Why are we differentiating between validation and training projects when it comes to autonomous driving functions? There are some profound differences between the two types of projects.
A project with the goal to train a machine learning model on a certain task. To do so, data needs to be collected and annotated, so that the model can learn from it. In autonomous driving we are talking about perception algorithms that help self-driving functions to recognize their environment recorded by the vehicles' sensors.
A project with the goal to train an AI-model to perfection certainly requires a decent amount of data of high variance in different conditions. Yet, the amount of validation data to ensure proper functioning in an environment where low performance can lead to fatalities is orders of magnitudes higher than what will be needed for training.
Flexible workflows which can be easily optimized for large project requirements
Rock-solid processing of petabytes of data
Easy onboarding of new labelling experts whenever required
Watch the recorded webcast Where to start with AI-powered annotation automation. Let Daniel Rödler, Director of Product explain how understand.ai deploys AI to automate annotations and share years of experience working on autonomous driving annotation projects.
An algorithm can only get as good as the data it was trained on. If quality requirements in terms of box tightness, consistency, completeness and coverage are not met, the model is likely to not be able to unfold its potential in real world scenarios. To achieve a potentially usable model requires high manual quality assurance efforts.
Sensors enabling machines to see have far surpassed human capabilities, yet their ability to 'connect the dots' has its limits. Does 'depth' make a difference?
What does it take to get the highest throughput in large scale annotation? Learn how the engineering and R&D forerunner LTTS teamed up with UAI Annotator to give automotive customers an edge to lead in the autonomous driving race.
More to find in our large scale annotation tool testimonial here.
In machine perception, there’s one approach that utilizes the benefits of both 2D and 3D data while avoiding the downsides of both. It’s Sensor Fusion - a method of merging the information coming from two or more sensor sources to create a new representation of the environment - one superior to each of the individual ones.
Learn more here
Why is semantic segmentation so essential for perception data annotation? Which type is the most useful for machine learning? What are caveats to it?
Discover more in our blog on semantic segmentation.
Diversity is data is essential in both use cases, for training as well for validation you’d aim for a well-balanced and representative dataset of normal scenarios, edge cases and critical situations.
To run a successful validation project on huge amounts of data, watch out for a high automation rate and the right quality criteria. Then you’re all set for fast delivery at an affordable price point.