Level Up Your Object Detection Game: A Practical Guide to Training YOLOv8 on Custom Data

Object detection is a game-changer in AI, and YOLO models lead the way. Since its groundbreaking inception in 2016, the YOLO (You Only Look Once) suite has revolutionized real-time object identification.

Ready to master the latest version?

This guide dives into training YOLOv8 on custom data, enabling you to build powerful object detectors tailored to your specific needs by fine-tuning a custom version of YOLOv8 using RoboFlow and DigitalOcean GPU Droplets with the new Ultralytics API.

Is This Tutorial Right for You? Pre-requisites

Before you jump in, make sure you have these basics covered:

• Python Fundamentals: Elementary knowledge of Python coding.
• Deep Learning Essentials: An understanding of Neural Networks, CNNs, and object detection principles.
• Framework Familiarity: Exposure to either PyTorch or TensorFlow.
• Image Processing: An understanding of OpenCV.
• CUDA & GPU: Experience with GPU acceleration and CUDA.
• RoboFlow Ready: You should have a RoboFlow account.
• Git Basics: Managing code and version control.

How YOLO Works: A High-Level Overview

YOLO works by dividing an image into a grid. Each grid cell then predicts:

• Bounding Boxes: The location of objects within the cell.
• Class Probabilities: The likelihood of each object class being present.

YOLO's magic lies in processing the entire image in a single pass, optimizing detection performance. This makes it incredibly fast and efficient.

What’s New in YOLOv8? Exciting Upgrades Explained

YOLOv8 brings significant architectural changes & improvements. Since the original papers are still in progress, here's an overview of what has been identified thus far:

• New Backbone Network: The updated model backbone and head structures.
• Anchor-Free Detection Head: Simplifying object localization.
• New Loss Function: Improving training and accuracy.
• C2f Module: Enhanced information flow between Bottleneck layers.
• 3x3 Conv Block: Replaces the initial 6x6 Conv for finer feature extraction.
• Decoupled Head: Eliminating the objectness branch for streamlined processing.

These changes contribute to improved performance and efficiency.

Key Advantages of YOLOv8

• Ultralytics API: Facilitates training and inference.
• Anchor-Free Bounding Boxes: Simplifies the detection process.
• Mosaic Augmentation Control: Optimizes training by strategically stopping mosaic augmentation.
• Superior Performance: Enhanced speed and accuracy compared to previous versions.

Performance Statistics: A Head-to-Head Comparison

YOLOv8 consistently outperforms previous versions, including YOLOv7, YOLOv6-2.0, and YOLOv5-7.0, in terms of mean Average Precision (mAP), size, and latency during training. The increase in mAP signifies a notable enhancement in object detection capabilities.

Hands-on: Fine-Tuning YOLOv8 on Your Data

Ready to get your hands dirty? Here's how to fine-tune YOLOv8:

1. Dataset Preparation: Create and label your dataset using a platform like RoboFlow.
2. Model Training: Train YOLOv8 with your custom data, taking advantage of the Ultralytics API.
3. Deployment: Deploy your trained model for real-world object detection tasks.

Setting Up a GPU-Powered Environment

To follow along, a GPU-powered machine is crucial. Consider using cloud-based GPU services of DigitalOcean's GPU Droplets. Using a Jupyter Notebook environment, clone the following repo using the code snippet below, and launch the Jupyter environment.

git clone https://github.com/gradient-ai/YOLOv8-Ballhandler
cd YOLOv8-Ballhandler
jupyter lab

Configuring Your Custom Dataset

Once the demo runs, pull your data in from tools such as RoboFlow. The command below is used to get the data into a Notebook environment.

!pip install roboflow

from roboflow import Roboflow
rf = Roboflow(api_key="")
project = rf.workspace("james-skelton").project("ballhandler-basketball")
dataset = project.version(11).download("yolov8")
!mkdir datasets
!mv ballhandler-basketball-11/ datasets/

Remember: Tailor the workspace and project values to match your unique dataset. Also change the API Key.

Training Code: Unleash the Power of Ultralytics

Utilize the ultralytics library to kickstart the training process.

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)

# Use the model
results = model.train(data="datasets/ballhandler-basketball-11/data.yaml", epochs=10) # train the model

Evaluating Model Performance

After training, evaluate your model's performance using the model.val() method.

results = model.val() # evaluate model performance on the validation set

Testing the Model on Images

Finally, test your trained model on individual images:

from ultralytics import YOLO
from PIL import Image
import cv2

# from PIL
im1 = Image.open("assets/samp.jpeg")
results = model.predict(source=im1, save=True) # save plotted images
print(results)
display(Image.open('runs/detect/predict/image0.jpg'))