Training YOLO with a Custom Dataset: A Step-by-Step Guide
Object detection has become an essential technology in various industries, including security, automation, and robotics. YOLO (You Only Look Once) is one of the most popular real-time object detection models due to its speed and accuracy. In this blog post, we will walk you through training YOLO with your custom dataset, making it ready for real-world applications.
Step 1: Install Dependencies
To begin, install the necessary dependencies. The latest versions of YOLOv5 or YOLOv8 make training simpler and more efficient.
# Clone the YOLOv5 repository
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
# Install required packages
pip install -r requirements.txt
For YOLOv8, you can install the Ultralytics package directly:
pip install ultralytics
Step 2: Prepare Your Dataset
YOLO requires data in a specific format, where each image has an associated annotation file in the YOLO format:
<class_id> <x_center> <y_center> <width> <height>
All values are normalized between 0 and 1. Below is the correct dataset folder structure:
/dataset
├── images
│ ├── train
│ │ ├── img1.jpg
│ │ ├── img2.jpg
│ ├── val
│ ├── img3.jpg
│ ├── img4.jpg
├── labels
│ ├── train
│ │ ├── img1.txt
│ │ ├── img2.txt
│ ├── val
│ ├── img3.txt
│ ├── img4.txt
├── data.yaml
Creating the data.yaml
File
This file defines the dataset structure and class names:
train: /path/to/dataset/images/train
val: /path/to/dataset/images/val
nc: 2 # Number of object classes
names: ['person', 'car'] # Object class names
Step 3: Train the Model
To train YOLOv5, run the following command:
python train.py --img 640 --batch 16 --epochs 50 --data dataset/data.yaml --weights yolov5s.pt --cache
For YOLOv8, use:
yolo train model=yolov8n.pt data=dataset/data.yaml epochs=50 imgsz=640
Step 4: Monitor Training Progress
YOLO logs various performance metrics during training. If using YOLOv5, results will be stored in runs/train/exp/
. You can visualize training performance using TensorBoard:
tensorboard --logdir=runs/train
Step 5: Evaluate and Test the Model
Once training is complete, test the model on new images:
python detect.py --weights runs/train/exp/weights/best.pt --img 640 --source test_images/
For YOLOv8:
yolo detect model=runs/train/exp/weights/best.pt source=test_images/
Step 6: Export for Deployment
YOLO models can be exported to multiple formats for deployment:
python export.py --weights runs/train/exp/weights/best.pt --include onnx torchscript
For YOLOv8:
yolo export model=runs/train/exp/weights/best.pt format=onnx
Final Thoughts
Training YOLO with a custom dataset enables real-world object detection for applications such as security, traffic monitoring, and automation. By following this step-by-step guide, you can prepare, train, and deploy your YOLO model effectively.
Would you like help automating the dataset preparation or optimizing training settings? Let us know in the comments!
Get in Touch with us
Related Posts
- Smart Vision System for Continuous Material Defect Detection
- Building a Real-Time Defect Detector with Line-Scan + ML (Reusable Playbook)
- How to Read Source Code: Frappe Framework Sample
- Interface-Oriented Design: The Foundation of Clean Architecture
- Understanding Anti-Drone Systems: Architecture, Hardware, and Software
- RTOS vs Linux in Drone Systems: Modern Design, Security, and Rust for Next-Gen Drones
- Why Does Spring Use So Many Annotations? Java vs. Python Web Development Explained
- From Django to Spring Boot: A Practical, Visual Guide for Web Developers
- How to Build Large, Maintainable Python Systems with Clean Architecture: Concepts & Real-World Examples
- Why Test-Driven Development Makes Better Business Sense
- Continuous Delivery for Django on DigitalOcean with GitHub Actions & Docker
- Build a Local Product Recommendation System with LangChain, Ollama, and Open-Source Embeddings
- 2025 Guide: Comparing the Top Mobile App Frameworks (Flutter, React Native, Expo, Ionic, and More)
- Understanding `np.meshgrid()` in NumPy: Why It’s Needed and What Happens When You Swap It
- How to Use PyMeasure for Automated Instrument Control and Lab Experiments
- Supercharge Your Chatbot: Custom API Integration Services for Your Business
- How to Guess an Equation Without Math: Exploring Cat vs. Bird Populations
- How to Build an AI-Resistant Project: Ideas That Thrive on Human Interaction
- Build Your Own Cybersecurity Lab with GNS3 + Wazuh + Docker: Train, Detect, and Defend in One Platform
- How to Simulate and Train with Network Devices Using GNS3