GstInference/Metadatas/GstEmbeddingMeta: Difference between revisions
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=Fields= | =Fields= | ||
The facenet element and embedding overlay uses similar metadata as the classification plugins. GstEmbeddingMeta consist on the following fields: | The facenet element and embedding overlay uses similar metadata as the classification plugins. GstEmbeddingMeta consist on the following fields: | ||
{| class="wikitable" style="margin-right: 22em;" | |||
|- | |||
! field | |||
! type | |||
! description | |||
|- | |||
| num_dimensions | |||
| gint | |||
| The number of labels outputted by the model. This can vary from model to model. Facenet uses 128.. | |||
|- | |||
| embedding | |||
| gdouble * | |||
| The embedding produced by the network | |||
|} | |||
*num_dimensions: The number of labels outputted by the model. This can vary from model to model. Facenet uses 128. | *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 | *embedding: The embedding produced by the network |
Revision as of 18:42, 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 |
Benchmarks |
Model Zoo |
Project Status |
Contact Us |
|
This metadata consist on a variable size elements array, that is filed with the embedding produced by the net, like facenet that uses a 128 elements array size.
Fields
The facenet element and embedding overlay uses similar metadata as the classification plugins. GstEmbeddingMeta consist on the following fields:
field | type | description |
---|---|---|
num_dimensions | gint | The number of labels outputted by the model. This can vary from model to model. Facenet uses 128.. |
embedding | gdouble * | The embedding produced by the network |
- 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.