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imagenet_pi

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

Homepage: https://github.com/google-research-datasets/imagenet_pi/
Source code: tfds.datasets.imagenet_pi.Builder
Versions:
- 1.0.0 (default): Initial release.
Download size: Unknown size
Dataset size: Unknown size
Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
manual_dir should contain two files: ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar. You need to register on http://www.image-net.org/download-images in order to get the link to download the dataset.
Auto-cached (documentation): Unknown
Splits:

Split	Examples

Feature structure:

FeaturesDict({
    '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
	FeaturesDict
annotator_confidences	Tensor	(16,)	float32
annotator_labels	Tensor	(16,)	int64
clean_label	ClassLabel		int64
file_name	Text		string
image	Image	(None, None, 3)	uint8

Supervised keys (See as_supervised doc): ('image', 'annotator_labels')
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe): Missing.
Citation:

@inproceedings{tram,
  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
               Information},
  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       = {https://proceedings.mlr.press/v162/collier22a.html},
}
@article{ILSVRC15,
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},
volume={115},
number={3},
pages={211-252}
}