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.
Creating a Parametric Model Enterprise Architect User Guide
ArxivPapers Dataset
2022-8-7 arXiv roundup: Adam and sharpness, Recursive self-improvement for coding, Training and model tweaks
BDD100K: A Large-scale Diverse Driving Video Database – The Berkeley Artificial Intelligence Research Blog
BDD100K Dataset Papers With Code
Exploring the Berkeley Deep Drive Autonomous Vehicle Dataset, by Jimmy Guerrero, Voxel51
How to Test Code Coupled to APIs or Databases
ContrXT: Generating contrastive explanations from any text classifier - ScienceDirect
Object detection results of YOLO V3 on BDD dataset. Left to right
Berkeley DeepDrive
DDI Dataset Papers With Code
Exploring the Berkeley Deep Drive Autonomous Vehicle Dataset, by Jimmy Guerrero, Voxel51