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D2Det-mmdet2.1

• The project adopts mmdetection v2.1.0 to re-implement the original D2Det.
• The config files for large vocabulary datasets LVIS and Objects365 are also provided.

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 pytorch1.4.0, cuda10.0, and mmcv0.6.1.

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 your 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_r50_fpn_lvis.py ./D2Det_instance_r50_fpn_lvis.pth ./demo/demo.jpg --out ./demo/lvis.jpg

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.