BDD-X Dataset Papers With Code

Berkeley Deep Drive-X (eXplanation) is a dataset is composed of over 77 hours of driving within 6,970 videos. The videos are taken in diverse driving conditions, e.g. day/night, highway/city/countryside, summer/winter etc. On average 40 seconds long, each video contains around 3-4 actions, e.g. speeding up, slowing down, turning right etc., all of which are annotated with a description and an explanation. Our dataset contains over 26K activities in over 8.4M frames.

Exploring the Berkeley Deep Drive Autonomous Vehicle Dataset, by Jimmy Guerrero, Voxel51

BDD100K Dataset Papers With Code

How to Test Code Coupled to APIs or Databases

Exploring the Berkeley Deep Drive Autonomous Vehicle Dataset, by Jimmy Guerrero, Voxel51

BDD100K: A Large-scale Diverse Driving Video Database – The Berkeley Artificial Intelligence Research Blog

GitHub - gy20073/BDD_Driving_Model

Berkeley DeepDrive

Machine Learning Datasets

GitHub - microsoft/X-Decoder: [CVPR 2023] Official Implementation of X-Decoder for generalized decoding for pixel, image and language

PDF] Zero-Suppressed BDDs for Set Manipulation in Combinatorial Problems

Making Sense of the Metadata: Clustering 4,000 Stack Overflow tags with BigQuery k-means - Stack Overflow