Achieving Optimal Speed and Accuracy in Object Detection (YOLOv4)

Table of Contents Achieving Optimal Speed and Accuracy in Object Detection (YOLOv4)

An Incremental Improvement with Darknet-53 and Multi-Scale Predictions (YOLOv3)

Mean Average Precision (mAP) Using the COCO Evaluator

A Better, Faster, and Stronger Object Detector (YOLOv2)

Detection frameworks have become increasingly fast and accurate, as seen in our last post on YOLOv1; however, most detection methods are still constrained to a small set of objects like 20 classes in PASCAL VOC and 80 classes in Microsoft COCO.

Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1)

Table of Contents Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1)

Introduction to the YOLO Family

Text Detection and OCR with Microsoft Cognitive Services

Table of Contents Text Detection and OCR with Microsoft Cognitive Services Microsoft Cognitive Services for OCR

Text Detection and OCR with Amazon Rekognition API

How to run uWSGI (config)

Improving Text Detection Speed with OpenCV and GPUs

In this tutorial, you will learn to improve text detection speed with OpenCV and GPUs.

OCR’ing Video Streams

In this tutorial, you will learn how to OCR video streams. This lesson is part 3 of a 4-part series on Optical Character Recognition with Python

Multi-Column Table OCR

In this tutorial, you will: Discover a technique for associating rows and columns together Learn how to detect tables of text/data in an image

U-Net Image Segmentation in Keras

In this tutorial, you will learn how to create U-Net, an image segmentation model in TensorFlow 2 / Keras. We will first present a brief introduction on image segmentation, U-Net architecture, and then walk through the code implementation with a Colab notebook.

Image Super Resolution

In this tutorial, you will learn to use image super resolution. This lesson is part of a 3-part series on Super Resolution: OpenCV Super Resolution with Deep Learning Image Super Resolution (this tutorial) Pixel Shuffle Super Resolution with TensorFlow, Kera

Django Rest Framework. Добавление аутентификации по токену

Стартовая статья по серии примеров работы с Django Rest Framework. В данной статье показан пример настройки получения токена аутентификации, настройка swagger документации, а также имеется пример кода на QML/Felgo для получения токена в мобильном приложении.

Anime Faces with WGAN and WGAN-GP

Django - Как написать функционал auto populate field

Использование функционала auto populate field на примере простого MarkdownField для генерирования html контента в обычный TextField при сохранении объекта в Django Framework

Torch Hub Series #6: Image Segmentation

In this tutorial, you will learn the concept behind Fully Convolutional Networks (FCNs) for segmentation. In addition, we will see how we can use Torch Hub to import a pre-trained FCN model and use it in our projects to get… The post Torch Hub Series #6: Image Segmentation appeared first on PyImageSearch.

Torch Hub Series #5: MiDaS — Model on Depth Estimation

Torch Hub Series #4: PGAN — Model on GAN

In this tutorial, you will learn the architectural details of Progressive GAN, which enable it to generate high-resolution images. In addition, we will see how we can use Torch Hub to import a pre-trained PGAN model and use it in our projects to generate high-quality images.