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Dataset Card for Animals with Attributes 2 (AwA2)
Dataset Details
Dataset Description
The Animals with Attributes 2 (AwA2) dataset is originally designed for zero-shot learning (ZSL), transfer learning, and attribute-based classification. It consists of 37,322 images across 50 animal classes, making it a widely used benchmark for recognizing unseen categories using shared semantic attributes.
However, in our repository, we use AwA2 as a standard image classification dataset, focusing on image-label pairs rather than attribute-based learning. The dataset provides images with their respective animal class labels, enabling supervised learning and evaluation of traditional deep learning models.
- License: Public domain images with individual license files in the archive.
Dataset Sources
- Homepage: https://cvml.ista.ac.at/AwA2/
- Paper: Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2018). Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. IEEE transactions on pattern analysis and machine intelligence, 41(9), 2251-2265.
Dataset Structure
Total images: 37,322
Classes: 50 animal categories
Image specs: JPEG format, RGB images
Class Labels: antelope, grizzly bear, killer whale, beaver, dalmatian, persian cat, horse, german shepherd, blue whale, siamese cat, skunk, mole, tiger, hippopotamus, leopard, moose, spider monkey, humpback whale, elephant, gorilla, ox, fox, sheep, seal, chimpanzee, hamster, squirrel, rhinoceros, rabbit, bat, giraffe, wolf, chihuahua, rat, weasel, otter, buffalo, zebra, giant panda, deer, bobcat, pig, lion, mouse, polar bear, collie, walrus, raccoon, cow, dolphin.
Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("randall-lab/awa2", split="test", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]
image.show() # Display the image
print(f"Label: {label}")
Citation
BibTeX:
@article{xian2018zero, title={Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly}, author={Xian, Yongqin and Lampert, Christoph H and Schiele, Bernt and Akata, Zeynep}, journal={IEEE transactions on pattern analysis and machine intelligence}, volume={41}, number={9}, pages={2251--2265}, year={2018}, publisher={IEEE} }
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