Drone Vision Project

Welcome to the Drone Vision Project! This repository contains a collection of resources, experiments, and a complete end-to-end pipeline for training object detection models with YOLOv8.

Project Structure

This repository is organized into several key modules, each serving a specific purpose in the drone vision pipeline and learning journey.

.
├── data/                    # Data handling: scripts for synthetic data generation, cleaning, and dataset preparation
├── experiments/             # OpenCV learning hub: tutorials, code examples, and interactive scripts
│   ├── _assets/             # Images/videos used in experiments
│   └── ...
├── models/                  # Model development: YOLOv8 training scripts, model weights, and related notebooks
│   ├── notebooks/           # Notebooks for model-related experiments
│   ├── training/            # Scripts for model training
│   └── weights/             # Directory for trained model weights
├── Slides/                  # Project presentations and related materials
├── .gitignore               # Files/directories to be ignored by Git
├── Opencv.md                # Detailed documentation for OpenCV experiments
├── README.md                # Project overview and navigation (this file)
├── requirements.txt         # Python dependencies
└── YOLO.md                  # Detailed documentation for the YOLOv8 pipeline

• data/: Contains scripts and configurations related to data engineering. This includes generating synthetic data, cleaning raw datasets, and preparing data for model training.
• experiments/: A comprehensive collection of OpenCV tutorials and examples, organized by topic. This module serves as a learning hub for various computer vision techniques.
• models/: Houses everything related to the YOLOv8 object detection model, from training scripts to pre-trained weights and evaluation notebooks.
• Slides/: This directory is designated for project presentations, reports, and any other related static materials. You can upload your presentations here.
• Opencv.md: Detailed documentation and guides for working with the OpenCV experiments.
• YOLO.md: In-depth documentation covering the end-to-end YOLOv8 model training pipeline.

Modules

This project is divided into two main parts:

1. OpenCV Experiments: A comprehensive set of tutorials and code examples for learning and experimenting with OpenCV. This is a great place to start if you are new to computer vision or want to explore specific techniques.

   • Go to OpenCV Documentation

2. YOLOv8 End-to-End Pipeline: A complete, step-by-step guide and implementation for training a YOLOv8 model on a custom dataset. This includes data collection, synthetic data generation, data cleaning, labeling, and model training.

   • Go to YOLOv8 Documentation

External Resources

This section is for linking to external project-related materials, such as online notebooks or hosted presentations.

Presentations

Project presentations and reports can be found in the Slides/ directory.

Google NotebookLM

• Vefa Project: YOLOv8 Data Preparation Pipeline
• Python Computer Vision with OpenCV: Fundamentals and Implementation
• YOLO Ultralytics: End-to-end

Getting Started

To get started with the project, you can explore the directories and documentation linked above.

Installation

If you want to run the code, you'll need to set up a Python virtual environment and install the required packages.

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt