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 data presentation is tricky, but […]
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 a DL model. But how […]
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 […]
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 beyond the standard […]
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.
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.
Data Explorer now provides a text interface where you simply type what are searching for, and Data Explorer will provide the relevant images.