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README.md

PP-PicoDet Legacy Model-ZOO (2021.10)

Model Input size mAPval
0.5:0.95		 mAPval
0.5		 Params
(M)		 FLOPS
(G)		 LatencyNCNN
(ms)		 LatencyLite
(ms)		 Download Config
PicoDet-S 320*320 27.1 41.4 0.99 0.73 8.13 6.65 model | log config
PicoDet-S 416*416 30.7 45.8 0.99 1.24 12.37 9.82 model | log config
PicoDet-M 320*320 30.9 45.7 2.15 1.48 11.27 9.61 model | log config
PicoDet-M 416*416 34.8 50.5 2.15 2.50 17.39 15.88 model | log config
PicoDet-L 320*320 32.9 48.2 3.30 2.23 15.26 13.42 model | log config
PicoDet-L 416*416 36.6 52.5 3.30 3.76 23.36 21.85 model | log config
PicoDet-L 640*640 40.9 57.6 3.30 8.91 54.11 50.55 model | log config

More Configs

Model Input size mAPval
0.5:0.95		 mAPval
0.5		 Params
(M)		 FLOPS
(G)		 LatencyNCNN
(ms)		 LatencyLite
(ms)		 Download Config
PicoDet-Shufflenetv2 1x 416*416 30.0 44.6 1.17 1.53 15.06 10.63 model | log config
PicoDet-MobileNetv3-large 1x 416*416 35.6 52.0 3.55 2.80 20.71 17.88 model | log config
PicoDet-LCNet 1.5x 416*416 36.3 52.2 3.10 3.85 21.29 20.8 model | log config
PicoDet-LCNet 1.5x 640*640 40.6 57.4 3.10 - - - model | log config
PicoDet-R18 640*640 40.7 57.2 11.10 - - - model | log config
Table Notes:
  • Latency: All our models test on Qualcomm Snapdragon 865(4xA77+4xA55) with 4 threads by arm8 and with FP16. In the above table, test latency on NCNN and Lite->Paddle-Lite. And testing latency with code: MobileDetBenchmark.
  • PicoDet is trained on COCO train2017 dataset and evaluated on COCO val2017.
  • PicoDet used 4 or 8 GPUs for training and all checkpoints are trained with default settings and hyperparameters.
  • Deploy models
Model Input size ONNX Paddle Lite(fp32) Paddle Lite(fp16)
PicoDet-S 320*320 model model model
PicoDet-S 416*416 model model model
PicoDet-M 320*320 model model model
PicoDet-M 416*416 model model model
PicoDet-L 320*320 model model model
PicoDet-L 416*416 model model model
PicoDet-L 640*640 model model model
PicoDet-Shufflenetv2 1x 416*416 model model model
PicoDet-MobileNetv3-large 1x 416*416 model model model
PicoDet-LCNet 1.5x 416*416 model model model

Cite PP-PicoDet

@misc{yu2021pppicodet,
      title={PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices},
      author={Guanghua Yu and Qinyao Chang and Wenyu Lv and Chang Xu and Cheng Cui and Wei Ji and Qingqing Dang and Kaipeng Deng and Guanzhong Wang and Yuning Du and Baohua Lai and Qiwen Liu and Xiaoguang Hu and Dianhai Yu and Yanjun Ma},
      year={2021},
      eprint={2111.00902},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}