YOLOv8 with Custom Object Detection

Object detection has come a long way, and the YOLO (You Only Look Once) series has been a key contributor to its progress. The latest in this line, YOLOv8, offers a powerful, flexible, and easy-to-train framework for both detection and segmentation. In this post, we will walk through how to train YOLOv8 on your own custom dataset.

What Is YOLOv8?

YOLOv8 is an object detection model developed by Ultralytics. Unlike previous versions, YOLOv8 supports not only detection, but also classification and segmentation tasks out of the box. It is optimized for speed and accuracy, and it comes with a modern Python-based interface.

Step 1: Install YOLOv8

First, install the Ultralytics package using pip:

pip install ultralytics

This gives you access to the `yolo` CLI and Python API for training and inference.

Step 2: Prepare Your Dataset

YOLOv8 expects data in the YOLO format. Each image should have a corresponding `.txt` annotation file with bounding boxes in the format:

<class_id> <x_center> <y_center> <width> <height>

Create a folder structure like this:

dataset/
├── images/
│   ├── train/
│   └── val/
├── labels/
│   ├── train/
│   └── val/

Also, create a `data.yaml` file that specifies the dataset paths and class names.

Step 3: Train the Model

Use the following command to start training:

yolo detect train data=data.yaml model=yolov8n.pt epochs=50 imgsz=640

This trains YOLOv8 using the Nano model variant. You can also choose `yolov8s.pt`, `yolov8m.pt`, or `yolov8l.pt` depending on your accuracy and speed needs.

Step 4: Run Inference

Once trained, you can use your model to make predictions on images or videos:

yolo detect predict model=runs/detect/train/weights/best.pt source=path/to/image.jpg

You can also use the Python API for more control:

from ultralytics import YOLO

model = YOLO("path/to/best.pt")
results = model("image.jpg")
results[0].save("output.jpg")

Tips for Better Accuracy

• Use higher-resolution images for better object detection
• Label your data accurately and consistently
• Balance your dataset across classes
• Train longer or use a larger model if accuracy is low

Conclusion

YOLOv8 makes custom object detection easier than ever. With its clean interface, support for multiple tasks, and high performance, it is a great choice for developers and researchers alike. Whether you're detecting products on shelves or vehicles in traffic, YOLOv8 gives you the flexibility to build what you need.