By Maxim Bonnaerens, Matthias Freiberger and Joni Dambre.
This paper proposes anchor pruning for object detection in one-stage anchor-based detectors. While pruning techniques are widely used to reduce the computational cost of convolutional neural networks, they tend to focus on optimizing the backbone networks where often most computations are. In this work we demonstrate an additional pruning technique, specifically for object detection: anchor pruning. With more efficient backbone networks and a growing trend of deploying object detectors on embedded systems where post-processing steps such as non-maximum suppression can be a bottleneck, the impact of the anchors used in the detection head is becoming increasingly more important. In this work, we show that many anchors in the object detection head can be removed without any loss in accuracy. With additional retraining, anchor pruning can even lead to improved accuracy. Extensive experiments on SSD and MS COCO show that the detection head can be made up to 44% more efficient while simultaneously increasing accuracy. Further experiments on RetinaNet and PASCAL VOC show the general effectiveness of our approach. We also introduce ` overanchorized' models that can be used together with anchor pruning to eliminate hyperparameters related to the initial shape of anchors.
@article{bonnaerens2022anchor,
title={Anchor pruning for object detection},
author={M. Bonnaerens, M. Freiberger and J. Dambre},
journal={Computer Vision and Image Understanding},
pages={103445},
year={2022},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.cviu.2022.103445},
}| Anchor Configuration | AP .50:.95 | FLOPS head | BBoxes | Config | Download |
|---|---|---|---|---|---|
| SSD Baseline | 25.6 | 4231M | 8732 | config | model |
| SSD Configuration-A retrained | 25.4 | 3607M | 7814 | config | model |
| SSD Configuration-B retrained | 25.6 | 2476M | 4926 | config | model |
| SSD Configuration-C retrained | 25.2 | 1628M | 3121 | config | model |
| SSDConfiguration-D retrained | 22.8 | 774M | 1291 | config | model |
| RetinaNet Baseline | 36.5 | 129B | config | model | |
| RetinaNet Pruned | 34.8 | 31B | config | model |
Above results are on the COCO validation set while the results in the paper are on the COCO test set.
This repository builds upon MMDetection.
See The MMDetection documentation for installation instructions. Last confirmed working version is mmdet v2.25.0 with mmcv-full v1.4.8
pip install openmim
mim install mmcv-full==1.4.8
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
git switch --detach v2.25.0
pip install -v -e .Next, clone our repository and install the anchor pruning package
git clone https://github.com/Mxbonn/anchor_pruning.git
cd anchor_pruning
pip install -e .Please see Tutorial.ipynb for a general guide on how to do anchor pruning.
To run the given pretrained models above run
python tools/mmdet_test.py configs/ssd/configuration_B.py pretrained_models/configuration_B.pth --eval bboxafter modifying the paths to the mmdet base config in configuration_X.py and linking your dataset directory to data/ similarly as required for mmdetection.
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