};
- armnn::TensorShape inputTensorShape({ 1, 299, 299, 3 });
+ armnn::TensorShape inputTensorShape({ 1, 224, 224, 3 });
using DataType = uint8_t;
using DatabaseType = ImagePreprocessor<DataType>;
retVal = armnn::test::ClassifierInferenceTestMain<DatabaseType,
ParserType>(
argc, argv,
- "quant_resnet_v2_50_model.tflite", // model name
+ "resnet_v2_50_default_minmax.tflite", // model name
true, // model is binary
"input", // input tensor name
"output", // output tensor name
auto inputBinding = model.GetInputBindingInfo();
return DatabaseType(
dataDir,
- 299,
- 299,
+ 224,
+ 224,
imageSet,
inputBinding.second.GetQuantizationScale(),
inputBinding.second.GetQuantizationOffset());