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  • Description:

ImageNet-PI is a relabelled version of the standard ILSVRC2012 ImageNet dataset in which the labels are provided by a collection of 16 deep neural networks with different architectures pre-trained on the standard ILSVRC2012. Specifically, the pre-trained models are downloaded from tf.keras.applications.

In addition to the new labels, ImageNet-PI also provides meta-data about the annotation process in the form of confidences of the models on their labels and additional information about each model.

For more information see: ImageNet-PI

Split Examples
  • Feature structure:
    'annotator_confidences': Tensor(shape=(16,), dtype=float32),
    'annotator_labels': Tensor(shape=(16,), dtype=int64),
    'clean_label': ClassLabel(shape=(), dtype=int64, num_classes=1000),
    'file_name': Text(shape=(), dtype=string),
    'image': Image(shape=(None, None, 3), dtype=uint8),
  • Feature documentation:
Feature Class Shape Dtype Description
annotator_confidences Tensor (16,) float32
annotator_labels Tensor (16,) int64
clean_label ClassLabel int64
file_name Text string
image Image (None, None, 3) uint8
  author    = {Mark Collier and
               Rodolphe Jenatton and
               Effrosyni Kokiopoulou and
               Jesse Berent},
  editor    = {Kamalika Chaudhuri and
               Stefanie Jegelka and
               Le Song and
               Csaba Szepesv{\'{a} }ri and
               Gang Niu and
               Sivan Sabato},
  title     = {Transfer and Marginalize: Explaining Away Label Noise with Privileged
  booktitle = {International Conference on Machine Learning, {ICML} 2022, 17-23 July
               2022, Baltimore, Maryland, {USA} },
  series    = {Proceedings of Machine Learning Research},
  volume    = {162},
  pages     = {4219--4237},
  publisher = { {PMLR} },
  year      = {2022},
  url       = {},
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal   = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},