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--- |
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license: apache-2.0 |
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task_categories: |
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- image-classification |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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pretty_name: DogPoseCV |
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--- |
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# Dataset Card for DogPoseCV |
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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. |
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## Dataset Details |
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- **Curated by:** Jason Stock and Tom Cavey, Computer Science, Colorado State University |
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- **Paper:** [arxiv.org/abs/2101.02380](https://arxiv.org/abs/2101.02380) ([BibTeX](#citation)) |
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- **Repository:** [github.com/stockeh/canine-embedded-ml](https://github.com/stockeh/canine-embedded-ml) |
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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. |
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### Dataset Structure |
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The dataset contains 20,578 RGB images of 120 dog breeds. Images are labeled as one of four classes: |
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- standing (4143 images) |
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- sitting (3038 images) |
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- lying down (7090 images) |
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- undefined (6307 images) |
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- |
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Images have varying resolutions, with 50% between 361x333 and 500x453 pixels. |
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#### Data Collection and Processing |
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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. |
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## Bias, Risks, and Limitations |
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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. |
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**Recommendations**: When using this dataset, be aware of the class imbalance and consider oversampling or augmentation techniques if needed. |
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## Citation |
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``` |
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@article{stock2021s, |
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title={Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine Learning}, |
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author={Stock, Jason and Cavey, Tom}, |
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journal={arXiv preprint arXiv:2101.02380}, |
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year={2021} |
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} |
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``` |
