SIFT10M
Donated on 2/22/2016
In SIFT10M, each data point is a SIFT feature which is extracted from Caltech-256 by the open source VLFeat library. The corresponding patches of the SIFT features are provided.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Causal-Discovery
Feature Type
Integer
# Instances
11164866
# Features
-
Dataset Information
Additional Information
In SIFT10M, the titles of the png files indicate the columns position of the SIFT features. This data set has been used for evaluating the approximate nearest neighbour search methods. The patches can be used for visualisation purpose and helps for analysing the performance of the corresponding approximate nearest neighbour search methods.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
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| no |
0 to 10 of 128
Additional Variable Information
Each SIFT feature is a 128D column, and the corresponding patch is saved in 41*41 png format. The png files are compressed into 307 tar files for downloading.
Dataset Files
| File | Size |
|---|---|
| SIFT10M.tar.gz | 7.3 GB |
| README.txt | 1.3 KB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset sift10m = fetch_ucirepo(id=353) # data (as pandas dataframes) X = sift10m.data.features y = sift10m.data.targets # metadata print(sift10m.metadata) # variable information print(sift10m.variables)
Fu, X., McCane, B., Mills, S., Albert, M., & Szymanski, L. (2016). SIFT10M [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5S603.
Creators
Xiping Fu
Brendan McCane
Steven Mills
Michael Albert
Lech Szymanski
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.