Say Goodbye to Uncertainty about Ground Truth Video Data
We all know bad data in = bad data out.
This simple truth has major consequences in the world of video annotation.
Many data science teams have experienced the spiral of problems that erupt in the model training process when errors in ground truth video data aren’t discovered and resolved properly.
In our most recent piece, we explore why these kinds of errors can happen so easily and how Alegion Control ensures that they won’t.
We explore the significance of ground truth video data errors for annotation: how they happen, the most common problems, how competitor platforms handle issues (spoiler: it’s not ideal), and why Alegion chose to go another way.
We talk through things like:
- How video data works and where problems typically begin
- The tension between encoding standards and decoding options
- How decoders fool annotators (and create big problems)
- Missing frames and compression artifacts
- The problems with forced pre-processing
- Why Alegion Control can give you peace of mind
Read Part 3 here for how we optimized our platform for annotation quality, user experience, and handling potential issues with ground truth video data.