> All the fields in this README are **mandatory** for others to understand what you have achieved in this implementation. If you still feel unclear about the requirements, please read our [contribution guide](https://mmocr.readthedocs.io/en/dev-1.x/notes/contribution_guide.html), [projects FAQ](../faq.md), or approach us in [Discussions](https://github.com/open-mmlab/mmocr/discussions).
> For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`.
All the commands below rely on the correct configuration of `PYTHONPATH`, which should point to the project's directory so that Python can locate the module files. In `example_project/` root directory, run the following line to add the current directory to `PYTHONPATH`:
> List the results as usually done in other model's README. [Example](https://github.com/open-mmlab/mmocr/blob/1.x/configs/textdet/dbnet/README.md#results-and-models)
>
> You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project.
> The PIC (person in charge) or contributors of this project should check all the items that they believe have been finished, which will further be verified by codebase maintainers via a PR.
>
> OpenMMLab's maintainer will review the code to ensure the project's quality. Reaching the first milestone means that this project suffices the minimum requirement of being merged into 'projects/'. But this project is only eligible to become a part of the core package upon attaining the last milestone.
>
> Note that keeping this section up-to-date is crucial not only for this project's developers but the entire community, since there might be some other contributors joining this project and deciding their starting point from this list. It also helps maintainers accurately estimate time and effort on further code polishing, if needed.
>
> A project does not necessarily have to be finished in a single PR, but it's essential for the project to at least reach the first milestone in its very first PR.
> The code's design shall follow existing interfaces and convention. For example, each model component should be registered into `mmocr.registry.MODELS` and configurable via a config file.
> Each major object should contain a docstring, describing its functionality and arguments. If you have adapted the code from other open-source projects, don't forget to cite the source project in docstring and make sure your behavior is not against its license. Typically, we do not accept any code snippet under GPL license. [A Short Guide to Open Source Licenses](https://medium.com/nationwide-technology/a-short-guide-to-open-source-licenses-cf5b1c329edd)
> If you are reproducing the result from a paper, make sure your model's inference-time performance matches that in the original paper. The weights usually could be obtained by simply renaming the keys in the official pre-trained weights. This test could be skipped though, if you are able to prove the training-time correctness and check the second milestone.
> If you are reproducing the result from a paper, checking this item means that you should have trained your model from scratch based on the original paper's specification and verified that the final result matches the report within a minor error range.
> Ideally *all* the methods should have [type hints](https://www.pythontutorial.net/python-basics/python-type-hints/) and [docstrings](https://google.github.io/styleguide/pyguide.html#381-docstrings). [Example](https://github.com/open-mmlab/mmocr/blob/76637a290507f151215d299707c57cea5120976e/mmocr/utils/polygon_utils.py#L80-L96)
> Unit tests for each module are required. [Example](https://github.com/open-mmlab/mmocr/blob/76637a290507f151215d299707c57cea5120976e/tests/test_utils/test_polygon_utils.py#L97-L106)
