[doc] Add object detection

doc/databases/detect/cxr8.rst

+.. SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+.. _mednet.databases.detect.cxr8:
+
+=======
+ CXR-8
+=======
+
+* DataModule and support code: :py:mod:`.data.detect.cxr8`
+* Splits:
+
+  .. list-table::
+     :align: left
+
+     * - Config. key
+       - Module
+     * - ``cxr8-detect``
+       - :py:mod:`.config.detect.data.cxr8.default`

doc/databases/detect/index.rst

+.. SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+.. _mednet.databases.detect:
+
+==================
+ Object Detection
+==================
+
+.. toctree::
+   :maxdepth: 1
+
+   cxr8
+   jsrt
+   montgomery
+   shenzhen

doc/databases/detect/jsrt.rst

+.. SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+.. _mednet.databases.detect.jsrt:
+
+======
+ JSRT
+======
+
+* DataModule and support code: :py:mod:`.data.detect.jsrt`
+* Splits:
+
+  .. list-table::
+     :align: left
+
+     * - Config. key
+       - Module
+     * - ``jsrt-detect``
+       - :py:mod:`.config.detect.data.jsrt.default`

doc/databases/detect/montgomery.rst

+.. SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+.. _mednet.databases.detect.montgomery:
+
+===================
+ Montgomery County
+===================
+
+* DataModule and support code: :py:mod:`.data.detect.montgomery`
+* Splits:
+
+  .. list-table::
+     :align: left
+
+     * - Config. key
+       - Module
+     * - ``montgomery-detect``
+       - :py:mod:`.config.detect.data.montgomery.default`

doc/databases/detect/shenzhen.rst

+.. SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+.. _mednet.databases.detect.shenzhen:
+
+===================
+ Shenzhen Hospital
+===================
+
+* DataModule and support code: :py:mod:`.data.detect.shenzhen`
+* Splits:
+
+  .. list-table::
+     :align: left
+
+     * - Config. key
+       - Module
+     * - ``shenzhen-detect``
+       - :py:mod:`.config.detect.data.shenzhen.default`

doc/databases/index.rst

@@ -15,3 +15,4 @@
 
    classify/index
    segment/index
+   detect/index

doc/models.rst

@@ -89,4 +89,22 @@ Pre-configured models supporting semantic segmentation tasks.
      - :py:class:`.models.segment.unet.Unet`
 
 
+.. _mednet.models.detect:
+
+Object Detection
+----------------
+
+Pre-configured models supporting object detection tasks.
+
+.. list-table:: Pre-configured object detection models
+   :align: left
+
+   * - Config. key
+     - Module
+     - Base type
+   * - ``faster-rcnn``
+     - :py:mod:`.config.detect.models.faster_rcnn`
+     - :py:class:`.models.detect.faster_rcnn.FasterRCNN`
+
+
 .. include:: links.rst

doc/usage/evaluation.rst

@@ -34,7 +34,10 @@ pre-configured :ref:`datamodule <mednet.datamodel>`, run the one of following:
    mednet predict -vv pasa montgomery --weight=<results/model.ckpt> --output-folder=predictions
 
    # example for a segmentation task
-   mednet predict -vv lwnet drive  --weight=<results/model.ckpt> --output-folder=predictions
+   mednet predict -vv lwnet drive --weight=<results/model.ckpt> --output-folder=predictions
+
+   # example for a object detection task
+   mednet predict -vv faster-rcnn montgomery-detect --weight=<results/model.ckpt> --output-folder=predictions
 
 
 Replace ``<results/model.ckpt>`` to a path leading to the pre-trained model.
@@ -71,5 +74,8 @@ the following:
    # segmentation task
    mednet segment evaluate -vv --predictions=path/to/predictions.json
 
+   # object detection task
+   mednet detect evaluate -vv --predictions=path/to/predictions.json
+
 
 .. include:: ../links.rst

doc/usage/experiment.rst

@@ -29,6 +29,11 @@ performance curves, run the one of following:
    $ mednet experiment -vv lwnet drive
    # check results in the "results" folder
 
+   # example object detection task using the "faster-rcnn" network model
+   # on the "montgomery" (for object detection) datamodule
+   $ mednet experiment -vv montgomery-detect faster-rcnn
+   # check results in the "results" folder
+
 You may run the system on a GPU by using the ``--device=cuda``, or
 ``--device=mps``  option.
 

doc/usage/index.rst

@@ -8,14 +8,14 @@
  Usage
 =======
 
-This package supports a fully reproducible research experimentation cycle for
-medical image classification and segmentation with support for the following
+This package supports a fully reproducible research experimentation cycle for medical
+image classification, segmentation, and object detection with support for the following
 activities:
 
-* Training: Images are fed to a deep neural network that is trained to match
-  (classification) or reconstruct (segmentation) annotations automatically, via
-  error back propagation. The objective of this phase is to produce a model.
-  We support training on CPU and a few GPU architectures (``cuda`` or ``mps``).
+* Training: Images are fed to a deep neural network that is trained to match labels
+  (classification), reconstruct (segmentation), or find objects (detections)
+  automatically, via error back propagation. The objective of this phase is to produce a
+  model. We support training on CPU and a few GPU architectures (``cuda`` or ``mps``).
 * Prediction (inference): The model is used to generate predictions
 * Evaluation: Predictions are used evaluate model performance against provided
   annotations, or visualize prediction results overlayed on the original raw
@@ -32,7 +32,7 @@ generate intermediate outputs required for subsequent commands:
 .. graphviz:: img/cli-core-dark.dot
    :align: center
    :class: only-dark
-   :caption: Overview of core CLI commands for model training, inference and evaluation. Clicking on each item leads to the appropriate specific documentation. The workflow is the same across different task types (e.g. classification or segmentation), except for evaluation, that remains task-specific. The right implementation is chosen based on the type of datamodule being used.
+   :caption: Overview of core CLI commands for model training, inference and evaluation. Clicking on each item leads to the appropriate specific documentation. The workflow is the same across different task types (e.g. classification, segmentation or object detection), except for evaluation, that remains task-specific. The right implementation is chosen based on the type of datamodule being used.
 
 The CLI interface is configurable using :ref:`clapper's extensible
 configuration framework <clapper.config>`.  In essence, each command-line

doc/usage/training.rst

@@ -27,6 +27,10 @@ For example, to train a model on a pre-configured :ref:`datamodule
    mednet train -vv lwnet drive
    # check results in the "results" folder
 
+   # example object detection task
+   $ mednet train -vv montgomery-detect faster-rcnn
+   # check results in the "results" folder
+
 You may run the system on a GPU by using the ``--device=cuda``, or
 ``--device=mps``  option.
 

src/mednet/models/detect/faster_rcnn.py

@@ -65,7 +65,7 @@ class FasterRCNN(Model):
         num_classes: int = 1,
         variant: typing.Literal[
             "resnet50-v1", "resnet50-v2", "mobilenetv3-large", "mobilenetv3-small"
-        ] = "resnet50-v1",
+        ] = "mobilenetv3-small",
     ):
         super().__init__(
             name=f"faster-rcnn[{variant}]",