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
Bringing sophisticated AI-based driving functions to the road safely requires a growing amount of annotated data. Watch Daniel explain what challenges are connected to the annotation process and what can be done with it.
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.
Discover in an on-demand webinar on how to automate 3D object annotation in autonomous driving. 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.
Watch the webinar on annotation automation now!
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.