Interactive Tutorials (Python)¶

This collection of Python tutorials are written for running on Jupyter notebooks. The tutorials provide an introduction to the OpenVINO™ toolkit and explain how to use the Python API and tools for optimized deep learning inference. You can run the code one section at a time to see how to integrate your application with OpenVINO libraries.

Notebooks with a Binder button button can be run without installing anything. Once you have found the tutorial of your interest, just click the button next to the name of it and Binder will start it in a new tab of a browser. Binder is a free online service with limited resources (for more information about it, see the Additional Resources section).

Note

For the best performance, more control and resources, you should run the notebooks locally. Follow the Installation Guide in order to get information on how to run and manage the notebooks on your machine.

Contents:

Getting Started

Getting Started ¶

The Jupyter notebooks are categorized into four classes, select one related to your needs or give them all a try. Good Luck!

First steps with OpenVINO ¶

Brief tutorials that demonstrate how to use Python API for inference in OpenVINO.

Notebook	Description	Preview
001-hello-world	Classify an image with OpenVINO.
002-openvino-api	Learn the OpenVINO Python API.
003-hello-segmentation	Semantic segmentation with OpenVINO.
004-hello-detection	Text detection with OpenVINO.

Convert & Optimize ¶

Tutorials that explain how to optimize and quantize models with OpenVINO tools.

Notebook	Description	Preview
101-tensorflow-to-openvino	Convert TensorFlow models to OpenVINO IR.
102-pytorch-onnx-to-openvino	Convert PyTorch models to OpenVINO IR.
103-paddle-onnx-to-openvino	Convert PaddlePaddle models to OpenVINO IR.
104-model-tools	Download, convert and benchmark models from Open Model Zoo.

Explore more notebooks here.

Notebook	Description
105-language-quantize-bert	Optimize and quantize a pre-trained BERT model
106-auto-device	Demonstrates how to use AUTO Device
107-speech-recognition-quantization	Optimize and quantize a pre-trained Data2Vec speech model
107-speech-recognition-quantization	Optimize and quantize a pre-trained Wav2Vec2 speech model
108-gpu-device	Working with GPUs in OpenVINO™
109-performance-tricks	Performance tricks in OpenVINO™
110-ct-segmentation-quantize	Quantize a kidney segmentation model and show live inference
111-yolov5-quantization-migration	Migrate YOLOv5 POT API based quantization pipeline on Neural Network Compression Framework (NNCF)
112-pytorch-post-training-quantization-nncf	Use Neural Network Compression Framework (NNCF) to quantize PyTorch model in post-training mode (without model fine-tuning)
113-image-classification-quantization	Quantize mobilenet image classification
114-quantization-simplified-mode	Quantize Image Classification Models with POT in Simplified Mode
115-async-api	Use Asynchronous Execution to Improve Data Pipelining
116-sparsity-optimization	Improve performance of sparse Transformer models
117-model-server	Improve performance of sparse Transformer models
118-optimize-preprocessing	Improve performance of image preprocessing step

Model Demos ¶

Demos that demonstrate inference on a particular model.

Notebook	Description	Preview
205-vision-background-removal	Remove and replace the background in an image using salient object detection.
209-handwritten-ocr	OCR for handwritten simplified Chinese and Japanese.	的人不一了是他有为在责新中任自之我们
211-speech-to-text	Run inference on speech-to-text recognition model.
215-image-inpainting	Fill missing pixels with image in-painting.
218-vehicle-detection-and-recognition	Use pre-trained models to detect and recognize vehicles and their attributes with OpenVINO.

Explore more notebooks below.

