BDD100K Dataset

Instance segmentation, object detection, drivable areas and lane markings – all you can find in Berkley DeepDrive 100K Dataset. It consists of more than 100 000 HD videos recorded at various times, seasons and weather. The dataset includes localization, timestamp and IMU data.

Data were collected in 4 locations which 3 are close to each other (SF, Berkeley and Bay Area), and the last one is New York.

BDD100K Locations
Locations [4]
BDD100K Weather Distribution
Weather distribution [4]
Distribution across various weather conditions is not uniform – plots are in log scale!

BDD100K: Object detection
Object detection [2]
BDD100K Object Detection Instances
Object Detection Instances [4]
In this dataset, there are only 10 object categories:

  1. bike
  2. bus
  3. car
  4. motor
  5. person
  6. rider
  7. traffic light
  8. traffic sign
  9. train
  10. truck

BDD100K: Instance segmentation
Instance segmentation [2]
BDD100K Instance Segmentation Instances

Instance Segmentation Instances [4]Instance segmentation categories:

  1. banner
  2. billboard
  3. lane divider
  4. parking sign
  5. pole
  6. polegroup
  7. street light
  8. traffic cone
  9. traffic device
  10. traffic light
  11. traffic sign
  12. sign frame
  13. person
  14. rider
  15. bicycle
  16. bus
  17. car
  18. caravan
  19. motorcycle
  20. trailer
  21. train
  22. truck

BDD100K: Drivable area
Drivable area [2]
Drivable area categories:

  • area/alternative
  • area/drivable

BDD100K: Lane Markings
Lane Markings [2]
BDD100K Lane Markings Instances
Lane Markings Instances [4]
Lane marking categories:

  • lane/crosswalk
  • lane/double other
  • lane/double white
  • lane/double yellow
  • lane/road curb
  • lane/single other
  • lane/single white
  • lane/single yellow

To sum up, we get a new dataset with the relatively low number of categories. These categories focused on road object detection.

I highly recommend training your own neural network using this dataset!
If you need any help – please contact me!

Links

  1. Source Code on Github
  2. BDD100K Page
  3. BAIR Post
  4. arXiv Paper

 

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