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.

Locations [4]

Weather distribution [4]

Object detection [2]

Object Detection Instances [4]

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

Instance segmentation [2]

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

Drivable area [2]

• area/alternative
• area/drivable

Lane Markings [2]

Lane Markings Instances [4]

• 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