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54 lines
1.4 KiB
Markdown
54 lines
1.4 KiB
Markdown
# MNIST Sample - Number recognition
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This sample uses the MNIST model from the Model Zoo: https://github.com/onnx/models/tree/master/vision/classification/mnist
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## Requirements
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* MacOS Catalina
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* Xcode 11
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* Compiled libonnxruntime.dll / lib
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## Build
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Command line:
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```
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$ xcodebuild -project SwiftMnist.xcodeproj
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```
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From Xcode, open `SwiftMnist.xcodeproj` and run with Command-R.
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## How to use it
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Just draw a number on the surface, when you lift your finger from the
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mouse or the trackpad, the guess will be displayed.
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Note that when drawing numbers requiring multiple drawing strokes, the
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model will be run at the end of each stroke with probably wrong
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predictions (but it's amusing to see and avoids needing to press a
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'run model' button).
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## How it works
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(Add once it is added)
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### Preprocessing the data
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MNIST's input is a {1,1,28,28} shaped float tensor, which is basically
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a 28x28 floating point grayscale image (0.0 = background, 1.0 =
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foreground).
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### Postprocessing the output
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MNIST's output is a simple {1,10} float tensor that holds the
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likelihood weights per number. The number with the highest value is
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the model's best guess.
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The MNIST structure uses std::max_element to do this and stores it in
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result_:
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https://github.com/microsoft/onnxruntime/blob/521dc757984fbf9770d0051997178fbb9565cd52/samples/c_cxx/MNIST/MNIST.cpp#L31
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