Umsakwa/vit-beans-classifier

Umsakwa/Uddayvit-image-classification-model

This Vision Transformer (ViT) model has been fine-tuned for image classification tasks on the Beans Dataset, 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 (angularleafspot, bean_rust, healthy)
• —Input Shape: 224x224 RGB images
• —Training Dataset: Beans Dataset
• —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

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:

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)