| --- |
| license: cc-by-4.0 |
| task_categories: |
| - object-detection |
| - image-segmentation |
| language: |
| - en |
| pretty_name: ShitSpotter |
| --- |
| # Dataset Card for ShitSpotter ("ScatSpotter") |
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| ShitSpotter (or "ScatSpotter" in formal settings) is an open dataset of images containing dog feces. |
| This dataset contains full-resolution smartphone images of dog feces ("poop") collected in urban outdoor |
| environments taken using a "before/after/negative" protocol. |
| It includes thousands of polygon annotations of feces in varied lighting, seasonal, and terrain conditions. |
| The dataset is designed for training and evaluating object detection and segmentation models, |
| with potential applications in public sanitation, AR-assisted navigation, and pet owner assistance. |
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| This dataset card was generated via ChatGPT (based on existing documentation) and edited by the author. |
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| ## Dataset Details |
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| ### Dataset Description |
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| <!-- Provide a longer summary of what this dataset is. --> |
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| Images were collected using a smartphone and annotated with manually or AI-assisted drawn polygons using the Segment Anything Model (SAM). |
| The goal is to support the development of systems that can detect and segment dog feces in real-world environments. |
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| The dataset is publicly released with open-source licenses and is distributed via IPFS, BitTorrent, |
| a centralized Girder server, and HuggingFace. |
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| - **Curated by:** Jonathan Crall |
| - **Funded by:** Not funded; this is a self-motivated project. |
| - **Shared by:** Kitware (institutional affiliation of the author) |
| - **License:**, Code: Apache 2.0, Data: Creative Commons Attribution 4.0 International (CC BY 4.0) |
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| ### Dataset Sources |
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| - **Repository:** https://github.com/Erotemic/shitspotter |
| - **Paper:** Preprint: https://www.arxiv.org/abs/2412.16473 |
| - **Demo:** No live demo at this time. (Attempting a react-native-vision-camera app, but progress is slow. Help wanted.) |
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| ## Uses |
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| ### Direct Use |
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| The dataset is intended for training, evaluating, and benchmarking computer vision algorithms, particularly: |
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| * Object detection and segmentation (single-class) |
| * Dataset analysis, robustness testing |
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| Trained models could enable: |
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| * Deployment in mobile or AR applications for locating feces |
| * Urban cleanliness monitoring by municipalities |
| * AR poop collision warning systems |
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| ### Out-of-Scope Use |
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| The images are high resolution, but the poops themselves sometimes are not. |
| There are images from multiple scales, but the majority occupy smaller areas of the image. |
| Thus more fine-grained analysis of the poops themselves may not be possible. |
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| The dataset was not collected or labeled with health diagnostics in mind, |
| so caution should be used when applying it to medical contexts. |
| However, it does contain healthy and sick poops, the data may still offer |
| useful signals for future health-related research if properly validated. |
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| As with any open dataset, respectful use is expected. |
| This dataset was created to advance research and practical applications |
| in public health, computer vision, and sanitation. |
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| ## Dataset Structure |
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| The dataset consists of RGB JPEG images (mostly 4032×3024), many with EXIF metadata and kwcoco-format metadata. |
| Most data follows a “before/after/negative” triplet protocol: |
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| “Before” image: includes visible feces |
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| “After” image: same scene after cleanup (used as high-correlation negative) |
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| “Negative” image: a different, nearby image without feces (used as low-correlation negative) |
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| Only the “before” images contain annotations. Annotations are polygons in COCO-style JSON format. |
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| Data is split into: |
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| * Train: Author collected images from 2021–2023 and 2/3 of 2024, and 2025+. |
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| * Val: Author collected from 2020 and 1/3 of 2024 |
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| * Test: 121 contributed images with annotations, held out for evaluation |
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| ## Dataset Creation |
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| ### Curation Rationale |
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| The dataset began after a real-world frustration: the creator couldn’t find their dog’s poop in a leaf-covered park. |
