alexeygrigorev/kfserving-keras-transformer

This project is a helper service that sits in front of an image recognition model and prepares images before the model sees them. If you have a model trained to recognize types of clothing, it usually cannot accept just any random image file from the web. It needs the image to be a specific size and format. This service takes an image URL, automatically resizes and formats the picture, and then hands it off to your model to get a prediction. In a typical machine learning setup, a tool called KFServing is used to host models and make them available over the internet. However, KFServing expects the incoming data to already be perfectly formatted for the model. This tool acts as a middleman, or transformer, between your application and KFServing. When you send it a simple web link to an image, it downloads the picture, transforms it to the exact specifications your model requires, and forwards the formatted data to the underlying model. It then passes the model's answer back to you. Someone building an automated clothing classifier would use this so they do not have to write image processing code directly into their main application. For example, if an e-commerce startup wants to automatically tag photos of dresses, shirts, or pants, their app can simply send a photo link to this service. The startup's developers only need to know the list of categories their model recognizes and the specific image size their model expects. The project is designed to run locally during development or packaged into a container for live deployment. The README provides specific examples for a clothing classifier that recognizes ten categories of apparel using a popular image recognition model. It is built to integrate directly into existing machine learning infrastructure, meaning teams already hosting models with KFServing can plug it in by listing the image dimensions and category labels their specific model uses.