ML Project 2 : Create an Interactive Teaching Tool for Medical Doctors to Understand how Machine Learning Models Work (and how they Break!)
https://bias-ml-slayer.streamlit.app/
Website (local) :
- Install requirements from requirements.txt.
- Go to website foler
- run this command : "streamlit run Main.py"
Dataset :
- The dataset is stored on Google Drive and available at the following link: https://drive.google.com/drive/folders/1CGFnv5D0TNlTD7U5iKnAIoQ3haoKkdAA?usp=sharing. Downloading the dataset from there allows you to use the structure required with our pipeline, without modifying any path. Because it is stored on Google Drive, you can create a shortcut to our link on your own Google Drive and run the code comfortably with a Google Colab notebook. This will save you a potentially long download time.
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src
| model_tools.py Tools to define the architecture of the model
| pipeline_tools.py Tools to import image for the pipeline
| poison_images.py Script used to resize and poison images with dot/date/invisible_dot
| predToBinary.py Script used to get Binary predition (0 or 1) from range prediction file ([0..1])
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results # contains some of our results from the data exploration
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notebooks
| data_exploration.ipynb : analyze the distribution of the dataset images according to the width/height ratio
| gradcam.ipynb : run Grad-CAM on a pre-trained model and output the Grad-CAM image
| ml_pipeline.ipynb : import dataset, train, test model and export predictions
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data # raw data used to compute metrics in the site
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generated # generated data from notebooks
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res # resources
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website
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img # images used in the website
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pages # Pages of the website sequentially followed by user
| helper_website.py Tools used to avoid redundancy between the website pages
| Main.py File used to launch the site, main page
| metrics.py Computation and display of metrics in the website
| path_constants.py Dictionnaries of constants to get path of the resource needed by the site
| style.css Style sheet
- Joris Monnet
- Colin Pelletier
- Killian Raude