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.

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

Artificial Intelligence (Subject Code - 843) : General Instructions

An example of our TrafficQA dataset showing that a white sedan had

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

Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow

Binary decision diagram - Wikipedia

BDD100K Dataset Papers With Code

Listen to Talking Papers Podcast podcast

GitHub - mseg-dataset/mseg-semantic: An Official Repo of CVPR '20 MSeg: A Composite Dataset for Multi-Domain Segmentation

BDD100K val Benchmark (Semantic Segmentation)

BDD-X Dataset Papers With Code

Solved Question 1: state.X77 (25 points) In # Explanatory

Solved Intro to Machine Learning 1. Please use Python for

J. Imaging, Free Full-Text