diff --git a/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/SegmentModel.cpp b/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/SegmentModel.cpp
index 85da2ce6..6a6948ba 100644
--- a/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/SegmentModel.cpp
+++ b/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/SegmentModel.cpp
@@ -38,7 +38,7 @@ enum OnnxDataType : long {
 } OnnxDataType;
 
 
-const int32_t opset = 12;
+const int32_t opset = 13;
 
 
 /****	Style transfer model	****/
@@ -83,12 +83,52 @@ void WindowsHello::InitializeSession(int w, int h) {
     SetImageSize(w, h);
 
     // Preprocessing: grayscale model
+    m_sessionPreprocess = CreateLearningModelSession(GrayScale(1, 3, m_imageHeightInPixels, m_imageWidthInPixels));
+    m_bindingPreprocess = LearningModelBinding(m_sessionPreprocess);
 
     m_session = CreateLearningModelSession(GetModel());
     m_binding = LearningModelBinding(m_session);
 
 }
 
+void WindowsHello::Run(IDirect3DSurface src, IDirect3DSurface dest)
+{
+    VideoFrame inVideoFrame = VideoFrame::CreateWithDirect3D11Surface(src);
+    VideoFrame outVideoFrame = VideoFrame::CreateWithDirect3D11Surface(dest);
+    SetVideoFrames(inVideoFrame, outVideoFrame);
+    auto outputBindProperties = PropertySet();
+
+    // Shape validation
+    assert((UINT32)m_inputVideoFrame.Direct3DSurface().Description().Height == m_imageHeightInPixels);
+    assert((UINT32)m_inputVideoFrame.Direct3DSurface().Description().Width == m_imageWidthInPixels);
+
+    // Preprocessing: grayscale
+    std::vector<int64_t> shape = { 1, 1, m_imageHeightInPixels, m_imageWidthInPixels };
+    ITensor intermediateTensor = TensorFloat::Create(shape);
+    hstring inputName = m_sessionPreprocess.Model().InputFeatures().GetAt(0).Name();
+    hstring outputName = m_sessionPreprocess.Model().OutputFeatures().GetAt(0).Name();
+
+    m_bindingPreprocess.Bind(inputName, m_inputVideoFrame);
+    outputBindProperties.Insert(L"DisableTensorCpuSync", PropertyValue::CreateBoolean(true));
+    m_bindingPreprocess.Bind(outputName, intermediateTensor, outputBindProperties);
+    m_sessionPreprocess.Evaluate(m_bindingPreprocess, L"");
+
+    // Run through actual model
+    std::vector<int64_t> helloOutputShape = {1, -1, 4}; // TODO: How many bboxes? 
+    ITensor helloOutput = TensorFloat::Create(helloOutputShape);
+    m_binding.Bind(m_session.Model().InputFeatures().GetAt(0).Name(), intermediateTensor);
+    m_binding.Bind(m_session.Model().OutputFeatures().GetAt(0).Name(), helloOutput, outputBindProperties);
+    m_session.Evaluate(m_binding, L"");
+
+    //m_.CopyToAsync(outVideoFrame);
+    // Draw bboxes on videoframe?? For now just print them out
+    auto bbox = helloOutput.as<TensorFloat>().GetAsVectorView();
+    m_inputVideoFrame.CopyToAsync(outVideoFrame).get();
+    OutputDebugString(L"BOUNDING BOX: ");
+    OutputDebugString(std::to_wstring(bbox.GetAt(0)).c_str());
+    OutputDebugString(L"\n");
+}
+
 LearningModel WindowsHello::GetModel() {
     auto modelPath = std::filesystem::path(m_modelBasePath.c_str());
     modelPath.append("retina_rgb.onnx");
@@ -226,6 +266,21 @@ LearningModel Invert(long n, long c, long h, long w)
     return builder.CreateModel();
 }
 
+LearningModel GrayScale(long n, long c, long h, long w) 
+{
+    // Go from [n, c, h, w] -> [1, 1, h, w] 
+    // Start with simple reduction of mean across channels
+    auto builder = LearningModelBuilder::Create(opset)
+        .Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input", TensorKind::Float, { 1, c, h, w }))
+        .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output", TensorKind::Float, { 1, 1, h, w }))
+        .Operators().Add(LearningModelOperator(L"ReduceMean")
+            .SetInput(L"data", L"Input")
+            .SetConstant(L"axes", TensorFloat::CreateFromIterable({ 1 }, { 1.f }))
+            .SetOutput(L"reduced", L"Output")
+        );
+    return builder.CreateModel();
+}
+
 LearningModel Normalize0_1ThenZScore(long h, long w, long c, const std::array<float, 3>& means, const std::array<float, 3>& stddev)
 {
     assert(means.size() == c);
diff --git a/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/SegmentModel.h b/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/SegmentModel.h
index ca72cc89..f3ee3e31 100644
--- a/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/SegmentModel.h
+++ b/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/SegmentModel.h
@@ -110,7 +110,7 @@ protected:
 class StyleTransfer : public StreamModelBase 
 {
 public:
-    StyleTransfer() : StreamModelBase() {};
+    StyleTransfer() : StreamModelBase(){};
     void InitializeSession(int w, int h);
     void Run(IDirect3DSurface src, IDirect3DSurface dest);
 private: 
@@ -119,12 +119,16 @@ private:
 
 class WindowsHello : public StreamModelBase {
 public:
-    WindowsHello() : StreamModelBase() {};
+    WindowsHello() : 
+        StreamModelBase(),
+        m_sessionPreprocess(nullptr),
+        m_bindingPreprocess(nullptr) {};
     void InitializeSession(int w, int h);
     void Run(IDirect3DSurface src, IDirect3DSurface dest);
 private:
     LearningModel GetModel();
-
+    LearningModelSession m_sessionPreprocess;
+    LearningModelBinding m_bindingPreprocess;
 };
 
 class BackgroundBlur : public StreamModelBase