Update inception v3 readme

examples/inception_v3/README.md

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+# Inception v3
+
+Inception v3 Inference on CUDANet
+
+## Usage
+
+1. Export pytorch Inception v3 weights pretrained on ImageNet (requires pytorch and torchvision):
+
+```sh
+python inception_v3.py
+```
+
+2. Follow the instructions from the repository root to build the CUDANet library.
+
+3. Build Inception v3 (requires [OpenCV](https://opencv.org/) for image loading and preprocessing):
+
+```sh
+mkdir build
+cd build
+cmake -S ..
+make
+```
+
+4. (Optional) Run tests
+
+Generate test input/output and resources by running `inception_blocks.py` in the `test` folder
+
+Build and run tests (requires [Google Test](https://github.com/google/googletest))
+
+```sh
+cd build
+make test_inception_v3
+./tests/test_inception_v3
+```
+
+5. Run Inception v3 inference:
+```sh
+inception_v3 ../inception_v3_weights.bin ../image.jpg
+```
+
+## Note on Preprocessing
+
+The image preprocessing in this implementation uses OpenCV, which may produce slightly different results compared to PyTorch's Pillow-based preprocessing due to differences in interpolation methods during resizing.