computer-vision-projects's Introduction

Computer Vision Projects

This repository hosts a collection of computer vision projects using deep learning techniques, focusing on various real-world applications. Each project is designed to demonstrate the power of transfer learning and convolutional neural networks (CNN) in solving practical problems. Here's what you'll find in this repository:

Project Highlights:

Cataract Detection: Detect cataracts in eye images using pre-trained models and fine-tuning.
Traffic Sign Detection: Identify and classify traffic signs from images for improved road safety.
Pneumonia Detection: Utilize deep learning to diagnose pneumonia from chest X-ray images.
Emotion Detection: Build models to recognize and classify emotions from facial expressions.
MNIST Digit Classification: Develop a model to classify handwritten digits from the MNIST dataset, a fundamental task for beginners in computer vision.
Driver Drowsiness Detection: Enhance road safety with Driver Drowsiness Detection! Utilize Transfer Learning and Convolutional Neural Networks with Parallel Convolution Architecture to identify and classify driver drowsiness.
Eye Diseases: Contribute to healthcare with a deep learning project focused on classifying eye diseases. Employ Convolutional Neural Networks (CNNs), including those with a Parallel Convolution Architecture, for accurate disease classification.
Lane Detection for Autonomous Vehicles: Contribute to the development of autonomous vehicles with a lane detection algorithm using computer vision techniques. Highlight detected lanes on the road, providing a visual representation crucial for vehicle navigation and safety.
Object Detection Using YOLOv3: Experience the speed and accuracy of the YOLOv3 algorithm for real-time object detection. This repository provides code for implementing object detection and showcases the versatility of YOLOv3 in identifying and tracking various objects.

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