B2B AI Training Dataset

16K+ labeled pet stool condition image dataset

B2B data for AI research teams and pet health product teams. Review the data structure, fields, and quality documents first, then discuss licensing.

This dataset is for research, education, ML development, and service PoCs. Poop bounding boxes within images are not provided, and the dataset does not replace medical or veterinary diagnosis.

Review representative records right away

Representative data preview

Ways to use pet image data for AI training

Use dog and cat stool images with label data for computer vision model training, image classification research, and AI image training data review. The source images and metadata are suitable for research institutes, university labs, pet health R&D teams, and data buyers to review quality.

  • AI research at institutes and universities - Research institutes, universities, and graduate labs can use the data to train and validate computer vision models.
  • Graduate theses and industry academic projects - Use the image labeling data for cross-disciplinary research in AI, data science, and veterinary medicine.
  • Computer vision image classification - Train and compare models that classify color and shape labels in pet stool images.
  • Pet health R&D and product PoCs - Pet healthcare companies and veterinary R&D teams can use the data for observation support service PoCs.
  • Label validation for AI and data teams - Review preprocessing, label consistency, and error analysis for AI training image datasets.
  • AI data sourcing and procurement review - Companies, institutions, and data purchasing teams can review source data and labeled data.

Quarterly records and coverage

Record counts are based on annotation records included in each year and quarter. Because the data is entered directly by users, it may differ from the actual condition or may not be fully accurate. Coverage means the share of records with actual values, excluding null, the "null" string, and empty values. This dataset is intended as reference material for research, education, and ML development, and it does not replace standalone medical diagnosis or treatment decisions. Additional domain validation and filtering are recommended before use.

Quarterly annotation records

  • 16K+ Labeled image scale - Images are provided together with condition labels.
  • Image + metadata Review fields - Review key fields including petType, breed, color, and shape.
  • Quarterly report Quality signal - Representative label ranges and quality review details are provided.
  • 2022 1Q: 99 records, 2022 2Q: 546 records, 2022 3Q: 542 records, 2022 4Q: 774 records
  • 2023 1Q: 1,387 records, 2023 2Q: 1,845 records, 2023 3Q: 2,629 records, 2023 4Q: 2,675 records
  • 2024 1Q: 2,647 records, 2024 2Q: 2,922 records

Field-level coverage

  • Birth date (petBirth): 99.43% - 15,975 records / missing 91. Records where petBirth contains a real date string
  • Body size (petSize): 87.41% - 14,044 records / missing 2,022. Records entered as small, medium, or large
  • Hair length (petHair): 92.80% - 14,910 records / missing 1,156. Records entered as l or s
  • Neutered (neuterYn): 99.80% - 16,034 records / missing 32. Records entered as y or n
  • Stool color (poopColor): 81.71% - 13,128 records / missing 2,938. Records with a non-null color label
  • Stool shape (poopShape): 96.79% - 15,550 records / missing 516. Records with a non-null shape or condition label

Actual enum value distribution

Animal type (petType)

Four raw petType values

  • dog: 62.91% - 10,107 records
  • cat: 36.99% - 5,943 records
  • small_animal: 0.05% - 8 records
  • other: 0.05% - 8 records

Sex (petSex)

Two raw petSex values

  • f: 52.74% - 8,474 records
  • m: 47.26% - 7,592 records

Body size (petSize)

Body-size values and null share

  • small: 50.20% - 8,065 records
  • medium: 26.55% - 4,266 records
  • large: 10.66% - 1,713 records
  • "null": 12.59% - 2,022 records

Hair length (petHair)

Hair-length values and null share

  • l: 49.50% - 7,953 records
  • s: 43.30% - 6,957 records
  • "null": 7.20% - 1,156 records

Neutered (neuterYn)

Neuter flag values and null share

  • y: 67.29% - 10,811 records
  • n: 32.51% - 5,223 records
  • "null": 0.20% - 32 records

Stool color (poopColor)

Full color-label distribution

  • Chocolate: 75.48% - 12,126 records
  • "null": 18.29% - 2,938 records
  • Yellow: 3.08% - 495 records
  • Black: 1.53% - 245 records
  • Bloody: 1.18% - 189 records
  • Green: 0.43% - 69 records
  • Purple: 0.02% - 4 records

Stool shape (poopShape)

Full shape and condition-label distribution

  • NORMAL_1: 64.45% - 10,354 records
  • DIARRHEA_2: 10.18% - 1,635 records
  • NORMAL_2: 8.65% - 1,389 records
  • DIARRHEA_1: 5.52% - 887 records
  • DRY_1: 3.53% - 567 records
  • "null": 3.21% - 516 records
  • DIARRHEA_4: 2.88% - 462 records
  • DIARRHEA_3: 1.59% - 256 records

Review the data package and ask about licensing

Email us with your purchase purpose and intended use. We will review the scope we can provide.

Business information

  • Business name: Barabom
  • Representative: KimJeonghun
  • Business registration number: 460-08-02680
  • Business address: 50 Haedeung-ro, Dobong-gu, Seoul, Republic of Korea
  • Business type/item: Information and communication / Application software publishing
  • Mail-order business report number: 제 2026-서울도봉-0268 호
  • Customer center: +82 10-2286-9185

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