GraphFPN: Graph Feature Pyramid Network for Object Detection with Retinanet

Currently working with the author Gangming Zhao, we will improve our code on mmdetection in the future.

For demonstraction with Graph_fpn, run:

python demo.py

For demonstraction with fpn, run:

python demo.py --no_graph

For training , run:

python train.py

For test with Graph_fpn, run

python test.py

For test with fpn, run

python test.py --no_graph

If You need COCO API for test, you can download from here. You need to set the backend of DGL to tensorflow, here is tutorial link

Folder structure

${ROOT}
└── checkpoint/
└── COCO/    
│   └── coco/
│   │    ├── .config 
│   │    ├── 2017/
│   │
│   ├── downloads/
│
│
└── data_demo/
|   ├── data/
|   |    ├── coco
|   |    ├── checkpoint
|   ├── data.zip
|
├── results/
├── src/     
|   ├── configs/
|   |    ├── configs.py
|   |
|   ├── detection/
|   |    ├── datasets/
|   |    |      ├── coco.py
|   |    ├── utils/
|   |
|   ├── model/
|   ├── init_path.py
|   ├── demo.py
|   ├── train.py
|   ├── test.py
├── README.md 
└── requirements.txt

References

[1] Retinanet: Focal Loss for Dense Object Detection
[2] Graph-FPN: GraphFPN: Graph Feature Pyramid Network for Object Detection
[3] Object Detection with RetinaNet: Keras Implementation