Giving YOLOv8 a Second Look (Part 1)

This article is the first in a three-part series that explores YOLOv8, a state-of-the-art object detection model. In this part, you will learn how to generate, load, and visualize YOLOv8 predictions using FiftyOne, an open-source computer vision toolkit, and Ultralytics, the library that provides access to YOLOv8. The series is organized into three parts: generating and loading YOLOv8 predictions, evaluating YOLOv8 model predictions, and fine-tuning YOLOv8 models for custom computer vision applications. The article begins with a brief history of YOLO models since their initial release in 2015 and how they have evolved over time to become one of the most popular object detection models in the field. It then introduces YOLOv8, which was released by Ultralytics in late 2022 and comes with a new backbone that includes general-purpose models for object detection and instance segmentation tasks. To get started, you need to install the Ultralytics and FiftyOne Python packages and import relevant modules from both libraries. The article demonstrates how to apply YOLOv8 models to individual images or a set of images in a directory using the command line interface provided by Ultralytics. It also explains how to load your images into a FiftyOne Dataset and export them in YOLOv5Dataset format, which is compatible with YOLOv8. The article then focuses on visualizing YOLOv8 predictions using the FiftyOne App. It demonstrates how to generate detection predictions for a subset of the MS COCO dataset from the command line and add these predictions to the dataset in FiftyOne's Detections format. The article also provides functions to read YOLOv8 detection prediction files, convert them into FiftyOne Detections objects, and efficiently add these detections to all samples in a dataset by batching the read and write operations to the underlying MongoDB database. Finally, the article concludes with a brief overview of how to visualize YOLOv8 predictions on images using the FiftyOne App and how to convert YOLOv8 instance segmentations to FiftyOne instance segmentations. In Part 2 of this series, you will learn how to evaluate the quality of YOLOv8 model predictions, identify edge cases, and assess potential modes of failure.