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Surface defects are one of the most persistent challenges in steel and metal manufacturing. From scratches and dents to inclusions and cracks, even the smallest...
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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.
This article was originally published on InsideBigData. It is time to shift from a model-centric mindset to a data-centric approach. AI is a massive part of human life today and is woven into the fabric of our everyday society. From medical imagery scanning to the ubiquity of facial recognition software
The challenge Imagine you’ve just been given a new batch of 10,000 images or hours of video and you need to find only a small portion of relevant images. How would you go about isolating that subset of the data? In many cases, we receive a batch of visual data,
ECCV 2022 was a truly remarkable event. With two full days of incredible workshops and tutorials, followed by three additional days of the main conference, Computer Vision was celebrated in every corner. During the conference, giants like Amazon, Google and Meta, giants-in-the-making like Akriadata, and a whole host of awesome
nuScenes is a public dataset for autonomous driving. In this post, we’ll break down how Akridata Data Explorer makes exploration easy.
The autonomous world is rapidly becoming one of the most significant developments in human history. From self-driving cars to automated retail stores and warehouses, AI and machine learning have seamlessly integrated into our daily lives. The next frontier in AI is to push machines toward even greater autonomy, and achieving
Class imbalance-in-visual data sets is an all too common problem in real-world applications that use machine learning and AI. Many applications of computer vision suffer from imbalanced class distribution, including fraud detection, anomaly detection, medical diagnosis, oil spillage detection, facial recognition, and more. The consequences of imbalanced class data can
Explore how Akridata Data Explorer enhances visual data and AI modeling. Learn about its powerful features for dataset curation and efficient AI development.
CVPR Recap: Akridata Key Takeaways We are all jazzed up! We attended CVPR in New Orleans at the end of June and had a great time seeing everyone there, talking computer vision, visual data, and displaying Akridata Data Explorer. Couldn’t make the event? No problem! Below we’ll dive into our
GTC 2022 is Just Around the Corner! Next week, Nvidia’s GTC Conference and Training will be firing up with some of the foremost in AI across numerous sectors, and we’re looking forward to connecting, engaging, informing, and learning with all of you from March 21-24. This year, Akridata’s Senior Director
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
