YOLOV8: Instance Segmentation on Custom Data | Step By Step Guide

In this video, we will guide you through the process of training YOLOv8, a powerful model for instance segmentation, on a custom dataset. Instance segmentation goes beyond object detection by not only identifying objects within an image but also segmenting them at the pixel level, providing precise boundaries for each instance.

We'll start by setting up the environment and ensuring GPU access for faster training. Then, we'll walk you through the installation of YOLOv8 using the pip install method, which is the recommended approach. Once YOLOv8 is installed, we'll explain how to prepare a custom dataset, which can be a time-consuming process. However, we'll introduce you to Roboflow, a tool that simplifies dataset collection, labeling, and formatting, making the process more efficient.

With the custom dataset ready, we'll dive into the custom training process, where YOLOv8 will learn to perform instance segmentation on the objects within the dataset. We'll guide you through the necessary commands and parameters to initiate the training process and monitor its progress.

After training, we'll move on to validating the custom model to assess its performance and fine-tune it if necessary. We'll showcase the validation results and highlight the importance of this step in ensuring accurate instance segmentation.

Next, we'll demonstrate how to perform inference using the trained model on new images. You'll witness the power of YOLOv8 as it accurately identifies and segments objects in real-world scenarios. We'll showcase the inference results on sample images, providing visual representations of the model's instance segmentation capabilities.

Finally, we'll conclude the tutorial by summarizing the key steps and highlighting the potential applications of YOLOv8 instance segmentation. Whether you're new to instance segmentation or an experienced practitioner, this tutorial provides a comprehensive guide to training YOLOv8 on a custom dataset for precise and accurate instance segmentation.

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