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.
SentiSight.ai 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.
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.
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 SentiSight.ai platform.
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.
Image annotation is a process of classifying images and creating labels to describe objects within them. It is a crucial stepping stone in a supervised machine learning project because the quality of the initial data determines the quality of the final model. A mislabeled image could lead to the model getting trained incorrectly, consequently producing undesirable results. To develop a neural network model well, data scientists are collecting vast amounts of data that contains hundreds of images. Therefore, labeling all of them correctly is a tedious, resource-heavy and lengthy process. The more people are working on the same project annotating, the more confusing it can get. Images can get duplicated, mislabeled or not labeled at all. Therefore, having a good management system is a must. To make the image annotation process more efficient programmers have developed numerous data labeling tools that allow for quicker and more precise annotation. One of these powerful tools, called SentiSight.ai is being offered by us.
Today we are releasing a new version of our platform and we have decided to start our blog to keep you updated about the progress in our development and other related news. The most significant update of this new version is AI-assisted labeling. Some AI-assisted labeling functionalities, such as smart labeling tool, have already been part of SentiSight.ai platform, but now we are bringing those to a whole new level.