OpenVINO notebooks documentation¶
Getting Started¶
Convert & Optimize¶
- Object Detection Quantization
- Quantize a Segmentation Model and Show Live Inference
- Quantize NLP models with OpenVINO Post-Training Optimization Tool
- Convert a PaddlePaddle Model to ONNX and OpenVINO IR
- Post-Training Quantization of PyTorch models with NNCF
- INT8 Quantization with Post-training Optimization Tool (POT) in Simplified Mode tutorial
- Quantization of Image Classification Models
- Quantize a Segmentation Model and Show Live Inference
- Compare FP32 and INT8 Model
- Show Live Inference
- Automatic Device Selection with OpenVINO™
- Convert a PyTorch Model to ONNX and OpenVINO IR
- Working with Open Model Zoo Models
- Quantize Speech Recognition Models with OpenVINO Post-Training Optimization Tool
- Convert a TensorFlow Model to OpenVINO
Model Demos¶
- Live Inference and Benchmark CT-scan Data with OpenVINO
- Optical Character Recognition (OCR) with OpenVINO
- Deblur Photos with DeblurGAN-v2 and OpenVINO
- PaddlePaddle Image Classification with OpenVINO
- Super Resolution with PaddleGAN and OpenVINO
- Monodepth Estimation with OpenVINO
- OpenVINO optimizations for Knowledge graphs
- Windows specific settings
- Import the packages needed for successful execution
- Vehicle Detection And Recognition with OpenVINO
- Imports
- Download Models
- Load Models
- Use detection model to detect vehicles
- Single Image Super Resolution with OpenVINO
- Image In-painting with OpenVINO™
- Style Transfer on ONNX Models with OpenVINO
- Video Super Resolution with OpenVINO
- Photos to Anime with PaddleGAN and OpenVINO
- Image Background Removal with U^2-Net and OpenVINO
- Speech to Text with OpenVINO
- Handwritten Chinese and Japanese OCR
- License Plate Recognition with OpenVINO
- Document Entity Extraction with OpenVINO