This work reproduces the paper Mask-Guided Attention Network for Occluded Pedestrian Detection. Original authors have their own implementations, but they remove training parts. We use their codes and rewrite the training parts on our own. Same as the original implementation, we also work on the open-source toolbox mmdetetcion.
- We are using a detetction toolbox based on PyTorch: mmdetetcion. All the information is available on GitHub.
- Follow their documents to install the toolbox completely.
After successful installation, we only need to add few codes to set up our self-defined detector. This repository only include added codes. You can just add them into mmdetection framework.
mmdet/model/detector/mgan.py: for MGAN forward training and lossmodel/detector/__init__.py: addfrom .mgan import MGAN, and add'MGAN'in__all__listmmdet/model/mgan_head/: MGAN brachmmdet/utilis/: tools for network calculationsmmdet/models/backbones/vgg.py: VGG backboneconfigs/mgan.py: network structure claim, hyperparamter, and directories
The whole mmdetection files structure looks like:
|-- LICENSE
|-- README.md
|-- build
|-- configs
| `-- mgan.py # MGAN network structure
|-- data
| `-- cityscapes -> $DATASET_DIR # link to dataset
|-- demo
|-- docker
|-- docs
|-- mmcv
|-- mmdet
| |-- __init__.py # add MGAN class
| |-- __pycache__
| |-- apis
| |-- core
| |-- datasets
| |-- models
| | |-- __init__.py # add 'mgan_head' class
| | |-- __pycache__
| | |-- anchor_heads
| | |-- backbones # add backbone VGG
| | | |-- __init__.py
| | | `-- vgg.py
| | |-- bbox_heads
| | |-- builder.py
| | |-- detectors # add MGAN detectot
| | | |-- __init__.py
| | | |-- mgan.py
| | |-- losses
| | |-- mask_heads
| | |-- mgan_heads # add MGAN head
| | | |-- __init__.py
| | | |-- __pycache__
| | | `-- mgan_head.py
| | |-- necks
| | |-- plugins
| | |-- registry.py
| | |-- roi_extractors
| | |-- shared_heads
| | `-- utils
| |-- ops
| |-- utils
| `-- version.py
|-- mmdet.egg-info
|-- models
| `-- 50_65.pth # checkpoint
|-- pytest.ini
|-- requirements
|-- requirements.txt
|-- result
|-- setup.py
|-- tests
|-- tools
`-- work_dirs
Then you can train MGAN network by:
python tools/train.py configs/mgan.pyYou can test your codes by:
python tools/test_mgan.py configs/mgan.py models/50_65.pth --eval bbox --out result/50_65_bdd.pklwhere the checkpoint can be downloaded from original authors' GitHub.
You can also use our docker to reproduce it.
The dataset should have the same annotation convention as COCO dataset. For example, for BDD dataset can be converted in this way.
ToDo
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