Update README.md

README.md

@@ -63,7 +63,7 @@ You can use the raw model for image segmentation. See the [model hub](https://hu
 - Code snippet from [`infer_onnx.py`](infer_onnx.py) on how to use
 ```python
     parser = argparse.ArgumentParser(description='SemanticFPN model')
-    parser.add_argument('--onnx_path', type=str, default='FPN_int.onnx')
+    parser.add_argument('--onnx_path', type=str, default='FPN_int_NHWC.onnx')
     parser.add_argument('--save_path', type=str, default='./data/demo_results/senmatic_results.png')
     parser.add_argument('--input_path', type=str, default='data/cityscapes/cityscapes/leftImg8bit/test/bonn/bonn_000000_000019_leftImg8bit.png')
     parser.add_argument('--ipu', action='store_true',
@@ -95,12 +95,12 @@ You can use the raw model for image segmentation. See the [model hub](https://hu
 
  - Run inference for a single image
   ```python
-  python infer_onnx.py --onnx_path FPN_int.onnx --input_path /Path/To/Your/Image --ipu --provider_config Path/To/vaip_config.json
+  python infer_onnx.py --onnx_path FPN_int_NHWC.onnx --input_path /Path/To/Your/Image --ipu --provider_config Path/To/vaip_config.json
   ```
 
  - Test accuracy of the quantized model
   ```python
-  python test_onnx.py --onnx_path FPN_int.onnx --dataset citys --test-folder ./data/cityscapes --crop-size 256 --ipu --provider_config Path/To/vaip_config.json
+  python test_onnx.py --onnx_path FPN_int_NHWC.onnx --dataset citys --test-folder ./data/cityscapes --crop-size 256 --ipu --provider_config Path/To/vaip_config.json
   ```
 ### Performance