This is a quick proof-of-concept for building a model for image classification and then deploying it to be used in a web app using Fast API for backend and Vue for frontend.
The model is built using Pytorch and I use MLflow to track experiments and log metrics.
The model and training data was originally part of a project I completed during the Udacity Deep Learning Nanodegree. The training data was provided by Udacity and is a subset of the Google Landmark Recognition dataset.
The three components each have their own folder with their own readme within the project.
All the code for training, building, and deploying the model is within the model folder. I built one model from scratch using a CNN architecture and then a second one using transfer learning built on the ResNet50 model.
The backed is built in Python using Fast API and can be found in the backend folder. The backend is responsible for serving the model and making predictions via the /predict endpoint.
The frontend is built in Vue and can be found in the frontend folder. The frontend is responsible for displaying the web app and making requests to the backend.