12k labelled instances of dogs in-the-wild with 2D keypoint and segmentations.
Dataset released with our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.
To understand how the dataset can be used, please read demo.ipynb.
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All annotations, segmentations and metadata are sourced in a single .json file for ease of download. However, you will also need to download the Stanford Dogs Dataset dataset to access the raw images.
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For segmentation decoding, install
pycocotools
python -m pip install "git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI"
- The
demo.ipynbcode is trivial to adapt to work with the full StanfordExtra dataset, by editing the line
img_dir = "sample_imgs" # Edit this to the location of the extracted tar file (e.g. /.../Images).
To download the dataset, please fill in this Google form.
You may also find the other datasets useful for your animal work:
We are also delighted to hear about your animal related activities! Please do visit Ben's webpage to get in touch.
If you make use of this annotation dataset, please cite the following paper:
@inproceedings{biggs2020wldo,
title={{W}ho left the dogs out: {3D} animal reconstruction with expectation maximization in the loop},
author={Biggs, Benjamin and Boyne, Oliver and Charles, James and Fitzgibbon, Andrew and Cipolla, Roberto},
booktitle={ECCV},
year={2020}
}
and the Stanford Dog Dataset from which the images are derived:
@inproceedings{KhoslaYaoJayadevaprakashFeiFei_FGVC2011,
author = "Aditya Khosla and Nityananda Jayadevaprakash and Bangpeng Yao and Li Fei-Fei",
title = "Novel Dataset for Fine-Grained Image Categorization",
booktitle = "First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition",
year = "2011",
month = "June",
address = "Colorado Springs, CO",
}
(c) Benjamin Biggs, Oliver Boyne, James Charles, Andrew Fitzgibbon and Roberto Cipolla. Department of Engineering, University of Cambridge 2020
By downloading this dataset, you agree to the Creative Commons Attribution-NonCommercial 4.0 International license. This license allows users to use, share and adapt the dataset, so long as credit is given to the authors (e.g. by citation) and the dataset is not used for any commercial purposes.
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