Extracted Features on AmsterTime Dataset (dataset)

Extracted Features on AmsterTime Dataset

The DOI displayed 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

Datacite citation style

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

Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Version 4 - 2022-06-07 (latest)

Version 3 - 2022-04-12 Version 2 - 2021-06-08 Version 1 - 2021-05-12

Usage statistics

3018

views

3970

downloads

Keywords

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

Geolocation

Amsterdam

Licence

CC BY 4.0

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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.

History

2021-05-12 first online

2022-06-07 published, posted

Publisher

4TU.ResearchData

Format

Pickle file

Associated peer-reviewed publication

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

References

https://github.com/seyrankhademi/AmsterTime

Organizations

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

DATA

Files (14)

20,282,404 bytesMD5:91099171982d0e87ce2386b8a8f403acap_gem_landmarks.p
2,705,170 bytesMD5:b2c3b30a622b05f8a19eab807075dcaalift_piccadilly_128.p
322,832,359 bytesMD5:ce32fec7843201fc40bcdbf7ba45a8d8netvlad_pittsburgh250k.p
20,282,404 bytesMD5:a5a399839030986b593dcd407c834abfresnet101_imagenet.p
20,282,404 bytesMD5:3c264112b53e464319b34cb0f642b4c0resnet50_imagenet.p
2,705,170 bytesMD5:89c297423fe08eaba61c81fc437c8f75sift_128.p
20,282,404 bytesMD5:812e4c62b7b042f2a696212e02d4e167simsiam_resnet50_scratch_amstertime_ep10000_bs128.p
20,282,404 bytesMD5:2335e737bea264e62ca117717458176dsimsiam_resnet50_scratch_gldv2_ep100_bs128.p
20,282,404 bytesMD5:91fc99d22dd6afef11fc19d1fc846d06simsiam_resnet50_scratch_imagenet_ep100_bs256.p
247,199,719 bytesMD5:65e5e811533dacbdb9f2d8c4c8c7c299simsiam_vgg16_scratch_amstertime_ep10000_bs128.p
247,199,719 bytesMD5:13e9d4efcd279b942f80348c53bc87ccsimsiam_vgg16_scratch_gldv2_ep100_bs128.p
247,199,719 bytesMD5:e74387a8628fa6ec3fc676016ec1ba11simsiam_vgg16_scratch_imagenet_ep100_bs128.p
5,153,797 bytesMD5:883aabaf2968e4b6f23730802cad0526triplet_resnet18_imagenet.p
247,199,717 bytesMD5:ea1f6b6a7dd89bed2083931cbcb351fdvgg16_imagenet.p
download all files (zip)
1,443,889,794 bytes unzipped