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 (angular_leaf_spot, 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)
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