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.

A large set of images of cats and dogs.

Homepage: https://www.microsoft.com/en-us/download/details.aspx?id=54765

Source code: tfds.image_classification.CatsVsDogs

Versions:

4.0.0 (default): New split API (https://tensorflow.org/datasets/splits) Download size: 786.68 MiB

Source: https://www.tensorflow.org/datasets/catalog/cats_vs_dogs

In this competition, you'll write an algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. Your computer will find it a bit more difficult.

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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 (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.

Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:

Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. Image recognition attacks

While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. State of the art

The current literature suggests machine classifiers can score above 80% accuracy on this task [1]. Therfore, Asirra is no longer considered safe from attack. We have created this contest to benchmark the latest computer vision and deep learning approaches to this problem. Can you crack the CAPTCHA? Can you improve the state of the art? Can you create lasting peace between cats and dogs?

Submission Format

Your submission should have a header. For each image in the test set, predict a label for its id (1 = dog, 0 = cat):

id,label 1,0 2,0 3,0 etc...

DALL-E-Cats is a dataset meant to produce a synthetic animal dataset. This is a successor to DALL-E-Dogs. DALL-E-Dogs and DALL-E-Cats will be fed into an image classifier to see how it performs. This is under the BirdL-AirL License.

This model is based on how house cats utilize wildlands near human habituation. These predators can have detrimental effects on wildlife populations (Alterio et al. 1998). We based our model on the data collected by Odell and Knight (2001) that investigated habitat utilization of these predators with regard to distance from housing and on the probability for a homeowner to possess a house cat. We buffered the populated areas distance layer in ARC/INFO using a probability function [P = 0.216 - 0.96 * Distance (km)] where any cell with distance less than 0.18km received a probability between 0.216 to 0. All distances greater than or equal to 0.18km from populated areas were assigned a probability of 0. The resulting dataset was then resampled to 180m using the bilinear interpolation option.

Free-roaming domestic cats (Felis catus) are known to pose threats to ecosystem health via transmission of zoonotic diseases and predation of native wildlife. Likewise, free-roaming cats are also susceptible to predation or disease transmission from native wildlife. Physical interactions are required for many of these risks to be manifested, necessitating spatial and temporal overlap between cats and wildlife species. Therefore, knowledge of the location and extent of shared habitat and activity periods would benefit management programs. We used data from a 3-year camera trap survey to model species-specific occupancy and identify landscape variables that contribute to the distribution of free-roaming domestic cats and eight native mammal species in Washington, DC. (USA). Our analysis includes five species that are common prey items of domestic cats, and three species that are potential disease vectors or are otherwise known to be a risk to cats. We then predicted the probability of occupancy and estimated the probability of spatial overlap between cats and each native wildlife species at multiple scales. We also used kernel density estimations to calculate temporal overlap between cats and each native wildlife species. Across spatial scales, occupancy for potential disease vector species was generally positively correlated with canopy cover and open water. Prey species were also generally positively correlated with canopy cover, but displayed negative associations with human population density and inconsistent associations with average per capita income. Domestic cat occupancy was negatively correlated with natural habitat characteristics and positively correlated with human population density. Predicted spatial overlap between domestic cats and native wildlife was greatest for potential disease vector species. Temporal overlap was high (>0.50) between cats and all but two native wildlife species, indicating that temporal overlap is probable wherever species overlap spatially. Our findings indicate that the risk to and from domestic cats varies across urban landscapes, but primarily arises from human activities. As such, humans are implicated in the negative outcomes that result from cats interacting with wildlife. Data-driven management to reduce such interactions can aid in cat population management, biodiversity conservation, and public health campaigns.

