Edge Devices

TensorFlow Lite: TFLite Model Optimization for On-Device Machine Learning

The recent trend in developing larger and larger Deep Learning models for a slight increase in accuracy raises concerns about their computational efficiency and wide scaled usability We can not

CNN, Deep Learning, Edge Devices, Image Classification, Model Optimization, OpenCV Tutorials, Post Training Quantization, Raspberry Pi

Object Detection With Depth Measurement Using Pre-trained Models With OAK-D

This is the third blog post in the Oak series If you haven 8217 t checked out the previous posts on OAK check them below In this post we are

3D Computer Vision, CNN, Edge Devices, Face Detection, OAK

AUTOSAR C++ compliant deep learning inference with TensorRT

If you are unfamiliar with safety compliance and are debating whether or not to read this blog post let s answer the most important question first Why should you read

CNN, Deep Learning, Edge Devices, Image Segmentation, Machine Learning

Top 10 Sources to Find Computer Vision and AI Models

The AI community generously shares code model architectures and even models trained on large datasets We are standing on the shoulders of giants which is why the industry is adopting

Computer Vision, Deep Learning, Edge Devices, Image Segmentation, Object Detection

Introduction to Epipolar Geometry and Stereo Vision

We will learn the concepts of Epipolar geometry and point correspondences We will then use these concepts discuss how to calculate depth from stereo disparity

3D Computer Vision, Classical Computer Vision, Edge Devices, OAK

PyTorch to CoreML model conversion

Neural network usage usually takes a lot of computations but in our modern world even a smartphone can be a device to run your trained neural model Today we will

Deep Learning, Edge Devices, PyTorch