readme: added results section

README.md

@@ -16,15 +16,15 @@ pip3 install git+https://github.com/ENOT-AutoDL/ti-vit.git@main
 
 To export the model version with maximum performance, run the following command:
 ```commandline
-export-ti-vit -o npu-max-perf.onnx -t npu-max-perf
+export-ti-vit -o max-perf.onnx -t max-perf
 ```
 This variant of model contains MLP blocks that can be run on TI DSP. GELU operation is approximated.
 
 ### MLP blocks partially on TI DSP (minimal loss of accuracy)
 
 To export the model version with minimal loss of accuracy, run the following command:
 ```commandline
-export-ti-vit -o npu-max-acc.onnx -t npu-max-acc
+export-ti-vit -o max-acc.onnx -t max-acc
 ```
 This variant of model contains MLP blocks that partially can be run on TI DSP. GELU operation is not approximated.
 
@@ -33,3 +33,15 @@ This variant of model contains MLP blocks that partially can be run on TI DSP. G
 It is important to disable compilation of all nodes except nodes from MLP blocks ("Squeeze" node from MLP must be 
 disabled too). The list of operations for ["deny_list:layer_name"](https://github.com/TexasInstruments/edgeai-tidl-tools/blob/08_06_00_05/examples/osrt_python/README.md#options-to-enable-control-on-layer-level-delegation-to-ti-dsparm) 
 compiler option can be found in the file "output-onnx-dir/output-onnx-name.deny_list", that is generated with onnx file.
+
+## Results
+
+### TorchVision ViT B16
+|          | CPU only | max-acc | max-perf |
+|----------|----------|---------|----------|
+| Time sec | 3.398    | 2.233   | 1.382    |
+
+### ENOT optimized ViT B16
+|          | CPU only | max-acc | max-perf |
+|----------|----------|---------|----------|
+| Time sec | 0.871    | 0.574   | 0.361    |

src/ti_vit/export.py

@@ -31,7 +31,7 @@ def export(
     output_onnx_path : Union[str, Path]
         Path to the output ONNX file.
     model_type : str
-        Type of the final model. Possible values are "npu-max-acc", "npu-max-perf" or "cpu".
+        Type of the final model. Possible values are "max-acc", "max-perf" or "cpu".
     checkpoint_path : Optional[Union[str, Path]] = None
         Path to the PyTorch model checkpoint. If value is None, then ViT_B_16 pretrained torchvision model is used.
         Default value is None.
@@ -49,9 +49,9 @@ def export(
     try:
         transform_model_func = {
             "cpu": lambda model: model,
-            "npu-max-acc": TICompatibleVitOrtMaxAcc,
-            "npu-max-perf": lambda model: TICompatibleVitOrtMaxPerf(model=model, ignore_tidl_errors=False),
-            "npu-max-perf-experimental": lambda model: TICompatibleVitOrtMaxPerf(model=model, ignore_tidl_errors=True),
+            "max-acc": TICompatibleVitOrtMaxAcc,
+            "max-perf": lambda model: TICompatibleVitOrtMaxPerf(model=model, ignore_tidl_errors=False),
+            "max-perf-experimental": lambda model: TICompatibleVitOrtMaxPerf(model=model, ignore_tidl_errors=True),
         }[model_type]
     except KeyError as exc:
         raise ValueError(f"Got unknown transformation type ('{model_type}')") from exc
@@ -105,9 +105,9 @@ def export_ti_compatible_vit() -> None:  # pylint: disable=missing-function-docs
         "--model-type",
         type=str,
         required=False,
-        default="npu-max-perf",
-        help='Type of the final model (optional argument). Possible values are "npu-max-acc", "npu-max-perf", or "cpu".'
-        ' Default value is "npu-max-perf".',
+        default="max-perf",
+        help='Type of the final model (optional argument). Possible values are "max-acc", "max-perf", or "cpu".'
+        ' Default value is "max-perf".',
     )
     parser.add_argument(
         "-c",