• 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.

• PaddlePaddle >= 2.2.2

• Installation guide
• Prepare dataset

• 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.

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.

• 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

• 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
  

• PaddlePaddle >= 2.2.2
• PaddleSlim >= 2.2.2

Install:

pip install paddleslim==2.2.2

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

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

Please reduce the batch_size of TrainReader in config.

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

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

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)