imagenet2012

  • Description:

ILSVRC 2012, aka ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy.

Note that labels were never publicly released for the test set, so we only include splits for the training and validation sets here.

  • Homepage: http://image-net.org/
  • Source code: tfds.image.imagenet.Imagenet2012
  • Versions:
    • 5.0.0 (default): New split API (https://tensorflow.org/datasets/splits)
    • 2.0.1: No release notes.
  • Download size: 144.02 GiB
  • 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/manual/imagenet2012/):
    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): No
  • Splits:
Split Examples
'train' 1,281,167
'validation' 50,000
  • Features:
FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})
@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}
}