I am using the model called duke in this
repo it’s a person attribute recognition model, I’ve convert the model to tensorRT and now try to parse the output of that model, Could you recommend a way to parse that model output, specially adding the labels at the parsing function ??
and could i get the tensorMeta output from a classifier ?
• DeepStream Version: 5.0
• Driver Version: 460.80 )
• Issue Type( new requirements)
This solved my question, thank you ^^
but I need now to make a pre-processing for each object before the secondary model classify them, pre-processing such as:
Resize each object to (288, 188)
Normalize the objects with this formula T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), but i dnt know how to make this using the sgie ??
Unfortunately, current gst-nvinfer dose not support different deviation for different channel. You can only set one deviation for all channels by “net-scale-factor”.
The mean values can be set by “offsets”.
Please read the document carefully.
Gst-nvinfer — DeepStream 5.1 Release documentation
All parameters are explained in details.
but what about resizing an object before fed it to the classifier ?
The gst-nvinfer already knows the model dimensions by “infer-dims”, so it will scale the input to the model dimensions.
@Fiona.Chen could you check this new topic, it’s related to this one
I am trying to make a pre-processing on the objects that came out from yolo,
At my original model i used this function for normalizing
transforms = T.Compose([
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
So the means are : [0.485, 0.456, 0.406]
the stds are: [0.229, 0.224, 0.225]
and this compose function work as this equation : (x.sub(mean).div(std))
When I try to make this at deepstream I found that the offset means “t…
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