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ImageNet Large Scale Visual Recognition Challenge 2017 (ILSVRC2017)

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Citation

When using the DET or CLS-LOC dataset, please cite:
    Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. arXiv:1409.0575, 2014. paper | bibtex
Development kit

Please be sure to consult the included readme.txt file for competition details. Additionally, the development kit includes

  • Overview and statistics of the data.
  • Meta data for the competition categories.
  • Matlab routines for evaluating submissions.

Object localization


This dataset is unchanged since ILSVRC2012. There are a total of 1,281,167 images for training. The number of images for each synset (category) ranges from 732 to 1300. There are 50,000 validation images, with 50 images per synset. There are 100,000 test images. All images are in JPEG format.

Object detection


    DET dataset. 55GB. MD5: 237b95a860e9637b6a27683268cb305a

    The dataset is unchanged from ILSVRC2016. There are a total of 456567 images for training. The number of positive images for each synset (category) ranges from 461 to 67513. The number of negative images ranges from 42945 to 70626 per synset. There are 20121 validation images and 60000 test images. All images are in JPEG format.

    DET test dataset(new). 428MB. MD5: e9c3df2aa1920749a7ec35d1847280c6

    The file only contains 5500 new images and the test.txt file.

Object detection from video


    Please download from here.

Taster challenges with amazon bin image dataset


    Please download from here.

Terms of use: by downloading the image data from the above URLs, you agree to the following terms:

  1. You will use the data only for non-commercial research and educational purposes.
  2. You will NOT distribute the above URL(s).
  3. Stanford University and Princeton University and UNC Chapel Hill and MIT make no representations or warranties regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose.
  4. You accept full responsibility for your use of the data and shall defend and indemnify Stanford University and Princeton University and UNC Chapel Hill and MIT, including their employees, officers and agents, against any and all claims arising from your use of the data, including but not limited to your use of any copies of copyrighted images that you may create from the data.