We live now in an era where AI is everywhere around us, where applications and services rely heavily on automated systems with an AI-based component embedded in them. In order to perform well, these systems rely on getting an accurate output from various ML models, and while researcher and DS teams work tirelessly to improve […]
Akridata Data Explorer: Revolutionizing Video Sequencing for Data Scientists Visual data has always been challenging for data scientists, but when it comes to video data, the complexities multiply. The massive size, high frame rates, and the sheer volume of content make video datasets incredibly unwieldy. The traditional methods of cleaning, curating, and searching through these […]
Santiago Valdarrama, an AI and Computer Vision expert, was recently featured on a webinar addressing trending topics in AI/computer vision. This blog will provide an overview of Valdarrama’s takes on the topics, including the important role of data in computer vision, deep learning vs. classical computer vision, and AI bias. Hot Topic # 1 The […]
Saliency Maps are essentially heat maps outlining which parts of the image influenced the model more, and which were less important. Once we understand that, we can decide what and how to change, in order to improve the model’s accuracy.
In previous posts we saw how a video could be visualized and how to search for an object within a video. Now, we can go one step further and search for an event.
Data Explorer now provides a text interface where you simply type what are searching for, and Data Explorer will provide the relevant images.
By defining the Region of Interest within visual datasets, organizations can better train their computer vision models and improve their accuracy, image segmentation, and data tracking and analysis.
Akridata recently hosted a webinar highlighting the challenges data scientists working with visual data often face and how Data Explorer solves these challenges But, not all visual data is created equal. Data quality varies greatly when it comes to visual datasets, and common issues like data noise, misleading color contrast and imaging, and occlusion that […]
A dataset of images, used for computer vision tasks, could be the key to success or failure. A clean dataset could lead the way to a great algorithm and model
A visual dataset used for computer vision tasks, could be the key to success or failure. So how do you make sure your algorithm and model are based on strong foundations?