CURE-OR: Challenging Unreal and Real Environment for Object Recognition

CURE-OR: Challenging Unreal and Real Environment for Object Recognition

Citation Author(s):
Dogancan
Temel
Georgia Institute of Technology
Jinsol
Lee
Georgia Institute of Technology
Ghassan
AlRegib
Georgia Institute of Technology
Submitted by:
Dogancan Temel
Last updated:
Sun, 10/13/2019 - 17:05
DOI:
10.21227/h4fr-h268
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE-OR dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives.  Please refer to our GitHub page for code, papers, and more information.

Instructions: 

 

 

Image name format : 

"backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg"

 

backgroundID: 

1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office

 

 

objectOrientationID: 

1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top

 

 

objectID:

 1-100

 

 

challengeType: 

No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise

challengeLevel: 

A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.

 

 

 

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[1] Dogancan Temel, Jinsol Lee, Ghassan AlRegib, "CURE-OR: Challenging Unreal and Real Environment for Object Recognition", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/h4fr-h268. Accessed: Feb. 18, 2020.
@data{h4fr-h268-19,
doi = {10.21227/h4fr-h268},
url = {http://dx.doi.org/10.21227/h4fr-h268},
author = {Dogancan Temel; Jinsol Lee; Ghassan AlRegib },
publisher = {IEEE Dataport},
title = {CURE-OR: Challenging Unreal and Real Environment for Object Recognition},
year = {2019} }
TY - DATA
T1 - CURE-OR: Challenging Unreal and Real Environment for Object Recognition
AU - Dogancan Temel; Jinsol Lee; Ghassan AlRegib
PY - 2019
PB - IEEE Dataport
UR - 10.21227/h4fr-h268
ER -
Dogancan Temel, Jinsol Lee, Ghassan AlRegib. (2019). CURE-OR: Challenging Unreal and Real Environment for Object Recognition. IEEE Dataport. http://dx.doi.org/10.21227/h4fr-h268
Dogancan Temel, Jinsol Lee, Ghassan AlRegib, 2019. CURE-OR: Challenging Unreal and Real Environment for Object Recognition. Available at: http://dx.doi.org/10.21227/h4fr-h268.
Dogancan Temel, Jinsol Lee, Ghassan AlRegib. (2019). "CURE-OR: Challenging Unreal and Real Environment for Object Recognition." Web.
1. Dogancan Temel, Jinsol Lee, Ghassan AlRegib. CURE-OR: Challenging Unreal and Real Environment for Object Recognition [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/h4fr-h268
Dogancan Temel, Jinsol Lee, Ghassan AlRegib. "CURE-OR: Challenging Unreal and Real Environment for Object Recognition." doi: 10.21227/h4fr-h268