Decoupled R-CNN

This code is an official implementation of "IoU Regression with H+L-Sampling for Accurate Detection Confidence" based on the open source object detection toolbox mmdetection.

Introduction

It is a common paradigm in object detection frameworks that the samples in training and testing have consistent distributions for the two main tasks: Classification and bounding box regression. This paradigm is popular in sampling strategy for training an object detector due to its intuition and practicability. For the task of localization quality estimation, there exist two ways of sampling: The same sampling with the main tasks and the uniform sampling by manually augmenting the ground-truth. The first method of sampling is simple but inconsistent for the task of quality estimation. The second method of uniform sampling contains all IoU level distributions but is more complex and difficult for training. In this paper, we propose an H+L-Sampling strategy, selecting the high and low IoU samples simultaneously, to effectively and simply train the branch of quality estimation. This strategy inherits the effectiveness of consistent sampling and reduces the training difficulty of uniform sampling. Finally, we introduce accurate detection confidence, which combines the classification probability and the localization accuracy, as the ranking keyword of NMS. Extensive experiments show the effectiveness of our method in solving the misalignment between classification confidence and localization accuracy and improving the detection performance.

Installation

Requirements

Linux (Windows is not officially supported)
Python 3.6+
PyTorch 1.3 or higher
CUDA 9.0 or higher
GCC 5+
mmcv

Install mmdetection

a. Create a conda virtual environment and activate it.

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

b. Install Pytorch and torchvision.

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch

c. Install mmcv.

 pip install mmcv-full==1.1.2 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html

d. Clone the mmdetection repository.

git clone --branch v.2.4.0 https://github.com/open-mmlab/mmdetection.git
cd mmdetection

e. Install build requirements and then install mmdetection.

pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

Train and Inference

All our model is trained on 4 TITAN X GPUs with a total batch size of 8 (2 images per GPU). The learning rate is initialized as 0.01.

Train with a single GPU

python tools/train.py ${CONFIG_FILE}

Train with multiple GPUs

./tools/dist_train.sh ${CONFIG_FILE} 4 [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 IoU-Aware R-CNN is in configs/iou-aware_rcnn_final, please refer to getting_started.md for more details.

Results

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

Acknowledgement

Many thanks to the open source codes, i.e., mmdetection.

shouwangzhe134/IoU-Aware-R-CNN