Model Info Section

The OpenVINO™ Intermediate Representation (IR) includes an XML file with description of network topology as well as conversion and runtime metadata.

If “model_proc” file is not present, Intel® Deep Learning Streamer parses “model_info” section located at the end of the XML model file. An example section looks as in the code snippet below:

<rt_info>
    ...
    <model_info>
        <iou_threshold value="0.7" />
        <labels value="person bicycle ... " />
        <model_type value="yolo_v8" />
        <pad_value value="114" />
        <resize_type value="fit_to_window_letterbox" />
        <reverse_input_channels value="True" />
        <scale_values value="255" />
    </model_info>
</rt_info>

Intel® Deep Learning Streamer supports the following fields in the model info section:

Field	Type	Possible values or example	Description	Corresponding ‘model-proc’ key
model_type	string	label detection_output yolo_v8	The converter to parse output tensors and map to GStreamer meta data.	converter
confidence_threshold	float	[0.0, 1.0]	The confidence level to report inference results, typically depends on training accuracy.	threshold (command line param)
iou_threshold	float	[ 0.0, 1.0 ]	Threshold for non-maximum suppression (NMS) intersection over union (IOU) filtering.	iou_threshold
multilabel	boolean	True False	Classification model predicts a set of labels per input image.	method=multi
output_raw_scores	boolean	True False	Classification model outputs all non-normalized scores for all detected labels.	method=softmax
labels	string list	person bicycle …	List of labels for predicted object classes.	labels
resize_type	string	crop standard fit_to_window fit_to_window_letterbox	Resize method to map input video images to model input tensor.	resize
reverse_input_channels	boolean	True False	Convert input video image to RGB format (model trained with RGB images)	color_space=”RGB”
scale	float	255.0	Divide input image values by ‘scale’ before mapping to model input tensor (typically used when model was trained with input data normalized in <0,1> range).	range: [0.0, 1.0]

Please also refer to Deep Learning Workbench for more information on the “model_info” section.