CenterNet

Develop

This document provides tutorials to develop CenterNet. lib/src/opts lists a few more options that the current version supports.

New dataset

Basically there are three steps:

• Convert the dataset annotation to COCO format. Please refer to src/tools/convert_kitti_to_coco.py for an example to convert kitti format to coco format.
• Create a dataset intilization file in src/lib/datasets/dataset. In most cases you can just copy src/lib/datasets/dataset/coco.py to your dataset name and change the category information, and annotation path.
• Import your dataset at src/lib/datasets/dataset_factory.

New task

You will need to add files to src/lib/datasets/sample/, src/lib/datasets/trains/, and src/lib/datasets/detectors/, which specify the data generation during training, the training targets, and the testing, respectively.

New architecture

• Add your model file to src/lib/models/networks/. The model should accept a dict heads of {name: channels}, which specify the name of each network output and its number of channals. Make sure your model returns a list (for multiple stages. Single stage model should return a list containing a single element.). The element of the list is a dict contraining the same keys with heads.
• Add your model in model_factory of src/lib/models/model.py.

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