Released a new series of PP-PicoDet models: (2022.03.20)
- (1) It was used TAL/ETA Head and optimized PAN, which greatly improved the accuracy;
- (2) Moreover optimized CPU prediction speed, and the training speed is greatly improved;
- (3) The export model includes post-processing, and the prediction directly outputs the result, without secondary development, and the migration cost is lower.

Legacy Model

Please refer to: PicoDet 2021.10

Introduction

We developed a series of lightweight models, named PP-PicoDet. Because of the excellent performance, our models are very suitable for deployment on mobile or CPU. For more details, please refer to our report on arXiv.

🌟 Higher mAP: the first object detectors that surpass mAP(0.5:0.95) 30+ within 1M parameters when the input size is 416.
🚀 Faster latency: 150FPS on mobile ARM CPU.
😊 Deploy friendly: support PaddleLite/MNN/NCNN/OpenVINO and provide C++/Python/Android implementation.
😍 Advanced algorithm: use the most advanced algorithms and offer innovation, such as ESNet, CSP-PAN, SimOTA with VFL, etc.

Benchmark

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{CPU^{^(ms)}}	Latency^{Lite^{^(ms)}}	Weight	Config	Inference Model
PicoDet-XS	320*320	23.5	36.1	0.70	0.67	3.9ms	7.81ms	model \| log	config	w/ postprocess \| w/o postprocess
PicoDet-XS	416*416	26.2	39.3	0.70	1.13	6.1ms	12.38ms	model \| log	config	w/ postprocess \| w/o postprocess
PicoDet-S	320*320	29.1	43.4	1.18	0.97	4.8ms	9.56ms	model \| log	config	w/ postprocess \| w/o postprocess
PicoDet-S	416*416	32.5	47.6	1.18	1.65	6.6ms	15.20ms	model \| log	config	w/ postprocess \| w/o postprocess
PicoDet-M	320*320	34.4	50.0	3.46	2.57	8.2ms	17.68ms	model \| log	config	w/ postprocess \| w/o postprocess
PicoDet-M	416*416	37.5	53.4	3.46	4.34	12.7ms	28.39ms	model \| log	config	w/ postprocess \| w/o postprocess
PicoDet-L	320*320	36.1	52.0	5.80	4.20	11.5ms	25.21ms	model \| log	config	w/ postprocess \| w/o postprocess
PicoDet-L	416*416	39.4	55.7	5.80	7.10	20.7ms	42.23ms	model \| log	config	w/ postprocess \| w/o postprocess
PicoDet-L	640*640	42.6	59.2	5.80	16.81	62.5ms	108.1ms	model \| log	config	w/ postprocess \| w/o postprocess

Table Notes:

Latency: All our models test on Intel core i7 10750H CPU with MKLDNN by 12 threads and Qualcomm Snapdragon 865(4xA77+4xA55) with 4 threads by arm8 and with FP16. In the above table, test CPU latency on Paddle-Inference and testing Mobile latency with Lite->Paddle-Lite.
PicoDet is trained on COCO train2017 dataset and evaluated on COCO val2017. And PicoDet used 4 GPUs for training and all checkpoints are trained with default settings and hyperparameters.
Benchmark test: When testing the speed benchmark, the post-processing is not included in the exported model, you need to set -o export.benchmark=True or manually modify runtime.yml.

Benchmark of Other Models

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{NCNN^{^(ms)}}
YOLOv3-Tiny	416*416	16.6	33.1	8.86	5.62	25.42
YOLOv4-Tiny	416*416	21.7	40.2	6.06	6.96	23.69
PP-YOLO-Tiny	320*320	20.6	-	1.08	0.58	6.75
PP-YOLO-Tiny	416*416	22.7	-	1.08	1.02	10.48
Nanodet-M	320*320	20.6	-	0.95	0.72	8.71
Nanodet-M	416*416	23.5	-	0.95	1.2	13.35
Nanodet-M 1.5x	416*416	26.8	-	2.08	2.42	15.83
YOLOX-Nano	416*416	25.8	-	0.91	1.08	19.23
YOLOX-Tiny	416*416	32.8	-	5.06	6.45	32.77
YOLOv5n	640*640	28.4	46.0	1.9	4.5	40.35
YOLOv5s	640*640	37.2	56.0	7.2	16.5	78.05

Testing Mobile latency with code: MobileDetBenchmark.

Quick Start

Requirements:

PaddlePaddle >= 2.2.2

Installation

Training and Evaluation

Training model on single-GPU:

# training on single-GPU
export CUDA_VISIBLE_DEVICES=0
python tools/train.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --eval

If the GPU is out of memory during training, reduce the batch_size in TrainReader, and reduce the base_lr in LearningRate proportionally. At the same time, the configs we published are all trained with 4 GPUs. If the number of GPUs is changed to 1, the base_lr needs to be reduced by a factor of 4.

Training model on multi-GPU:

# training on multi-GPU
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --eval

Evaluation:

python tools/eval.py -c configs/picodet/picodet_s_320_coco_lcnet.yml \
              -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams

Infer:

python tools/infer.py -c configs/picodet/picodet_s_320_coco_lcnet.yml \
              -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams

Detail also can refer to Quick start guide.

