A Compilation of all MIVisionX applications available open-source
MIVisionX has several applications built on top of OpenVX and its modules, it uses AMD optimized libraries to build applications that 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 is set to showcase how to use AMD's OpenVX and RunVX application.
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.
This sample application is set to showcase how to use AMD's OpenVX and RunVX application.
This sample application is used to recognize handwritten digits.
This sample application uses Kubernetes infrastructure to launch Dockers to perform inference on multi-GPU multi-Rack to acheive linear scaling in Inference applications
MIVisionX ML Model Validation Tool using pre-trained ONNX / NNEF / Caffe models to analyze, summarize, & validate.
ADAT provides you with tools for accomplishing your tasks throughout the whole neural net life-cycle, from verifing the model to validating the model to deploying them for your target platforms.
This sample application does inference using a client-server system.
MIVisionX Inference Analyzer Application using pre-trained ONNX / NNEF / Caffe models to analyze and summarize images.
This sample application demonstrates the 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.
This sample application shows how to run supported pre-trained caffe models with MIVisionX RunTime.
This sample application shows how to run supported ONNX models with MIVisionX RunTime on Windows.
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 the MIVisionX toolkit.