Over the last several decades, feral cats have moved from the fringes to the mainstream in animal welfare and sheltering. Although many best practice guidelines have been published by national non-profits and veterinary bodies, little is known about how groups “in the trenches” actually operate. Our study sought to address that gap through an online survey of feral cat care and advocacy organizations based in the United States. Advertised as “The State of the Mewnion,” its topics included a range of issues spanning non-profit administration, public health, caretaking and trapping, adoptions of friendly kittens and cats, veterinary medical procedures and policies, data collection and program efficacy metrics, research engagement and interest, and relationships with wildlife advocates and animal control agencies. Respondents from 567 organizations participated, making this the largest and most comprehensive study on this topic to date. Respondents came primarily from grassroots organizations. A majority reported no paid employees (74.6%), served 499 or fewer feral cats per year (75.0%), engaged between 1 and 9 active volunteers (54.9%), and did not operate a brick and mortar facility (63.7%). Some of our findings demonstrate a shared community of practice, including the common use of a minimum weight of 2.0 pounds for spay/neuter eligibility, left side ear tip removals to indicate sterilization, recovery holding times after surgery commonly reported as 1 night for male cats and 1 or 1 nights for females, requiring or recommending to adopters of socialized kittens/cats that they be kept indoor-only, and less than a quarter still engaging in routine testing of cats for FIV and FeLV. Our survey also reveals areas for improvement, such as most organizations lacking a declared goal with a measurable value and a time frame, only sometimes scanning cats for microchips, and about a third not using a standardized injection site for vaccines. This study paints the clearest picture yet available of what constitutes the standard practices of organizations serving feral and community cats in the United States.

The US Cat Food Market is segmented by Pet Food Product (Food, Pet Nutraceuticals/Supplements, Pet Treats, Pet Veterinary Diets) and by Distribution Channel (Convenience Stores, Online Channel, Specialty Stores, Supermarkets/Hypermarkets). The market volume and value are presented in metric ton and USD respectively. The key data points include the market size of pet food by products, distribution channels, and pets.

Graph and download economic data for Producer Price Index by Industry: Dog and Cat Food Manufacturing: Dog Food (PCU3111113111111) from Dec 1985 to May 2025 about pets, food, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

IntroductionAlthough multiple studies have explored behavioral changes in pets during the strict lockdown of the COVID-19 pandemic between May and July 2020, this longitudinal study uniquely investigated the phases beyond strict lockdown. The primary objective of this research was to evaluate the pandemic’s impact on pet behavior.MethodsTo achieve this goal, we conducted an online survey, completed by pet owners residing in the United States between June 2020 (including retrospective data for February and April 2020) and December 2021. The study encompassed four distinct pandemic phases: Pre-pandemic (data collected retrospectively), Strict lockdown, Prolonged lockdown, and Re-opening.Results and discussionThe data collected from surveys completed by 3,278 pet owners across one to six time points revealed declining trends in all investigated behavior problems of both dogs and cats. Concurrently, human-animal interaction activities also showed variations across different COVID-19 phases. The association between human-animal interactions and the occurrence of behavior problems underscored the importance of not only exercise quantity but also adherence to consistent schedules, particularly for dogs, in mitigating behavior problems. Interestingly, among cats, sleeping outside the bedroom was associated with a reduced occurrence of aggression, anxiety, fear, and physiological behavior changes. However, sleep location displayed limited or no association with behavior problems in dogs. In summary, this study highlights the importance of tailoring interventions to the unique needs of each species through human-animal interactions to mitigate the occurrence of behavioral problems and enhance human-animal relationships.

The United States Pet Food Market report segments the industry into Pet Food Product (Food, Pet Nutraceuticals/Supplements, Pet Treats, Pet Veterinary Diets), Pets (Cats, Dogs, Other Pets), and Distribution Channel (Convenience Stores, Online Channel, Specialty Stores, Supermarkets/Hypermarkets, Other Channels). Get five years of historical data alongside five-year market forecasts.

The Lower Keys marsh rabbit is one of many endangered endemic species of the Florida Keys. The main threats are habitat loss and fragmentation from sea level rise, development, and habitat succession. Exotic predators such as free-ranging domestic cats pose an additional threat to these endangered small mammals. Management strategies have focused on habitat restoration and exotic predator control. However, the effectiveness of predator removal and the effects of anthropogenic habitat modifications and restoration have not been evaluated. Between 2013-2015, we used camera traps to survey marsh rabbits and free-ranging cats at 84 sites in the National Key Deer Refuge, Florida, USA. We used dynamic occupancy models to determine factors associated with marsh rabbit occurrence, colonization, extinction, and the co-occurrence of rabbits and cats during a period of predator removal. Rabbit occurrence was positively related to freshwater habitat and patch size, but was negatively related to the number of individual cats detected at each site. Furthermore, marsh rabbit colonization was negatively associated with relative increases in the number of individual cats at each site between survey years. Cat occurrence was negatively associated with increasing distance from human developments. The probability of cat site extinction was positively related to a two year trapping effort, indicating that predator removal reduced the cat population. Dynamic co-occurrence models suggested that cats and rabbits co-occur less frequently than randomly expected, whereas co-detections were site and survey-specific. Rabbit site extinction and colonization were not strongly conditional on cat presence, but corresponded with a negative association. Our results suggest that while rabbits can colonize and persist at sites where cats occur, it is the number of individual cats at a site that more strongly influences rabbit occupancy and colonization. These findings indicate that continued predator management would likely benefit endangered small mammals as they re-colonize restored habitats

