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One-to-Few Label Assignment for End-to-End Dense Detection

This repo hosts the code for implementing the one-to-few label assignemnt, as presented in our CVPR 2023 paper.

Introduction

One-to-one (o2o) label assignment plays a key role for transformer based end-to-end detection, and it has been recently introduced in fully convolutional detectors for end-to-end dense detection. However, o2o can degrade the feature learning efficiency due to the limited number of positive samples. Though extra positive samples are introduced to mitigate this issue in recent DETRs, the computation of self- and cross- attentions in the decoder limits its practical application to dense and fully convolutional detectors. In this work, we propose a simple yet effective one-to-few (o2f) label assignment strategy for end-to-end dense detection. Apart from defining one positive and many negative anchors for each object, we define several soft anchors, which serve as positive and negative samples simultaneously. The positive and negative weights of these soft anchors are dynamically adjusted during training so that they can contribute more to "representation learning" in the early training stage, and contribute more to "duplicated prediction removal" in the later stage. The detector trained in this way can not only learn a strong feature representation but also perform end-to-end dense detection. Experiments on COCO and CrowdHuman datasets demonstrate the effectiveness of the o2f scheme.

Installation

This implementation is based on MMDetection. Therefore the installation is the same as original MMDetection.

Results and Models on COCO Detection

Backbone MS
train		 Lr
schd		 box AP
(val)		     Download    
R-50 N 1x 39.0 model | log
R-50 Y 3x 42.2 model | log
R-101 N 1x 41.0 model | log
R-101 Y 3x 43.8 model | log

Results and Models on COCOSegmentation

Backbone MS
train		 Lr
schd		 box AP
(val)		     Download    
R-50 N 1x 35.9 model | log
R-50 Y 3x 38.0 model | log

Results and Models on CrowdHuman Detection

Backbone AP MAR Recall     Download    
R-50 91.0 45.3 98.0 model | log

Inference

Assuming you have put the COCO dataset into data/coco/ and have downloaded the models into the weights/, you can now evaluate the models on the COCO val2017 split:

bash dist_test.sh configs/r50_1x.py weights/r50_1x.pth 8 --eval bbox

Training

The following command line will train r50_fpn_1x_coco on 8 GPUs:

bash dist_train.sh configs/r50_1x.py 8 --work-dir weights/r50_1x

Citation

@inproceedings{shuai2023o2f,
  title={One-to-Few Label Assignment for End-to-End Dense Detection},
  author={Li, Shuai and Li, Minghan and Li, Ruihuang and He, Chenhang and Zhang, Lei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2023}
}