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.

ContrXT: Generating contrastive explanations from any text classifier - ScienceDirect

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

Object detection results of YOLO V3 on BDD dataset. Left to right

2022-8-7 arXiv roundup: Adam and sharpness, Recursive self-improvement for coding, Training and model tweaks

DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model

Evaluation of Detection and Segmentation Tasks on Driving Datasets

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

Berkeley DeepDrive

Number of nodes and code sizes for BDD machine and QDD machine.

PDF] DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model