🚀 Mastering Custom Object Detection with YOLOv8: A Step-by-Step Guide to Precision in Computer Vision 📷🤖

Object detection is a crucial task in computer vision, with applications ranging from autonomous vehicles 🚗 to surveillance systems 🔍 and medical imaging 🏥. While there are many deep learning models available for this purpose, YOLO (You Only Look Once) stands out for its speed ⚡ and accuracy 📏. YOLOv8, an evolution of the YOLO series, is a powerful model that has gained popularity for custom object detection. In this blog, we’ll explore YOLOv8 and guide you through the process of custom object detection with this state-of-the-art model. 🤖

What is YOLOv8?

YOLOv8, short for You Only Look Once Version 8, is the latest iteration of the YOLO family of object detection models. Developed by Alexey Bochkovskiy, the YOLOv8 architecture combines the best features of its predecessors and integrates a range of optimizations for improved performance and accuracy. YOLOv8 is faster ⚡, more efficient 🏎️, and more capable of detecting objects accurately, even in complex scenarios. 🎯

Getting Started

To embark on the journey of custom object detection with YOLOv8, you’ll need to follow these steps:

Data Collection 📷:

The foundation of any machine learning project is high-quality data. Gather a dataset containing images of the objects you want to detect. Make sure to have a diverse set of images, capturing the objects from different angles 📸, under various lighting conditions ☀️🌙, and in different environments 🌆.

Data Annotation 🖊️:

Annotate your dataset by marking the objects’ bounding boxes in the images. Use the CVAT tool for annotation because they have a YOLO export format, making it easier for training. ✍️

Folder Making 📁:

After annotating the images, export the images with their respective label files given by CVAT. Folder making is as follows:

• Create a ‘data’ folder.
• In the ‘data’ folder create the ‘images’ and ‘labels’ folder.
• In the ‘images’ folder, create the ‘train’, ‘validation’, and ‘test’ folder.
• Split the images into respective folders based on the split ratio you’re using.
• Now create the same ‘train’, ‘validation’, and ‘test’ folders in the ‘labels’ folder and put the respective labels.

4. Setting up config.yaml File:

Create a config.yaml file. The format of this file is as follows:

path: '/content/drive/MyDrive/data' # dataset root dir
train: images/train  # train images (relative to 'path')
val: images/validation  # val images (relative to 'path')
test: images/test

# Classes
names:
  0: Hi,
  1: Good,
  2: Bad, ....... (For Example)

5. YOLO:

First, install the Ultralytics library ->

pip install ultralytics

Then run this code in your Python file:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # build a new model YOLOv8n from scratch
# YOLO version can also be changed 

# Use the model
model.train(data=" config.yaml path ", epochs=200) # epoch can be changed

Also after your model has completed the epochs, it will give a ‘runs’ folder where you can get the weights of the model along with metric graphs.

To get metrics of your model run this ->

metrics = model.val()

And that's how you train a custom object detection YOLOv8 model!!!

If you want to predict the results of an image run this ->

results = model.predict(source='bathroom.7fd18799-6459-11ee-930f-28cdc400a7b6.jpg', show=True)
print(results[0].boxes.conf)  # confidence scores
print(results[0].boxes.cls)  # Label

This will show a new window with the object detection on the image and also print what labels its detected with what confidence score respectively.

Thank you for embarking on this incredible journey! 🙌🚀📷

Feel free to explore the limitless possibilities of custom object detection with YOLOv8 and make your mark in the field of computer vision. Happy detecting! 🌟🤖🔭