Facial re-identification is a commodity task in the CV field: there is no rocket science in doing that, at least academically. However, the commercial efficiency of such a solution is still a concern for customers. The article presents a high-performance pipeline developed with the Savant framework, which can be used in doorbell security or video content annotation systems.
Continue reading Facial Identification With Savant, YOLOV5-Face, AdaFace and HNSWLIB
Instance segmentation is an important task in the computer vision field. High-quality instance segmentation couldn’t run in real-time on more than new hardware for a long time. However, recent advances in the CV field have made it possible to run instance segmentation in real-time. The YOLOV8 family, invented and published by Ultralitics, broke through the next frontier of computer vision, enabling object segmenting efficiently. It opens doors for a broad range of applications and increases the quality of CV, which is more important.
Continue reading Real-Time Instance Segmentation With YOLOV8M-seg And Savant Framework
When we develop Savant, we keep in mind that pipelines work in accelerated environments backed with specialized hardware. However, recently Nvidia stepped out of providing end-to-end acceleration in certain products. Specifically, Jetson Orin Nano, Nvidia Tesla A100, and Nvidia Tesla H100 don’t include hardware-accelerated video encoder devices (NVENC).
Continue reading Savant Explained: Software Video Encoder
Savant 0.2.5 introduces a new pipeline syntax structure — Element Group (EG). The purpose of an EG is to specify a group of elements conditionally loaded during the pipeline initialization based on a specified expression. E.g., imagine that your pipeline can use YOLOV8 or PeopleNet for people detection, and you want to specify the environment variable DETECTOR, determining which model will be loaded.
Continue reading Savant Explained: Pipeline Element Groups