It is hard to imagine the field of modern medicine without the development and harnessing of technology. X-rays, MRIs and other solutions have been widely utilized in various medical fields to improve diagnosis accuracy and efficiency, whilst also minimizing instances of human error.
Users of SentiSight.ai are able to train a variety of image recognition models of their choice, but deploying these models correctly can often be a tricky decision. At SentiSight.ai we are offering three options to use the model you have trained.
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.
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.
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.
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.
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.