Sampling-Free for Object Detection
Development, Maintenance @ChenJoya. Please feel free to contact me: [email protected]
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 - Focal Loss + Sampling-Free | retinanet_R_50_FPN_1x | 36.8 | -- |
| FCOS | fcos_R_50_FPN_1x | 37.1 | -- |
| FCOS - Focal Loss + Sampling-Free | fcos_R_50_FPN_1x | 37.6 | -- |
| Faster R-CNN | faster_rcnn_R_50_FPN_1x | 36.8 | -- |
| Faster R-CNN -Biased Sampling + 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 - Biased Sampling + Sampling-Free | mask_rcnn_R_50_FPN_1x | 39.0 | 34.9 |
| PAA | paa_R_50_FPN_1x | 40.4 | -- |
| PAA - Focal Loss + Sampling-Free | paa_R_50_FPN_1x | 41.0 | -- |
PASCAL VOC dataset (07+12 for training)
| Model | Config | mAP (07test) |
|---|---|---|
| RetinaNet | retinanet_voc_R_50_FPN_0.2x | 79.3 |
| RetinaNet - Focal Loss + 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 - Biased Sampling + 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 Cheng and
Enhong Chen},
title = {Is Heuristic Sampling Necessary in Training Deep Object Detectors?},
journal = {IEEE Transactions on Image Processing},
year = {2021},
volume = {},
number = {},
pages = {1-1},
}
License
sampling-free is released under the MIT license. See LICENSE for additional details.