Building and Deploying a Face Mask Detection Application Using NGC Collections

Originally published at: Building and Deploying a Face Mask Detection Application Using NGC Collections | NVIDIA Developer Blog

AI workflows are complex. Building an AI application is no trivial task, as it takes various stakeholders with domain expertise to develop and deploy the application at scale. Data scientists and developers need easy access to software building blocks, such as models and containers, that are not only secure and highly performant, but which have…

Hello and thanks for the information.

I’m trying to recreate the code in my environment but I cannot find any clue to set the following parameters:

–category-limit $CATEGORY_LIMIT
–tlt-input-dims_width $TLT_INPUT_DIMS_WIDTH
–tlt-input-dims_height $TLT_INPUT_DIMS_HEIGHT \

I will really appreciate some guidance about this or any point out to a reference.

Thanks in advance!

Great question! We actually go into more detail about the input-dims parameter in this blog which is definitely worth checking out if you’re interested.

-category-limit 

Determines the number of inference categories to display in DeepStream.

tlt-input-dims_width

Tells TLT what resolution to use. So while I can’t give you an absolute answer without knowing what camera/feed/input sensor/dataset you’re using, you should try updating them to match the height/width of your images/feed.

The full docs are here.