Computer Vision Projects

Automatic Eye Cataract Detection is an AI-based tool leveraging YOLOv8 for precise and quick cataract diagnosis, enhancing efficiency and accuracy in eye care.

This project utilizes YOLOv8 to build a crop disease detection and classification system in Google Colab. The system processes images and videos to identify diseases, providing an interactive interface for real-time analysis using Gradio.

Banana Leaf Disease Detection leverages Deep Learning and Computer Vision to identify plant diseases early, helping farmers protect their crops, improve cultivation, and reduce losses with smarter, more sustainable farming methods.

Our project uses deep learning to detect leaf diseases from images. By training models like VGG16 and EfficientNet on a robust dataset, we accurately diagnose plant conditions, aiding farmers in early disease detection and promoting healthier crops.

Diffusers and stable diffusion models can be used to improve image production. This project enables realistic synthesis with advanced deep learning techniques, interactive image creation via Gradio UI, and customizable training.

YOLOv8 is used in this project to identify human poses in real time. As the COCO dataset is used to train the model, its performance is checked, and poses in photos and videos are predicted. Pose recognition compresses the video output so that it can be s

YOLOv8 and OCR models are used for accurate and quick results in automated license plate identification and recognition.

You can improve UNet training by using checkpoints, LR adjustments, label encoding, and seeing examples to make sure they work.