News, updates, and handy materials related to the Savant ecosystem
Savant now supports direct CSI camera capture on NVIDIA Jetson through a new nvarguscamerasrc_bin pipeline source element. Frames land in GPU memory with zero encoding or network overhead — the...
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Hardware H.264 encoding works on the host but fails inside Docker on Jetson Orin NX with JetPack 6.2.2. The root cause turns out to be a missing two-megabyte package —...
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DeepStream is the most optimized SDK for video analytics on NVIDIA hardware. Savant is built on top of it. This article explains the relationship between the two, what each brings to the table, and which to reach for depending on your project's requirements.
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PyTorch and Savant are both Python-first and CUDA-capable, yet they solve fundamentally different problems. This article explains the design philosophies, target audiences, and use cases of each — and when it makes sense to use them together.
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A step-by-step guide to building a fan-out/fan-in video analytics pipeline that distributes a stream across multiple parallel modules, runs independent inference, and merges results back into a single frame using Savant and savant-rs.
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Savant ecosystem gains Meta-Merge, a new service that completes the Fan-Out/Fan-In pattern by merging metadata from parallel inference branches back into a single unified frame.
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A step-by-step guide to exporting Ultralytics models to the ONNX format for use in Savant's TensorRT-based inference pipeline.
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Savant 0.6.0 brings Blackwell GPU support through custom-built DeepStream 7.1 with TensorRT 10.9, plus Python 3.12 for improved CPU-bound performance.
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An in-depth landscape overview of current and legacy tech stacks for computer vision applications, covering frameworks from OpenCV to DeepStream to Savant.
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Savant ecosystem expands with Router, a new microservice for simplifying and optimizing the routing of video streams across complex CV pipelines.
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Retina RTSP Service enables seamless integration of real-time video streaming into Savant-powered AI video pipelines with low latency and high performance.
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Savant 0.5.1 brings DeepStream 7.0 support, 34 real-world samples, Key-Value API, original resolution processing, and many more enhancements.
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We explain the advantages of USB/CSI cameras over RTSP cameras for projects working on Nvidia Jetson Orin hardware.
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A deep dive into camera properties that matter for computer vision: sensor quality, lens selection, chip processing, and software encoding.
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A minor bugfix update to Savant 0.4.x, fixing issues with JSON/image/video sink adapters.
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A minor bugfix update to Savant 0.4.x for JSON/image/video sink adapters.
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Savant 0.4.1 continues the 0.4.x release cycle with new features, bug fixes, and a new watchdog service for pipeline health monitoring.
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Learn how to record and replay video streaming traffic for reproducible testing, troubleshooting, and code improvement in Savant pipelines.
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Savant now supports hardware-accelerated video transcoding and composition, enabling custom dashboards, resolution changes, and video walls.
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Savant 0.4.0 focuses on system usability, interfacing, advanced computer vision, and video analytics on top of DeepStream 6.4.
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New adapters integrate Amazon Kinesis Video Streams with Savant, combining video-optimized streaming and storage in the cloud.
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Learn how to dynamically attach and detach video sources to a running Savant pipeline using Docker, including Unix domain sockets and TCP/IP.
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Savant 0.3.11 adds MJPEG USB camera support -- a popular format for high-resolution, high-FPS streaming and stereo cameras.
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How to simulate an MJPEG USB camera in Linux using FFmpeg and a V4L2 loopback device for development and testing.
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Benchmark results demonstrating Savant's performance advantage over PyTorch for video inference, including hardware-accelerated decoding.
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Learn how to implement keypoint detection for pose estimation, facial recognition alignment, and action recognition using Savant.
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Using Nvidia PeopleNet and Savant to accurately count and monitor people in user-defined polygonal areas for various applications.
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Building a license plate detection and recognition pipeline using YOLOv8 and Nvidia NGC models with the Savant framework.
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Media Pass-Through allows copying the incoming video stream to output without transcoding, optimizing pipelines that don't need draw functionality.
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Why you may find it beneficial to use Savant instead of DeepStream, OpenVINO, PyTorch, or OpenCV in your next computer vision project.
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How to build scalable computer vision applications using Kafka and KeyDB for handling real-time and delayed video processing at scale.
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Exploring the challenges of real-time video analytics and practical approaches to overcome them using modern frameworks and hardware acceleration.
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Build a pipeline that calculates vehicles passing through metering zones and sends statistics to Grafana for visualization.
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Introducing RocksQ, a high-performance persistent queue built on RocksDB for buffering video frames and metadata in Savant.
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How to dynamically reconfigure pipeline parameters at runtime using Etcd for adaptive computer vision applications.
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Savant 0.2.5 brings significant changes, new features, and improvements to developer experience and deployment.
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How to use OpenTelemetry to trace, log, and collect metrics for deep learning computer vision pipelines in Savant.
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A high-performance facial re-identification pipeline using Savant for doorbell security and video content annotation systems.
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Running real-time instance segmentation with the YOLOv8 family on Savant, enabling efficient object segmentation on edge and data center hardware.
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How Savant handles video encoding on hardware that lacks NVENC, such as Jetson Orin Nano, Tesla A100, and Tesla H100.
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Element Groups allow conditionally loading pipeline elements during initialization based on environment variables and expressions.
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