You can find more DJL example code here. DJL also provided an Android app with semantic_segmentation which can take a picture and run semantic segmentation with a variety of options. In summary, using the Deep Java Library, it is easy to load a deep learning model for semantic segmentation and use it to identify objects in images at the pixel level. This can be useful for applications such as self-driving cars, where it is important to accurately detect and identify objects in the environment. With the Deep Java Library, you can quickly and easily run deep learning models in Java, making it a valuable tool for any Java developer working in the field of computer vision.

This blog posts looks at using Apache Groovy with the Deep Java Library (DJL) and backed by the Apache MXNet engine to detect objects within an image. Deep learning falls under the branches of machine learning and artificial intelligence. It involves multiple layers (hence the "deep") of an artificial neural network. There are lots of ways to configure such networks and the details are beyond the scope of this blog post, but we can give some basic details. We will have four input nodes corresponding to the measurements of our four characteristics.

Many AWS customers--startups and large enterprises--are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to fraud detection and collaboration. However, until recently, the adoption learning curve was steep and required development of internal technical expertise in new programming languages (e.g., Python) and frameworks, with cascading effect on the whole software development lifecycle, from coding to building, testing, and deployment. The approach outlined in this blog post enables enterprises to leverage existing talent and resources (frameworks, pipelines, and deployments) to integrate machine learning capabilities. Spring Boot, one of the most popular and widespread open source frameworks for microservices development, has simplified the implementation of distributed systems.

Specifically, enterprises now use machine learning for image recognition in a wide variety of use cases. There are applications in the automotive industry, healthcare, security, retail, automated product tracking in warehouses, farming and agriculture, food recognition and even real-time translation by pointing your phone's camera. Thanks to machine learning and visual recognition, machines can detect cancer and COVID-19 in MRIs and CT scans. Today, many of these solutions are primarily developed in Python using open source and proprietary ML toolkits, each with their own APIs. Despite Java's popularity in enterprises, there aren't any standards to develop machine learning applications in Java.

Apache Spark is a widely used technology for data processing and is used heavily by machine learning users. Spark can be used to classify products, forecast demand, and personalize recommendations. While Spark supports a variety of programming languages, the preferred Spark SDK is implemented for Scala, which is not well supported by most deep learning frameworks. Most machine learning frameworks favor Python with their SDKs, leaving Spark developers with suboptimal options: porting their code to Python or implementing a custom Scala wrapper. These options impact developer velocity and threaten production environments with brittle code.

At the 2019 AWS re:Invent conference, Amazon released Deep Java Library (DJL), an open-source library with Java APIs to simplify training, testing, deploying, and making predictions with deep-learning models. While Java remains the first or second most popular programming language since the late 90s, Python is the most used language for machine learning, with numerous resources and deep-learning frameworks. DJL aims to make deep-learning open-source tools accessible to Java developers, using familiar concepts and intuitive APIs. Java developers can use their favorite IDE with DJL or Jupyter Notebook-based code execution for Java. DJL is framework agnostic; it abstracts away commonly used deep-learning functions, using Java Native Access (JNA) on top of existing deep-learning frameworks, currently providing implementations for Apache MXNet and TensorFlow.

Amazon's DJL is a deep learning toolkit used to develop machine learning (ML) and deep learning (DL) models natively in Java while simplifying the use of deep learning frameworks. A toolkit open-sourced just in time for re:Invent 2019, DJL provides a set of high-level APIs to train, test and run inference. Java developers can develop their own models or utilize pre-trained models developed by data scientists in Python from their Java code. DJL stays true to Java's motto, "write once, run anywhere (WORA)", by being engine and deep learning framework-agnostic. Developers can write code once that runs on any engine.