Detect Objects in Images Using DJL and MXNet Model Zoo

Detect Objects in Images Using DJL and MXNet Model Zoo

Table of Contents

1. Introduction
2. What is Deep Java Library (DJL)?
3. Setting up a Maven project in Eclipse
4. Adding Dependencies to the POM.xml
5. Using DJL for Object Detection
   • Criteria for Loading the Model
   • Downloading and Saving Sample Images
   • Running the Object Detection Program
   • Viewing the Output
6. Caching in DJL
7. Troubleshooting DJL
8. Conclusion

Introduction

In this article, we will explore how to use Deep Java Library (DJL) to detect objects in images using the MXNet deep learning engine. DJL is a framework-agnostic toolkit for Java that supports multiple deep learning engines such as TensorFlow, MXNet, and PyTorch. We will learn how to set up a Maven project in Eclipse, add the necessary dependencies, and use DJL's pre-trained models to detect objects in images. Additionally, we will delve into caching in DJL and troubleshooting common issues.

What is Deep Java Library (DJL)?

Deep Java Library (DJL) is a powerful deep learning toolkit for Java that provides an abstraction layer over various deep learning frameworks. It allows developers to build and deploy machine learning models using Java in a simple and efficient manner. With DJL, you can work with popular frameworks like TensorFlow, MXNet, and PyTorch seamlessly, leveraging their capabilities to perform tasks such as object detection, image classification, and natural language processing.

Setting up a Maven project in Eclipse

To get started with DJL, we need to set up a Maven project in Eclipse. Maven is a widely used build automation and dependency management tool for Java projects. In Eclipse, we can create a Maven project by following these steps:

1. Open Eclipse and navigate to the workspace where you want to create the project.
2. Click on "File" and then select "New" and "Maven Project".
3. Choose a suitable location for the project and click "Next".
4. Select "Create a simple project" and click "Next".
5. Enter the Group Id and Artifact Id for the project and click "Finish".
6. Set the JRE version of the project to Java 1.8+ or higher.

Adding Dependencies to the POM.xml

Next, we need to add the required dependencies to the project's pom.xml file. These dependencies include DJL API, MXNet ModelZoo, MXNet Engine, and MXNet Native Auto. We can add these dependencies by editing the pom.xml file as follows:

<!-- DJL API -->
<dependency>
    <groupId>ai.djl</groupId>
    <artifactId>djl-api</artifactId>
    <version>0.8.0</version> <!-- Use the latest version -->
</dependency>

<!-- MXNet ModelZoo -->
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-model-zoo</artifactId>
    <version>0.8.0</version> <!-- Use the same version as DJL API -->
</dependency>

<!-- MXNet Engine -->
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-engine</artifactId>
    <version>0.8.0</version> <!-- Use the same version as DJL API -->
</dependency>

<!-- MXNet Native Auto -->
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-auto</artifactId>
    <version>1.8.0</version> <!-- Use the appropriate version for your system -->
</dependency>

<!-- SLF4J for logging -->
<dependency>
    <groupId>org.slf4j</groupId>
    <artifactId>slf4j-simple</artifactId>
    <version>1.7.32</version> <!-- Use the latest version -->
</dependency>

These dependencies can be found in the Maven repository at [maven repository URL]. Make sure to use the correct versions and update them if necessary.

Using DJL for Object Detection

Once we have set up the Maven project and added the required dependencies, we can start using DJL for object detection. The process involves loading a pre-trained model from the MXNet ModelZoo, downloading and saving sample images, running the object detection program, and viewing the output. Let's walk through each step in detail.

Criteria for Loading the Model

To load the model for object detection, we need to define the criteria based on which the model will be selected. The criteria include the application type, input and output types, filter, and optimization progress. For object detection, we specify the criteria as follows:

Criteria<Image, DetectedObjects> criteria =
    Criteria.builder(Image.class, DetectedObjects.class)
        .optApplication(Application.CV.OBJECT_DETECTION) // Application type
        .setTypes(Image.class, DetectedObjects.class) // Input and output types
        .optFilter("backbone", "resnet50") // Filter
        .optProgress(new ProgressBar()) // Show progress bar
        .build();

Downloading and Saving Sample Images

In order to test our object detection program, we need sample images to work with. We can download and save these images from the example project provided with DJL. Once downloaded, we can save them in the resources directory of our project.

Running the Object Detection Program

With the model loaded and sample images available, we can now run our object detection program. The program will take an image as input and use the pre-trained model to identify objects in the image. The detected objects will be displayed along with their probabilities and coordinates.

Viewing the Output

After running the program, we can view the output to see the detected objects. The program will generate output images with the detected objects highlighted. These images can be found in the output directory of our project. We can open them to visually verify the accuracy of the object detection.

Caching in DJL

DJL utilizes cache directories to store downloaded models and engine-specific native files. By default, these cache directories are located in the current user's home directory. The cache directories can be customized by setting the DJL_CACHE_DIR and DJL_ENGINE_CACHE_DIR environment variables. DJL automatically caches the downloaded models to avoid repeated downloads, which improves performance for subsequent runs of the program.

Troubleshooting DJL

While working with DJL, you may encounter some common issues. One such issue is the "No deep learning engine found" exception, which can occur due to missing DLL libraries or not adding any engine dependency. To troubleshoot such issues, you can refer to the DJL troubleshooting guide and the DJL GitHub repository for known issues and solutions.

Conclusion

In this article, we explored how to use Deep Java Library (DJL) to detect objects in images using the MXNet deep learning engine. We learned how to set up a Maven project in Eclipse, add the necessary dependencies, and run a sample object detection program. Additionally, we discussed caching in DJL and troubleshooting common issues. DJL provides a convenient and powerful framework for Java developers to leverage the capabilities of deep learning engines and perform complex tasks like object detection with ease.

Resources:

• Maven URL: [maven repository URL]
• DJL GitHub repository: [DJL GitHub repository URL]

Note: The sample project discussed in this article is available in the video description.