Official Implementation of DDOD (Disentangle your Dense Object Detector), ACM MM2021

Last update: Jan 07, 2023

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Overview

Disentangle Your Dense Object Detector

This repo contains the supported code and configuration files to reproduce object detection results of Disentangle Your Dense Object Detector. It is based on mmdetection.

Results and Models

Model	Backbone	Lr Schd	box mAP	AP50	AP75	APs	APm	APl
ATSS(IoU)	ResNet50	1x	39.4	56.6	42.6	23.9	42.5	49.6
DDOD	ResNet50	1x	41.6	59.9	45.2	23.9	44.9	54.4

Usage

Installation

Please refer to get_started.md for installation and dataset preparation.

Inference

# multi-gpu testing
tools/dist_test.sh coco_cfg/ddod_r50_1x.py <DET_CHECKPOINT_FILE> 8 --eval bbox

Training

To train a detector with pre-trained models, run:

# multi-gpu training
tools/dist_train.sh coco_cfg/ddod_r50_1x.py 8

Citing DDOD

@misc{chen2021disentangle,
      title={Disentangle Your Dense Object Detector}, 
      author={Zehui Chen and Chenhongyi Yang and Qiaofei Li and Feng Zhao and Zhengjun Zha and Feng Wu},
      year={2021},
      eprint={2107.02963},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Owner

loveSnowBest

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Official Implementation of DDOD (Disentangle your Dense Object Detector), ACM MM2021

Related tags

Overview

Disentangle Your Dense Object Detector

Results and Models

Usage

Installation

Inference

Training

Citing DDOD

Owner

loveSnowBest

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