In this video, I'll guide you through the process of training a YOLOv8 object detection model on your own custom dataset of images. Key topics include:
Gathering and preparing a suitable dataset of images
Annotating the objects of interest in the images
Configuring YOLOv8 for training on the custom data
Running the training process and monitoring performance
Evaluating the trained model's detection accuracy
Deploying the custom YOLOv8 model for real-world use
Whether you want to detect specialized objects, create a unique computer vision system, or fine-tune an existing YOLOv8 model, you'll learn the essential steps to make it happen. By the end, you'll have the skills to build your own tailored object detection solution using the powerful YOLOv8 framework.
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⏳ *Timestamps*
0:00 Intro
1:20 What is YOLO?
2:25 Step 1: Set up Python & YOLO
5:40 Step 2: Create the Dataset
9:40 Step 3: Annotate the Dataset
16:42 Step 4: Split the Dataset
23:18 Train the Model
31:30 Test the model
