Lost and Found: Object Detection System This project implements a machine learning-based system for detecting and identifying lost and found objects using image processing techniques. It aims to streamline the process of locating lost items in public areas by automating the detection and categorization of objects.

Table of Contents Project Overview Installation Dataset Model Architecture The Lost and Found project is designed to detect objects in images using a deep learning approach. This system can be applied in airports, railway stations, schools, and other public places where lost and found services are essential. The system classifies objects into different categories, helping to automate the process of finding lost items.

Installation- Follow these steps to set up the project locally:

Clone the repository: git clone https://github.com/rishabhGit24/Lost-and-Found-.git cd Lost-and-Found-

Dataset: The dataset for this project consists of images of various objects that are commonly found in lost and found departments (e.g., wallets, bags, keys, phones, etc.). You can either create your own dataset or use publicly available datasets related to object detection.

Model Architecture: The model architecture uses a deep learning-based Convolutional Neural Network (CNN) for image classification. The CNN is designed to extract features from the images and categorize them into predefined object classes.