This tutorial is a part of the series that shows how to train, optimize, quantize and show live inference on a segmentation model with PyTorch Lightning and OpenVINO.
This is the final part that shows benchmarking results and live inference on the quantized segmentation model. You will see real-time segmentation of kidney CT scans running on a CPU, iGPU, or combining both devices for higher throughput. The processed frames are 3D scans that are shown as individual slices. The visualization slides through the slices with detected kidneys overlayed in red. A pre-trained and quantized model is provided, so running the previous tutorials in the series is not required.
For a minimum installation:
- Create a virtual environment, with
python -m venv openvino_env(on Linux you may need to typepython3) and activate it withopenvino_env\Scripts\activateon Windows orsource openvino_env/bin/activateon macOS or Linux. - Clone the openvino_notebooks repository:
git clone https://github.com/openvinotoolkit/openvino_notebooks/ - Change to the directory:
cd openvino_notebooks - Check out the live_inference branch
git checkout live_inference - Change to the notebook directory:
cd notebooks/210-ct-scan-live-inference - Install the requirements with
pip install --upgrade pip && pip install -r requirements.txt. - Run the notebook by typing
jupyter laband doubleclicking on the notebook from the left sidebar.
NOTE: This notebook needs an Internet connection to download data. If you use a proxy server, enable the proxy server in the terminal before typing
jupyter lab.
NOTE: To use other notebooks in the openvino_notebooks repository, follow the instructions in the Installation Guide
- Data Preparation for 2D Segmentation of 3D Medical Data
- Train a 2D-UNet Medical Imaging Model with PyTorch Lightning
- Convert and Quantize a UNet Model and Show Live Inference
- Live Inference and Benchmark CT-scan data (this notebook)