format model docs with Rewrap

Automatic formatting changes by Rewrap (stkb) have been staged separately to avoid unintended modifications to the codebase. Feel free to review and revert if preferred.

Author: qin-yu

docs/models.rst

@@ -1,86 +1,89 @@
 Models
-------------------------------
+-------------------------------
 
 ``from cellpose import models``
 
-Each model will be downloaded automatically to your ``models.MODELS_DIR``
-(see Installation instructions for more details on MODELS_DIR).
-You can also directly download a model by going to the URL, e.g.:
+Each model will be downloaded automatically to your ``models.MODELS_DIR`` (see
+Installation instructions for more details on MODELS_DIR). You can also directly
+download a model by going to the URL, e.g.:
 
 ``https://www.cellpose.org/models/MODEL_NAME``
 
 All built-in models were trained with the ROIs resized to a diameter of 30.0
-(``diam_mean = 30``),
-except the `'nuclei'` model which was trained with a diameter of 17.0
-(``diam_mean = 17``). User-trained models will be trained with the same ``diam_mean``
-as the model they are initalized with.
-The models will internally take care of rescaling the images given a
-user-provided diameter (or with the diameter from
-auto-diameter estimation in full models).
+(``diam_mean = 30``), except the `'nuclei'` model which was trained with a
+diameter of 17.0 (``diam_mean = 17``). User-trained models will be trained with
+the same ``diam_mean`` as the model they are initalized with. The models will
+internally take care of rescaling the images given a user-provided diameter (or
+with the diameter from auto-diameter estimation in full models).
 
 Full built-in models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 These models have Cellpose model weights and a size model. This means you can
-run with ``diameter=0`` or ``--diameter 0`` and the model can estimate the ROI size.
-However, we recommend that you set the diameter for your ROIs rather than having Cellpose
-guess the diameter.
+run with ``diameter=0`` or ``--diameter 0`` and the model can estimate the ROI
+size. However, we recommend that you set the diameter for your ROIs rather than
+having Cellpose guess the diameter.
 
-These models can be loaded and used in the notebook with ``models.Cellpose(model_type='cyto3')``
-or in the command line with ``python -m cellpose --pretrained_model cyto3``.
+These models can be loaded and used in the notebook with
+``models.Cellpose(model_type='cyto3')`` or in the command line with ``python -m
+cellpose --pretrained_model cyto3``.
 
-We have a ``nuclei`` model and a super-generalist ``cyto3`` model. There are also two
-older models, ``cyto``, which is trained on only the Cellpose training set, and ``cyto2``,
-which is also trained on user-submitted images.
+We have a ``nuclei`` model and a super-generalist ``cyto3`` model. There are
+also two older models, ``cyto``, which is trained on only the Cellpose training
+set, and ``cyto2``, which is also trained on user-submitted images.
 
-FYI we are no longer using the 4 different versions and ``--net_avg`` is deprecated.
+FYI we are no longer using the 4 different versions and ``--net_avg`` is
+deprecated.
 
 Cytoplasm model (``'cyto3'``, ``'cyto2'``, ``'cyto'``)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The cytoplasm models in cellpose are trained on two-channel images, where
-the first channel is the channel to segment, and the second channel is
-an optional nuclear channel. Here are the options for each:
-1. 0=grayscale, 1=red, 2=green, 3=blue
-2. 0=None (will set to zero), 1=red, 2=green, 3=blue
+The cytoplasm models in cellpose are trained on two-channel images, where the
+first channel is the channel to segment, and the second channel is an optional
+nuclear channel. Here are the options for each: 1. 0=grayscale, 1=red, 2=green,
+3=blue 2. 0=None (will set to zero), 1=red, 2=green, 3=blue
 
-Set channels to a list with each of these elements, e.g.
-``channels = [0,0]`` if you want to segment cells in grayscale or for single channel images, or
+Set channels to a list with each of these elements, e.g. ``channels = [0,0]`` if
+you want to segment cells in grayscale or for single channel images, or
 ``channels = [2,3]`` if you green cells with blue nuclei.
 
