mobilenetv3_centernet

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mobilenetv3_centernet

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A tensorflow implement mobilenetv3 centernet, which can be easily deployeed on android(MNN) and ios(CoreML).

mobilenetv3_centernet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# mobilenetv3_centernet

### there is a [pytorch version](https://github.com/610265158/mobile_centernet), trained with mobilenetv2, it is more simple.

## introduction

This is a tensorflow implement mobilenetv3-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|
| :------:                  |:------:   |:------:  |:------:  |:------:  |
|[mbv3-large-0.75-modified_head](https://drive.google.com/drive/folders/13zvokhOmfSexXNt6fDeFvjedllvLMJfZ?usp=sharing)  |512x512     | 0.251| 0.423|0.258  |

## requirment

+ tensorflow 1.14

+ tensorpack 0.9.9  (for data provider)

+ opencv

+ python 3.6

+ MNNConverter

+ coremltools

## useage

### MSCOCO

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

2. download pretrained model from
[mbv3-large0.75](https://storage.googleapis.com/mobilenet_v3/checkpoints/v3-large_224_0.75_float.tgz)
relese it in the current dir.

3. 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

4. After training, freeze the model as .pb  by

` python tools/auto_freeze.py --pretrained_mobile ./model/yourmodel.ckpt`

it will produce a detector.pb

#### 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
                                --is_show 1

ps, no test time augmentation is used.
```

### finetune
1. download the trained model,
modify the config config.MODEL.pretrained_model='yourmodel.ckpt',
and set config.MODEL.continue_train=True
2. `python train.py`

### visualization

if u get a trained model and dont need to work on mobile device, run `python tools/auto_freeze.py`, it will read the checkpoint file in ./model, and produce detector.pb, then

`python visualization/vis.py`

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

+ 4.1.1 convert model

just use the MNN converter, for example:
        `./MNNConvert -f TF --modelFile detector.pb --MNNModel centernet.mnn --bizCode biz  --fp16 1`

+ 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

+ 4.2.1 convert

`python tools/converter_to_coreml.py`

+ 4.2.2 visualization with coreml python wrapper

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

ps, if you want to do quantization, please reffer to the official doc, it is easy.

### TODO: 
- [ ] Android project.

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