Notebook	Description	Preview
201-vision-monodepth	Monocular depth estimation with images and video.
202-vision-superresolution-image	Upscale raw images with a super resolution model.	→
202-vision-superresolution-video	Turn 360p into 1080p video using a super resolution model.	→
203-meter-reader	PaddlePaddle pre-trained models to read industrial meter’s value
206-vision-paddlegan-anime	Turn an image into anime using a GAN.	→
207-vision-paddlegan-superresolution	Upscale small images with superresolution using a PaddleGAN model.	→
208-optical-character-recognition	Annotate text on images using text recognition resnet.
212-pyannote-speaker-diarization	Run inference on speaker diarization pipeline
213-question-answering	Answer your questions basing on a context.
214-grammar-correction	Grammatical Error Correction with OpenVINO
217-vision-deblur	Deblur Images with DeblurGAN-v2.
219-knowledge-graphs-conve	Optimize the knowledge graph embeddings model (ConvE) with OpenVINO
220-yolov5-accuracy-check-and-quantization	Quantize the Ultralytics YOLOv5 model and check accuracy using the OpenVINO POT API
221-machine-translation	Real-time translation from English to German
222-vision-image-colorization	Use pre-trained models to colorize black & white images using OpenVINO
223-text-prediction	Use pretrained models to perform text prediction on an input sequence
224-3D-segmentation-point-clouds	Process point cloud data and run 3D Part Segmentation with OpenVINO
225-stable-diffusion-text-to-image	Text-to-image generation with Stable Diffusion method
226-yolov7-optimization	Optimize YOLOv7 using NNCF PTQ API
227-whisper-subtitles-generation	Generate subtitles for video with OpenAI Whisper and OpenVINO
228-clip-zero-shot-image-classification	Perform Zero-shot Image Classification with CLIP and OpenVINO
229-distilbert-sequence-classification	Sequence Classification with OpenVINO
230-yolov8-optimization	Optimize YOLOv8 using NNCF PTQ API
231-instruct-pix2pix-image-editing	Image editing with InstructPix2Pix
232-clip-language-saliency-map	Language-Visual Saliency with CLIP and OpenVINO™
233-blip-visual-language-processing	Visual Question Answering and Image Captioning using BLIP and OpenVINO™
234-encodec-audio-compression	Audio compression with EnCodec and OpenVINO™
235-controlnet-stable-diffusion	A Text-to-Image Generation with ControlNet Conditioning and OpenVINO™
236-stable-diffusion-v2	Text-to-Image Generation and Infinite Zoom with Stable Diffusion v2 and OpenVINO™
237-segment-anything	Prompt based object segmentation mask generation using Segment Anything and OpenVINO™

Model Training ¶

Tutorials that include code to train neural networks.

Notebook	Description	Preview
301-tensorflow-training-openvino	Train a flower classification model from TensorFlow, then convert to OpenVINO IR.
301-tensorflow-training-openvino-pot	Use Post-training Optimization Tool (POT) to quantize the flowers model.
302-pytorch-quantization-aware-training	Use Neural Network Compression Framework (NNCF) to quantize PyTorch model.
305-tensorflow-quantization-aware-training	Use Neural Network Compression Framework (NNCF) to quantize TensorFlow model.

Live Demos ¶

Live inference demos that run on a webcam or video files.

Notebook	Description	Preview
401-object-detection-webcam	Object detection with a webcam or video file.
402-pose-estimation-webcam	Human pose estimation with a webcam or video file.
403-action-recognition-webcam	Human action recognition with a webcam or video file.
404-style-transfer-webcam	Style Transfer with a webcam or video file
405-paddle-ocr-webcam	OCR with a webcam or video file
406-3D-pose-estimation-webcam	3D display of human pose estimation with a webcam or video file
407-person-tracking-webcam	Person tracking with a webcam or video file

Notebook		Preview
Vision-monodepth	Monocular depth estimation with images and video.
Vision-background-removal	Remove and replace the background in an image using salient object detection.
Object-detection-webcam	Object detection with a webcam or video file.
Pose-estimation-webcam	Human pose estimation with a webcam or video file.
Action-recognition-webcam	Human action recognition with a webcam or video file.

Additional Resources ¶

Contributors ¶

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