1 files changed, 7 insertions, 7 deletions
diff --git a/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp b/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
index e23dbdc9d4..84d5292195 100644
--- a/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
+++ b/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
@@ -23,7 +23,7 @@ int main(int argc, char* argv[])
             {"shark.jpg", 669},
         };
 
-        armnn::TensorShape inputTensorShape({ 2, 224, 224, 3 });
+        armnn::TensorShape inputTensorShape({ 1, 224, 224, 3 });
 
         using DataType = uint8_t;
         using DatabaseType = ImagePreprocessor<DataType>;
@@ -34,11 +34,11 @@ int main(int argc, char* argv[])
         retVal = armnn::test::ClassifierInferenceTestMain<DatabaseType,
                                                           ParserType>(
                      argc, argv,
-                     "vgg_16_u8.tflite",                  // model name
-                     true,                                // model is binary
-                     "content_vgg/concat",                // input tensor name
-                     "content_vgg/prob",                  // output tensor name
-                     { 0, 1, 2 },                         // test images to test with as above
+                     "vgg_16_u8_batch1.tflite", // model name
+                     true,                      // model is binary
+                     "content_vgg/concat",      // input tensor name
+                     "content_vgg/prob",        // output tensor name
+                     { 0, 1, 2 },               // test images to test with as above
                      [&imageSet](const char* dataDir, const ModelType & model) {
                          // we need to get the input quantization parameters from
                          // the parsed model
@@ -53,7 +53,7 @@ int main(int argc, char* argv[])
                              {{0, 0, 0}},
                              {{1, 1, 1}},
                              DatabaseType::DataFormat::NCHW,
-                             2);
+                             1);
                      },
                      &inputTensorShape);
     }