Savant 0.2.4

Savant is an open-source, high-level framework for building real-time, streaming, highly efficient multimedia AI applications on the Nvidia stack. It helps to develop dynamic, fault-tolerant inference pipelines that utilize the best Nvidia approaches for data center and edge accelerators. Savant is built on DeepStream and provides a high-level abstraction layer for building inference pipelines. It is designed to be easy to use, flexible, and scalable. It is a great choice for building smart CV and video analytics applications for cities, retail, manufacturing, and more.

Tags conversion capture artificial-intelligence image-recognition visualization
License Apache
State stable

Recent Releases

0.2.417 Jul 2023 07:42 major feature: New demos: - Age/gender prediction example showing how to use YoloV5-Face, how to work with a custom attributive model predicting age and gender, and advanced in-GPU affine transformations based on facial landmarks with OpenCV-CUDA and Python; - Conditional video encoding example demonstrating the pipeline that draws on frames and encodes a video stream only when a user requests that (in the sample, only when a model detects objects); it shows how to avoid wasting computing resources when the footage is required based on certain external condition; - Multiple RTSP streams example featuring a simple pipeline that processes two RTSP streams and casts them to RTSP; Savant is very different from what people expect regarding the dynamic stream processing; they try to overcomplicate things, so we implemented a simple pipeline processing multiple streams simultaneously to show how it works. New Features - Conditional Drawing and Encoding, which helps to decrease traffic and use CPU/GPU resources wisely; - New FFmpeg-based RTSP source adapter, which works much better than GStreamer-based when streams include B-frames; - New generic FFmpeg-based source adapter, which can work with every input supported by FFmpeg; Quality Assurance - Now, we track possible performance regressions when merging every ticket; our idea is to make Savant faster, not slower, so we want to monitor how our code affects performance; - Move from Python-based internals to Rust-based: we implement a core functionality library, Savant-rs, where we test the code carefully.