environmental or geographic conditions of the sampled plants.
- **Limitations**: Performance may degrade on datasets significantly different from the training dataset.

### Recommendations

- Users should ensure the model is evaluated on their specific dataset before deployment.
- Additional fine-tuning may be required for domain-specific applications.

## How to Get Started with the Model

To use this model for inference:

```python
from transformers import ViTForImageClassification, ViTImageProcessor

# Load model and processor
model = ViTForImageClassification.from_pretrained("Umsakwa/Uddayvit-image-classification-model")
processor = ViTImageProcessor.from_pretrained("Umsakwa/Uddayvit-image-classification-model")

# Prepare an image
image = processor(images="path_to_image.jpg", return_tensors="pt")

# Run inference
outputs = model(**image)
predictions = outputs.logits.argmax(-1)