djl-demo

djl-demo

Demo applications showcasing DJL

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The Deep Java Library (DJL) is a framework-agnostic Java API for deep learning. It provides a unified interface to popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. DJL makes it easy to develop deep learning applications in Java, and it can be used for a variety of tasks, including image classification, object detection, natural language processing, and speech recognition.

README:

Deep Java Library examples

DJL Demo Nightly test

The repository contains the source code of the examples for Deep Java Library (DJL) - an framework-agnostic Java API for deep learning.

Inference examples

An example application show you how to run python code in DJL.

An example application detects malicious urls based on a trained Character Level CNN model.

An example application detects Pneumonia based on X-ray images using a trained Keras model.

An example application detects live objects from web camera.

A web based DoodleDraw game built with DJL.

A web application that runs DJL code in browser.

Training examples

An example application trains footwear classification model using DJL.

An example application features a web UI to track and visualize metrics such as loss and accuracy.

Android

An example that shows how to build deep learning android app with ease.

A Doodle draw android game that is built with PyTorch model.

This app will take your image and convert it to the style of Van Gogh or Monet among others.

An app that takes in an image and colors the objects in it.

Write in the French text that you wanted translated and receive an English output.

An example that shows how to build speech recognition app.

An example that shows how to build object detection app with ONNX model.

AWS services

An example application that reads the output of a KVS Stream.

An example application that serves deep learning model with AWS Lambda.

Build a micro service to deploy on AWS Elastic Beanstalk.

Build a micro service to deploy on AWS Elastic Beanstalk.

An example application that runs low cost/high performance inference with AWS Inferentia.

Big data integration

Contains Spark image classification demos.

An example application using Apache Beam to predict the click-through rate for online advertisements.

An example using Apache Flink to run sentiment analysis.

An example application that demonstrates simple HTTP-service to classify images using Zoo Model.

Other demos

An example application that runs multiple deep learning frameworks in one Java Process.

An example application that demonstrates compile DJL apps into native executables.

An example application that serves deep learning models using Quarkus.

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