Data Curation for Labeling

The Issue

Often image sensors are video streams which inherently means multiple frames per second (30 FPS – 60 FPS).
If you are trying to build a robust training set, you probably want to have a diverse set of examples that is representative of the scenario you want the model to learn.
Naturally, this implies that if you want a frame in a video sequence, the neighboring 30/60 frames will be nearly identical. Picking identical frames may result in having a less diverse dataset and less impact on model performance.
Primitive methods such as downsampling or random sampling may miss out on valuable information and a hope-based approach.

The Akridata Solution

Here’s how Akridata Data Explorer helps to solve this issue. Let’s review step-by step.

Get Started with Akridata Data Explorer

Labeling is a slow and expensive process. So it is important to be able to build quality training that will be labeled.
With Akridata Data Explorer, you can exploit the capability of coreset sampling and many other choices of tunables to remove nearly identical frames or adjoining frames and construct a diverse representation of your target image.
Data Explorer allows you to extract relevant information from 1% or any user-defined percent of the dataset while still preserving the underlying representation. With a few clicks, you save yourself a lot of time and money.
Get more bang for your buck on labeling spends. Try Data Explorer today.