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@@ -4,7 +4,7 @@ Credits: S. Dahan, LZJ. Williams | |
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| This repository provides a reference Docker algorithm container for a SLCN 2022 Challenge submission, on the grand-challenge plateform. | ||
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| It should serve as an example or/and a template for your own algorithm container implementaion. | ||
| It should serve as an example or/and a template for your own algorithm container implementaion, on Linux/MacOS. | ||
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| 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). | ||
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| 1. [Prerequisites](#prerequisites) | ||
| 2. [Overview of the project structure](#overview) | ||
| 3. [Requirements for Grand Challenge submission (input/output)](#requirements) | ||
| 4. [Build, test and export your algorithm container](#container) | ||
| 4. [Tips and general advice](#advice) | ||
| 5. [Contacts](#contact) | ||
| 6. [Acknowledgements](#acknowledgments) | ||
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| The structure of this repository is based on the Algorithm Container for Classification in evalutils. | ||
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| 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: | ||
| 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 cloning this repository and make the appropriate modifications: | ||
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| ``` | ||
| git clone https://github.com/metrics-lab/SLCN_challenge | ||
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| ├── 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 | ||
| │ ├── <uid>.mha # An example image for the dataset (converted into .mha) | ||
| │ └── expected_output.json # Output file expected to be produced by the algorithm container | ||
| ``` | ||
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| The Dockerfile | ||
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| ## 3. Requirements for Grand Challenge submissions (input/output) <a name="requirements"></a> | ||
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| 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. | ||
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| ## 4. Build, test and export your algorithm container <a name="container"></a> | ||
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| First you should try to build your docker image. | ||
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| ``` | ||
| bash build.sh | ||
| ``` | ||
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| This will install all required libraries and copy files and folders into the docker container. | ||
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| Then, the testing script will build your container (again), run the process.py script and check if the ./output/birth-age.json is similar to the ./test/expected-output.json | ||
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| ``` | ||
| bash test.sh | ||
| ``` | ||
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| Finally, once your algorithm container is working, you can export it as a .tar file for submission with | ||
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| ``` | ||
| bash export.sh | ||
| ``` | ||
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| ## 4. Tips and general advice <a name="advice"></a> | ||
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| To remove all unused docker images you can use | ||
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| ``` | ||
| docker image prune -y | ||
| ``` | ||
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| ## 4. Contacts <a name="contacts"></a> | ||
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| Please email [email protected] if you have any questions on slcn submissions, or open an issue. | ||
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| ## 5. Acknowledgements <a name="acknowledgements"></a> | ||
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| The repository is greatly inspired from [MIDOG_reference_docker](https://github.com/DeepPathology/MIDOG_reference_docker). | ||
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| The code for Surface Vision Transformer is from [surface-vision-transformers](https://github.com/metrics-lab/surface-vision-transformers). | ||