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mobilenetv3_centernet

introduction

This is a pytorch implement mobilenet-centernet framework, which can be easily deployeed on Android(MNN) and IOS(CoreML) mobile devices, end to end.

Purpose: Light detection algorithms that work on mobile devices is widely used, such as face detection. So there is an easy project contains model training and model converter.

** contact me if u have question 2120140200@mail.nankai.edu.cn **

pretrained model , and preformance

mscoco

no test time augmentation.

model input_size map map@0.5 map@0.75
mbv2_100-centernet_stride8 512x512 0.224 0.383 0.228
mbv2_100-centernet_stride4 512x512 0.234 0.385 0.242

requirment

  • pytorch

  • tensorpack

  • opencv

  • python 3.6

  • MNNConverter

  • coremltools

useage

MSCOCO

train

  1. download mscoco data, then run python prepare_coco_data.py --mscocodir ./mscoco

  2. then, modify in config=mb3_config in train_config.py, then run:

    python train.py

    and if u want to check the data when training, u could set vis in confifs/mscoco/mbv3_config.py as True

evaluation

python model_eval/custome_eval.py [--model [TRAINED_MODEL]] [--annFile [cocostyle annFile]]
                          [--imgDir [the images dir]] [--is_show [show the result]]

python model_eval/custome_eval.py --model model/detector.pb
                                --annFile ../mscoco/annotations/instances_val2017.json
                                --imgDir ../mscoco/val2017

ps, no test time augmentation is used.

visualization

python visualization/vis.py --model yout.pth --imgDir yourimgdir

u can check th code in visualization to make it runable, it's simple.

model convert for mobile device

I have carefully processed the postprocess, and it can works within the model, so it could be deployed end to end.

4.1 MNN

convert to onnx first

+ 4.1.1 convert model to onnx

    `python tools/converter_to_coreml.py --model your.pth`
    
+ 4.1.2 convert onnx to mnn
    
    './MNNConvert -f ONNX --modelFile centernet.onnx --MNNModel centernet.mnn --bizCode biz --weightQuantBits  8`

+ 4.1.2 visualization with mnn python wrapper

    `python visualization/vis_with_mnn.py --mnn_model centernet.mnn --imgDir 'your image dir'`

4.2 coreml

##some bugs in coremltools now, convert carefully. try to find the answer in coremltools issue
+ 4.2.1 convert

    `python tools/converter_to_coreml.py --model your.pth`

+ 4.2.2 visualization with coreml python wrapper

    `python visualization/vis_with_coreml.py --coreml_model centernet.mlmodel --imgDir 'your image dir'`