More cleanup

docs/execution-providers/QNN-ExecutionProvider.md

@@ -124,6 +124,7 @@ python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_p
 Quantization for QNN EP requires the use of calibration input data. Using a calibration dataset that is representative of typical model inputs is crucial in generating an accurate quantized model.
 
 The following snippet defines a sample `DataReader` class that generates random float32 input data. Note that using random input data will most likely produce an inaccurate quantized model.
+Refer to the [implementation of a Resnet data reader](https://github.com/microsoft/onnxruntime-inference-examples/blob/main/quantization/image_classification/cpu/resnet50_data_reader.py) for one example of how to create a `CalibrationDataReader` that provides input from image files on disk.
 
 ```python
 # data_reader.py
@@ -200,7 +201,7 @@ if __name__ == "__main__":
 
 Running `python quantize_model.py` will generate a quantized model called `model.qdq.onnx` that can be run on Windows ARM64 devices via ONNX Runtime's QNN EP.
 
-Refer to the following pages for more information on quantization utilities usage:
+Refer to the following pages for more information on usage of the quantization utilities:
 - [Quantization example for mobilenet on CPU EP](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/quantization/image_classification/cpu)
 - [quantization/execution_providers/qnn/preprocess.py](https://github.com/microsoft/onnxruntime/blob/23996bbbbe0406a5c8edbf6b7dbd71e5780d3f4b/onnxruntime/python/tools/quantization/execution_providers/qnn/preprocess.py#L16)
 - [quantization/execution_providers/qnn/quant_config.py](https://github.com/microsoft/onnxruntime/blob/23996bbbbe0406a5c8edbf6b7dbd71e5780d3f4b/onnxruntime/python/tools/quantization/execution_providers/qnn/quant_config.py#L20-L27)
@@ -264,12 +265,12 @@ print(result)
 
 Running `python run_qdq_model.py` will execute the quantized `model.qdq.onnx` model on the QNN HTP backend.
 
-Notice that the session has been optionally configured to raise an exception if the entire model cannot be executed on the QNN HTP backend. This is useful to check that the quantized model is fully supported by QNN EP.
+Notice that the session has been optionally configured to raise an exception if the entire model cannot be executed on the QNN HTP backend. This is useful for verifying that the quantized model is fully supported by QNN EP.
 Available session configurations include:
 - [session.disable_cpu_ep_fallback](https://github.com/microsoft/onnxruntime/blob/a4cfdc1c28ac95ec6fd0667e856b6a6b8dd1020c/include/onnxruntime/core/session/onnxruntime_session_options_config_keys.h#L229): Disables fallback of unsupported operators to the CPU EP.
 - [ep.context_enable](https://github.com/microsoft/onnxruntime/blob/a4cfdc1c28ac95ec6fd0667e856b6a6b8dd1020c/include/onnxruntime/core/session/onnxruntime_session_options_config_keys.h#L243): [Enable QNN context cache](./QNN-ExecutionProvider.md#qnn-context-binary-cache-feature) feature to dump a cached version of the model in order to decrease session creation time.
 
-Also, the above snippet only specifies the `backend_path` provider option. Refer to the [Configuration options section](./QNN-ExecutionProvider.md#configuration-options) for a list of all available QNN EP provider options.
+The above snippet only specifies the `backend_path` provider option. Refer to the [Configuration options section](./QNN-ExecutionProvider.md#configuration-options) for a list of all available QNN EP provider options.
 
 ## QNN context binary cache feature
 There's a QNN context which contains QNN graphs after converting, compiling, filnalizing the model. QNN can serialize the context into binary file, so that user can use it for futher inference direclty (without the QDQ model) to improve the model loading cost.