GstInference - Introduction

GstInference
Introduction
Getting started
Getting the code Building the plugin Quick Starting Guide
Supported architectures
InceptionV1 InceptionV2 InceptionV3 InceptionV4 TinyYoloV2 TinyYoloV3 YoloV2 ResNetV1 MobileNetV2 MobileNetV2 + SSD AlexNet
Supported backends
TensorFlow Tensorflow-Lite Coral from Google Caffe TensorRT ONNXRT ONNXRT ACL ONNXRT OpenVINO
Metadata and Signals
GstClassificationMeta GstInferenceMeta Signals
Overlay Elements
Inference Overlay
Utils Elements
InferenceFilter InferenceCrop InferenceDebug InferenceBin
Legacy pipelines
PC NANO TX2 Xavier IMX8
Example pipelines
Example applications
Classification Detection Hand Pose Recognition
Benchmarks
Model Zoo
Project Status
Releases Roadmap
Contact Us

Deep Learning has revolutionized classic computer vision techniques to enable even more intelligent and autonomous systems. Multimedia frameworks, such as GStreamer, are a basic complement of automatic recognition and classification systems. GstInference is an ongoing open-source project from Ridgerun Engineering that allows easy integration of deep learning networks into your existing pipeline.

Software Stack

The following diagram shows how GstInference plays along other software and hardware modules.

GstInference Software Stack

HW Modules

A deep learning model can be inferred in different hardware units. In the most general case, even though performance won't be great, the general purpose CPU may be used. This is ideal for quick prototyping without requiring specialized hardware.

One of the most popular processors to infer models is the GPGPU. Platforms such as CUDA or OpenCL have enabled the concurrent processing power of these units to execute in a very efficient way deep learning models.