Deep convolutional network to predict 23 classes of skin diseases found in http://www.dermnet.com/dermatology-pictures-skin-disease-pictures from raw images. We make use of a pretrained ResNet152 since many visual semantics like edges or object shapes should be transferrable from Imagenet. We append 2 fully connected layers to fine-tune for our use case.
Run the setup.sh script to download raw images and ResNet152 embeddings. The script will also download a trained model (this is a 2-fully-connected network stacked on top of ResNet152). Unzipping the files should produce the following folders: train/, test/, train_emb/, test_emb/. trained_models includes a trained version of the fine-tune net with 0.53 percent accuracy on held-out test data.
To install all the libraries needed, run pip install -r requirements.txt.
To start the Flask app, do python run.py. There is a single POST route (/predict) that takes a JSON from key image to a base64 encoded image. It will return a class and a score.