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Building an Object Detection Model for Autonomous Vehicles using PyTorch: Developer Spotlight

Ramya_Ravi
Employee
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In autonomous vehicles, it is important to understand where the other vehicles are on the road and know the environment to anticipate where they are driving next. Object Detection is an important concept in the field of computer vision. Bad weather conditions like rain, fog, and snow can cause image corruption and significant challenges in Object Detection.

Akash Agrawal, in his blog, proposed a solution to such problems by developing object detection and segmentation model. This model can identify objects along with lane detection capabilities while considering the impact of real-world scenarios on accuracy and safety. The proposed model uses the following Intel AI tools and frameworks:

  1. Intel® Extension for PyTorch*

  2. Intel® Developer Cloud

  3. TensorFlow* Optimizations from Intel

Read and learn more about the project at Object Detection and Segmentation Model for Autonomous Vehicles Using HybridNets and PyTorch.

Learn more about TensorFlow Optimizations from Intel and Intel Extension for PyTorch!!!

TensorFlow Optimizations from Intel:

Intel collaborates with Google* to upstream most optimizations into the stock distribution of TensorFlow, with the newest optimizations and features being released earlier as Intel® Extension for TensorFlow*. These optimizations can be enabled with a few lines of code and will accelerate TensorFlow-based training and inference performance on Intel CPU and GPU hardware.

Intel Extension for PyTorch:

The Intel extension expands PyTorch with up-to-date features and optimizations for an extra performance boost on Intel hardware. Check out how to install Intel Extension for PyTorch. The extension can be loaded as a Python module or linked as a C++ library. Python users can enable it dynamically by importing intel_extension_for_pytorch.

  • The CPU tutorial gives detailed information about Intel Extension for PyTorch for Intel CPUs. Source code is available at the master branch.

  • The GPU tutorial gives detailed information about Intel Extension for PyTorch for Intel GPUs. Source code is available at the xpu-master branch.

What's Next?

We encourage you to check out and incorporate Intel's other AI/ML Framework optimizations and end-to-end portfolio of tools into your AI workflow and learn about the unified, open, standards-based oneAPI programming model that forms the foundation of Intel's AI Software Portfolio to help you prepare, build, deploy, and scale your AI solutions.

For more details about the new 4th Gen Intel® Xeon® Scalable processors, visit Intel's AI Solution Platform portal to learn how Intel empowers developers to run end-to-end AI pipelines on these powerful CPUs.

About the Author:

Akash Agrawal is currently pursuing his masters in Robotics and Autonomous Systems (Department of Cyber-Physical Systems) at the Indian Institute of Science Bangalore, India. He is keenly interested in Machine Learning, Deep Learning, Computer Vision, and Embedded Systems domains. He is currently collaborating with Indian Space Research Organisation (ISRO) for his master thesis on "Deep Learning Based Visual Analytic Approach for Automated Cyclone Tracking".

 

About the Author
Product Marketing Engineer bringing cutting edge AI/ML solutions and tools from Intel to developers.