Welcome to the Object Detection Project! This project aims to develop and deploy an object detection system using deep learning techniques. Object detection involves identifying and localizing objects within an image or video, making it a crucial task in various applications such as autonomous driving, surveillance, and image search engines.

In this project, we utilize state-of-the-art object detection models like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) to detect and classify multiple objects within images. These models are known for their accuracy and speed, making them ideal for real-time applications.

• Multiple Object Detection: Capable of detecting and classifying various objects in a single image.
• Bounding Boxes and Labels: Provides precise bounding boxes and labels for detected objects.
• Pre-trained Models: Includes pre-trained model weights for quick setup and use.
• Custom Training: Supports training on custom datasets for specialized applications.
• Visualization: Offers tools for visualizing detection results with bounding boxes and labels.

• Autonomous Driving: Enhances vehicle safety by detecting pedestrians, other vehicles, and obstacles.
• Surveillance: Improves security by identifying suspicious activities and objects.
• Image Search Engines: Facilitates image search by recognizing objects within images.
• Retail: Assists in inventory management and automated checkout systems by detecting products.

The primary goal of this project is to provide a robust and flexible object detection framework that can be easily adapted for various use cases. Whether you are a researcher, developer, or enthusiast, this project aims to provide the tools and resources needed to build and deploy effective object detection systems.

• Deep Learning Frameworks: TensorFlow, PyTorch
• Computer Vision: OpenCV
• Data Processing: NumPy, Pandas
• Visualization: Matplotlib, Seaborn

We hope this project serves as a valuable resource for your object detection needs. Happy detecting!