Go package for computer vision using OpenCV 4 and beyond.

Pre-trained Deep Learning models and demos (high quality and extremely fast)

Deep learning gateway on Raspberry Pi and other edge devices

Contains examples for the Movidius Neural Compute Stick.

Trainable models and NN optimization tools

Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.

YoloV3/tiny-YoloV3+RaspberryPi3/Ubuntu LaptopPC+NCS/NCS2+USB Camera+Python+OpenVINO

A scalable inference server for models optimized with OpenVINO™

A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin]

[High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + Background Multi-transparent(Simple multi-class segmentation) + FaceDetection + MultiGraph + MultiProcessing + MultiClustering

Fast and accurate face landmark detection library using PyTorch; Support 68-point semi-frontal and 39-point profile landmark detection; Support both coordinate-based and heatmap-based inference; Up to 100 FPS landmark inference speed with SOTA face detector on CPU.

Real-time 3D multi-person pose estimation demo in PyTorch. OpenVINO backend can be used for fast inference on CPU.

Working online speech recognition based on RNN Transducer. ( Trained model release available in release )

Fast and accurate single-person pose estimation, ranked 10th at CVPR'19 LIP challenge. Contains implementation of "Global Context for Convolutional Pose Machines" paper.

World's fastest ANPR / ALPR implementation for CPUs, GPUs, VPUs and FPGAs using deep learning (Tensorflow, Tensorflow lite, TensorRT & OpenVINO). Multi-OS (Android, Linux, Windows, Raspberry Pi, NVIDIA Jetson) and Multi-Arch (ARM, x86).

Tutorial for Using Custom Layers with OpenVINO (Intel Deep Learning Toolkit)

[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.

This is implementation of YOLOV4,YOLOV4-relu,YOLOV4-tiny in OpenVINO2020R4 .

Sample projects for Computer Vision with Raspberry Pi and Movidius Neural Compute Stick

Tensorflow based Fast Pose estimation. OpenVINO, Tensorflow Lite, NCS, NCS2 + Python.

The smart city reference pipeline shows how to integrate various media building blocks, with analytics powered by the OpenVINO™ Toolkit, for traffic or stadium sensing, analytics and management tasks.

Optimized media, analytics and graphics software stack images. Use the dockerfile(s) in your project or as a recipe book for bare metal installation.

This is the python implementation of openvino models.

A fast stacked hourglass network for human pose estimation on OpenVino

A ROS package to wrap openvino inference engine and get it working with Myriad and GPU

OpenVINO Training Extensions Object Detection

OpenVINO+NCS2/NCS+MutiModel(FaceDetection, EmotionRecognition)+MultiStick+MultiProcess+MultiThread+USB Camera/PiCamera. RaspberryPi 3 compatible. Async.

Intel Neural Compute Stick 2 Running on RPI 3 with Threading