# Compairing performances across multiple Keras models by training them and applying transfer learning.
Author: alexcla99
Version: 2.0.0
## Getting started
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+-+- ft_data/ # The folder containing the fine tuning dataset
| +--- coma/ # The folder containing coma MRIs
| +--- control/ # The folder containing control MRIs
|
+-+- train_data/ # The folder containing the train dataset
| +--- abnormal/ # The folder containing abnormal MRIs
| +--- control/ # The folder containing control MRIs
|
+-+- models/ # The folder containing available models
| +--- LeNet17.py # The LeNet17 model
|
+--- results/ # The folder containing the train, transfer learning and tests results
+--- __init__.py # An empty file to make this directory being a Python library
+--- dataset.py # The dataset loader
+--- fine_tune.py # A script to apply fine tuning on a model
+--- README.md # This file
+--- requirements.txt # The Python libraries to be installed in order to run the project
+--- Results.ipynb # The obtained results and a description on how the model has been fine-tuned
+--- settings.json # The settings of the model and the train phase
+--- test_trained_model.py # A script to test a trained model
+--- test_fine_tuned_model.py # A script to test a fine_tuned model
+--- tf_config.py # A script to configure TensorFlow
+--- train.py # A script to train from scratch a model
+--- utils.py # Some utils
```
## Integrate with your tools
-[ ] [Set up project integrations](https://gitlab.irit.fr/sig/anoxia_mri/anoxia_mri_classification/-/settings/integrations)
## Collaborate with your team
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-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-[ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
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-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
### Usage:
***
This library has been implemented and used with Python>=3.8.0
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
## Name
Choose a self-explaining name for your project.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## Badges
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## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
Requirements:
```Shell
pip3 install -r requirements
```
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
Train a model:
```Shell
python3 train.py <model:str>
# Example: python3 train.py LeNet17
```
Data to be used are selected from the "train_data" folder and results are saved in the "results" folder.
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
Available networks:
See the `models` folder.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
Fine tune a model:
```Shell
python3 fine_tune.py <model:str>
# Example: python3 fine_tune.py LeNet17
```
Data to be used are selected from the "ft_data" folder and results are saved in the "results" folder.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
Test a trained model:
```Shell
python3 test_trained_model.py <model:str>
# Example: python3 test_trained_model.py LeNet17
```
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
### Obtained results:
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
See the `Results.ipynb` file.
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
### Many thanks to:
## License
For open source projects, say how it is licensed.
3D images classification: https://keras.io/examples/vision/3D_image_classification/
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
