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!
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