In the world of automotive technology, accurate image segmentation is crucial. Enter FFNet-78S-Quantized, a powerful model optimized for mobile devices that helps in segmenting street scene images into per-pixel classes like road, sidewalk, and pedestrian. In this user-friendly guide, we’ll explore how to install, configure, and deploy this model on Qualcomm-based devices.

Getting Started with FFNet-78S-Quantized

FFNet-78S-Quantized is a semantic segmentation model trained on the Cityscapes dataset. To put it simply, think of it as a neural artisan, capable of painting a digital map of busy streets by identifying different elements in an image. Just like an artist uses color and brushstrokes, FFNet uses parameters to delineate sidewalks, roads, and pedestrians with stunning precision.

Model Installation Steps

• Install the Model: To get started, install the FFNet-78S-Quantized model via pip. Open your terminal or command prompt and run:

pip install qai-hub-models[ffnet_78s_quantized]

Configuring Qualcomm® AI Hub

Configure the Qualcomm® AI Hub to run FFNet-78S-Quantized on cloud-hosted devices.

• Sign in to Qualcomm® AI Hub with your Qualcomm® ID.
• Navigate to Account – Settings – API Token to obtain the API token.
• Run the following command to configure your client:

qai-hub configure --api_token API_TOKEN

Running the Demo

The package comes with a simple end-to-end demo that lets you experience how the model works with sample input. You can run it by executing:

python -m qai_hub_models.models.ffnet_78s_quantized.demo

If you want to run it in a Jupyter Notebook or Google Colab, use:

%run -m qai_hub_models.models.ffnet_78s_quantized.demo

Deploying the Model on Android

Once you’re comfortable with the demo, you can deploy the model on an Android device. There are two primary ways to do this:

• TensorFlow Lite: Export the model for TensorFlow Lite. Follow this tutorial for guidance.
• QNN (.so export): Use the provided sample app to integrate the .so shared library into your application.

Troubleshooting Tips

If you encounter issues while setting up the FFNet-78S-Quantized, here are some ideas to resolve them:

• Check API Token: Ensure that your API token is correctly inputted and that you have the necessary permissions.
• Performance Issues: Verify that your device meets the minimum specifications required for the parameter settings of the model.
• Demo Failures: Ensure you have all dependencies installed and are running the command in the correct environment.

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Conclusion

By following these steps, you’ll be able to utilize the FFNet-78S-Quantized model effectively for automotive image segmentation on mobile devices. Remember that settings may need to adjust based on the device specifications and requirements.

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.