update docs

docs/source/cli.rst

@@ -1,23 +1,12 @@
 Inference via CLI
 -----------------
 
-You can use the CLI to run inference on an image or folder containing several images.
+Command Line Interface (CLI)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+You can use the CLI to run inference on an image or folder containing several images. To do that, you can use the following command
 
-To do that, simply use the following command:
+.. code-block:: console
 
-.. code-block::
+   $ spotiflow predict --input PATH
 
-    spotiflow-predict PATH
-
-
-where PATH can be either an image or a folder.
-
-By default, the command will use the `general` pretrained model. You can specify a different model by using the `-\-pretrained-model` flag or the `-\-model-dir` flag for a model you trained yourself.
-
-Moreover, spots are saved to a subfolder `spotiflow_results` created inside the input folder (this can be changed with the `-\-out-dir` flag).
-
-For more information, please refer to the help message of the CLI:
-
-.. code-block::
-
-    spotiflow-predict -h
+where ``PATH`` can be either an image or a folder. By default, the command will use the ``general`` pretrained model. You can specify a different model by using the ``--pretrained-model`` flag. Moreover, spots are saved to a subfolder ``spotiflow_results`` created inside the input folder (this can be changed with the ``--out-dir`` flag). For more information, please refer to the help message of the CLI (``spotiflow-predict -h``).

docs/source/index.rst

@@ -4,7 +4,7 @@
 Spotiflow
 =========
 
-Spotiflow is a learning-based spot detection method for fluorescence microscopy images. For more information, please refer to our `paper <https://PLACEHOLDER/>`__.
+Spotiflow is a learning-based subpixel-accurate spot detection method for 2D and 3D fluorescence microscopy. It is primarily developed for spatial transcriptomics workflows that require transcript detection in large, multiplexed FISH-images, although it can also be used to detect spot-like structures in general fluorescence microscopy images and volumes. For more information, please refer to our `paper <https://doi.org/10.1101/2024.02.01.578426/>`__. 
 
 Getting Started
 ---------------
@@ -13,36 +13,36 @@ Installation
 ~~~~~~~~~~~~
 
 
-First, create and activate a new conda environment. 
+First, create and activate a fresh ``conda`` environment (we currently support Python 3.9 to 3.12). If you don't have ``conda`` installed, we recommend using `miniforge <https://github.com/conda-forge/miniforge>`__.
 
 .. code-block:: console
 
-   (base) $ conda create -n spotiflow python=3.9
-   (base) $ conda activate spotiflow
+   $ conda create -n spotiflow python=3.12
+   $ conda activate spotiflow
 
+**Note (for MacOS users):** if using MacOS, there is a known bug causing the installation of PyTorch with conda to sometimes break OpenMP. You can avoid installing PyTorch with ``conda`` and let install it automatically via pip instead.
 Then, install Pytorch using ``conda``/ ``mamba``. Please follow the `official instructions for your system <https://pytorch.org/get-started/locally>`__.
 
-As an example, for MacOS:
+As an example, for a Linux system with CUDA (note that you should change the CUDA version to match the one installed on your system):
 
 .. code-block:: console
 
-   (spotiflow) $ conda install pytorch::pytorch torchvision -c pytorch
+   $ conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia
 
-For a linux system with CUDA (note that you should change the CUDA version to match the one installed on your system):
-
-.. code-block:: console
-
-   (spotiflow) $ conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia
+**Note (for Windows users):** if using Windows, if using Windows, please install the latest `Build Tools for Visual Studio <https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2022>`__ (make sure to select the C++ build tools during installation) before proceeding to install Spotiflow.
 
 Finally, install ``spotiflow`` using ``pip``:
 
 .. code-block:: console
 
-   (spotiflow) $ pip install spotiflow
+   $ pip install spotiflow
 
 Predicting spots in an image
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+Python API
+^^^^^^^^^^
+
 The snippet below shows how to retrieve the spots from an image using one of the pretrained models:
 
 .. code-block:: python
@@ -73,8 +73,20 @@ If a custom model is used, simply change the model loadings step to:
 
    # Load a custom model
    model = Spotiflow.from_folder("/path/to/model")
-   
 
+Command Line Interface (CLI)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+You can use the CLI to run inference on an image or folder containing several images. To do that, you can use the following command
+
+.. code-block:: console
+
+   $ spotiflow predict --input PATH
+
+where ``PATH`` can be either an image or a folder. By default, the command will use the ``general`` pretrained model. You can specify a different model by using the ``--pretrained-model`` flag. Moreover, spots are saved to a subfolder ``spotiflow_results`` created inside the input folder (this can be changed with the ``--out-dir`` flag). For more information, please refer to the help message of the CLI (``spotiflow-predict -h``).
+
+Napari plugin
+^^^^^^^^^^^^^
+Spotiflow also can be run easily in a graphical user interface as a `napari <https://napari.org/>`__ plugin. See :ref:`napari:Predicting spots using the napari plugin` for more information. 
 
 Contents
 --------

docs/source/train.rst

@@ -78,6 +78,7 @@ You can then load it by simply calling:
 In the 3D case, you should initialize a :py:mod:`spotiflow.model.config.SpotiflowModelConfig` object and pass it to the `Spotiflow` constructor with the appropriate parameter set (see other options for the configuration at the end of the section):
 
 .. code-block:: python
+
     # Same imports as before
     from spotiflow.model import SpotiflowModelConfig
 
@@ -117,7 +118,7 @@ You can also pass other parameters relevant for training to the `fit` method. Fo
     )
 
 
-In order to change the model architecture (`e.g.` number of input/output channels, number of layers, variance for the heatmap generation, etc.), you can create a :py:mod:`spotiflow.model.config.SpotiflowModelConfig` object and populate it accordingly. Then you can pass it to the `Spotiflow` constructor (note that this is necessary for 3D). For example, if our image is RGB and we need the network to use 3 input channels, we can do the following:
+In order to change the model architecture (`e.g.` number of input channels, number of layers, variance for the heatmap generation, etc.), you can create a :py:mod:`spotiflow.model.config.SpotiflowModelConfig` object and populate it accordingly. Then you can pass it to the `Spotiflow` constructor (note that this is necessary for 3D). For example, if our image is RGB and we need the network to use 3 input channels, we can do the following:
 
 .. code-block:: python