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June 3, 2021
The Evolution of Object Detection

The Evolution of Object Detection

Even though object detection seems like an innovative computer vision technology, it has been all around us since the early 1960s. Its first applications included character pattern recognition systems in office automation related tasks, assembly and verification processes in the semiconductor industry that directly contributed to various countries’ economic development.
May 19, 2021
Changes to the Subscription Model

New features and changes

The most prominent change is to our subscription model with the introduction of a pay-as-you-go wallet system that allows users to pay for only what they use on the platform. Other exciting operational changes and features include the possibility to retrain a whole image classification network that allows more accurate models for large data sets, introduction of the object detection model building as a tool available to be used for all users, as well as changes to the capabilities of REST API operations.
May 13, 2021
Mussel defect detection

Deep Dive: Role of Image Recognition in Defect Detection

Since the dawn of the industrial era, innovations in machinery and technology have helped manufacturers to increase efficiency, reduce production costs and standardize quality at a vast scale. However, the diminishing human involvement in the production process has reduced the manufacturers’ ability to spot defective goods or products before they reach the final consumer.
April 29, 2021
Online image labeling annotation

Image labeling using online vs offline tools

Image labeling (sometimes known as image annotation) is the process of creating a textual and visual description of the content of images. These labels / annotations are then used to train deep learning computer vision models for tasks such as object detection.
April 13, 2021
Human pose estimation

Human Pose estimation using

Human pose estimation, is defined as the localization of major human joints such as elbows, knees, wrists, etc.It continues to be one of the most popular research areas regarding computer vision tasks.
March 30, 2021
Image recognition using AI

Image recognition: Choosing the right AI model for your project offers three different image recognition model types, single-label classification, multi-label classification and object detection, all of which have similarities as well as differences, with each of them excelling at different types of tasks. While the three models can be used to classify the content within images, the approach they undertake is dependent on the task aims and envisioned results. This article defines key similarities and differences between the 3 models, as well as providing examples of the use cases of each model, to help you to decide which model type is needed for your project requirements.
March 15, 2021
AI based image recognition

AI – based Image Recognition: 6 different Industry use cases

Since the dawn of artificial intelligence, image recognition has long been recognised as one of the most prosperous and beneficial utilisations of the technology. Closely linked to computer vision, image recognition is the interdisciplinary computer science field that deals with a computer’s ability to identify and understand the content within images. Nowadays, most image recognition tasks are performed by using deep learning algorithms.
March 1, 2021
Optical text recognition / Text Recognition

Optical Character Recognition Using

On February 8th, 2021 we released a new version of our platform that introduced a text recognition pre-trained model, otherwise known as optical character recognition software. We created this short guide on what text recognition is, its history and usage scenarios, how it works and how to make the best of it on the platform.
February 8, 2021
Quickstart guide for training object detection model using

Quickstart guide for training object detection model using

Object detection is one of the most praised use cases of artificial intelligence. In simple terms it is an algorithm searching for objects in an image and assigning suitable labels to them. It is sometimes confused with image classification due to their similar use case scenarios. In particular, the goal of object detection is to identify the object and mark its position with a bounding box, while image classification identifies which category the given image belongs to. Needless to say, the former is more suitable for images that have a few objects of interest in them or if the object constitutes only a small part of the image. Example images below show which tool suits a picture better.