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.
YOLOv8 performance
YOLOv8-P5 model structure
Results and models
COCO
| Backbone | Arch | size | Mask Refine | SyncBN | AMP | Mem (GB) | box AP | TTA box AP | Config | Download |
|---|---|---|---|---|---|---|---|---|---|---|
| YOLOv8-n | P5 | 640 | No | Yes | Yes | 2.8 | 37.2 | config | model | log | |
| YOLOv8-n | P5 | 640 | Yes | Yes | Yes | 2.5 | 37.4 (+0.2) | 39.9 | config | model | log |
| YOLOv8-s | P5 | 640 | No | Yes | Yes | 4.0 | 44.2 | config | model | log | |
| YOLOv8-s | P5 | 640 | Yes | Yes | Yes | 4.0 | 45.1 (+0.9) | 46.8 | config | model | log |
| YOLOv8-m | P5 | 640 | No | Yes | Yes | 7.2 | 49.8 | config | model | log | |
| YOLOv8-m | P5 | 640 | Yes | Yes | Yes | 7.0 | 50.6 (+0.8) | 52.3 | config | model | log |
| YOLOv8-l | P5 | 640 | No | Yes | Yes | 9.8 | 52.1 | config | model | log | |
| YOLOv8-l | P5 | 640 | Yes | Yes | Yes | 9.1 | 53.0 (+0.9) | 54.4 | config | model | log |
| YOLOv8-x | P5 | 640 | No | Yes | Yes | 12.2 | 52.7 | config | model | log | |
| YOLOv8-x | P5 | 640 | Yes | Yes | Yes | 12.4 | 54.0 (+1.3) | 55.0 | config | model | log |
Note
- 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.
- The performance is unstable and may fluctuate by about 0.3 mAP and the highest performance weight in
COCOtraining inYOLOv8may not be the last epoch. The performance shown above is the best model. - We provide scripts to convert official weights to MMYOLO.
SyncBNmeans using SyncBN,AMPindicates training with mixed precision.- The performance of
Mask Refinetraining is for the weight performance officially released by YOLOv8.Mask Refinemeans refining bbox by mask while loading annotations and transforming afterYOLOv5RandomAffine, and the L and X models useCopy Paste. TTAmeans that Test Time Augmentation. It's perform 3 multi-scaling transformations on the image, followed by 2 flipping transformations (flipping and not flipping). You only need to specify--ttawhen testing to enable. see TTA for details.