PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices
2021
The better accuracy and efficiency trade-off has been a challenging problem
in object detection. In this work, we are dedicated to studying key
optimizations and neural network architecture choices for object detection to
improve accuracy and efficiency. We investigate the applicability of the
anchor-free strategy on lightweight object detection models. We enhance the
backbone structure and design the lightweight structure of the neck, which
improves the feature extraction ability of the network. We improve label
assignment strategy and loss function to make training more stable and
efficient. Through these optimizations, we create a new family of real-time
object detectors, named PP-PicoDet, which achieves superior performance on
object detection for mobile devices. Our models achieve better trade-offs
between accuracy and latency compared to other popular models. PicoDet-S with
only 0.99M parameters achieves 30.6% mAP, which is an absolute 4.8% improvement
in mAP while reducing mobile CPU inference latency by 55% compared to
YOLOX-Nano, and is an absolute 7.1% improvement in mAP compared to NanoDet. It
reaches 123 FPS (150 FPS using Paddle Lite) on mobile ARM CPU when the input
size is 320. PicoDet-L with only 3.3M parameters achieves 40.9% mAP, which is
an absolute 3.7% improvement in mAP and 44% faster than YOLOv5s. As shown in
Figure 1, our models far outperform the state-of-the-art results for
lightweight object detection. Code and pre-trained models are available at
https://github.com/PaddlePaddle/PaddleDetection.
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