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README.md

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PP-PicoDet

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.

Comming Soon

  • More series of model, such as smaller or larger model.
  • Pretrained models for more scenarios.
  • More features in need.

Benchmark

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.

Benchmark of Other Models

Model Input size mAPval
0.5:0.95		 mAPval
0.5		 Params
(M)		 FLOPS
(G)		 LatencyNCNN
(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

Quick Start

Requirements:
  • PaddlePaddle >= 2.1.2
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.yml --eval
  • Training model on multi-GPU:
# training on single-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.yml --eval
  • Evaluation:
python tools/eval.py -c configs/picodet/picodet_s_320_coco.yml \
              -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams
  • Infer:
python tools/infer.py -c configs/picodet/picodet_s_320_coco.yml \
              -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams

Detail also can refer to Quick start guide.

Deployment

Export and Convert Model

1. Export model (click to expand) 
cd PaddleDetection
python tools/export_model.py -c configs/picodet/picodet_s_320_coco.yml \
              -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams --output_dir=inference_model
2. Convert to PaddleLite (click to expand)
  • Install Paddlelite>=2.10.rc:
pip install paddlelite
  • Convert model:
# FP32
paddle_lite_opt --model_dir=inference_model/picodet_s_320_coco --valid_targets=arm --optimize_out=picodet_s_320_coco_fp32
# FP16
paddle_lite_opt --model_dir=inference_model/picodet_s_320_coco --valid_targets=arm --optimize_out=picodet_s_320_coco_fp16 --enable_fp16=true
3. Convert to ONNX (click to expand) 
pip install onnx
pip install paddle2onnx
  • Convert model:
paddle2onnx --model_dir output_inference/picodet_s_320_coco/ \
            --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 onnx-simplifier >= 0.3.6:
    pip install onnx-simplifier
    • simplify onnx model:
    python -m onnxsim picodet_s_320_coco.onnx picodet_s_processed.onnx
  • 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

Deploy

Android demo visualization:

Quantization

Requirements:
  • PaddlePaddle >= 2.2.0rc0
  • PaddleSlim >= 2.2.0rc0

Install:

pip install paddleslim==2.2.0rc0
Quant aware (click to expand)

Configure the quant config and start training:

python tools/train.py -c configs/picodet/picodet_s_320_coco.yml \
          --slim_config configs/slim/quant/picodet_s_quant.yml --eval
Post quant (click to expand)

Configure the post quant config and start calibrate model:

python tools/post_quant.py -c configs/picodet/picodet_s_320_coco.yml \
          --slim_config configs/slim/post_quant/picodet_s_ptq.yml
  • Notes: Now the accuracy of post quant is abnormal and this problem is being solved.

Unstructured Pruning

Toturial:

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.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}
}