Harness the Power of DJL: Object Detection with MXNet

Table of Contents

1. Introduction
2. Understanding Deep Java Library (DJL)
3. Setting Up a Maven Project in Eclipse
4. Adding Dependencies to the Project
5. Loading Pre-Trained Models
6. Configuring Object Detection Criteria
7. Running the Object Detection Program
8. Caching Models and Engine Specific Files
9. Troubleshooting DJL Issues
10. Conclusion

👉 Introduction

In this article, we will explore how to use the Deep Java Library (DJL) along with the MXNet deep learning engine to detect objects in an image. We will start by understanding what DJL is and its significance in the world of Java-based deep learning. Then, we will walk through the step-by-step process of setting up a Maven project in Eclipse and adding the necessary dependencies. Next, we will delve into loading pre-trained models and configuring object detection criteria. Finally, we will run a sample program to detect objects and discuss the caching of models and engine-specific files. So, let's dive in and learn how to harness the power of DJL for object detection!

👉 Understanding Deep Java Library (DJL)

Before we begin exploring object detection using DJL, it's essential to have a clear understanding of what DJL really is. DJL is a framework-agnostic deep learning toolkit for Java that provides support for multiple deep learning engines, including TensorFlow, MXNet, PyTorch, and others. It offers a rich set of APIs and tools that allow developers to leverage the power of deep learning in their Java projects seamlessly. The flexibility of DJL lies in its engine-agnostic nature, allowing developers to switch between different deep learning engines based on their requirements. So, whether you're a beginner or an advanced Java developer, DJL simplifies the process of integrating deep learning capabilities into your projects.

👉 Setting Up a Maven Project in Eclipse

To get started with DJL and object detection, we need to set up a Maven project in Eclipse. Follow the steps below to create a Maven project for your object detection application:

1. Open Eclipse and create a new workspace.
2. Click on "File" > "New" > "Maven Project".
3. Choose the option to create a "Simple Project" and click "Next".
4. Provide the necessary details such as "Group Id" and "Artifact Id" for your project.
5. Click "Finish" to create the Maven project.

This will create a basic Maven project structure that we will use throughout this Tutorial. Now that we have our project set up, we can move on to adding the necessary dependencies to enable object detection with DJL.

👉 Adding Dependencies to the Project

Before we can utilize DJL and MXNet for object detection, we need to add the required dependencies to our Maven project's pom.xml file. These dependencies include the DJL API, the MXNet ModelZoo, and the MXNet Engine. Below is an example of the necessary dependencies we need to include:

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

<!-- DJL ModelZoo -->
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-modelzoo</artifactId>
    <version>0.8.0</version>
</dependency>

<!-- DJL MXNet Engine -->
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-engine</artifactId>
    <version>0.8.0</version>
</dependency>

<!-- DJL MXNet Native -->
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-auto</artifactId>
    <version>1.7.0</version>
</dependency>

<!-- SLF4J -->
<dependency>
    <groupId>org.slf4j</groupId>
    <artifactId>slf4j-simple</artifactId>
    <version>1.7.32</version>
</dependency>

Make sure to replace the version numbers with the latest versions of the dependencies. Once you have added these dependencies to your pom.xml file, save it, and Maven will automatically download and include them in your project.

👉 Loading Pre-Trained Models

Now that we have set up our project and added the necessary dependencies, it's time to load the pre-trained models for object detection. DJL simplifies the process of loading and working with pre-trained models. To load a pre-trained model, we need to define the criteria for object detection, including the input and output types, the backbone, and the model flavor. Here's an example of how to load a pre-trained object detection model using DJL:

Criteria<Image, DetectedObjects> criteria = Criteria.builder()
        .optApplication(Application.CV.OBJECT_DETECTION)
        .setTypes(Image.class, DetectedObjects.class)
        .optFilter("backbone", "resnet50")
        .optProgress(new ProgressBar())
        .build();

ObjectDetectionModel model = ModelZoo.loadModel(criteria);

System.out.println("Model loaded successfully!");

In this example, we define the criteria for object detection by specifying the application type as Application.CV.OBJECT_DETECTION. We set the input type as Image.class and the output type as DetectedObjects.class. Additionally, we specify the backbone as resnet50, which is a popular choice for object detection. We also enable a progress bar to track the model loading progress. Finally, we load the model using the ModelZoo.loadModel() method. If the model is successfully loaded, the message "Model loaded successfully!" will be printed.

👉 Configuring Object Detection Criteria

To fine-tune our object detection, we can modify the criteria by specifying different parameters such as the backbone, the model flavor, and the dataset. DJL provides flexibility in choosing the right combination of criteria for our specific requirements. Here's an example of how we can configure the object detection criteria:

Criteria<Image, DetectedObjects> criteria = Criteria.builder()
        .optApplication(Application.CV.OBJECT_DETECTION)
        .setTypes(Image.class, DetectedObjects.class)
        .optFilter("backbone", "resnet50")
        .optFilter("flavor", "v1.0")
        .optFilter("dataset", "coco")
        .build();

In this example, we specify the backbone as resnet50, the flavor as v1.0, and the dataset as coco. These criteria help DJL identify the specific object detection model that fits our requirements. Feel free to experiment with different combinations of these criteria to achieve the desired results.

