The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/14572644

Yildiz, Burak; Khademi, Seyran (2022): Extracted Features on AmsterTime Dataset. Version 4. 4TU.ResearchData. dataset. https://doi.org/10.4121/14572644.v4

Dataset

x choose version: version 3 - 2022-04-12 version 2 - 2021-06-08 version 1 - 2021-05-12

• Artificial Intelligence and Image Processing
• Urban and Regional Planning

Amsterdam, AmsterTime, Cross-time, deep learning, features, LIFT features, NetVLAD features, ResNet features, SIFT features, VGGNet features

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Burak Yildiz , Seyran Khademi

This collection contains feature files where the features are extracted on AmsterTime dataset using various methods and models such as SIFT, LIFT, and pre-trained VGG-16, ResNets, NetVLAD, AP-GeM and supervisely and self-supervisely trained models. The details of feature extraction procedure and other details can be found on https://github.com/seyrankhademi/AmsterTime.

• 2021-05-12 first online

• 2022-06-07 published, posted

4TU.ResearchData

Pickle file

AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift

• https://github.com/seyrankhademi/AmsterTime

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science Intelligent Systems