yolov8

YOLOv8

Abstract

Ultralytics YOLOv8, developed by Ultralytics, is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.

performance

YOLOv8-P5 model structure

Results and models

COCO

Backbone	Arch	size	SyncBN	AMP	Mem (GB)	box AP	Config	Download
YOLOv8-n	P5	640	Yes	Yes	2.8	37.2	config	model | log
YOLOv8-s	P5	640	Yes	Yes	4.0	44.2	config	model | log
YOLOv8-m	P5	640	Yes	Yes	7.2	49.8	config	model | log

Note

In the official YOLOv8 code, the bbox annotation, random_perspective and copy_paste data augmentation in COCO object detection task training uses mask annotation information, which leads to higher performance. Object detection should not use mask annotation, so only box annotation information is used in MMYOLO. We trained the official YOLOv8s code with 8xb16 configuration and its best performance is also 44.2. We will support mask annotations in object detection tasks in the next version.

1. We use 8x A100 for training, and the single-GPU batch size is 16. This is different from the official code, but has no effect on performance.
2. The performance is unstable and may fluctuate by about 0.3 mAP and the highest performance weight in COCO training in YOLOv8 may not be the last epoch. The performance shown above is the best model.
3. We provide scripts to convert official weights to MMYOLO.
4. SyncBN means use SyncBN, AMP indicates training with mixed precision.

Citation