Commit 4298a8c6 authored by remi cresson's avatar remi cresson
Browse files

DOC: correct typo in markdown

parent 0d7fad46
......@@ -133,7 +133,7 @@ otbcli_TensorflowModelTrain -source1.il spot6pms.tif -source1.placeholder x1 -so
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As you can note, there is `$OTB_TF_NSOURCES` + 1 sources for practical purpose: because we need at least 1 source for input data, and 1 source for the truth.
## Serve the model
The **TensorflowModelServe** application perform model serving, it can be used to produce output raster with the desired tensors. Thanks to the streaming mechanism, very large images can be produced. The application uses the `TensorflowModelFilter` and a `StreamingFilter` to force the streaming of output. This last can be optionally disabled by the user, if he prefers using the extended filenames to deal with chunk sizes. however, it's still very useful when the application is used in other composites applications, or just without extended filename magic. Some models can consume a lot of memory. In addition, the native tiling strategy of OTB consists in strips but this might not always the best. For Constitutional Neural Networks for instance, square tiles are more interesting because the padding required to perform the computation of one single strip of pixels induces to input a lot more pixels that to process the computation of one single tile of pixels.
The **TensorflowModelServe** application perform model serving, it can be used to produce output raster with the desired tensors. Thanks to the streaming mechanism, very large images can be produced. The application uses the `TensorflowModelFilter` and a `StreamingFilter` to force the streaming of output. This last can be optionally disabled by the user, if he prefers using the extended filenames to deal with chunk sizes. however, it's still very useful when the application is used in other composites applications, or just without extended filename magic. Some models can consume a lot of memory. In addition, the native tiling strategy of OTB consists in strips but this might not always the best. For Convolutional Neural Networks for instance, square tiles are more interesting because the padding required to perform the computation of one single strip of pixels induces to input a lot more pixels that to process the computation of one single tile of pixels.
So, this application takes in input one or multiple images (remember that you can change the number of inputs by setting the `OTB_TF_NSOURCES` to the desired number) and produce one output of the specified tensors.
Like it was said before, the user is responsible of giving the *perceptive field* and *name* of input placeholders, as well as the *expression field*, *scale factor* and *name* of the output tensors. The user can ask for multiple tensors, that will be stack along the channel dimension of the output raster. However, if the sizes of those output tensors are not consistent (e.g. a different number of (x,y) elements), an exception will be thrown.
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