YOLOv8: How to Train for Object Detection on a Custom Dataset

Video Description

YOLOv8 is the latest installment of the highly influential YOLO (You Only Look Once) architecture. YOLOv8 was developed by Ultralytics, a team known for its work on YOLOv3 and YOLOv5. Following the trend set by  YOLOv6 and YOLOv7, we have at our disposal object detection, but also instance segmentation, and image classification. The model itself is created in PyTorch and runs on both the CPU and GPU. As with YOLOv5, we also have a number of various exports such as TF.js or CoreML. In this video, I'll take you through a step-by-step tutorial on Google Colab, and show you how to train your own YOLOv8 object detection model. Chapters: 0:00 Introduction 0:51 Overview 3:09 Setting up the Python environment 5:36 New API: CLI vs. Python SDK 8:51 Prepare the YOLOv8 object detection dataset 12:29 Train YOLOv8 model on custom dataset 13:54 YOLOv8 model evaluation 16:47 YOLOv8 model inference on images and videos 18:44 YOLOv8 model deployment and inference via hosted API 19:58 Conclusion Resources: 🌏 Roboflow: https://roboflow.com 🌌 Roboflow Universe: https://universe.roboflow.com 📝 How to Train YOLOv8 Object Detection on a Custom Dataset Blogpost: https://blog.roboflow.com/how-to-train-yolov8-on-a-custom-dataset 📓How to Train YOLOv8 Object Detection on a Custom Dataset Notebook: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb ⭐ YOLOv8 repository: https://github.com/ultralytics/ultralytics 📄 YOLOv8 docs: https://v8docs.ultralytics.com 📓 Learn more about YOLOv8 and other Computer Vision models with Roboflow Notebooks: https://github.com/roboflow/notebooks 🎬 Automatically Label Computer Vision Data: https://youtu.be/eSfGTZarFNQ 🆕 What's New in YOLOv8 Architecture: https://blog.roboflow.com/whats-new-in-yolov8/ 💯 RF100 Dataset https://blog.roboflow.com/roboflow-100 Stay up to date with the projects I'm working on at https://github.com/roboflow and https://github.com/SkalskiP! ⭐