diff --git a/mediapipe/calculators/tensor/image_to_tensor_converter_opencv.cc b/mediapipe/calculators/tensor/image_to_tensor_converter_opencv.cc
index 95e38f89c..bb4c6de79 100644
--- a/mediapipe/calculators/tensor/image_to_tensor_converter_opencv.cc
+++ b/mediapipe/calculators/tensor/image_to_tensor_converter_opencv.cc
@@ -92,13 +92,14 @@ class OpenCvProcessor : public ImageToTensorConverter {
     const int dst_data_type = output_channels == 1 ? mat_gray_type_ : mat_type_;
     switch (tensor_type_) {
       case Tensor::ElementType::kInt8:
-        RET_CHECK_GE(output_shape.num_elements(),
-                     tensor_buffer_offset / sizeof(int8) + num_elements_per_img)
+        RET_CHECK_GE(
+            output_shape.num_elements(),
+            tensor_buffer_offset / sizeof(int8_t) + num_elements_per_img)
             << "The buffer offset + the input image size is larger than the "
                "allocated tensor buffer.";
-        dst = cv::Mat(
-            output_height, output_width, dst_data_type,
-            buffer_view.buffer<int8>() + tensor_buffer_offset / sizeof(int8));
+        dst = cv::Mat(output_height, output_width, dst_data_type,
+                      buffer_view.buffer<int8_t>() +
+                          tensor_buffer_offset / sizeof(int8_t));
         break;
       case Tensor::ElementType::kFloat32:
         RET_CHECK_GE(
@@ -113,12 +114,12 @@ class OpenCvProcessor : public ImageToTensorConverter {
       case Tensor::ElementType::kUInt8:
         RET_CHECK_GE(
             output_shape.num_elements(),
-            tensor_buffer_offset / sizeof(uint8) + num_elements_per_img)
+            tensor_buffer_offset / sizeof(uint8_t) + num_elements_per_img)
             << "The buffer offset + the input image size is larger than the "
                "allocated tensor buffer.";
-        dst = cv::Mat(
-            output_height, output_width, dst_data_type,
-            buffer_view.buffer<uint8>() + tensor_buffer_offset / sizeof(uint8));
+        dst = cv::Mat(output_height, output_width, dst_data_type,
+                      buffer_view.buffer<uint8_t>() +
+                          tensor_buffer_offset / sizeof(uint8_t));
         break;
       default:
         return InvalidArgumentError(
diff --git a/mediapipe/calculators/tensor/tensor_converter_calculator_test.cc b/mediapipe/calculators/tensor/tensor_converter_calculator_test.cc
index bdea0795e..2cfbd3d1e 100644
--- a/mediapipe/calculators/tensor/tensor_converter_calculator_test.cc
+++ b/mediapipe/calculators/tensor/tensor_converter_calculator_test.cc
@@ -41,7 +41,7 @@ constexpr char kTransposeOptionsString[] =
 
 using RandomEngine = std::mt19937_64;
 using testing::Eq;
-const uint32 kSeed = 1234;
+const uint32_t kSeed = 1234;
 const int kNumSizes = 8;
 const int sizes[kNumSizes][2] = {{1, 1}, {12, 1}, {1, 9},   {2, 2},
                                  {5, 3}, {7, 13}, {16, 32}, {101, 2}};
@@ -49,7 +49,7 @@ const int sizes[kNumSizes][2] = {{1, 1}, {12, 1}, {1, 9},   {2, 2},
 class TensorConverterCalculatorTest : public ::testing::Test {
  protected:
   // Adds a packet with a matrix filled with random values in [0,1].
-  void AddRandomMatrix(int num_rows, int num_columns, uint32 seed,
+  void AddRandomMatrix(int num_rows, int num_columns, uint32_t seed,
                        bool row_major_matrix = false) {
     RandomEngine random(kSeed);
     std::uniform_real_distribution<> uniform_dist(0, 1.0);
@@ -229,7 +229,7 @@ TEST_F(TensorConverterCalculatorTest, CustomDivAndSub) {
   MP_ASSERT_OK(graph.StartRun({}));
   auto input_image = absl::make_unique<ImageFrame>(ImageFormat::GRAY8, 1, 1);
   cv::Mat mat = mediapipe::formats::MatView(input_image.get());
-  mat.at<uint8>(0, 0) = 200;
+  mat.at<uint8_t>(0, 0) = 200;
   MP_ASSERT_OK(graph.AddPacketToInputStream(
       "input_image", Adopt(input_image.release()).At(Timestamp(0))));
 
@@ -286,7 +286,7 @@ TEST_F(TensorConverterCalculatorTest, SetOutputRange) {
     MP_ASSERT_OK(graph.StartRun({}));
     auto input_image = absl::make_unique<ImageFrame>(ImageFormat::GRAY8, 1, 1);
     cv::Mat mat = mediapipe::formats::MatView(input_image.get());
-    mat.at<uint8>(0, 0) = 200;
+    mat.at<uint8_t>(0, 0) = 200;
     MP_ASSERT_OK(graph.AddPacketToInputStream(
         "input_image", Adopt(input_image.release()).At(Timestamp(0))));
 
diff --git a/mediapipe/calculators/tensor/tensors_to_classification_calculator.cc b/mediapipe/calculators/tensor/tensors_to_classification_calculator.cc
index 5bfc00ed7..7041c02e4 100644
--- a/mediapipe/calculators/tensor/tensors_to_classification_calculator.cc
+++ b/mediapipe/calculators/tensor/tensors_to_classification_calculator.cc
@@ -84,7 +84,7 @@ class TensorsToClassificationCalculator : public Node {
  private:
   int top_k_ = 0;
   bool sort_by_descending_score_ = false;
-  proto_ns::Map<int64, LabelMapItem> local_label_map_;
+  proto_ns::Map<int64_t, LabelMapItem> local_label_map_;
   bool label_map_loaded_ = false;
   bool is_binary_classification_ = false;
   float min_score_threshold_ = std::numeric_limits<float>::lowest();
@@ -98,7 +98,8 @@ class TensorsToClassificationCalculator : public Node {
   // These are used to filter out the output classification results.
   ClassIndexSet class_index_set_;
   bool IsClassIndexAllowed(int class_index);
-  const proto_ns::Map<int64, LabelMapItem>& GetLabelMap(CalculatorContext* cc);
+  const proto_ns::Map<int64_t, LabelMapItem>& GetLabelMap(
+      CalculatorContext* cc);
 };
 MEDIAPIPE_REGISTER_NODE(TensorsToClassificationCalculator);
 
@@ -252,7 +253,7 @@ bool TensorsToClassificationCalculator::IsClassIndexAllowed(int class_index) {
   }
 }
 
-const proto_ns::Map<int64, LabelMapItem>&
+const proto_ns::Map<int64_t, LabelMapItem>&
 TensorsToClassificationCalculator::GetLabelMap(CalculatorContext* cc) {
   return !local_label_map_.empty()
              ? local_label_map_