JialeCao001 / D2Det

D2Det: Towards High Quality Object Detection and Instance Segmentation (CVPR2020)

Home Page:https://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

D2Det

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}]

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.

About

D2Det: Towards High Quality Object Detection and Instance Segmentation (CVPR2020)

https://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf

License:MIT License


Languages

Language:Python 87.0%Language:Cuda 7.4%Language:C++ 5.4%Language:Shell 0.1%Language:Dockerfile 0.0%