We’re thrilled to announce the release of Savant version 0.2.4, a cutting-edge computer vision and video analytics framework optimized for Nvidia hardware! Here’s what’s new:
Recent Posts
🦀🐍 Documenting Native Python Extensions Made With Rust and PyO3
Native Python extensions written in Rust become more and more popular. The PyO3 framework allows developing them effortlessly, providing the necessary tools to integrate Rust’s performance with Python’s flexibility.
However, there is a lack of information on how to document those extensions to help their users get acquainted with public API. Recently we developed an extension with a sophisticated API, so the problem became critical.
To solve the issue, we dived into the topic and are ready to share with you our recipe allowing to build of beautiful documentation for Rust/PyO3-based Native Python extensions.
YOLO v7 Inference Acceleration With Structural Pruning
Delve into the realm of deep learning optimization with our in-depth article on structured pruning for YOLOv7 DNN. Discover how this advanced technique revolutionizes inference speed without compromising model accuracy. We dissect the intricacies of structured pruning, showcasing its ability to streamline deep learning pipelines by reducing model complexity.
With concrete evidence from rigorous experimentation, we demonstrate the impressive benefits of structured pruning in accelerating YOLOv7 DNN performance. If you’re an AI enthusiast, researcher, or developer seeking to optimize object detection models, this article is a must-read. Follow the link below to gain technical insights into leveraging structured pruning for faster YOLOv7 DNN inference.
🚀 Savant: v0.2.3 is out: Pythonic Computer Vision And Video Analytics Framework For Nvidia Hardware
We’re thrilled to announce the release of Savant version 0.2.3, a cutting-edge computer vision and video analytics framework optimized for Nvidia hardware! Here’s what’s new:
Building a High-Performance Car Classification Pipeline With Savant
Savant, a new open-source product that simplifies the use of Nvidia DeepStream for ML engineers. It is a framework that handles the heavy lifting, allowing you to focus on building optimized inference pipelines with declarative syntax and Python functions.
With Savant, you can easily handle multiple streams simultaneously, deliver reliable, production-ready inference pipelines quickly and achieve top-notch performance with TensorRT. Visit the website to know more.
In the current article, we introduce you to a classification pipeline that uses a detector model, a tracker, and three classifier models to perform a pretty common task related to car traffic profiling: car detection, tracking, and classification. The pipeline is a remake of one of Nvidia’s earliest test applications.
The source for the current pipeline is in Savant’s samples/nvidia_car_classificiation directory.
Read the full article on Medium.