PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling

A PyTorch implementation of PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling. IEEE Transactions on Image Processing, 2022.

Introduction

We propose a Prediction-target-Decoupled Network (PDNet) for object detection, which decouples the prediction of different targets (i.e., object category and boundaries) into their respective proper positions. The figure below is a visualization of the decoupled regression predictions for localizing each object box boundary. This allows us to collect accurate predictions for these targets separately. For more details on Prediction Decoupling, please refer to our paper.

Installation

This implementation is based on maskrcnn-benchmark, so the installation procedure is pretty much the same. Please check INSTALL.md for details. Additionally, please remember to build the CUDA implementation of the prediction collection module with the following commands.

cd pdnet_core/pred_collect_ops
python setup.py build develop

Model Zoo

We provide the following trained models with different backbones or training settings.

Model	Training iterations	Training scales	AP (test-dev)	Link
PDNet_R_50_FPN_2x	180k	[640, 800]	44.3	config / weights
PDNet_R_50_FPN_2xx	180k	[480, 960]	45.0	config / weights
PDNet_R_101_FPN_2x	180k	[640, 800]	45.7	config / weights
PDNet_R_101_FPN_2xx	180k	[480, 960]	46.6	config / weights
PDNet_X_101_64x4d_FPN_2x	180k	[640, 800]	47.4	config / weights
PDNet_X_101_64x4d_FPN_2xx	180k	[480, 960]	48.7	config / weights

[1] 2x means the model is trained for 180k iterations ( $\approx$ 24 epochs) with multi-scale training ([640, 800]), while 2xx denotes using a wider training scale range [480, 960].
[2] With efficient prediction decoupling, our model has a similar inference time to FCOS while achieving significantly higher accuracy.

Inference

Evaluate the trained model on the minival split of MS COCO dataset:

python tools/test_net.py --config-file configs/pdnet/pdnet_R_50_FPN_1x.yaml --ckpt PDNet_R_50_FPN_1x.pth

• You can change the arguments of --config-file and --ckpt to evaluate different models.

Training

We train our detection model on 4 GPUs with 16 images per minibatch using the following command:

python -m torch.distributed.launch --nproc_per_node=4 tools/train_net.py --config-file configs/pdnet/pdnet_R_50_FPN_1x.yaml

• We also provide configuration files with different backbones or training settings in configs/pdnet/.

Citation

Please cite our paper if this project helps your research.

@article{yang2022pdnet,
  title={PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling},
  author={Yang, Li and Xu, Yan and Wang, Shaoru and Yuan, Chunfeng and Zhang, Ziqi and Li, Bing and Hu, Weiming},
  journal={IEEE Transactions on Image Processing},
  volume={31},
  pages={5121--5133},
  year={2022},
  publisher={IEEE}
}