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doc: remove section tensor-agnostic processing

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Daniel Morin 2024-05-14 21:41:23 -04:00
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subprojects/gst-docs/markdown/additional/design/machine-learning-analytics.md
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@ -251,21 +251,6 @@ specific to machine-learning techniques and can also be used to store analysis
results from computer-vision, heuristics or other techniques. It can be used as
a bridge between different techniques.
### Semantically-Agnostic Tensor Processing
Not all tensor processing is model dependent. Sometime the processing can be
done uniformly on all tensor's values. For example normalization, range
adjustment, offset adjustment, quantization are examples of operations that do
not require knowledge of how the information is encoded in the tensor. To the
contrary of tensor-decoder, elements implementing these types of processing
don't need to know how information is encoded in the tensor but need to know
general information about the tensor like: cardinality, dimension and data type.
Note GStreamer already does a lot of semantically-agnostic tensor processing,
remember image/frame are also a form of tensor, processing like scaling,
cropping, color-space conversion, ...
#### Semantically-Agnostic Tensor Processing With Graph-Computing Framework
Graph computing frameworks, like ONNX, can also be used this type of operation.
#### Tensor-Decoder Bin And Auto-Plugging
Since tensor-decoders are model specific, we expect that many will be created
and one way to simplify analytics pipeline creation an promote re-usability is to provide