CBNetV2: A Novel Composite Backbone Network Architecture for Object Detection

By Tingting Liang*, Xiaojie Chu*, Yudong Liu*, Yongtao Wang, Zhi Tang, Wei Chu, Jingdong Chen, Haibin Ling.

This repo is the official implementation of CBNetV2. It is based on mmdetection and Swin Transformer for Object Detection.

Introduction

CBNetV2 achieves strong single-model performance on COCO object detection (60.1 box AP and 52.3 mask AP on test-dev) without extra training data.

Partial Results and Models

More results and models can be found in model zoo

Faster R-CNN

Backbone	Lr Schd	box mAP (minival)	#params	FLOPs	config	log	model
DB-ResNet50	1x	40.8	69M	284G	config	github	github

Mask R-CNN

Backbone	Lr Schd	box mAP (minival)	mask mAP (minival)	#params	FLOPs	config	log	model
DB-Swin-T	3x	50.2	44.5	76M	357G	config	github	github

Cascade Mask R-CNN (1600x1400)

Backbone	Lr Schd	box mAP (minival/test-dev)	mask mAP (minival/test-dev)	#params	FLOPs	config	model
DB-Swin-S	3x	56.3/56.9	48.6/49.1	156M	1016G	config	github

Improved HTC (1600x1400)

We use ImageNet-22k pretrained checkpoints of Swin-B and Swin-L. Compared to regular HTC, our HTC uses 4conv1fc in bbox head.

Backbone	Lr Schd	box mAP (minival/test-dev)	mask mAP (minival/test-dev)	#params	FLOPs	config	model
DB-Swin-B	20e	58.4/58.7	50.7/51.1	235M	1348G	config	github
DB-Swin-L	1x	59.1/59.4	51.0/51.6	453M	2162G	config (test only)	github
DB-Swin-L (TTA)	1x	59.6/60.1	51.8/52.3	453M	-	config (test only)	github

TTA denotes test time augmentation.

Notes:

Pre-trained models of Swin Transformer can be downloaded from Swin Transformer for ImageNet Classification.

Usage

Installation

Please refer to get_started.md for installation and dataset preparation.

Inference

# single-gpu testing (w/o segm result)
python tools/test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval bbox 

# multi-gpu testing (w/ segm result)
tools/dist_test.sh <CONFIG_FILE> <DET_CHECKPOINT_FILE> <GPU_NUM> --eval bbox segm

Training

To train a detector with pre-trained models, run:

# multi-gpu training
tools/dist_train.sh <CONFIG_FILE> <GPU_NUM>

For example, to train a Faster R-CNN model with a Duel-ResNet50 backbone and 8 gpus, run:

# path of pre-training model (resnet50) is already in config
tools/dist_train.sh configs/cbnet/faster_rcnn_cbv2d1_r50_fpn_1x_coco.py 8

Another example, to train a Mask R-CNN model with a Duel-Swin-T backbone and 8 gpus, run:

tools/dist_train.sh configs/cbnet/mask_rcnn_cbv2_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py 8 --cfg-options model.pretrained=<PRETRAIN_MODEL>

Apex (optional):

Following Swin Transformer for Object Detection, we use apex for mixed precision training by default. To install apex, run:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Documents and Tutorials

We list some documents and tutorials from MMDetection, which may be helpful to you.

Citation

If you use our code/model, please consider to cite our paper CBNetV2: A Novel Composite Backbone Network Architecture for Object Detection.

@article{liang2021cbnetv2,
  title={CBNetV2: A Composite Backbone Network Architecture for Object Detection}, 
  author={Tingting Liang and Xiaojie Chu and Yudong Liu and Yongtao Wang and Zhi Tang and Wei Chu and Jingdong Chen and Haibing Ling},
  journal={arXiv preprint arXiv:2107.00420},
  year={2021}
}

License

The project is only free for academic research purposes, but needs authorization for commerce. For commerce permission, please contact wyt@pku.edu.cn.

wuusn/myCBNetV2