diff --git a/tests/MobileNetDatabase.cpp b/tests/MobileNetDatabase.cpp
new file mode 100644
index 0000000..66f297c
--- /dev/null
+++ b/tests/MobileNetDatabase.cpp
@@ -0,0 +1,133 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include "InferenceTestImage.hpp"
+#include "MobileNetDatabase.hpp"
+
+#include <boost/numeric/conversion/cast.hpp>
+#include <boost/assert.hpp>
+#include <boost/format.hpp>
+
+#include <iostream>
+#include <fcntl.h>
+#include <array>
+
+namespace
+{
+
+inline float Lerp(float a, float b, float w)
+{
+    return w * b + (1.f - w) * a;
+}
+
+inline void PutData(std::vector<float> & data,
+                    const unsigned int width,
+                    const unsigned int x,
+                    const unsigned int y,
+                    const unsigned int c,
+                    float value)
+{
+    data[(3*((y*width)+x)) + c] = value;
+}
+
+std::vector<float>
+ResizeBilinearAndNormalize(const InferenceTestImage & image,
+                           const unsigned int outputWidth,
+                           const unsigned int outputHeight)
+{
+    std::vector<float> out;
+    out.resize(outputWidth * outputHeight * 3);
+
+    // We follow the definition of TensorFlow and AndroidNN: The top-left corner of a texel in the output
+    // image is projected into the input image to figure out the interpolants and weights. Note that this
+    // will yield different results than if projecting the centre of output texels.
+
+    const unsigned int inputWidth = image.GetWidth();
+    const unsigned int inputHeight = image.GetHeight();
+
+    // How much to scale pixel coordinates in the output image to get the corresponding pixel coordinates
+    // in the input image
+    const float scaleY = boost::numeric_cast<float>(inputHeight) / boost::numeric_cast<float>(outputHeight);
+    const float scaleX = boost::numeric_cast<float>(inputWidth) / boost::numeric_cast<float>(outputWidth);
+
+    uint8_t rgb_x0y0[3];
+    uint8_t rgb_x1y0[3];
+    uint8_t rgb_x0y1[3];
+    uint8_t rgb_x1y1[3];
+
+    for (unsigned int y = 0; y < outputHeight; ++y)
+    {
+        // Corresponding real-valued height coordinate in input image
+        const float iy = boost::numeric_cast<float>(y) * scaleY;
+
+        // Discrete height coordinate of top-left texel (in the 2x2 texel area used for interpolation)
+        const float fiy = floorf(iy);
+        const unsigned int y0 = boost::numeric_cast<unsigned int>(fiy);
+
+        // Interpolation weight (range [0,1])
+        const float yw = iy - fiy;
+
+        for (unsigned int x = 0; x < outputWidth; ++x)
+        {
+            // Real-valued and discrete width coordinates in input image
+            const float ix = boost::numeric_cast<float>(x) * scaleX;
+            const float fix = floorf(ix);
+            const unsigned int x0 = boost::numeric_cast<unsigned int>(fix);
+
+            // Interpolation weight (range [0,1])
+            const float xw = ix - fix;
+
+            // Discrete width/height coordinates of texels below and to the right of (x0, y0)
+            const unsigned int x1 = std::min(x0 + 1, inputWidth - 1u);
+            const unsigned int y1 = std::min(y0 + 1, inputHeight - 1u);
+
+            std::tie(rgb_x0y0[0], rgb_x0y0[1], rgb_x0y0[2]) = image.GetPixelAs3Channels(x0, y0);
+            std::tie(rgb_x1y0[0], rgb_x1y0[1], rgb_x1y0[2]) = image.GetPixelAs3Channels(x1, y0);
+            std::tie(rgb_x0y1[0], rgb_x0y1[1], rgb_x0y1[2]) = image.GetPixelAs3Channels(x0, y1);
+            std::tie(rgb_x1y1[0], rgb_x1y1[1], rgb_x1y1[2]) = image.GetPixelAs3Channels(x1, y1);
+
+            for (unsigned c=0; c<3; ++c)
+            {
+                const float ly0 = Lerp(float(rgb_x0y0[c]), float(rgb_x1y0[c]), xw);
+                const float ly1 = Lerp(float(rgb_x0y1[c]), float(rgb_x1y1[c]), xw);
+                const float l = Lerp(ly0, ly1, yw);
+                PutData(out, outputWidth, x, y, c, l/255.0f);
+            }
+        }
+    }
+
+    return out;
+}
+
+} // end of anonymous namespace
+
+
+MobileNetDatabase::MobileNetDatabase(const std::string& binaryFileDirectory,
+                                     unsigned int width,
+                                     unsigned int height,
+                                     const std::vector<ImageSet>& imageSet)
+:   m_BinaryDirectory(binaryFileDirectory)
+,   m_Height(height)
+,   m_Width(width)
+,   m_ImageSet(imageSet)
+{
+}
+
+std::unique_ptr<MobileNetDatabase::TTestCaseData>
+MobileNetDatabase::GetTestCaseData(unsigned int testCaseId)
+{
+    testCaseId = testCaseId % boost::numeric_cast<unsigned int>(m_ImageSet.size());
+    const ImageSet& imageSet = m_ImageSet[testCaseId];
+    const std::string fullPath = m_BinaryDirectory + imageSet.first;
+
+    InferenceTestImage image(fullPath.c_str());
+
+    // this ResizeBilinear result is closer to the tensorflow one than STB.
+    // there is still some difference though, but the inference results are
+    // similar to tensorflow for MobileNet
+    std::vector<float> resized(ResizeBilinearAndNormalize(image, m_Width, m_Height));
+
+    const unsigned int label = imageSet.second;
+    return std::make_unique<TTestCaseData>(label, std::move(resized));
+}