diff --git a/machine-learning/export/ann/run.py b/machine-learning/export/ann/run.py
index 7cbe8de3ae..0912f18338 100644
--- a/machine-learning/export/ann/run.py
+++ b/machine-learning/export/ann/run.py
@@ -12,6 +12,7 @@ from huggingface_hub import login, upload_file
 import onnx2tf
 import numpy as np
 import onnxsim
+from shutil import rmtree
 
 # i can explain
 # armnn only supports up to 4d tranposes, but the model has a 5d transpose due to a redundant unsqueeze
@@ -167,9 +168,9 @@ def onnx_make_fixed(input_path: str, output_path: str, input_shape: tuple[int, .
     simplified, success = onnxsim.simplify(input_path)
     if not success:
         raise RuntimeError(f"Failed to simplify {input_path}")
-    onnx.save(simplified, input_path)
-    infer_shapes_path(input_path, check_type=True, strict_mode=True, data_prop=True)
-    model = onnx.load_model(input_path)
+    onnx.save(simplified, output_path, save_as_external_data=True, all_tensors_to_one_file=False)
+    infer_shapes_path(output_path, check_type=True, strict_mode=True, data_prop=True)
+    model = onnx.load_model(output_path)
     make_input_shape_fixed(model.graph, model.graph.input[0].name, input_shape)
     fix_output_shapes(model)
     onnx.save(model, output_path, save_as_external_data=True, all_tensors_to_one_file=False)
@@ -218,20 +219,23 @@ class ExportBase:
     
     def to_tflite(self, output_dir: str) -> tuple[str, str]:
         input_path = self.to_onnx_static()
-        os.makedirs(output_dir, exist_ok=True)
         tflite_fp32 = os.path.join(output_dir, "model_float32.tflite")
         tflite_fp16 = os.path.join(output_dir, "model_float16.tflite")
         if not os.path.isfile(tflite_fp32) or not os.path.isfile(tflite_fp16):
-            print(f"Exporting {self.model_name} ({self.task}) to TFLite")
+            print(f"Exporting {self.model_name} ({self.task}) to TFLite (this might take a few minutes)")
             onnx2tf.convert(
                 input_onnx_file_path=input_path,
                 output_folder_path=output_dir,
+                keep_shape_absolutely_input_names=self.inputs,
+                verbosity="warn",
                 copy_onnx_input_output_names_to_tflite=True,
+                output_signaturedefs=True,
             )
         
         return tflite_fp32, tflite_fp16
 
     def to_armnn(self, output_dir: str) -> tuple[str, str]:
+        output_dir = os.path.abspath(output_dir)
         tflite_model_dir = os.path.join(output_dir, "tflite")
         tflite_fp32, tflite_fp16 = self.to_tflite(tflite_model_dir)
         
@@ -240,28 +244,38 @@ class ExportBase:
         armnn_fp32 = os.path.join(output_dir, "model.armnn")
         armnn_fp16 = os.path.join(fp16_dir, "model.armnn")
         
-        args = [
-            "./armnnconverter",
-            "-f",
-            "tflite-binary",
-        ]
+        args = ["./armnnconverter", "-f", "tflite-binary"]
         for input_ in self.inputs:
             args.extend(["-i", input_])
         for output_ in self.outputs:
             args.extend(["-o", output_])
             
         fp32_args = args.copy()
-        fp32_args.extend(["-m", tflite_fp32, "-p", tflite_fp32])
+        fp32_args.extend(["-m", tflite_fp32, "-p", armnn_fp32])
         
         print(f"Exporting {self.model_name} ({self.task}) to ARM NN with fp32 precision")
-        subprocess.run(fp32_args, capture_output=True)
+        try:
+            print(subprocess.check_output(fp32_args, stderr=subprocess.STDOUT).decode())
+        except subprocess.CalledProcessError as e:
+            print(e.output.decode())
+            try:
+                rmtree(tflite_model_dir, ignore_errors=True)
+            finally:
+                raise e
         print(f"Finished exporting {self.name} ({self.task}) with fp32 precision")
         
         fp16_args = args.copy()
-        fp32_args.extend(["-m", tflite_fp16, "-p", tflite_fp16])
+        fp16_args.extend(["-m", tflite_fp16, "-p", armnn_fp16])
             
         print(f"Exporting {self.model_name} ({self.task}) to ARM NN with fp16 precision")
-        subprocess.run(fp16_args, capture_output=True)
+        try:
+            print(subprocess.check_output(fp16_args, stderr=subprocess.STDOUT).decode())
+        except subprocess.CalledProcessError as e:
+            print(e.output.decode())
+            try:
+                rmtree(tflite_model_dir, ignore_errors=True)
+            finally:
+                raise e
         print(f"Finished exporting {self.name} ({self.task}) with fp16 precision")
         
         return armnn_fp32, armnn_fp16