MIVisionX has a number of applications built on top of OpenVX and its modules, it uses AMD optimized libraries to build applications which can be used as prototypes or used as models to develop products.
This sample application creates bubbles and donuts to pop using OpenVX & OpenCV functionality.
This sample application does inference using a client-server system.
This sample application is used to recognize hand written digits.
This sample application demonstrates a basic usage of RALI’s C API to load JPEG images from the disk and modify them in different possible ways and displays the output images.
MIVisionX Inference Analyzer
MIVisionX Inference Analyzer Application using pre-trained
Caffe models to analyze and summarize images.
MIVisionX OpenVX Classsification
This sample application shows how to run supported pre-trained caffe models with MIVisionX RunTime.
MIVisionX Validation Tool
MIVisionX ML Model Validation Tool using pre-trained
Caffe models to analyze, summarize, & validate.
MIVisionX WinML Classification
This sample application shows how to run supported ONNX models with MIVisionX RunTime on Windows.
MIVisionX WinML YoloV2
This sample application shows how to run tiny yolov2(20 classes) with MIVisionX RunTime on Windows.
MIVisionX-Classifier - This application runs know CNN image classifiers on live/pre-recorded video stream.
YOLOv2 - Run tiny yolov2 (20 classes) with AMD’s MIVisionX
Traffic Vision - This app detects cars/buses in live traffic at a phenomenal 50 frames/sec with HD resolution (1920x1080) using deep learning network Yolo-V2. The model used in the app is optimized for inferencing performance on AMD-GPUs using MIVisionX toolkit.
- RGBDSLAMv2-MIVisionX - This is an implementation of RGBDSLAM_V2 that utilizes AMD MIVisionX for feature detection and ROCm OpenCL for offloading computations to Radeon GPUs. This application is used to create 3D maps using RGB-D Cameras.