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---
license: apache-2.0
task_categories:
- image-classification
language:
- en
size_categories:
- 10K<n<100K
pretty_name: DogPoseCV
---
# Dataset Card for DogPoseCV
This dataset contains 20,578 images of dogs in various poses, labeled as `standing`, `sitting`, `lying down`, or `undefined`. It is intended for computer vision tasks to identify a dog's behavior from images.
## Dataset Details
- **Curated by:** Jason Stock and Tom Cavey, Computer Science, Colorado State University
- **Paper:** [arxiv.org/abs/2101.02380](https://arxiv.org/abs/2101.02380) ([BibTeX](#citation))
- **Repository:** [github.com/stockeh/canine-embedded-ml](https://github.com/stockeh/canine-embedded-ml)
The dataset is intended to be used to train computer vision models to identify a dog's pose/behavior (standing, sitting, lying down) from images. This can enable applications to automatically detect and respond to a dog's actions. The variety of dog breeds enables robust generalization for real-time inference of dog actions.
### Dataset Structure
The dataset contains 20,578 RGB images of 120 dog breeds. Images are labeled as one of four classes:
- standing (4143 images)
- sitting (3038 images)
- lying down (7090 images)
- undefined (6307 images)
Images have varying resolutions, with 50% between 361x333 and 500x453 pixels.
#### Data Collection and Processing
This dataset is an adaption of from the [Stanford Dog Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/), relabeling dog breeds to their associated position. We manually labeled each image as `standing`, `sitting`, `lying down`, or `undefined` if the pose was indistinguishable, e.g., between two positions.
## Bias, Risks, and Limitations
The dataset has a class imbalance, with nearly 2x as many "lying down" images compared to "sitting". Indistinguishable poses were labeled as "undefined", with most being close-up portraits. This may limit the ability to handle such images.
**Recommendations**: When using this dataset, be aware of the class imbalance and consider oversampling or augmentation techniques..
## Citation
```
@article{stock2021s,
title={Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine Learning},
author={Stock, Jason and Cavey, Tom},
journal={arXiv preprint arXiv:2101.02380},
year={2021}
}
``` |