add doc for mmdet coco

CHANGELOG.md

@@ -6,6 +6,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## \[Unreleased\]
+### New features
+- Support MMDetection COCO format
+  (<https://github.com/openvinotoolkit/datumaro/pull/1213>)
+
 ### Enhancements
 - Optimize Python import to make CLI entrypoint faster
   (<https://github.com/openvinotoolkit/datumaro/pull/1182>)

docs/source/docs/data-formats/formats/index.rst

@@ -32,6 +32,7 @@ Supported Data Formats
    mapillary_vistas
    market1501
    mars
+   mmdet
    mnist
    mot
    mots
@@ -141,6 +142,10 @@ Supported Data Formats
    * `Format specification <https://zheng-lab.cecs.anu.edu.au/Project/project_mars.html>`_
    * `Dataset example <https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mars_dataset>`_
    * `Format documentation <mars.md>`_
+* MMDet-COCO (``detection``, ``segmentation``)
+   * `Format specification <https://mmdetection.readthedocs.io/en/latest/user_guides/dataset_prepare.html>`_
+   * `Dataset example <https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/coco_dataset/mmdet_coco>`_
+   * `Format documentation <mmdet.md>`_
 * MNIST (``classification``)
    * `Format specification <http://yann.lecun.com/exdb/mnist/>`_
    * `Dataset example <https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mnist_dataset>`_

docs/source/docs/data-formats/formats/mmdet.md

@@ -0,0 +1,41 @@
+# MMDetection COCO
+
+## Format specification
+
+[MMDetection](https://mmdetection.readthedocs.io/en/latest/) is a training framework for object detection and instance segmentation tasks, providing a modular and flexible architecture that supports various state-of-the-art models, datasets, and training techniques. MMDetection has gained popularity in the research community for its comprehensive features and ease of use in developing and benchmarking object detection algorithms.
+MMDetection specifies their COCO format [here](https://mmdetection.readthedocs.io/en/latest/user_guides/dataset_prepare.html).
+
+Most of available tasks or formats are similar to the [original COCO format](./formats/coco), while only the image directories are separated with respect to subsets.
+In this document, we just describe the directory structure of MMDetection COCO format as per [here](https://mmdetection.readthedocs.io/en/latest/user_guides/dataset_prepare.html).
+MMDetection COCO dataset directory should have the following structure:
+
+<!--lint disable fenced-code-flag-->
+```
+└─ Dataset/
+    ├── <subset_name>/
+    │   ├── <image_name1.ext>
+    │   ├── <image_name2.ext>
+    │   └── ...
+    ├── <subset_name>/
+    │   ├── <image_name1.ext>
+    │   ├── <image_name2.ext>
+    │   └── ...
+    └── annotations/
+        ├── instances_<subset_name>.json
+        └── ...
+```
+
+### Import using CLI
+
+``` bash
+datum project create
+datum project import --format mmdet_coco <path/to/dataset>
+```
+
+### Import using Python API
+
+```python
+import datumaro as dm
+
+dataset = dm.Dataset.import_from('<path/to/dataset>', 'mmdet_coco')
+```