updated contribution docs

CONTRIBUTING.md

@@ -5,34 +5,42 @@
 
 ## Choosing a task
 * Check out our [Kanban board](https://github.com/OML-Team/open-metric-learning/projects/1).
-  You can work on one of the existing issues or create the new one.
-  Especially pay attention to the issues marked with the `good_first_issue` flag.
-* Start the conversation under the issue that you picked. We will discuss the design and content of the pull request, and
+  You can work on one of the existing issues or create a new one.
+* Start the conversation under the issue you picked. We will discuss the design and content of the pull request, and
   then you can start working on it.
 
-## Contributing
+## Contributing in general
 * Fork the repository.
 * Clone it locally.
 * Create a branch with a name that speaks for itself.
 * Set up the environment. You can install the library in dev mode via `pip install -e .`
   or build / pull [docker image](https://github.com/OML-Team/open-metric-learning#installation).
 * Implement the discussed functionality, **docstrings**, and **tests** for it.
-* Run tests locally via `make run_tests` or `make docker_tests` (preferable option).
+* Run tests locally using commands from `Makefile`.
 * Push the code to your forked repository.
 * Create a pull request to OpenMetricLearning.
 
-## Good to know
+## Contributing to documentation
 * If you want to change `README.md` you should go to `docs/readme`, change the desired section and then build
   readme via `make build_readme`. *So, don't change the main readme file directly, otherwise tests will fail.*
 * Don't forget to update the documentation if needed. Its source is located in `docs/source`. To inspect
   it locally, you should run `make html` (from `docs` folder) and then open `docs/build/html/index.html` in your
   browser.
-* If you want to add some new criterion, miner, model, optimizer, sampler, lr scheduler or transforms, don't forget to
-  add it to the corresponding registry (see `oml.registry`) and also add a config file (see `oml.configs`).
 
-## How to add new functionality in Pipelines (Config-API)?
-* Implement your changes in `extractor_training_pipeline` and/or in `extractor_validation_pipeline`
-* Add a new test run or modify one of the existing tests. If adding a new test:
+## Contributing to models ZOO
+* Add the model's implementation under `oml/models`.
+* Implement `from_pretrained()` and add the corresponding [transforms](https://github.com/OML-Team/open-metric-learning/blob/f0d151ace24aaa527d0605d055529f31ad027f49/oml/registry/transforms.py#L53).
+* Add the model to `oml/registry` and `oml/configs`.
+* Evaluate model on 4 benchmarks and add the results into ZOO table in the main Readme.
+
+## Contributing to pipelines
+* Implement your changes in one of the pipelines (`extractor_training_pipeline`, `extractor_validation_pipeline` or others).
+* Add a new test or modify an existing one under `tests/test_runs/test_pipelines`.
+* If adding a new test:
   * Add config file: `tests/test_runs/test_pipelines/configs/train_or_validate_new_feature.yaml`
   * Add python script: `tests/test_runs/test_pipelines/train_or_validate_new_feature.py`
-  * Add entry point to your test to: `tests/test_runs/test_pipelines/test_pipelines.py`
+  * Add test: `tests/test_runs/test_pipelines/test_pipelines.py`
+
+## Don't forget to update Registry
+* If you want to add some new criterion, miner, model, optimizer, sampler, lr scheduler or transforms, don't forget to
+  add it to the corresponding registry (see `oml.registry`) and also add a config file (see `oml.configs`).