open-mmlab / mmyolo

OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.

Home Page:https://mmyolo.readthedocs.io/zh_CN/dev/

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Why yolox-s in mmyolo can be 40.8%(mAP)? However, official mAP of yolox-s is 40.5%?What you have modified?

lixiangMindSpore opened this issue · comments

Prerequisite

💬 Describe the reimplementation questions

Why yolox-s in mmyolo can be 40.8%(mAP)? However, official mAP of yolox-s is 40.5%?What you have modified?

Environment

nothing special

Expected results

No response

Additional information

No response

@lixiangMindSpore It mainly includes 2 aspects:

  1. Using multi_label, the performance can be about 0.2
  2. Algorithms fluctuate

@lixiangMindSpore It mainly includes 2 aspects:

  1. Using multi_label, the performance can be about 0.2
  2. Algorithms fluctuate

How to use multi_label?

@lixiangMindSpore Please refer to this line in the config file:

https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py#L93

Official YOLOX don't possess it. How to modify it in Official YOLOX?

@lixiangMindSpore
In MMYOLO, it's the same setting.
https://github.com/open-mmlab/mmyolo/blob/main/configs/yolox/yolox_s_8xb8-300e_coco.py#L97

If you want to modify official YOLOX, you can go to the official YOLOX repo and open a new issue about it.