Java API Example

This sample application was created to present an example for TensorFlow Java API usage. It uses YOLOv2 model for object detection and Gradle as build and dependency management tool. TensorFlow Java API is a new opportunity to use TensorFlow from Java applications. It is well-suited to load models created in Python and execute them within a Java application, however it is still under development and it is not covered by the Tensorflow API stability guarantees.

For more information about the installation please read the official TensorFlow site.

Tensorflow Java Example application

On the pictures below you can see how the application works. You can also try it out as a live demo on the following links:

• Deployed to Google Cloud with YOLOv9000 model: http://35.190.150.169:8080/
• Deployed to Heroku with tiny-yolo model: https://still-crag-64816.herokuapp.com/

These demo applications are using Spring Boot and Spring MVC to create a web user interface, where you can upload images and detect the objects on them. The application is able to detect 20 classes of the Pascal VOC dataset: aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor. If you are interested in training YOLO models, please visit the following page: Complete guide to train YOLO. If you would like to build a server application like our live demo, please check the TensorFlow Java API with Spring page, what presents you a more sophisticated example.

At this time we show you a more simple object detection project. The source code is available on the Github here: https://github.com/szaza/tensorflow-example-java. This project contains a HelloTensorFlow Java class to check if your configuration works well, beside this an ObjectDetector class is present, what reads the YOLO protobuff file and executes it by using the TensorFlow Java API. It communicates through Java Native Interface (JNI) with your installed TensorFlow instance.

Installation of the TensorFlow Java API example project

Preconditions:

• Java JDK 1.8 or higher;
• TensorFlow 1.6 or higher;
• Git version control system;

Strongly recommended to install:

• nVidia CUDA Toolkit 8.0 or higher version;
• nVidia cuDNN GPU accelerated deep learning framework;

To compile and run this TensorFlow Java example application you have to do the following steps:

1. Clone the repository from the Github link: https://github.com/szaza/tensorflow-example-java.git;
2. Download the protobuff and label files from this link: https://drive.google.com/drive/folders/1GfS1Yle7Xari1tRUEi2EDYedFteAOaoN;
3. Place them under the src/main/resources/YOLO directory;
4. Copy your images into the /sample directory and modify the configuration file;
5. Build the application with Gradle by running the following command from the root directory: ./gradlew clean build;
6. Now, you can start the application with: ./gradlew run;
7. In the /out/images folder you can see the result;