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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:
-[ ] [Set up project integrations](https://gitlab.ec-lyon.fr/barryt/image-classification/-/settings/integrations)
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Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
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***
# 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
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## Description
## 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.
This BE is about image classification. The goal is to classify images from CIFAR10 dataset and evaluate the accuracy of the models, firstly by using a simple KNN and then by using a manually implemented MLP with one hidden layer. The MLP is implemented using numpy and the forward and backward passes are implemented using the chain rule. The MLP is trained using SGD and the gradients are computed using backpropagation. The MLP is trained using the CIFAR10 dataset and the accuracy is evaluated on the test set.
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## Authors and acknowledgment
## Documentation
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The `be_image_classification.ipynb` file contains the main code used to train and evaluate the models.
## License
The functions implemented and used in this BE are located in the `utils` folder.
For open source projects, say how it is licensed.
## Project status
The images located in the `images` folder are:
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.
-`knn_accuracy.png`: the evolution of the accuracy of the model with respect to the number of neighbors
-`mlp_loss.png`: the evolution of the loss of the model with respect to the number of epochs, for the manually implemented MLP
-`mlp_loss_tf.png`: the evolution of the accuracy of the equivalent model implemented using tensorflow with respect to the number of epochs to compare with the manually implemented MLP and check that the implementation is correct
