update README

fabibo3 · May 4, 2022 · 38e93c9 · 38e93c9
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@@ -2,17 +2,21 @@
 
 Credits: S. Dahan, LZJ. Williams
 
-This repository provides a reference algorithm Docker container for SLCN 2022 Challenge submission, on the grand-challenge plateform. 
+This repository provides a reference Docker algorithm container for a SLCN 2022 Challenge submission, on the grand-challenge plateform. 
 
 It should serve as an example or/and a template for your own algorithm container implementaion. 
 
-Here, a [Surface Vision Trasnformer](https://arxiv.org/abs/2203.16414) (SiT) model is used for the task of birth age prediction as an example. Code is based on this [Github](https://github.com/metrics-lab/surface-vision-transformers).
+Here, a [Surface Vision Transformer](https://arxiv.org/abs/2203.16414) (SiT) model is used for the task of birth age prediction as an example. Code is based on this [Github](https://github.com/metrics-lab/surface-vision-transformers).
 
 More information about algorithm container and submission can be found [here](https://grand-challenge.org/blogs/create-an-algorithm/). 
 
 ## Content:
 1. [Prerequisites](#prerequisites)
-2. [Requirements for Grand Challenge submission](#requirements)
+2. [Overview of the project structure](#overview)
+3. [Requirements for Grand Challenge submission (input/output)](#requirements)
+4. [Tips and general advice](#advice)
+5. [Contacts](#contact)
+6. [Acknowledgements](#acknowledgments)
 
 
 ## 1. Prerequisites <a name="prerequisites"></a>
@@ -27,9 +31,46 @@ Then, you will need to install evalutils, that you can pip install:
 pip install evalutils
 ```
 
+Optional: To have GPU support for local testing, you want to install the [NVIDIA container toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).
 
-## 2. Requirements for Grand Challenge submissions
+## 2. Overview of the project structure <a name="overview"></a>
+
+
+The structure of this repository is based on the Algorithm Container for Classification in evalutils. 
+
+You can either start a project from scratch by following guidelines in [evalutils documentation](https://comic.github.io/evalutils/usage.html#algorithm-container) or by clonign this repository: 
+
+```
+git clone https://github.com/metrics-lab/SLCN_challenge
+```
+
+Remark: As evalutils does not implement a class for Regression problem, we only adapted the Classification class to the case of regression problems. 
+
+No matter what methods you used to start your project (evalutils or cloning this repo), you should have at least the following files in your project repository: 
+
+```
+.
+└── slcn_project
+    ├── Dockerfile               # Defines how to build your algorithm container    
+    ├── build.sh                 # Builds your algorithm container
+    ├── test.sh                  # A script that runs your algorithm container using the example in ./test 
+    ├── .gitignore               # Define which files git should ignore (optional)
+    ├── process.py               # Contains your algorithm code - this is where you will extend the BaseAlgorithm class
+    ├── README.md                # For describing your algorithm to others
+    ├── requirements.txt         # The python dependencies of your algorithm container - add any new 
+    ├── test                     # A folder that contains an example test image for testing
+    │   ├── <uid>.mha            # An example test image
+    │   └── expected_output.json # Output file expected to be produced by the algorithm container
+```
+
+
+
+## 3. Requirements for Grand Challenge submissions (input/output) <a name="requirements"></a>
 
 You Docker container (via process.py) is supposed to read .mha image files. 
 
-Important: Images will be read successively and predictions will be made one by one, ie there will be one birth-age.json file per predicition. 
+Important: Images will be read successively and predictions will be made one by one, ie there will be one birth-age.json file per predicition. 
+
+## 4. Contacts <a name="contacts"></a>
+
+## 5. Acknowledgements <a name="acknowledgements"></a>