| This sparked the idea of building a vision model to solve the problem and contribute a novel, underexplored dataset to the ML community. |
| The dataset serves practical, research, and humorous goals. |
| It also explores data distribution over IPFS and the lifecycle of living datasets. |
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| ### Source Data |
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| #### Data Collection and Processing |
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| Collected with a Pixel 5 and other smartphones |
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| Annotation tool: LabelMe |
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| AI-assistance: Segment Anything Model (SAM), trained detectron models (always manually reviwed / edited). |
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| Preprocessing includes optional alignment of image pairs (not always reliable) |
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| #### Who are the source data producers? |
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| The vast majority of images were taken by the dataset curator. |
| A few contributors have added images, which are separately labeled. |
| Images were taken during normal dog walks in parks and sidewalks. |
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| ### Annotations |
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| #### Annotation process |
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| Annotations were created in LabelMe. |
| Some were SAM-initialized using click prompts and corrected manually. |
| Some were seeded with a detectron model. |
| Some were manually drawn. |
| Shadows and similar-looking natural objects are known challenges. |
| All annotations are manually reviewed. |
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| #### Who are the annotators? |
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| The dataset creator; contributors may annotate in future. |
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| #### Personal and Sensitive Information |
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| Some EXIF location metadata is stripped, but many images are unmodified. |
| Consent was received to publish EXIF metadata along with these images. |
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| ## Bias, Risks, and Limitations |
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| Geographic bias: Mostly Upstate NY. |
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| Sensor bias: Primarily Google Pixel 5 |
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| Collector/dog bias: Same person and 3 dogs over time |
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| Bias in freshness, specific dogs, and lighting: More recent, fresh, daytime |
| images with poops from the author's dogs dominate. |
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| ### Recommendations |
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| When applying models trained on this dataset to new domains (e.g., different locations, animals, lighting), |
| expect a performance gap unless domain adaptation is applied. |
| Future work may include generalizing across feces types. |
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| ## Citation |
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| The current paper is not peer-reviewed. When a peer-reviewed version of the |
| paper is published we will update the citation. |
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| **BibTeX:** |
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| ``` |
| @article{crall2024scatspotter, |
| title={"ScatSpotter" 2024 — A Distributed Dog Poop Detection Dataset}, |
| author={Crall, Jonathan}, |
| journal={arXiv preprint arXiv:2412.16473}, |
| year={2024} |
| } |
| ``` |
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| **APA:** |
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| Crall, J. (2024). "ScatSpotter" 2024 — A Distributed Dog Poop Detection Dataset. arXiv:2412.16473. |
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| ## Glossary |
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| B/A/N Protocol: Before/After/Negative triplets for contrastive learning |
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| IPFS: InterPlanetary File System, a decentralized, content-addressable data store |
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| Polygon annotation: High-precision mask of object shape |
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| ## More Information |
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| Dataset access via: IPFS, BitTorrent, and Girder |
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| Datasheet: Available in repo |
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| 2024 Paper Preprint: https://www.arxiv.org/abs/2412.16473 |
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| Torrent: https://academictorrents.com/details/ee8d2c87a39ea9bfe48bef7eb4ca12eb68852c49 |
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| IPNS address: /ipns/k51qzi5uqu5dje1ees96dtsoslauh124drt5ajrtr85j12ae7fwsfhxb07shit |
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| Recent IPFS CID: /ipfs/bafybeihsd6rwjha4kbeluwdjzizxshrkcsynkwgjx7fipm5pual6eexax4 |
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| Girder Mirror: https://data.kitware.com/#user/598a19658d777f7d33e9c18b/folder/66b6bc34f87a980650f41f90 |
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| ## Dataset Card Contact |
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| GitHub Issues: https://github.com/Erotemic/shitspotter/issues |
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| Email: crall.vision@gmail.com |
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