diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py
index ed5cec57..1c6148f6 100644
--- a/ultralytics/engine/exporter.py
+++ b/ultralytics/engine/exporter.py
@@ -41,6 +41,7 @@ Inference:
                          yolov8n.tflite             # TensorFlow Lite
                          yolov8n_edgetpu.tflite     # TensorFlow Edge TPU
                          yolov8n_paddle_model       # PaddlePaddle
+                         yolov8n_ncnn_model         # NCNN
 
 TensorFlow.js:
     $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
@@ -743,10 +744,10 @@ class Exporter:
             verbose=True,
             msg="https://github.com/ultralytics/ultralytics/issues/5161",
         )
+        import onnx2tf
+
         f = Path(str(self.file).replace(self.file.suffix, "_saved_model"))
         if f.is_dir():
-            import shutil
-
             shutil.rmtree(f)  # delete output folder
 
         # Pre-download calibration file to fix https://github.com/PINTO0309/onnx2tf/issues/545
@@ -760,8 +761,9 @@ class Exporter:
 
         # Export to TF
         tmp_file = f / "tmp_tflite_int8_calibration_images.npy"  # int8 calibration images file
+        np_data = None
         if self.args.int8:
-            verbosity = "--verbosity info"
+            verbosity = "info"
             if self.args.data:
                 # Generate calibration data for integer quantization
                 LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'")
@@ -778,16 +780,20 @@ class Exporter:
                 # mean = images.view(-1, 3).mean(0)  # imagenet mean [123.675, 116.28, 103.53]
                 # std = images.view(-1, 3).std(0)  # imagenet std [58.395, 57.12, 57.375]
                 np.save(str(tmp_file), images.numpy())  # BHWC
-                int8 = f'-oiqt -qt per-tensor -cind images "{tmp_file}" "[[[[0, 0, 0]]]]" "[[[[255, 255, 255]]]]"'
-            else:
-                int8 = "-oiqt -qt per-tensor"
+                np_data = [["images", tmp_file, [[[[0, 0, 0]]]], [[[[255, 255, 255]]]]]]
         else:
-            verbosity = "--non_verbose"
-            int8 = ""
+            verbosity = "error"
 
-        cmd = f'onnx2tf -i "{f_onnx}" -o "{f}" -nuo {verbosity} {int8}'.strip()
-        LOGGER.info(f"{prefix} running '{cmd}'")
-        subprocess.run(cmd, shell=True)
+        LOGGER.info(f"{prefix} starting TFLite export with onnx2tf {onnx2tf.__version__}...")
+        onnx2tf.convert(
+            input_onnx_file_path=f_onnx,
+            output_folder_path=str(f),
+            not_use_onnxsim=True,
+            verbosity=verbosity,
+            output_integer_quantized_tflite=self.args.int8,
+            quant_type="per-tensor",  # "per-tensor" (faster) or "per-channel" (slower but more accurate)
+            custom_input_op_name_np_data_path=np_data,
+        )
         yaml_save(f / "metadata.yaml", self.metadata)  # add metadata.yaml
 
         # Remove/rename TFLite models
@@ -884,7 +890,10 @@ class Exporter:
 
         quantization = "--quantize_float16" if self.args.half else "--quantize_uint8" if self.args.int8 else ""
         with spaces_in_path(f_pb) as fpb_, spaces_in_path(f) as f_:  # exporter can not handle spaces in path
-            cmd = f'tensorflowjs_converter --input_format=tf_frozen_model {quantization} --output_node_names={outputs} "{fpb_}" "{f_}"'
+            cmd = (
+                "tensorflowjs_converter "
+                f'--input_format=tf_frozen_model {quantization} --output_node_names={outputs} "{fpb_}" "{f_}"'
+            )
             LOGGER.info(f"{prefix} running '{cmd}'")
             subprocess.run(cmd, shell=True)