GstInference

GstInference A GStreamer deep learning inference framework.	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
GstInference is an open-source project from RidgeRun Engineering that provides a framework for integrating deep learning inference into GStreamer. Either use one of the included elements to do out of the box inference using the most popular deep learning architectures or leverage the base classes and utilities to support your own custom architecture. Checkout the GstInference proposal presentation that RidgeRun gave at the GStreamer 2018 conference, at Edinburgh, Scotland, UK. GstInference Benchmarks Overview Error creating thumbnail: Unable to save thumbnail to destination By using GstInference as the interface between Google’s Coral board and GStreamer, users can: Easily prototype GStreamer pipelines with common and basic GStreamer tools such as gst-launch and GStreamer Daemon. Easily test and benchmark TFLite models using GStreamer with Google's Coral board. Enable a world of possibilities to use Google’s Coral board with video feeds from cameras, video files, and network streams, and process the prediction information (detection, classification, estimation, segmentation) to monitor events and trigger actions. Develop intelligent media servers with recording, streaming, capture, playback, and display features. Abstract GStreamer complexity in terms of buffers and events handling. Abstract TensorFlow Lite complexity and configuration. Make use of GstInference helper elements and API to visualize and easily extract readable prediction information. GstInference can also be used in other platforms (Jetson, Desktop). For further information you can check: Benchmarks page. Framerate CPU Usage GstInference Videos Promo video: Get started with GstInference by clicking the button below! Error creating thumbnail: Unable to save thumbnail to destination
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