ntucllab/clmicroimagenet10

Dataset Card for CLMicroImageNet10

This Complementary labeled MicroImageNet10 dataset contains 3 human-annotated complementary labels for all 5000 images in the training split of TinyImageNet200. The workers are from Amazon Mechanical Turk. We randomly sampled 4 different labels for 3 different annotators, so each image would have 3 (probably repeated) complementary labels.

For more details, please visit our github or paper.

Dataset Structure

Data Instances

A sample from the training set is provided below:

{
    'images': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=64x64 at 0x7BC7F6139E40>, 
    'ord_labels': 0, 
    'cl_labels': [7, 8, 7]
}

Data Fields

• images: A PIL.Image.Image object containing the 64x64 image.

• ord_labels: The ordinary labels of the images, and they are labeled from 0 to 9 as follows:

  0: sulphur-butterfly 1: backpack 2: cardigan 3: kimono 4: magnetic-compass 5: oboe 6: scandal 7: torch 8: pizza 9: alp

• cl_labels: Three complementary labels for each image from three different workers.

Annotation Task Design and Deployment on Amazon MTurk

To collect human-annotated labels, we used Amazon Mechanical Turk (MTurk) to deploy our annotation task. The layout and interface design for the MTurk task can be found in the file design-layout-mturk.html.

In each task, a single image was enlarged to 200 x 200 for clarity and presented alongside the question: Choose any one "incorrect" label for this image? Annotators were given four example labels to choose from (e.g., dog, cat, ship, bird), and were instructed to select the one that does not correctly describe the image.