From e7b5ec7cbe0c4e165133cf64a6b6bea5a1ce4db7 Mon Sep 17 00:00:00 2001 From: Dubray Chloe <chloe.dubray@etu.ec-lyon.fr> Date: Sat, 4 Nov 2023 21:35:26 +0000 Subject: [PATCH] Update README.md --- README.md | 82 ++++++------------------------------------------------- 1 file changed, 8 insertions(+), 74 deletions(-) diff --git a/README.md b/README.md index 19d35e2..6603ec9 100644 --- a/README.md +++ b/README.md @@ -1,89 +1,23 @@ # Image classification - - -## 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: - -``` -cd existing_repo -git remote add origin https://gitlab.ec-lyon.fr/cdubray/image-classification.git -git branch -M main -git push -uf origin main -``` - -## Integrate with your tools - -- [ ] [Set up project integrations](https://gitlab.ec-lyon.fr/cdubray/image-classification/-/settings/integrations) - -## Collaborate with your team - -- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) -- [ ] [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) -- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) -- [ ] [Set auto-merge](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/) -- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) -- [ ] [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) - -*** - -# 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 -On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. +Ce répertoire contient le rendu pour le BE de classification d'image dans le cadre du MOD apprentissage profond et intelligence artificielle. L'objectif de ce projet est d'appliquer des méthodes de classification étudiées en cours sur un échantillon d'images fourni. -## 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. - -## 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. +Les méthodes testées ici sont celles des K-plus proches voisins (KNN) et des réseaux de neurones (NN). Le jeu de données qui servira durant cette étude est constitué de 60 000 images réparties en 10 classes (soient 6000 images par classe). ## 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. - -## 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. -## Roadmap -If you have ideas for releases in the future, it is a good idea to list them in the README. +Le répertoire est constitué des éléments suivants : +* Un dossier "data" qui contient les images à exploiter. Celui-ci contient 6 lots de 10 000 images chacun, ainsi que d'un fichier "batches_meta" qui contient un dictionnaire qui fait correspondre les indices des classes (1 à 10) à leur classe (automobile, avion, etc.). -## Contributing -State if you are open to contributions and what your requirements are for accepting them. +* Un script python read_cifar.py qui contient les fonctions servant à lire les données de "data" ainsi qu'une fonction split_dataset qui permet de diviser les données aléatoirement en un jeu d'entraînement et un jeu de test, selon un coefficient "split" compris entre 0 et 1 qui permet de déterminer la taille du jeu de test. En lançant le script, on teste la fonction split_dataset en vérifiant que les jeux d'entraînement et de test ont bien la bonne taille. -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. +* Un script python knn.py qui permet de tester la méthode des K-plus proches voisins sur le jeu de données. En lançant le script, on peut obtenir le taux de réussite (compris entre 0 et 1) de l'algorithme des K-plus proches voisins, avec des valeurs de split et de K qu'on peut faire varier (lignes 60 et 61). Pour obtenir le graphe de l'évolution du taux de réussite de l'algorithme selon le nombre de voisins, il suffit d'enlever les # devant les dernières lignes du script (lignes 69 à 77). -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. +* Un script python mlp.py qui permet de tester la méthode du réseau de neurones sur le jeu de données. -## Authors and acknowledgment -Show your appreciation to those who have contributed to the project. +* Un dossier "results" qui contient certains résultats significatifs de l'étude. Tout d'abord, on y retrouve le fichier knn.png qui est un screenshot du graphique représentant l'évolution du taux de réussite de l'algorithme des plus proches voisins selon le nombre de voisins K choisi (K variant de 1 à 20). ## License For open source projects, say how it is licensed. -- GitLab