BorderDet

This project provides an implementation for "BorderDet: Border Feature for Dense Object Detection" (ECCV2020 Oral) on PyTorch.

For the reason that experiments in the paper were conducted using internal framework, this project reimplements them on cvpods and reports detailed comparisons below.

Requirements

Python >= 3.6
PyTorch >= 1.3
torchvision >= 0.4.2
OpenCV
pycocotools: pip install cython; pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
GCC >= 4.9

Get Started

git clone --recursive https://github.com/Megvii-BaseDetection/BorderDet

cd BorderDet

# build cvpods (requires GPU)
pip install -r requirements.txt
python setup.py build develop

# preprare data path
mkdir datasets
ln -s /path/to/your/coco/dataset datasets/coco

cd playground/detection/coco/borderdet/borderdet.res50.fpn.coco.800size.1x

# Train
pods_train --num-gpus 8
# Test
pods_test --num-gpus 8 \
    MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
    OUTPUT_DIR /path/to/your/save_dir # optional

# Multi node training
## sudo apt install net-tools ifconfig
pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port"

Results on COCO

For your convenience, we provide the performance of the following trained models. All models are trained with 16 images in a mini-batch and frozen batch normalization. All model including X_101/DCN_X_101 will be released soon.

Model	Multi-scale training	Multi-scale testing	Testing time / im	AP (minival)	Link
FCOS_R_50_FPN_1x	No	No	54ms	38.7	Google
BD_R_50_FPN_1x	No	No	60ms	41.4	Google
BD_R_101_FPN_1x	Yes	No	76ms	45.0	Google

Acknowledgement

cvpods is developed based on Detectron2. For more details about official detectron2, please check DETECTRON2.

Contributing to the project