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controlled_noisy_web_labels

  • Description:

Controlled Noisy Web Labels is a collection of ~212,000 URLs to images in which every image is carefully annotated by 3-5 labeling professionals by Google Cloud Data Labeling Service. Using these annotations, it establishes the first benchmark of controlled real-world label noise from the web.

We provide the Red Mini-ImageNet (real-world web noise) and Blue Mini-ImageNet configs: - controlled_noisy_web_labels/mini_imagenet_red - controlled_noisy_web_labels/mini_imagenet_blue

Each config contains ten variants with ten noise-levels p from 0% to 80%. The validation set has clean labels and is shared across all noisy training sets. Therefore, each config has the following splits:

  • train_00
  • train_05
  • train_10
  • train_15
  • train_20
  • train_30
  • train_40
  • train_50
  • train_60
  • train_80
  • validation

The details for dataset construction and analysis can be found in the paper. All images are resized to 84x84 resolution.

  1. Download the splits and the annotations here
  2. Extract dataset_no_images.zip to dataset_no_images/.
  3. Download all images in dataset_no_images/mini-imagenet-annotations.json into a new folder named dataset_no_images/noisy_images/. The output filename must agree with the image id provided in mini-imagenet-annotations.json. For example, if "image/id": "5922767e5677aef4", then the downloaded image should be dataset_no_images/noisy_images/5922767e5677aef4.jpg. 4.Register on https://image-net.org/download-images and download ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar.

The resulting directory structure may then be processed by TFDS:

  • dataset_no_images/
    • mini-imagenet/
    • class_name.txt
    • split/
      • blue_noise_nl_0.0
      • blue_noise_nl_0.1
      • ...
      • red_noise_nl_0.0
      • red_noise_nl_0.1
      • ...
      • clean_validation
    • mini-imagenet-annotations.json
  • ILSVRC2012_img_train.tar
  • ILSVRC2012_img_val.tar
  • noisy_images/

    • 5922767e5677aef4.jpg
  • Auto-cached (documentation): No

  • Feature structure:

FeaturesDict({
    'id': Text(shape=(), dtype=object),
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'is_clean': bool,
    'label': ClassLabel(shape=(), dtype=int64, num_classes=100),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
id Text object
image Image (None, None, 3) uint8
is_clean Tensor bool
label ClassLabel int64
@inproceedings{jiang2020beyond,
  title={Beyond synthetic noise: Deep learning on controlled noisy labels},
  author={Jiang, Lu and Huang, Di and Liu, Mason and Yang, Weilong},
  booktitle={International Conference on Machine Learning},
  pages={4804--4815},
  year={2020},
  organization={PMLR}
}

controlled_noisy_web_labels/mini_imagenet_red (default config)

  • Dataset size: 1.19 GiB

  • Splits:

Split Examples
'train_00' 50,000
'train_05' 50,000
'train_10' 50,000
'train_15' 50,000
'train_20' 50,000
'train_30' 49,985
'train_40' 50,010
'train_50' 49,962
'train_60' 50,000
'train_80' 50,008
'validation' 5,000

Visualization

controlled_noisy_web_labels/mini_imagenet_blue

  • Dataset size: 1.39 GiB

  • Splits:

Split Examples
'train_00' 60,000
'train_05' 60,000
'train_10' 60,000
'train_15' 60,000
'train_20' 60,000
'train_30' 60,000
'train_40' 60,000
'train_50' 60,000
'train_60' 60,000
'train_80' 60,000
'validation' 5,000

Visualization