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D2Det

This code is an official implementation of "D2Det: Towards High Quality Object Detection and Instance Segmentation (CVPR2020)" based on the open source object detection toolbox mmdetection.
We also provide a new version using mmdetection v2.1.0, which can further support large vocabulary datasets LVIS and Objects365.

Introduction

We propose a novel two-stage detection method, D2Det, that collectively addresses both precise localization and accurate classification. For precise localization, we introduce a dense local regression that predicts multiple dense box offsets for an object proposal. Different from traditional regression and keypoint-based localization employed in two-stage detectors, our dense local regression is not limited to a quantized set of keypoints within a fixed region and has the ability to regress position-sensitive real number dense offsets, leading to more precise localization. The dense local regression is further improved by a binary overlap prediction strategy that reduces the influence of background region on the final box regression. For accurate classification, we introduce a discriminative RoI pooling scheme that samples from various sub-regions of a proposal and performs adaptive weighting to obtain discriminative features.

Installation

Please refer to INSTALL.md of mmdetection.
I use pytorch1.1.0, cuda9.0/10.0, and mmcv0.4.3.

Train and Inference

Please use the following commands for training and testing by single GPU or multiple GPUs.

Train with a single GPU

python tools/train.py ${CONFIG_FILE}

Train with multiple GPUs

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

Test with a single GPU

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]

Test with multiple GPUs

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]

CONFIG_FILE about D2Det is in configs/D2Det, please refer to GETTING_STARTED.md for more details.

Demo

With our trained model, detection results of an image can be visualized using the following command.

python ./demo/D2Det_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} [--out ${OUT_PATH}]
e.g.,
python ./demo/D2Det_demo.py ./configs/D2Det/D2Det_instance_r101_fpn_2x.py ./D2Det-instance-res101.pth ./demo/demo.jpg --out ./demo/aa.jpg

Results

We provide some models with different backbones and results of object detection and instance segmentation on MS COCO benchmark.

name	backbone	iteration	task	validation	test-dev	download
D2Det	ResNet50	24 epoch	object detection	43.7 (box)	43.9 (box)	model
D2Det	ResNet101	24 epoch	object detection	44.9 (box)	45.4 (box)	model
D2Det	ResNet101-DCN	24 epoch	object detection	46.9 (box)	47.5 (box)	model
D2Det	ResNet101	24 epoch	instance segmentation	39.8 (mask)	40.2 (mask)	model

All the models are based on single-scale training and all the results are based on single-scale inference.

Citation

If the project helps your research, please cite this paper.

@article{Cao_D2Det_CVPR_2020,
  author =       {Jiale Cao and Hisham Cholakkal and Rao Muhammad Anwer and Fahad Shahbaz Khan and Yanwei Pang and Ling Shao},
  title =        {D2Det: Towards High Quality Object Detection and Instance Segmentation},
  journal =      {Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  year =         {2020}
}

Acknowledgement

Many thanks to the open source codes, i.e., mmdetection and Grid R-CNN plus.