tariqshaban / disaster-classification-with-xai-server

A RESTful API that embeds classification of an array of both natural and unnatural disasters, conducted on a previously compiled CNN network, utilizing Python

Embedding a Disasters Image Classification Model into Restful APIs

This is a supplementary submission of final paper for the CIS726 course.

It contains the code necessary to host a local restful API that utilizes CNN model to predict incoming request values.

The model has the following hyperparameters:

• 50 Epochs
• Learning Rate of 0.001
• Adam optimizer
• ResNet50 has been selected as the pretrained model

The expected returned values are:

• Lime image annotation in Base64 format
• Grad-CAM image annotation in Base64 format
• Grad-CAM++ image annotation in Base64 format
• The predicted label

The weights of the model has been imported, rather than the whole architecture and configuration.

Due to hosting limitations, Lime num_samples attribute has been reduced from 1000 to 10 only.

Such hyperparameters returned the best results.

Getting Started

Clone the project from GitHub

$ git clone https://github.com/tariqshaban/disaster-classification-with-xai-server.git

It is encouraged to refer to FastAPI documentation.

You may need to configure the Python interpreter (depending on the used IDE).

You may encounter problem concerning CORS policy when the server is improperly hosted.

No further configuration is required.

Usage

Execute the uvicorn main:app command in the console, ensure that the port 8000 is not occupied, if need be, add the --port *YOUR_PORT* flag.

You can also issue direct API request using Heroku, example; Postman should be used for the image to be uploaded.

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