MeowMLCategorization

tags: Image Classification, Domestic Cats, Species Recognition Note: This is an AI-generated dataset so its content may be inaccurate or false Dataset Description: The "MeowMLCategorization" dataset comprises a collection of high-resolution images labeled with metadata for the purpose of training machine learning models in the domain of cat image classification. The dataset has been carefully curated to include a diverse array of domestic cat breeds and… See the full description on the dataset page: https://huggingface.co/datasets/asoria/MeowMLCategorization.

There is an abundance of research focusing on the nutritional needs of the cat, though aspects surrounding treat feeding have received far less attention. Feeding practices have the potential to cause nutrient imbalances and adverse health outcomes, including obesity. The objective of this study was to identify and describe the perceptions, motivations, and behaviours surrounding treats, and factors that influence treat feeding by cat caregivers. A 56-question online survey was disseminated to cat caregivers (n = 337) predominantly from Canada and the USA to collect data regarding caregiver and cat demographics, the pet-caregiver relationship, perceptions surrounding treats, and feeding practices and behaviours. Descriptive statistics, chi-square tests, Kruskal-Wallis one-way ANOVA, Wilcoxon signed-rank tests, and multivariable logistic regression models were used to analyze the survey data. Caregivers had varying interpretations of the term ‘treat’ and how treats relate to the primary diet, and these perceptions appeared to influence the quantity of treats provided. Aspects relating to the human-animal bond were highlighted as an important factor in decisions and behaviours surrounding treat feeding in our results. Though the majority (224/337, 66%) of respondents indicated they monitor their pet’s treat intake, using an eyeball estimate was the most frequent (139/337, 41%) method reported to measure treats. Multivariable logistic regression results revealed feeding jerky, bones, dental treats, and table scraps in select frequencies were predictive of caregivers perceiving their cat as overweight/obese. Results provide valuable new insights to cat caregiver feeding practices and perceptions of treats and can be used to inform veterinary nutrition support to caregivers. More research is warranted to further our understanding and ensure that cats receive optimal nutrition and care.

The statistic shows the total revenue of PETCO Animal Supplies in the United States from 2020 to 2024. The company generated revenue amounting to almost 6.3 billion U.S. dollars in the financial year of 2023. Petco's number of stores in North America amounted to roughly 1,500 in 2023. PETCO Animal Supplies PETCO Animal Supplies, Inc. is the second largest pet specialty retailer in the United States, following only its major competitor PetSmart. Petco is privately owned and was established in 1965. Today, the pet specialty firm is based in San Diego, California and employs about 25,000 people throughout the country. The company's product portfolio includes pet-related items such as supplies, products and food. In addition, PETCO offers grooming and veterinary services for pets, however, the retailer does not sell live animals, such as cats or dogs. Alternatively, the PETCO Foundation, an independent non-profit organization, helps find new homes for adoptable dogs, cats, and other companion animals through adoption events and the support of animal welfare groups. The PETCO Foundation, which was founded in 1999, is based on private donations. Besides their brick-and-mortar stores, PETCO provides an online shopping platform at petco.com, where consumers may place pet-related product orders right to their doorstep.

CaTS-Bench Dataset

  Setup

After downloading, run tar -xzvf CaTSBench.tar.gz

to untar the directory.

  Contents





  Train

train_data contains four subdirectories:

gt_captions: Ground-truth captions, generated with contextual and statistical metadata alongside the time series. metadata: Contextual and statistical metadata for each time series plots: One plot image per time series time series: The numerical time series values

train_data contains 16k total… See the full description on the dataset page: https://huggingface.co/datasets/neurips2025submission/CaTS-Bench.