Deployment

Export and Convert Model

1. Export model

cd PaddleDetection
python tools/export_model.py -c configs/picodet/picodet_s_320_coco_lcnet.yml \
              -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams \
              --output_dir=output_inference

If no post processing is required, please specify: -o export.post_process=False (if -o has already appeared, delete -o here) or manually modify corresponding fields in runtime.yml.
If no NMS is required, please specify: -o export.nms=True or manually modify corresponding fields in runtime.yml. Many scenes exported to ONNX only support single input and fixed shape output, so if exporting to ONNX, it is recommended not to export NMS.

2. Convert to PaddleLite (click to expand)

Install Paddlelite>=2.10:

pip install paddlelite

Convert model:

# FP32
paddle_lite_opt --model_dir=output_inference/picodet_s_320_coco_lcnet --valid_targets=arm --optimize_out=picodet_s_320_coco_fp32
# FP16
paddle_lite_opt --model_dir=output_inference/picodet_s_320_coco_lcnet --valid_targets=arm --optimize_out=picodet_s_320_coco_fp16 --enable_fp16=true

3. Convert to ONNX (click to expand)

Install Paddle2ONNX >= 0.7 and ONNX > 1.10.1, for details, please refer to Tutorials of Export ONNX Model

pip install onnx
pip install paddle2onnx==0.9.2

Convert model:

paddle2onnx --model_dir output_inference/picodet_s_320_coco_lcnet/ \
            --model_filename model.pdmodel  \
            --params_filename model.pdiparams \
            --opset_version 11 \
            --save_file picodet_s_320_coco.onnx

Simplify ONNX model: use onnx-simplifier to simplify onnx model.
- Install onnxsim >= 0.4.1:
```
pip install onnxsim
```
- simplify onnx model:
```
onnxsim picodet_s_320_coco.onnx picodet_s_processed.onnx
```

Deploy models

Model	Input size	ONNX(w/o postprocess)	Paddle Lite(fp32)	Paddle Lite(fp16)
PicoDet-XS	320*320	( w/ postprocess) \| ( w/o postprocess)	model	model
PicoDet-XS	416*416	( w/ postprocess) \| ( w/o postprocess)	model	model
PicoDet-S	320*320	( w/ postprocess) \| ( w/o postprocess)	model	model
PicoDet-S	416*416	( w/ postprocess) \| ( w/o postprocess)	model	model
PicoDet-M	320*320	( w/ postprocess) \| ( w/o postprocess)	model	model
PicoDet-M	416*416	( w/ postprocess) \| ( w/o postprocess)	model	model
PicoDet-L	320*320	( w/ postprocess) \| ( w/o postprocess)	model	model
PicoDet-L	416*416	( w/ postprocess) \| ( w/o postprocess)	model	model
PicoDet-L	640*640	( w/ postprocess) \| ( w/o postprocess) model	model

Deploy

Infer Engine	Python	C++	Predict With Postprocess
OpenVINO	Python	C++（postprocess coming soon）	✔︎
Paddle Lite	-	C++	✔︎
Android Demo	-	Paddle Lite	✔︎
PaddleInference	Python	C++	✔︎
ONNXRuntime	Python	Coming soon	✔︎
NCNN	Coming soon	C++	✘
MNN	Coming soon	C++	✘

Android demo visualization:

Quantization

Requirements:

PaddlePaddle >= 2.2.2
PaddleSlim >= 2.2.2

Install:

pip install paddleslim==2.2.2

Quant aware

Configure the quant config and start training:

python tools/train.py -c configs/picodet/picodet_s_416_coco_lcnet.yml \
          --slim_config configs/slim/quant/picodet_s_416_lcnet_quant.yml --eval

More detail can refer to slim document

Quant Aware Model ZOO：

Quant Model	Input size	mAP^val 0.5:0.95	Configs	Weight	Inference Model	Paddle Lite(INT8)
PicoDet-S	416*416	31.5	config \| slim config	model	w/ postprocess \| w/o postprocess	w/ postprocess \| w/o postprocess

Unstructured Pruning

Tutorial:

Please refer this documentation for details such as requirements, training and deployment.

Application

Pedestrian detection: model zoo of PicoDet-S-Pedestrian please refer to PP-TinyPose
Mainbody detection: model zoo of PicoDet-L-Mainbody please refer to mainbody detection

FAQ

Out of memory error.

Please reduce the batch_size of TrainReader in config.

How to transfer learning.

Please reset pretrain_weights in config, which trained on coco. Such as:

pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams

The transpose operator is time-consuming on some hardware.

Please use PicoDet-LCNet model, which has fewer transpose operators.

How to count model parameters.

You can insert below code at here to count learnable parameters.

params = sum([
    p.numel() for n, p in self.model. named_parameters()
    if all([x not in n for x in ['_mean', '_variance']])
]) # exclude BatchNorm running status
print('params: ', params)

Cite PP-PicoDet

If you use PicoDet in your research, please cite our work by using the following BibTeX entry:

@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}
}

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PP-PicoDet

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