Adding new/updated io.github.betaseg_stardist_train_0.1.0

solutions/io.github.betaseg/stardist_train/CHANGELOG.md

@@ -4,5 +4,5 @@ All notable changes to this project will be documented in this file.
 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).
 
-## [0.1.0] - 2023-06-28
+## [0.1.0] - 2023-12-28
 

solutions/io.github.betaseg/stardist_train/README.md

@@ -1,37 +1,94 @@
-# StarDist train album solution
+# StarDist Model Training Solution for Album
 
-This album solution can be used to train a StarDist model from the command line.
+## Introduction
+This is an album solution designed to train a StarDist model directly from the command line. The primary goal of this solution is to facilitate the training of models to segment structures using the StarDist approach.
 
-## documentation
+Please refer to the detailed documentation of [StarDist](https://github.com/stardist/stardist) for in-depth understanding and guidelines.
 
-The extensive documentation of StarDist can be found at https://github.com/stardist/stardist.
+<details>
+  <summary><h2>Example: Training a 3D StarDist Model for Secretory Granules Segmentation</h2></summary>
 
-## Example: 3D segmentation of secretory granules with 3D stardist
+![Sample Image of Secretory Granules](granules.png)
 
-![](granules.png)
-
-This demonstrates how to use this solution to train a Stardist model to segment secretory granules from 3D FIB-SEM data.
-The procedure is described in the paper:
+The purpose of this example is to guide users on training a StarDist model to segment secretory granules from 3D FIB-SEM data. The training procedure and its significance are elaborated in the paper:
 
 Müller, Andreas, et al. "3D FIB-SEM reconstruction of microtubule–organelle interaction in whole primary mouse β cells." Journal of Cell Biology 220.2 (2021).
 
-Download the example data (or adapt your own data into the same format)
-
+### Dataset Preparation
+Download the sample data (or you can prepare your own data in a similar format).
+```bash
 wget https://syncandshare.desy.de/index.php/s/5SJFRtAckjBg5gx/download/data_granules.zip
 unzip data_granules.zip
-which should result in the following folder structure:
+```
+
+After extracting, your data should be structured as follows:
 
+```bash
 data_granules
 ├── train
 │   ├── images
 │   └── masks
 └── val
     ├── images
     └── masks
+```
+</details>
+
+## Installation
+Make sure album is already installed. If not, download and install it as described [here](https://album.solutions/).
+Also, don't forget to add the catalog to your album installation, so you can install the solutions from the catalog.
+
+Install the `stardist_train` and `stardist_predict` solution by using the graphical user interface (GUI) of album or by running the following command in the terminal:
+```bash
+album install io.github.betaseg:stardist_train:0.1.0
+album install io.github.betaseg:stardist_predict:0.1.0
+```
+
+## How to use
+<details open>
+  <summary><h2>Training</h2></summary>
+To start the training process, provide the root directory of the data (using the "root" argument) and the desired output directory (using the "out" argument). By default, the solution will train a 3D stardist model for 100 epochs.
+
+The parameters can be set and run using either the GUI, or by adapting this example for command line usage:
+```bash
+album run stardist_train --root /data/stardist_train/data_granules --out /data/stardist_train/data_granules_out --epochs 10 --steps_per_epoch 15
+```
+
+During training, a _TensorBoard_ will be opened in your browser. You can monitor the training process and the model performance using the _TensorBoard_. The solution terminates when the training is finished and _TensorBoard_ is closed.
+
+The trained model will be saved in the specified output directory and contains the date and time of the run. The model can be used for inference using the `stardist_predict` solution.
+</details>
+
+<details>
+  <summary><h2>Predict</h2></summary>
+For inference, provide the path to an input file (TIF) or directory containing multiple files (TIF) using the `fname_input` argument. 
+State the `model_name` and its corresponding directory `model_dir`.  
+The output directory must be specified using the `output_dir` argument. 
+
+```bash
+album run stardist_predict --fname_input /data/stardist_train/data_granules/val/images/high_c1_raw_region_2.tif --model_name 2023_06_28-17_45_35_stardist --model_basedir /data/stardist_train/data_granules_out/ --output_dir /data/stardist_train/predictions
+```
+</details>
+
+### Further documentation: 
+For further options, parameters and default values, please refer to the info page of the solution:
+```bash
+album info stardist_train
+```
 
-Start the solution by only providing the root folder of the data (argument named "root")
-and the output folder (argument named "out") to reproduce the results of the paper. 
-This way the solution trains a 3D stardist model for 100 epochs.
+## Hardware Requirements
+Ensure that your hardware meets the specific requirements of [StarDist](https://stardist.net/docs/faq.html#what-hardware-do-you-recommend) for efficient training. For training a 3D model, we recommend to use a GPU with at least 8GB of memory.
 
-# todo: reproducable call
+## Citation & License
+Stardist is licensed under the [BSD 3-Clause License](https://github.com/stardist/stardist/blob/master/LICENSE.txt).
 
+If you use this solution, please cite the following paper:
+```
+title: Cell Detection with Star-Convex Polygons,
+doi: 10.1007/978-3-030-00934-2_30
+```
+and
+```
+title: Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy,
+doi: 10.1109/WACV45572.2020.9093435
+```

solutions/io.github.betaseg/stardist_train/solution.yml

@@ -105,6 +105,6 @@ solution_creators:
 tags:
 - StarDist
 - machine learning
-timestamp: '2023-06-28T16:56:30.305786'
+timestamp: '2023-12-28T20:35:10.961171'
 title: StarDist Train
 version: 0.1.0