CNN

TensorFlow Model Optimization Toolkit – A Deep Dive

In the previous posts of the TFLite series, we introduced TFLite and the process of creating a model. In this post, we will take a deeper dive into the TensorFlow

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

TensorFlow Lite Model Maker: Create Models for On-Device Machine Learning

In this article, we will learn how to create a TensorFlow Lite model using the TF Lite Model Maker Library. We will fine-tune a pre-trained image classification model on the

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

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’t checked out the previous posts on OAK, check them below. In this post, we are going to

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

Object Detection using YOLOv5 OpenCV DNN in C++ and Python

Object detection using YOLOv5 and OpenCV DNN. Learn how to YOLOv5 Ultralytics Github repository. From plethora of YOLO versions, which one is most appropriate for you? Continue reading the article