PAFNet: An Efficient Anchor-Free Object Detector Guidance

28 Apr 2021  ·  Ying Xin, Guanzhong Wang, Mingyuan Mao, Yuan Feng, Qingqing Dang, Yanjun Ma, Errui Ding, Shumin Han ·

Object detection is a basic but challenging task in computer vision, which plays a key role in a variety of industrial applications. However, object detectors based on deep learning usually require greater storage requirements and longer inference time, which hinders its practicality seriously. Therefore, a trade-off between effectiveness and efficiency is necessary in practical scenarios. Considering that without constraint of pre-defined anchors, anchor-free detectors can achieve acceptable accuracy and inference speed simultaneously. In this paper, we start from an anchor-free detector called TTFNet, modify the structure of TTFNet and introduce multiple existing tricks to realize effective server and mobile solutions respectively. Since all experiments in this paper are conducted based on PaddlePaddle, we call the model as PAFNet(Paddle Anchor Free Network). For server side, PAFNet can achieve a better balance between effectiveness (42.2% mAP) and efficiency (67.15 FPS) on a single V100 GPU. For moblie side, PAFNet-lite can achieve a better accuracy of (23.9% mAP) and 26.00 ms on Kirin 990 ARM CPU, outperforming the existing state-of-the-art anchor-free detectors by significant margins. Source code is at https://github.com/PaddlePaddle/PaddleDetection.

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Results from the Paper


 Ranked #1 on Object Detection on COCO test-dev (Hardware Burden metric)

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Object Detection COCO test-dev PAFNet (ResNet50-vd) AP50 59.8 # 131
AP75 45.3 # 115
APS 22.8 # 117
APM 45.8 # 105
APL 59.2 # 65
Hardware Burden None # 1
Operations per network pass None # 1

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