Published November 12, 2020
| Version v2
Dataset
Open
mini CURE-OR
Description
The webpage associated with this dataset can be found here.
File descriptions
- train.zip - the training set
- test.zip - the test set
- train.csv - the ground truth for the training images with the following information: imageID, class, background, perspective, challengeType, challengeLevel
- test.csv - the ground truth for the training images with the following information: imageID, class, background, perspective, challengeType, challengeLevel
Data fields
- imageID - an anonymous id unique to a given image
- class - the class of the object in the given image: 1-10
- 1: Canon camera
- 2: Training marker cone
- 3: Baseball
- 4: Pan
- 5: Toy
- 6: LG Cell phone
- 7: Hair brush
- 8: DYMO Label maker
- 9: Calcium bottle
- 10: Shoes
- background - the background of the object
- 1: 2D white
- 2: 2D living room
- 3: 2D kitchen
- perspective - the perspective/orientation of the object
- 1: Front
- 2: Left side - 90 degrees
- 3: Back - 180 degrees
- 4: Right side - 270 degrees
- 5: Top
- challengeType - the type of generated challenging conditions
- 01: 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 - the level of generated challenging conditions
- 0: No challenge (01) and Grayscale (10) only - no challenge level
- 1 - 4: the degree of a challenge from least to most
Files
test.csv
Additional details
Related works
- Is referenced by
- Conference paper: 10.1109/ICMLA.2018.00028 (DOI)
- Conference paper: 10.1109/ICIP.2019.8803317 (DOI)
References
- D. Temel*, J. Lee*, and G. AlRegib, "CURE-OR: Challenging Unreal and Real Environments for Object Recognition," in IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, Dec. 2018
- D. Temel*, J. Lee*, and G. AlRegib, "Object Recognition Under Multifarious Conditions: A Reliability Analysis and a Feature Similarity-Based Performance Estimation," in IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, Sep. 2019