Railways have long served as a crucial mode of transportation, ensuring the seamless movement of people and goods across vast distances. In recent years, the railway industry has witnessed a technological transformation, with artificial intelligence (AI) revolutionizing operations and safety. One of the most exciting advancements in this field is the application of computer vision models, such as YOLOv7, which enables railway organizations to harness the power of AI for enhanced efficiency and security. In this article, we will explore how Navan AI, a pioneering no-code computer vision platform, is empowering businesses to build and deploy computer vision models effortlessly, revolutionizing the railway industry.
In today's fast-paced world, businesses across various industries are seeking innovative ways to leverage artificial intelligence (AI) and computer vision technologies to gain a competitive edge. However, developing and deploying computer vision models has traditionally been a time-consuming and resource-intensive process. Fortunately, with the advent of no-code platforms like Navan.ai, organizations can now build and deploy powerful computer vision models in a matter of minutes, revolutionizing the landscape of AI development.
Computer vision has revolutionized various industries, enabling machines to perceive and understand visual data. Thanks to advancements in artificial intelligence (AI), developers can now create powerful computer vision applications without extensive coding knowledge. In this article, we will delve into the key differences between YOLOv4 and YOLOv7 models, explore their applications in computer vision, and help developers make informed decisions when choosing between these two models. Powered by Navan AI, the no-code platform for computer vision, developers can unlock their creativity and drive innovation effortlessly.
In today's fast-paced world, Artificial Intelligence (AI) has become an integral part of numerous industries, revolutionizing the way we solve complex problems. However, developing AI models often requires extensive coding knowledge and technical expertise, limiting the accessibility for businesses without dedicated data science teams. Enter EfficientNet models, a groundbreaking solution that allows users to build and deploy AI models without any coding.
In the previous blog, we discussed the applications of computer vision in the manufacturing industry.
This blog explains how to use navan.ai, a no-code computer vision platform to build an image classification model to classify damaged and intact medical packages.
Challenges in AI development
Artificial Intelligence market size is growing and it is said that it can grow up to $15.7 trillion by 2030, as quoted in the research paper https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI(2019)637967_EN.pdf
As AI grows, the impact and challenges rise parallely as well. Let's see some of the most common challenges in Artificial Intelligence Development.