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EVA SDK User's Manual
Image Analytic Plugins
99
5.1.12.1 Examples Using gst-launch
The following pipeline translates the onnxruntime inference result using the Microsoft Custom Vision detector (tiny
yolov2 type) model into a human-readable format.
gst-launch-1.0 videotestsrc ! adonnx model=detection.onnx mean="0,0,0" std="1,1,1"
rgbconv=False nchw=True device=gpu:0 ! adtrans_tinyyolov2_py class-num=2 label-
file=label.txt threshold=0.5 ! admetadrawer ! videoconvert ! ximagesink
Note
: The element ‘ximagesink’ is only supported on Linux. It is recommended to use the ‘glimagesink’ or
‘d3dvideosink’ elements to display the window on Windows.
5.1.12.2 Hierarchy
╰
──
╰
──
╰
──
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──
adtrans_tinadtrans_tinyyolov2Py
5.1.12.3 Pad Templates
sink
ANY
Presence –
always
Direction –
sink
src
ANY
Presence –
always
Direction –
src
5.1.12.4 Properties
batch-num
“batch-num” gint
The batch number of the deep learning model. Range: 1 to 256
Flags: Read, Write
Default value: 1
class-num
“class-num” gint
The class number of the deep learning model. Range: 1 to 65535
Flags: Read, Write
Default value: 20
Содержание EVA SDK
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