In today’s tech-savvy world, deploying machine learning models on mobile devices has become essential, especially in real-time applications such as object detection. The Yolo-v7-Quantized model, optimized for mobile and edge devices, allows you to perform efficient and speedy object detection. In this guide, we’ll learn how to install and run this model effectively.

What is Yolo-v7-Quantized?

Yolo-v7 is a machine learning model designed to predict bounding boxes and classes of objects in images. This version is specifically optimized for low-latency and low-computational scenarios by using int8 quantization, making it suitable for mobile deployment. The Yolo-v7-Quantized model is implemented and can be accessed here.

Installation Steps

To get started with Yolo-v7-Quantized, follow these steps:

• Install the required Python package using pip:

pip install qai-hub-models[yolov7_quantized]

Configuration for Qualcomm® AI Hub

Next, you need to configure the Qualcomm® AI Hub. Here’s how:

1. Sign in to Qualcomm® AI Hub with your Qualcomm® ID.
2. Navigate to Account -> Settings -> API Token to retrieve your API token.
3. Run the following command to configure your client:

qai-hub configure --api_token API_TOKEN

For more information, visit the documentation.

Running the Demo

The package comes with a simple demo to help you get started. You can run this demo as follows:

• For terminal use:

python -m qai_hub_models.models.yolov7_quantized.demo

• If you’re in a Jupyter Notebook or Google Colab environment:

%run -m qai_hub_models.models.yolov7_quantized.demo

Running on Cloud-Hosted Device

If you want to run the model on a cloud-hosted Qualcomm® device, follow these steps:

python -m qai_hub_models.models.yolov7_quantized.export

This command checks device performance and downloads compiled assets suitable for Android.

Deploying to Android

To deploy this model on Android, you can use the following methods:

• For TensorFlow Lite deployment, refer to the TensorFlow Lite quickstart guide.
• For deploying compiled assets using QNN, follow the instructions in this sample app.

Understanding the Code: An Analogy

Think of the Yolo-v7-Quantized model like a highly trained security guard (model) at a shopping mall (mobile device), who can quickly spot different types of shoppers (objects) by looking around and flagging anyone who doesn’t belong. The quantization process is like giving the guard special glasses that help him to see clearly even in varying light (size and performance). Just as the guard learns to be more efficient by focusing only on key shoppers (important data), the model becomes faster and more adaptable to different mobile devices.

Troubleshooting

If you encounter issues while running Yolo-v7-Quantized, consider the following troubleshooting steps:

• Ensure you have all the prerequisites installed on your device.
• Check your API token configuration to ensure it’s correct.
• If running in a Jupyter Notebook, verify that you added the right commands.
• For unexpected errors, search the Yolo-v7-Quantized GitHub repository for solutions.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Now you are ready to embrace the power of real-time object detection on your mobile device with Yolo-v7-Quantized. Happy coding!