CURE-OR-Sampled: Challenging Unreal and Real Environments for Object Recognition

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
• sample_submission.csv - a sample submission file in the correct format with column headers of imageID and class

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

For more information about CURE-OR dataset, please refer to the webpage.