Oh boy, it feels like just yesterday that YOLOv6,R,X, and YOLOv7 got released, and now we're already on YOLOv8?! Time sure does fly when you're busy making object detection models . But in all seriousness, YOLOv8 by Ultralytics is a real game changer . It's like the "Swiss Army Knife" of object detection and image segmentation models. With its extensibility feature, you can switch between different versions of YOLO like it's no big deal . It's like being able to change the lenses on your camera, instead of lenses, you're swapping out versions of YOLO.

YOLO (You Only Look Once) one-stage real-time object detection in videos and images. YOLOv6 is inspired by YOLOv5 by Ultralytics and is developed by researchers at Meituan. Models are available on https://github.com/meituan/YOLOv6, Using YOLOv6 is quite straightforward (Using google-colab for demo), this can also be done in the local system but please use python version below 3.10 as version 3.10 or above might give error while installing requirement due to non compatibility of onnx-simplifier. One can also create a virtual environment for this task. Finally running script for detection (Current repository supports (image, folder of images) as input for object detection.)

The field of computer vision has rapidly evolved in recent years and achieved results that seemed like science fiction a few years back. These breakthroughs have many causes -- building better, more accessible compute resources, but also the fact that they are the closest thing we have to Open Source Data Science (OSDS). Revealing the source code to the community unlocks the "wisdom of the crowd" and enables innovation and problem-solving at scale. One of the most popular OS projects in computer vision is YOLO (You Only Look Once). YOLO is an efficient real-time object detection algorithm, first described in the seminal 2015 paper by Joseph Redmon et al.

YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. Second, run inference with tools/infer.py