OpenVINO notebooks documentation¶
Getting Started¶
Convert & Optimize¶
- Working with Open Model Zoo Models
- Asynchronous Inference with OpenVINO™
- Convert a TensorFlow Model to OpenVINO™
- Convert a PaddlePaddle Model to ONNX and OpenVINO™ IR
- Quantize NLP models with Post-Training Optimization Tool in OpenVINO™
- Object Detection Quantization
- Quantize a Segmentation Model and Show Live Inference
- Compare FP32 and INT8 Model
- Show Live Inference
- Automatic Device Selection with OpenVINO™
- Quantization of Image Classification Models
- Convert a PyTorch Model to ONNX and OpenVINO™ IR
- Quantize Speech Recognition Models with OpenVINO™ Post-Training Optimization Tool
- Post-Training Quantization of PyTorch models with NNCF
- INT8 Quantization with Post-training Optimization Tool (POT) in Simplified Mode tutorial
- Quantize a Segmentation Model and Show Live Inference
Model Demos¶
- Machine translation demo
- OpenVINO optimizations for Knowledge graphs
- Windows specific settings
- Import the packages needed for successful execution
- Image Background Removal with U^2-Net and OpenVINO™
- Document Entity Extraction with OpenVINO
- License Plate Recognition with OpenVINO™
- Speech to Text with OpenVINO™
- Deblur Photos with DeblurGAN-v2 and OpenVINO™
- Industrial Meter Reader
- Photos to Anime with PaddleGAN and OpenVINO
- Handwritten Chinese and Japanese OCR with OpenVINO™
- Image In-painting with OpenVINO™
- Optical Character Recognition (OCR) with OpenVINO™
- Super Resolution with PaddleGAN and OpenVINO™
- Single Image Super Resolution with OpenVINO™
- Image Colorization with OpenVINO
- Style Transfer on ONNX Models with OpenVINO™
- PaddlePaddle Image Classification with OpenVINO™
- Quantize the Ultralytics YOLOv5 model and check accuracy using the OpenVINO POT API
- Monodepth Estimation with OpenVINO
- GPT-2 Text Prediction with OpenVINO
- Run
- Video Super Resolution with OpenVINO™
- Live Inference and Benchmark CT-scan Data with OpenVINO™
- Vehicle Detection And Recognition with OpenVINO™
- Imports
- Download Models
- Load Models
- Use the Detection Model to Detect Vehicles