Finalize documentation (#568)

Documentation updates
computational-cell-analytics · May 3, 2024 · fa4318e · fa4318e
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diff --git a/README.md b/README.md
@@ -5,8 +5,6 @@
 
 # Segment Anything for Microscopy
 
-**Attention: We are currently updating our software to a new release that will improve it and introduce new features. The documentation is not up-to-date with these changes yet, we will update it as soon as possible!**
-
 Tools for segmentation and tracking in microscopy build on top of [Segment Anything](https://segment-anything.com/).
 Segment and track objects in microscopy images interactively with a few clicks!
 
@@ -24,14 +22,14 @@ If you run into any problems or have questions regarding our tool please open an
 
 ## Installation and Usage
 
-Please check [the documentation](https://computational-cell-analytics.github.io/micro-sam/) for details on how to install and use `micro_sam`. You can also find a quickstart guide in [this video](TODO) and find all video tutorials [here](https://www.youtube.com/watch?v=ket7bDUP9tI&list=PLwYZXQJ3f36GQPpKCrSbHjGiH39X4XjSO&pp=gAQBiAQB). 
+Please check [the documentation](https://computational-cell-analytics.github.io/micro-sam/) for details on how to install and use `micro_sam`. You can also find a quickstart guide in [this video](TODO) and find all video tutorials [here](TODO). 
 
 
 ## Contributing
 
 We welcome new contributions!
 
-If you are interested in contributing to micro-sam, please see the [contributing guide](doc/contributing.md) and [developer documentation](doc/development.md). The first step is to [discuss your idea in a new issue](https://github.com/computational-cell-analytics/micro-sam/issues/new) with the current developers.
+If you are interested in contributing to micro-sam, please see the [contributing guide](https://computational-cell-analytics.github.io/micro-sam/micro_sam.html#contribution-guide). The first step is to [discuss your idea in a new issue](https://github.com/computational-cell-analytics/micro-sam/issues/new) with the current developers.
 
 
 ## Citation
@@ -55,6 +53,14 @@ Compared to these we support more applications (2d, 3d and tracking), and provid
 
 ## Release Overview
 
+**New in version 1.0.0**
+
+- TODO
+
+**New in version 0.5.0**
+
+- TODO
+
 **New in version 0.4.1**
 
 - Bugfix for the image series annotator. Before the automatic segmentation did not work correctly.

diff --git a/doc/annotation_tools.md b/doc/annotation_tools.md
@@ -17,6 +17,8 @@ The annotation tools are explained in detail below. We also provide [video tutor
 The annotation tools can be started from the napari plugin menu:
 <img src="https://raw.githubusercontent.com/computational-cell-analytics/micro-sam/master/doc/images/napari-plugin.png" width="768">
 
+You can find additional information on the annotation tools [in the FAQ section](usage-question).
+
 
 ## Annotator 2D
 

diff --git a/doc/faq.md b/doc/faq.md
diff --git a/doc/finetuned_models.md b/doc/finetuned_models.md
@@ -9,7 +9,7 @@ We currently offer the following models:
 - `vit_b`: Default Segment Anything model with vit-b backbone.
 - `vit_t`: Segment Anything model with vit-tiny backbone. From the [Mobile SAM publication](https://arxiv.org/abs/2306.14289). 
 - `vit_l_lm`: Finetuned Segment Anything model for cells and nuclei in light microscopy data with vit-l backbone. ([zenodo](TODO), [bioimage.io](TODO))
-- `vit_b_lm`: Finetuned Segment Anything model for cells and nuclei in light microscopy data with vit-b backbone. ([zenodo](TODO), [bioimage.io](TODO))
+- `vit_b_lm`: Finetuned Segment Anything model for cells and nuclei in light microscopy data with vit-b backbone. ([zenodo](https://zenodo.org/doi/10.5281/zenodo.11103797), [diplomatic-bug on bioimage.io](TODO))
 - `vit_t_lm`: Finetuned Segment Anything model for cells and nuclei in light microscopy data with vit-t backbone. ([zenodo](TODO), [bioimage.io](TODO))
 - `vit_l_em_organelles`: Finetuned Segment Anything model for mitochodria and nuclei in electron microscopy data with vit-l backbone. ([zenodo](TODO), [bioimage.io](TODO))
 - `vit_b_em_organelles`: Finetuned Segment Anything model for mitochodria and nuclei in electron microscopy data with vit-b backbone. ([zenodo](TODO), [bioimage.io](TODO))

