surveillance_monitoring/dependence/PaddleDetection/configs/picodet/legacy_model

PP-PicoDet Legacy Model-ZOO (2021.10)

More Configs

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

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	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

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}
}