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On February 3, 2023, a train made up of 38 cars derailed in East Palestine, Ohio, spilling hazardous materials and...
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Akridata’s Data Explorer offers an interactive platform, where the accuracy metrics are connected directly to the data, saving valuable time in analyzing model’s accuracy, understanding what caused inaccuracies and allowing DS teams to target the next training cycle exactly where the model misfires.
Philips recently settled over 700 lawsuits relating to their line of sleep apnea machines and ventilators. Not only did their payout amount to over one billion dollars but the company was forced to recall a million of their devices as well. Why? Poor inspection. Perhaps that’s an oversimplification of the
In this blog, we’ll see how Akridata leverages existing models like CLIP and DinoV2 for an improved labeling flow.
Explore AI’s transformative effects on the industry in 2024. Uncover key predictions and trends, from foundational models to data quality investments and more.
Until very recently, performing data science with image and video data was an incredibly difficult task. But now, Sanjay Pichaiah explains how advancement in AI powered tools has allowed more organizations to extract value from visual data, an increasingly important need for businesses.
Given the typically large visual datasets, it is impossible to manually inspect each image, but what if there was an automatic way to validate their quality?
Information can be presented in many different forms – starting from its raw format, with no processing or filtering, through graphs and statistics, to a short summary or even a single value. It all depends on the use case, available resources and the next step. Choosing the correct form of
Development cycles in modern computer vision rely heavily on large visual datasets – images of various types, videos from different sources or a mix of the two. The raw content is somehow curated to form a training set and a test set that are used to develop, train and test
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
Visual data is notoriously tricky for data scientists to work with. Visual datasets are known for their huge, unwieldy size, and the tedious, monotonous task of cleaning, curating, and searching through visual data demands significant time and effort from data scientists. But with video data in particular, the challenges go above and
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.
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.
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
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?
Discover how Data Explorer’s image-based search and patch search simplify dataset curation for computer vision tasks. Find relevant data quickly and efficiently.
In this blog, we will see how to automatically select a subset of images for training.
Data Explorer manipulates videos, modifies frame rate for faster processing, splits them into scenes and allows for further curation and exploration.
We see how to perform an image based search or a patch based search in a video, and increase the chances of finding results in different scenes.
Learn how Data Explorer simplifies dataset curation for computer vision tasks. Filter and visualize metadata to enhance algorithm and model development.
Use Data Explorer in the Kaggle RSNA Mammography competition or EDA and coreset selection
As new uses for cameras and the visual data created become available, the need for tools to find the most valuable subsets becomes essential.
AI guru and one of Time magazine’s 100 Most Influential People in 2012, Andrew Ng recently launched a campaign to drive the adoption of “Data-Centric AI.” Why? Because at this point in the evolution of AI, the success and the future of real-world AI applications hinges on the quality of data. While ML/AI hardware infrastructure and
Many of you are familiar with the comprehensive range of AI solutions that Nvidia currently offers. With chips optimized for AI, PCB boards, AI software dev kits, and the Omniverse platform with Digital Twin capabilities, Nvidia provides almost everything needed to successfully build an AI infrastructure that reaches across edge
The December edition of Tech Briefs from SAE International featured a short paper from Akridata on how a Data Supply Chain is a critical part of our emerging AI-infused world. This is best seen in the transportation industry where accelerating ADAS/AV development demands a cohesive modern Edge-to-Cloud perspective focused on data. As