diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp
index a373f55..d0aaf1d 100644
--- a/src/backends/backendsCommon/WorkloadData.cpp
+++ b/src/backends/backendsCommon/WorkloadData.cpp
@@ -1459,53 +1459,63 @@
 
 void DetectionPostProcessQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const
 {
-    ValidateNumInputs(workloadInfo, "DetectionPostProcessQueueDescriptor", 2);
+    const std::string& descriptorName = " DetectionPostProcessQueueDescriptor";
+    ValidateNumInputs(workloadInfo, descriptorName, 2);
 
     if (workloadInfo.m_OutputTensorInfos.size() != 4)
     {
-        throw InvalidArgumentException("DetectionPostProcessQueueDescriptor: Requires exactly four outputs. " +
+        throw InvalidArgumentException(descriptorName + ": Requires exactly four outputs. " +
                                        to_string(workloadInfo.m_OutputTensorInfos.size()) + " has been provided.");
     }
 
     if (m_Anchors == nullptr)
     {
-        throw InvalidArgumentException("DetectionPostProcessQueueDescriptor: Anchors tensor descriptor is missing.");
+        throw InvalidArgumentException(descriptorName + ": Anchors tensor descriptor is missing.");
     }
 
     const TensorInfo& boxEncodingsInfo =  workloadInfo.m_InputTensorInfos[0];
-    const TensorInfo& scoresInfo =  workloadInfo.m_InputTensorInfos[1];
-    const TensorInfo& anchorsInfo = m_Anchors->GetTensorInfo();
-    const TensorInfo& detectionBoxesInfo = workloadInfo.m_OutputTensorInfos[0];
+    const TensorInfo& scoresInfo       =  workloadInfo.m_InputTensorInfos[1];
+    const TensorInfo& anchorsInfo      = m_Anchors->GetTensorInfo();
+
+    const TensorInfo& detectionBoxesInfo   = workloadInfo.m_OutputTensorInfos[0];
     const TensorInfo& detectionClassesInfo = workloadInfo.m_OutputTensorInfos[1];
-    const TensorInfo& detectionScoresInfo = workloadInfo.m_OutputTensorInfos[2];
-    const TensorInfo& numDetectionsInfo = workloadInfo.m_OutputTensorInfos[3];
+    const TensorInfo& detectionScoresInfo  = workloadInfo.m_OutputTensorInfos[2];
+    const TensorInfo& numDetectionsInfo    = workloadInfo.m_OutputTensorInfos[3];
 
-    ValidateTensorNumDimensions(boxEncodingsInfo, "DetectionPostProcessQueueDescriptor", 3, "box encodings");
-    ValidateTensorNumDimensions(scoresInfo, "DetectionPostProcessQueueDescriptor", 3, "scores");
-    ValidateTensorNumDimensions(anchorsInfo, "DetectionPostProcessQueueDescriptor", 2, "anchors");
+    ValidateTensorNumDimensions(boxEncodingsInfo, descriptorName, 3, "box encodings");
+    ValidateTensorNumDimensions(scoresInfo, descriptorName, 3, "scores");
+    ValidateTensorNumDimensions(anchorsInfo, descriptorName, 2, "anchors");
 
-    ValidateTensorNumDimensions(detectionBoxesInfo, "DetectionPostProcessQueueDescriptor", 3, "detection boxes");
-    ValidateTensorNumDimensions(detectionScoresInfo, "DetectionPostProcessQueueDescriptor", 2, "detection scores");
-    ValidateTensorNumDimensions(detectionClassesInfo, "DetectionPostProcessQueueDescriptor", 2, "detection classes");
-    ValidateTensorNumDimensions(numDetectionsInfo, "DetectionPostProcessQueueDescriptor", 1, "num detections");
+    const std::vector<DataType> supportedInputTypes =
+    {
+        DataType::Float32,
+        DataType::QuantisedAsymm8,
+        DataType::QuantisedSymm16
+    };
 
-    ValidateTensorDataType(detectionBoxesInfo, DataType::Float32,
-                          "DetectionPostProcessQueueDescriptor", "detection boxes");
-    ValidateTensorDataType(detectionScoresInfo, DataType::Float32,
-                          "DetectionPostProcessQueueDescriptor", "detection scores");
-    ValidateTensorDataType(detectionClassesInfo, DataType::Float32,
-                          "DetectionPostProcessQueueDescriptor", "detection classes");
-    ValidateTensorDataType(numDetectionsInfo, DataType::Float32,
-                          "DetectionPostProcessQueueDescriptor", "num detections");
+    ValidateDataTypes(boxEncodingsInfo, supportedInputTypes, descriptorName);
+    ValidateDataTypes(scoresInfo, supportedInputTypes, descriptorName);
+    ValidateDataTypes(anchorsInfo, supportedInputTypes, descriptorName);
+
+    ValidateTensorNumDimensions(detectionBoxesInfo, descriptorName, 3, "detection boxes");
+    ValidateTensorNumDimensions(detectionScoresInfo, descriptorName, 2, "detection scores");
+    ValidateTensorNumDimensions(detectionClassesInfo, descriptorName, 2, "detection classes");
+    ValidateTensorNumDimensions(numDetectionsInfo, descriptorName, 1, "num detections");
+
+    // NOTE: Output is always Float32 regardless of input type
+    ValidateTensorDataType(detectionBoxesInfo, DataType::Float32, descriptorName, "detection boxes");
+    ValidateTensorDataType(detectionScoresInfo, DataType::Float32, descriptorName, "detection scores");
+    ValidateTensorDataType(detectionClassesInfo, DataType::Float32, descriptorName, "detection classes");
+    ValidateTensorDataType(numDetectionsInfo, DataType::Float32, descriptorName, "num detections");
 
     if (m_Parameters.m_NmsIouThreshold <= 0.0f || m_Parameters.m_NmsIouThreshold > 1.0f)
     {
-        throw InvalidArgumentException("DetectionPostProcessQueueDescriptor: Intersection over union threshold "
+        throw InvalidArgumentException(descriptorName + ": Intersection over union threshold "
                                        "must be positive and less than or equal to 1.");
     }
     if (scoresInfo.GetShape()[2] != m_Parameters.m_NumClasses + 1)
     {
-        throw InvalidArgumentException("DetectionPostProcessQueueDescriptor: Number of classes with background "
+        throw InvalidArgumentException(descriptorName + ": Number of classes with background "
                                        "should be equal to number of classes + 1.");
     }
 }