Oxford Pets: Shiba Inu

Image classification, object detection, image segmentation and instance segmentation datasets containing 37 categories of cats and dogs.

Conversion from original data:

  • Download the images and annotations archives and extract them, resulting in the annotations and images directories.

  • Create output directory at the same level as extracted archives.

  • Create virtual environment using these requirements:

    • python3 -m venv venv

    • ./venv/bin/pip install -r requirements.txt

  • Classification

    • Run the classification.py Python script: ./venv/bin/python classification.py . output

    • The output/subdir directory contains the image classification dataset

  • Object detection (head ROI)

    • Run the object_detection.py Python script: ./venv/bin/python object_detection.py . output

    • The output/voc-head directory contains the object detection dataset with the head ROI annotations

  • Object detection, image/instance segmentation (animal ROI)

    • Run the segmentation.py Python script: ./venv/bin/python segmentation.py . output

    • The output/bluechannel directory contains the image segmentation dataset

    • The output/adams directory contains the instance segmentation dataset in ADAMS format

    • Convert the ADAMS annotations into MS COCO using the wai.annotations library:

      wai-annotations convert from-adams-od -i "./adams/*.report" to-coco-od -o ./coco/annotations.json --sort-categories --category-output-file labels.txt

    • Convert the ADAMS annotations into VOC using the wai.annotations library:

      wai-annotations convert from-adams-od -i "./adams/*.report" coerce-box to-voc-od -o ./voc-animal


CC-BY-SA 4.0


O. M. Parkhi, A. Vedaldi, A. Zisserman, C. V. Jawahar: Cats and Dogs, IEEE Conference on Computer Vision and Pattern Recognition, 2012.