GstInference/Metadatas/GstEmbeddingMeta: Difference between revisions
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This metadata consist on an 128 elements array, that is filed with the embedding produced by facenet | This metadata consist on an 128 elements array, that is filed with the embedding produced by facenet. | ||
=Fields= | =Fields= |
Revision as of 18:01, 11 November 2019
Make sure you also check GstInference's companion project: R2Inference |
GstInference |
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Introduction |
Getting started |
Supported architectures |
InceptionV1 InceptionV3 YoloV2 AlexNet |
Supported backends |
Caffe |
Metadata and Signals |
Overlay Elements |
Utils Elements |
Legacy pipelines |
Example pipelines |
Example applications |
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Model Zoo |
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This metadata consist on an 128 elements array, that is filed with the embedding produced by facenet.
Fields
The facenet element and embedding overlay uses similar metadata as the classification plugins. GstEmbeddingMeta consist on the following fields:
- num_dimensions: The number of labels outputted by the model. This can vary from model to model. Facenet uses 128.
- embedding: The embedding produced by the network
Access metadata
If you want to access this metadata from your custom Gstreamer element instead the process is fairly easy:
- Add the Facenet element to your pipeline
- Include GstInference metadata header:
#include "gst/r2inference/gstinferencemeta.h"
- Get a GstClassificationMeta object from the buffer:
class_meta = (GstClassificationMeta *) gst_buffer_get_meta (frame->buffer, GST_CLASSIFICATION_META_API_TYPE);
FaceNet also raises a signal containing GstClassificationMeta, for details on how to use this signal please check the example applications section.