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

Related tags

Deep LearningDDOD
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
loveSnowBest
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