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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

Get Started

  • install cvpods locally (requires cuda to compile)
python3 -m pip install 'git+https://github.com/Megvii-BaseDetection/cvpods.git'
# (add --user if you don't have permission)

# Or, to install it from a local clone:
git clone https://github.com/Megvii-BaseDetection/cvpods.git
python3 -m pip install -e cvpods

# Or,
pip install -r requirements.txt
python3 setup.py build develop
  • prepare datasets
cd /path/to/cvpods
cd datasets
ln -s /path/to/your/coco/dataset coco
  • Train & Test
git clone https://github.com/Megvii-BaseDetection/BorderDet.git
cd BorderDet/playground/detection/coco/borderdet/borderdet.res50.fpn.coco.800size.1x  # for example

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 download
BD_R_50_FPN_1x No No 60ms 41.4 download
BD_R_101_FPN_1x Yes No 76ms 45.0 download
BD_X_101_32x8d_FPN_1x Yes No 124ms 45.6 download
BD_X_101_64x4d_FPN_1x Yes No 123ms 46.2 download
BD_DCNV2_X_101_32x8d_FPN_1x Yes No 150ms 47.9 download
BD_DCNV2_X_101_64x4d_FPN_1x Yes No 156ms 47.5 download

Acknowledgement

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

Contributing to the project

Any pull requests or issues are welcome.