👉 Running the Object Detection Program

With the pre-trained model loaded and the object detection criteria configured, we are ready to run our object detection program. We will use DJL to detect objects in an image and display the results. Here's an example of how we can run the object detection program:

// Load the image
Path imagePath = Paths.get("path/to/image.jpg");
Image img = ImageFactory.getInstance().fromFile(imagePath);

// Perform object detection
DetectedObjects detectionResult = model.detect(img);

// Display the results
System.out.println("Detected Objects:");
List<Classification> items = detectionResult.items();
for (Classification item : items) {
    System.out.println("Class: " + item.getClassName());
    System.out.println("Probability: " + item.getProbability());
    System.out.println("Bounding Box: " + item.getBoundingBox());
    System.out.println();
}

In this example, we first load the image from the specified file path using ImageFactory.getInstance().fromFile(). Then, we use the loaded model to perform object detection on the image using the model.detect() method. The result of the object detection is a DetectedObjects object, which contains a list of Classification items representing the detected objects. We then iterate over these items and print the class name, probability, and bounding box coordinates for each detected object.

By running this program, you can detect objects in an image and gain insights into their classes and probabilities, allowing for various application possibilities.

👉 Caching Models and Engine Specific Files

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. This caching mechanism enables DJL to cache models once they are downloaded, resulting in faster subsequent launches of the program.

If you want to change the default cache location or manage the cache directories manually, DJL provides environment variables that allow you to customize the cache directories. The two environment variables that control the cache directories are:

• DJL_CACHE_DIR: Specifies the root cache directory for DJL.
• DJL_ENGINE_CACHE_DIR: Specifies the cache directory for the specific engine being used.

You can set these environment variables to desired directory paths or use the default locations based on your requirements.

👉 Troubleshooting DJL Issues

While working with DJL, you may encounter some common issues or errors. Here are a few troubleshooting tips to help you address these issues:

1. No Deep Learning Engine Found Exception: This exception occurs when DJL cannot find a deep learning engine. Ensure that you have added the engine dependency (e.g., MXNet) to your pom.xml file. Also, make sure that the engine-specific native files are properly downloaded and accessible.

2. DLL Library Issues: If you encounter DLL library-related issues, ensure that the native libraries required by the deep learning engine are properly installed and accessible on your system. Refer to the engine's documentation for detailed instructions on configuring the native libraries.

3. IntelliJ Compatibility: If you are using IntelliJ IDEA as your IDE, ensure that the project's JDK version and IntelliJ's JDK version are compatible. DJL may require a specific JDK version for proper functioning.

4. Cradle Compatibility: If you are using Gradle as your build system instead of Maven, make sure to configure the build files accordingly and include the required DJL dependencies.

For more comprehensive troubleshooting guidance and known issues, refer to the DJL troubleshooting documentation.

👉 Conclusion

Congratulations! You have successfully learned how to use DJL along with the MXNet engine to detect objects in an image. We started by understanding DJL and its capabilities as a deep learning toolkit for Java. Then, we set up a Maven project in Eclipse and added the necessary dependencies. We explored loading pre-trained models, configuring object detection criteria, and running an object detection program using DJL. Finally, we discussed the caching of models and engine-specific files and provided troubleshooting tips for common DJL issues.

DJL empowers Java developers to harness the power of deep learning technologies in their applications seamlessly. With its versatile APIs and engine-agnostic nature, DJL makes it easy to leverage pre-trained models and perform complex tasks like object detection. So go ahead, explore DJL, and unlock the potential of deep learning in your Java projects!

Resources:

• Deep Java Library (DJL)
• MXNet ModelZoo

👉 Highlights

• DJL is a framework-agnostic deep learning toolkit for Java.
• We can use DJL with the MXNet deep learning engine for object detection.
• Setting up a Maven project in Eclipse is the first step to using DJL.
• Adding the necessary dependencies to the project enables object detection using DJL.
• Pre-trained models for object detection can be loaded with DJL.
• Configuring object detection criteria allows us to fine-tune the detection process.
• Running the object detection program with DJL helps identify objects in an image.
• DJL caches downloaded models and engine-specific files for faster subsequent launches.
• Troubleshooting DJL issues involves handling exceptions and configuring dependencies.
• DJL simplifies deep learning tasks in Java projects and unlocks their potential.

👉 FAQ

Q: Can I use DJL with other deep learning engines apart from MXNet? A: Yes, DJL is engine-agnostic and supports multiple deep learning engines such as TensorFlow, PyTorch, and more. You can easily switch between engines based on your project requirements.

Q: How can I change the cache directories for DJL? A: DJL uses environment variables to control the cache directories. You can set the DJL_CACHE_DIR variable to change the root cache directory and the DJL_ENGINE_CACHE_DIR variable to customize the cache directory for a specific engine.

Q: Are there any known issues or troubleshooting tips for working with DJL and IntelliJ IDEA? A: Yes, IntelliJ IDEA compatibility can sometimes cause issues with DJL projects. Ensure that the JDK version is compatible between your project and IntelliJ IDEA. Also, make sure that the necessary dependencies are correctly specified in your project's configuration.

Q: Can DJL be used for other tasks apart from object detection? A: Absolutely! DJL is a versatile deep learning toolkit that supports various domains, including image classification, natural language processing, and more. You can explore the DJL documentation and examples to learn more about its capabilities.

Q: Is DJL suitable for beginners in deep learning with Java? A: Yes, DJL provides a user-friendly interface and simplified APIs, making it suitable for beginners. It abstracts the complexities of deep learning frameworks and allows developers to focus on their application logic.

Q: Where can I find more examples and resources for DJL? A: The DJL GitHub repository (https://github.com/awslabs/djl) is an excellent resource for finding examples, documentation, and other resources related to DJL. Additionally, the official DJL documentation provides comprehensive information to help you get started.

Resources:

• Deep Java Library (DJL) Repository
• DJL MXNet ModelZoo Documentation