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

loveSnowBest

Last update: Jan 7, 2023

Related tags

Deep Learning DDOD

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}
}

Comments

FCOS version of DDOD

Hi, thanks for your great work!.

It seems that there is no FCOS version of DDOD (Use anchor point instead of anchor) implemented as indicated in the Table 3 of your paper, could you also share that part of code and config for reference?

Thanks!

opened by memoiry 7
Batch size for COCO

For WIDER FACE, the paper describes the batch size. "The model is trained with a batch size of 24 on 6 Titan V100s."

For COCO, is the total batch size 32? tools/dist_train.sh coco_cfg/ddod_r50_1x.py 8 samples_per_gpu=4,

Can we achieve similar AP with a total batch size of 16 and lr=0.01?

opened by shinya7y 6
About WiderFace.

When ddod is applied to wideface, does the testing process adopt the method in tinaface? In addition, could you release the code to apply ddod to widerface?

opened by StetchPlane 2
COCO pre-trained models
Hi! Awesome work! Do you have any plans to upload COCO weights?

Especially,

ATSS(IoU) R-50 1x

DDOD R-50 1x

DDOD R2-101-DCN 2x

DDOD-X R2-101-DCN 2x

It would be great if you also could provide configs for DDOD-X because the arXiv v1 seems to lack the explanation of DDOD-X and the details of multi-scale testing and stronger data augmentation.
opened by shinya7y 2
Missing retina_assigner?

Hi, thanks for your great work. But when I run this code, it threw an error with AttributeError: 'ConfigDict' object has no attribute 'retina_assigner'. I guess self.train_cfg.retina_assigner in atss_r50_1x.py is missing. Can you please provide it?

opened by yarkable 2
在WiderFace上训练的mAP为0
Environment info： sys.platform: linux Python: 3.7.13 (default, Oct 18 2022, 18:57:03) [GCC 11.2.0] CUDA available: True GPU 0,1: NVIDIA A100 80GB PCIe CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 PyTorch: 1.10.0 PyTorch compiling details: PyTorch built with:

GCC 7.3

C++ Version: 201402

Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications

Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)

OpenMP 201511 (a.k.a. OpenMP 4.5)

LAPACK is enabled (usually provided by MKL)

NNPACK is enabled

CPU capability usage: AVX512

CUDA Runtime 11.3 TorchVision: 0.11.0 OpenCV: 4.6.0 MMCV: 1.6.2 MMCV Compiler: GCC 9.3 MMCV CUDA Compiler: 11.3 MMDetection: 2.25.3+unknown

您好，我在mmdetection上根据您论文里Implementation Details的介绍（关于训练widerface），对ddod的优化器部分进行了以下改动： optimizer = dict(type='SGD', lr=0.0075, momentum=0.9, weight_decay=0.0005) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='CosineRestart', warmup='linear', warmup_iters=500, warmup_ratio=0.1, periods=[ 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30 ], restart_weights=[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ], min_lr_ratio=0.01) runner = dict(type='EpochBasedRunner', max_epochs=600) 此外，我也将reg loss改为了DIoU loss，但是每个epoch报告的mAP均为0。请问是什么情况呢？
opened by Cathy1900 1

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

Comments

FCOS version of DDOD

Batch size for COCO

About WiderFace.

COCO pre-trained models

Missing retina_assigner?

在WiderFace上训练的mAP为0

Owner

loveSnowBest

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