Udacity Self Driving Car

Overview

The original Udacity Self Driving Car Dataset is missing labels for thousands of pedestrians, bikers, cars, and traffic lights. This will result in poor model performance. When used in the context of self driving cars, this could even lead to human fatalities.

Re-labeled the dataset to correct errors and omissions.

Content

The dataset contains 97,942 labels across 11 classes and 15,000 images. There are 1,720 null examples (images with no labels).

All images are 1920x1200 (download size ~3.1 GB).

Annotations have been hand-checked for accuracy by Roboflow.

Annotation Distribution:

Use Cases

Udacity is building an open source self driving car! You might also try using this dataset to do person-detection and tracking.

Note: the dataset contains many duplicated bounding boxes for the same subject which we have not corrected. You will probably want to filter them by taking the IOU for classes that are 100% overlapping or it could affect your model performance (expecially in stoplight detection which seems to suffer from an especially severe case of duplicated bounding boxes).

About Roboflow

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Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:


Acknowledgements

Licensed by MIT. More details in the README.txt files. Provided by Roboflow License: MIT