Automated Egg Counting System

This project implements an Automated Egg Counting System that detects and counts eggs in real time on a conveyor belt.
It uses YOLOv5 for object detection and a Raspberry Pi for on-device inference, giving egg-packing facilities an accurate, low-overhead way to track production.

---

1 · Project Overview

• Goal: Reduce human error and speed up counting in egg-packaging lines.
• Approach: Train a custom YOLOv5 model, deploy it on a Raspberry Pi, and log counts as eggs pass a checkpoint on the belt.

---

2 · Key Features

Feature	Description
Real-time counting	Counts eggs as they cross the camera’s checkpoint.
High accuracy	YOLOv5’s detection keeps false positives/negatives low.
Data logging	Live on-screen count plus CSV logging for batch analysis.

---

3 · Hardware

• Raspberry Pi 3 or 4
• Pi Camera Module (or USB webcam)
• Consistent LED lighting over the conveyor

---

4 · Software

Layer	Requirement
Model training	Google Colab + YOLOv5 notebook
Runtime	Raspberry Pi OS, Python 3.8+
Python libs	torch, torchvision, opencv-python, numpy, pandas, matplotlib

---

5 · Setup & Workflow

5.1 Train the Model

1. Label egg images (e.g., Roboflow or CVAT).
2. In Google Colab, train YOLOv5 and export best.pt.

5.2 Prepare the Raspberry Pi

# Clone your repo
git clone https://github.com/<your-org>/egg-counter.git
cd egg-counter

# Install Python deps
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt