19 lines
1.4 KiB
Markdown
19 lines
1.4 KiB
Markdown
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# Develop
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This document provides tutorials to develop CenterNet. `lib/src/opts` lists a few more options that the current version supports.
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## New dataset
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Basically there are three steps:
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- Convert the dataset annotation to [COCO format](http://cocodataset.org/#format-data). Please refer to [src/tools/convert_kitti_to_coco.py](../src/tools/convert_kitti_to_coco.py) for an example to convert kitti format to coco format.
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- 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.
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- Import your dataset at `src/lib/datasets/dataset_factory`.
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## New task
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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.
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## New architecture
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- 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`.
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- Add your model in `model_factory` of `src/lib/models/model.py`.
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