Published November 12, 2020 | Version v2
Dataset Open

mini CURE-OR

  • 1. Georgia Institute of Technology

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

Files (3.4 GB)

Name Size Download all
md5:4c0c7330fd8f312d1ccacbac1f2a288a
109.5 kB Preview Download
md5:b495d36b5df29b18584d01c25c0bfefd
1.5 GB Preview Download
md5:a2489e30a7e01fc022d0c4924de9f5e6
153.4 kB Preview Download
md5:c370f94d0ba9f90f9abc75a5d1a2aca5
1.9 GB Preview Download

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