Identifying the landing position of the image : Rheumatoid Arthritis application

Steps to train U-net

1. save all images and labels in two separate folders (image and corresponding label should have the same name, i.e.: 'img00001.png')
2. define in src/data/segmentation_dataset.py the path where the images and labels are stored
3. create the .txt files in get_img_names.ipynb. For this, define the path of the images in this file
4. open the autoencoder.ipynb folder and define all necesary paths

Dataset

Characteristics and definition - what the dataset has and what it does not have.

1. Preprocessing dataset: Data augmentation, also understanding more on hand to understand results, edge cases
   1. Expanding dataset (via data augmentation techniques with skin color)
2. Discuss processing architecture :
3. Train the model - divide training, validation etc.
4. Fine tune the parameters and iterate

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Define knowledge into the system. Also we need to come up with Get an image -> Is the image good -> Preprocess the image for inputs to the algorithm -> Process with learned model -> Compute landing position -> Check if the answer is correct