mirror of https://github.com/open-mmlab/mmyolo.git
75fc8fc2a3
* add cfg * add copypaste * add todo * 在mosaic和mixup中处理gt_masks,改config * fix cat bug * add finetune box in affine * add repr * del albu config in l * add doc * add config * format code * fix loadmask * addconfig,fix mask * fix loadann * fix tra * update LoadAnnotations * update * support mask * fix error * fix error * fix config and no maskrefine bug * fix * fix * update config * format code * beauty config * add yolov5 config and readme * beauty yolov5 config * add ut * fix ut. bitmap 2 poly * fix ut and add mix transform ut. * fix bool * fix loadann * rollback yolov5 * rollback yolov5 * format * 提高速度 * update --------- Co-authored-by: huanghaian <huanghaian@sensetime.com> |
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| README.md | ||
| metafile.yml | ||
| yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_l_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_m_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_n_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_s_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco.py | ||
| yolov8_x_syncbn_fast_8xb16-500e_coco.py | ||
README.md
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 | Mask Refine | SyncBN | AMP | Mem (GB) | 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) | 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) | 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) | 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) | 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) | 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.