An estimated ** million households in the United States owned at least one dog according to a 2024/25 pet owners survey, making them the most widely owned type of pet across the U.S. at this time. Cats and freshwater fish ranked in second and third places, with around ** million and ** million households owning such pets, respectively. Freshwater vs. salt water fish Freshwater fish spend most or all their lives in fresh water. Fresh water’s main difference to salt water is the level of salinity. Freshwater fish have a range of physiological adaptations to enable them to live in such conditions. As the statistic makes clear, Americans keep a large number of freshwater aquatic species at home as pets. American pet owners In 2023, around ** percent of all households in the United States owned a pet. This is a decrease from 2020, but still around a ** percent increase from 1988. It is no surprise that as more and more households own pets, pet industry expenditure has also witnessed steady growth. Expenditure reached over *** billion U.S. dollars in 2022, almost a sixfold increase from 1998. The majority of pet product sales are still made in brick-and-mortar stores, despite the rise and evolution of e-commerce in the United States.

Dataset Summary

Mini(24 MB) Classification dataset for mini projects. Cats, dogs and rabbit are included as pet in this dataset.

  Supported Tasks and Leaderboards

image-classification: Based on a pet image, the goal of this task is to predict the type of pet (i.e., dog or cat or rabbit).

  Languages

English

  Class Label Mappings:

{ "cat": 0, "dog": 1, "rabbit": 2, }

  Load Dataset

from datasets import load_dataset

train_dataset =… See the full description on the dataset page: https://huggingface.co/datasets/rokmr/pets.

Pet Health Symptoms Dataset

  Overview

This dataset contains 2,000 LLM-generated pet health symptoms text samples covering 5 common pet health condition categories, designed to train ML models for automated pet health classification. Each entry is labeled with:

Pet health condition (1 of 5 distinct classes)
Record type (Owner Observation or Clinical Notes)

Owner observations are expressed in everyday language (e.g., "My cat scratches constantly"), whereas clinical… See the full description on the dataset page: https://huggingface.co/datasets/karenwky/pet-health-symptoms-dataset.

Dataset Card for Cats Vs. Dogs

  Dataset Summary

A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page:

The Asirra data set Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/cats_vs_dogs.

Between 2013 and 2017, Roland Kays et al. convinced hundreds of volunteers in the U.S., U.K., Australia, and New Zealand to strap GPS sensors on their pet cats. The aforelinked datasets include each cat's characteristics (such as age, sex, neuter status, hunting habits) and time-stamped GPS pings.

When using this dataset, please cite the original article.

Kays R, Dunn RR, Parsons AW, Mcdonald B, Perkins T, Powers S, Shell L, McDonald JL, Cole H, Kikillus H, Woods L, Tindle H, Roetman P (2020) The small home ranges and large local ecological impacts of pet cats. Animal Conservation. doi:10.1111/acv.12563

Additionally, please cite the Movebank data package:

McDonald JL, Cole H (2020) Data from: The small home ranges and large local ecological impacts of pet cats [United Kingdom]. Movebank Data Repository. doi:10.5441/001/1.pf315732

Values used in the calculation of the fraction (and total) of energy consumed by dogs and cats in the US.

Descriptive statistics of the study participants by their pet ownership status (No pet, Dog, and Cat).

The role of gut microbiomes as important regulators of mammalian health is increasingly recognized, although feline and canine gut microbiomes remain poorly characterized. In this proof-of-concept study, we assessed the utility of a direct-to-consumer approach to executing pet microbiome studies. We characterized the gut microbiomes of 238 pets (46 cats and 192 dogs) by generating ~11 million merged reads that were mapped to the V4 region of 16S ribosomal RNA gene at a sequencing depth of 45,806 (±22,325) reads per sample. Analyses of these reads revealed that both feline and canine gut microbiomes are dominated by three major phyla, namely Firmicutes, Proteobacteria, and Bacteroides and that alpha diversity is higher in the feline gut. In addition to interspecies differences between the feline and canine gut, we also detected appreciable intraspecies bacterial variation within the canine population. While the dogs in this dataset could be assigned to three distinct clusters based on their gut microbiome, no clustering was observed within the feline population. Integration of additional data obtained from survey questionnaires revealed that geography and body weight may be associated with canine gut microbiome composition. Furthermore, we found that both the inter and intraspecies differences are more pronounced at finer taxonomic levels, indicating that strain-level investigations may be necessary in the future. This study demonstrates that the direct-to-consumer approach overcomes existing limitations in pet microbiome research, for example, it allows collection of large numbers of pet samples. The direct-to-consumer approach has proven successful in human genomics as well as human microbiomics and this study demonstrates that by building partnerships with an engaged general public this approach can also propel the field of pet microbiomics forward.