mirror of
https://gitlab.freedesktop.org/gstreamer/gstreamer.git
synced 2024-06-15 20:40:39 +00:00
doc: remove section tensor-agnostic processing
This commit is contained in:
parent
019f7493d7
commit
9f7bce72db
|
@ -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
|
||||
|
|
Loading…
Reference in a new issue