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BDD-X Dataset Papers With Code

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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.

BDD100K Dataset Papers With Code

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Solved Intro to Machine Learning 1. Please use Python for

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

Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

Learning Binary Decision Diagrams (BDDs) from data in Python - Stack Overflow

Evaluation of Detection and Segmentation Tasks on Driving Datasets

Cryptography, Free Full-Text

ContrXT: Generating contrastive explanations from any text classifier - ScienceDirect

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

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

Benchmarking and scaling of deep learning models for land cover image classification - ScienceDirect