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Sampling-Free for Object Detection

Development, Maintenance @ChenJoya. Please feel free to contact me: chenjoya@foxmail.com

Introduction

To address the foreground-background imbalance, is heuristic sampling necessary in training deep object detectors?

Keep clam and try the sampling-free mechanism in this repository.

Sampling-free mechanism enables various object detectors (e.g. one-stage, two-stage, anchor-free, multi-stage) to drop sampling heuristics (e.g., undersampling, Focal Loss, objectness), but achieve better bounding-box or instance segmentation accuracy.

Technical report: https://arxiv.org/abs/1909.04868. This repository is based on maskrcnn-benchmark, including the implementation of RetinaNet/FCOS/Faster/Mask R-CNN. Other detectors will also be released.

Installation

Check INSTALL.md for installation instructions.

Training

See scripts/train.sh, you can easily train with the sampling-free mechanism.

Evaluation

See scripts/eval.sh, you can easily evaluate your trained model.

COCO dataset

Model Config Box AP (minival) Mask AP (minival)
RetinaNet retinanet_R_50_FPN_1x 36.4 --
RetinaNet + Sampling-Free retinanet_R_50_FPN_1x 36.8 --
FCOS fcos_R_50_FPN_1x 37.1 --
FCOS + Sampling-Free fcos_R_50_FPN_1x 37.6 --
Faster R-CNN faster_rcnn_R_50_FPN_1x 36.8 --
Faster R-CNN + Sampling-Free faster_rcnn_R_50_FPN_1x 38.4 --
Mask R-CNN mask_rcnn_R_50_FPN_1x 37.8 34.2
Mask R-CNN + Sampling-Free mask_rcnn_R_50_FPN_1x 39.0 34.9

PASCAL VOC dataset (07+12 for training)

Model Config mAP (07test)
RetinaNet retinanet_voc_R_50_FPN_0.2x 79.3
RetinaNet + Sampling-Free retinanet_voc_R_50_FPN_0.2x 80.1
Faster R-CNN faster_rcnn_voc_R_50_FPN_0.2x 80.9
Faster R-CNN + Sampling-Free faster_rcnn_voc_R_50_FPN_0.2x 81.5

Other Details

See the original benchmark maskrcnn-benchmark for more details.

Citations

Please consider citing this project in your publications if it helps your research. The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.

@article{sampling_free,
author    = {Joya Chen and
             Dong Liu and
             Tong Xu and
             Shiwei Wu and
             Yifei Chen and
             Enhong Chen},
title     = {Is Heuristic Sampling Necessary in Training Deep Object Detectors?},
journal   = {CoRR},
volume    = {abs/1909.04868},
url       = {http://arxiv.org/abs/1909.04868},
archivePrefix = {arXiv},
eprint    = {1909.04868},
}

License

sampling-free is released under the MIT license. See LICENSE for additional details.

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Is heuristic sampling necessary in training deep object detectors? Try sampling-free object detectors!

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