-The `'cyto3'` model is trained on 9 datasets, see the Cellpose3 paper for more details.
+The `'cyto3'` model is trained on 9 datasets, see the Cellpose3 paper for more
+details.
 
 Nucleus model (`'nuclei'`)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The nuclear model in cellpose is trained on two-channel images, where
-the first channel is the channel to segment, and the second channel is
-always set to an array of zeros. Therefore set the first channel as
-0=grayscale, 1=red, 2=green, 3=blue; and set the second channel to zero, e.g.
-``channels = [0,0]`` if you want to segment nuclei in grayscale or for single channel images, or
-``channels = [3,0]`` if you want to segment blue nuclei.
+The nuclear model in cellpose is trained on two-channel images, where the first
+channel is the channel to segment, and the second channel is always set to an
+array of zeros. Therefore set the first channel as 0=grayscale, 1=red, 2=green,
+3=blue; and set the second channel to zero, e.g. ``channels = [0,0]`` if you
+want to segment nuclei in grayscale or for single channel images, or ``channels
+= [3,0]`` if you want to segment blue nuclei.
 
 Other built-in models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The main built-in models are dataset-specific models trained on one of the 9 datasets
-in the Cellpose3 paper. These models do not have a size model.
-If the diameter is set to 0.0, then the model uses the default ``diam_mean`` for the
+The main built-in models are dataset-specific models trained on one of the 9
+datasets in the Cellpose3 paper. These models do not have a size model. If the
+diameter is set to 0.0, then the model uses the default ``diam_mean`` for the
 diameter (``30.0``).
 
 These models can be loaded and used in the notebook with e.g.
-``models.CellposeModel(model_type='tissuenet_cp3')`` or ``models.CellposeModel(model_type='livecell_cp3')``,
-or in the command line with ``python -m cellpose --pretrained_model tissuenet_cp3``.
-
-The dataset-specific models were trained on the training images provided in the following datasets:
-    - ``tissuenet_cp3``: `tissuenet dataset <https://datasets.deepcell.org/>`_.
-    - ``livecell_cp3``: `livecell dataset <https://sartorius-research.github.io/LIVECell/>`_
-    - ``yeast_PhC_cp3``: `YEAZ dataset <https://www.epfl.ch/labs/lpbs/data-and-software/>`_
-    - ``yeast_BF_cp3``: `YEAZ dataset <https://www.epfl.ch/labs/lpbs/data-and-software/>`_
-    - ``bact_phase_cp3``: `omnipose dataset <https://osf.io/xmury/>`_
-    - ``bact_fluor_cp3``: `omnipose dataset <https://osf.io/xmury/>`_
-    - ``deepbacs_cp3``: `deepbacs dataset <https://github.com/HenriquesLab/DeepBacs/wiki/Segmentation>`_
-    - ``cyto2_cp3``: `cellpose dataset <http://www.cellpose.org/dataset>`_
+``models.CellposeModel(model_type='tissuenet_cp3')`` or
+``models.CellposeModel(model_type='livecell_cp3')``, or in the command line with
+``python -m cellpose --pretrained_model tissuenet_cp3``.
+
+The dataset-specific models were trained on the training images provided in the
+following datasets:
+
+- ``tissuenet_cp3``: `tissuenet dataset <https://datasets.deepcell.org/>`_.
+- ``livecell_cp3``: `livecell dataset <https://sartorius-research.github.io/LIVECell/>`_
+- ``yeast_PhC_cp3``: `YEAZ dataset <https://www.epfl.ch/labs/lpbs/data-and-software/>`_
+- ``yeast_BF_cp3``: `YEAZ dataset <https://www.epfl.ch/labs/lpbs/data-and-software/>`_
+- ``bact_phase_cp3``: `omnipose dataset <https://osf.io/xmury/>`_
+- ``bact_fluor_cp3``: `omnipose dataset <https://osf.io/xmury/>`_
+- ``deepbacs_cp3``: `deepbacs dataset <https://github.com/HenriquesLab/DeepBacs/wiki/Segmentation>`_
+- ``cyto2_cp3``: `cellpose dataset <http://www.cellpose.org/dataset>`_
 
 
 User-trained models