Add limitations to README.
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31
README.md
31
README.md
@@ -79,7 +79,7 @@ After that, you can, for example, run the program as follows
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-l ../data/ilsvrc12/synset_words.txt \
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-l ../data/ilsvrc12/synset_words.txt \
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../data/samples/*.jpg
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../data/samples/*.jpg
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This will run the network on the deduplicated caffenet (see: limitations)
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This will run the network on the deduplicated caffenet (see: [limitations](#lmitations))
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with the correct weights file and the labels file supplied, on all jpegs
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with the correct weights file and the labels file supplied, on all jpegs
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located in the samples folder. More advanced usage can be discovered using
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located in the samples folder. More advanced usage can be discovered using
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the `-h` option of the executable.
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the `-h` option of the executable.
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@@ -88,3 +88,32 @@ the `-h` option of the executable.
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You can move around with the WASD keys, and look around using the mouse.
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You can move around with the WASD keys, and look around using the mouse.
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Arrow keys change the currently loaded input.
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Arrow keys change the currently loaded input.
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## Limitations
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The following documents the limitations currently present in the program,
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including possible workarounds. Note that these may change at any time.
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### No in-place layers
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Due to the way Caffe works, this program cannot properly extract state
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data from networks that use in-place computation, since it cannot observe
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the input- and output state of each layer.
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To work around this, a simple script `deinplace.py` is included in the tools
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folder. To use it, first run make (requires `protoc` to be on your PATH) and
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then run it on your program. To see what options it supports, use its `-h`
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option.
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### Single input/output
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The visualisation structuree only supports linear networks. That is: network
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where each layer only ever reads from one input layer and only produces one
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output. This is not a fundamental limitation, and could be reworked in future
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versions.
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### Data input layer only
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The program only supports reading input from image files, rather than using
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LLDB or LevelDB sources. Render your inputs to an image format of choice to
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use the program.
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