diff --git a/doc/installation.md b/doc/installation.md
@@ -5,6 +5,8 @@ There are three ways to install `micro_sam`:
 - [From source](#from-source) for setting up a development environment to use the development version and to change and contribute to our software.
 - [From installer](#from-installer) to install it without having to use mamba (supported platforms: Windows and Linux, only for CPU users). 
 
+You can find more information on the installation and how to troubleshoot it in [the FAQ section](installation-questions).
+
 ## From mamba
 
 [mamba](https://mamba.readthedocs.io/en/latest/) is a drop-in replacement for conda, but much faster.
@@ -61,8 +63,8 @@ $ pip install -e .
 ## From installer
 
 We also provide installers for Linux and Windows:
-- [Linux](TODO)
-- [Windows](TODO)
+- [Linux](https://owncloud.gwdg.de/index.php/s/nrNBuHr9ncJqid6)
+- [Windows](https://owncloud.gwdg.de/index.php/s/kZmpAIBDmUSu4e9)
 <!---
 - [Mac](https://owncloud.gwdg.de/index.php/s/7YupGgACw9SHy2P)
 -->

diff --git a/doc/python_library.md b/doc/python_library.md
@@ -36,4 +36,4 @@ The training logic is implemented in `micro_sam.training` and is based on [torch
 We also support training an additional decoder for automatic instance segmentation. This yields better results than the automatic mask generation of segment anything and is significantly faster.
 The notebook explains how to activate training it together with the rest of SAM and how to then use it.
 
-More advanced examples, including quantitative and qualitative evaluation, of finetuned models can be found in [finetuning](https://github.com/computational-cell-analytics/micro-sam/tree/master/finetuning), which contains the code for training and evaluating [our models](finetuned-models).
+More advanced examples, including quantitative and qualitative evaluation, of finetuned models can be found in [finetuning](https://github.com/computational-cell-analytics/micro-sam/tree/master/finetuning), which contains the code for training and evaluating [our models](finetuned-models). You can find further information on model training in the [FAQ section](fine-tuning-questions).
diff --git a/examples/README.md b/examples/README.md
@@ -1,11 +1,13 @@
-# micro_sam examples
+# Examples
 
 Examples for using the micro_sam annotation tools:
 - `annotator_2d.py`: run the interactive 2d annotation tool
 - `annotator_3d.py`: run the interactive 3d annotation tool
 - `annotator_tracking.py`: run the interactive tracking annotation tool
 - `image_series_annotator.py`: run the annotation tool for a series of images
 
+TODO reference the notebooks as recommended examples for using the python library for things below
+
 The folder `finetuning` contains example scripts that show how a Segment Anything model can be fine-tuned
 on custom data with the `micro_sam.train` library, and how the finetuned models can then be used within the annotatin tools.
 

diff --git a/examples/annotator_with_custom_model.py b/examples/annotator_with_custom_model.py
diff --git a/micro_sam/__init__.py b/micro_sam/__init__.py
@@ -4,6 +4,7 @@
 .. include:: ../doc/annotation_tools.md
 .. include:: ../doc/python_library.md
 .. include:: ../doc/finetuned_models.md
+.. include:: ../doc/faq.md
 .. include:: ../doc/contributing.md
 .. include:: ../doc/development.md
 .. include:: ../doc/band.md

diff --git a/notebooks/README.md b/notebooks/README.md
@@ -0,0 +1,3 @@
+# Example Noteboks
+
+TODO