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.

Dataset used in the article "Does Visual Stimulation by Photographs of Cats and Dogs Make People Happier and More Optimistic?"ColumnsIDis_preview: true - response by the researcher to check the questionnaire, it should be removedremove: respondent checked that his/her responses are not valid and should not be used in future analysisfinished_proc: percentage of the questionnaire finisheddate_time: filing of the questionnaire started at this timeduration_formatted: duration of the filling of the questionnairebrowserbrowser_versionOS: operating systempriming: true - primed group, false - control groupcat_dog: objects on photos showngenderage: in yerssex_o: attraction to people of the opposite sex (scale 1 - 7)sex_s: attraction to people of the same sex (scale 1 - 7) orientation: computed as the difference of previous twomood: actual mood (scale 0 - 5)condition_phys: physical condition (scale 0 - 5)condition_psych: mental condition (scale 0 - 5)life_quality: life quality (scale 0 - 5)optimism: mean of previous threeoptimism_zskore: z-score of the previous children_own: how many children does respondent havewanted_sons: total number of sons which respondent would like to havewanted_daughters: total number of daughters which respondent would like to havewanted_children: a sum of previous twoliking_dogs: how much respondent likes dogs (scale 1 - 100)present_whenever_dog: respondent has ever kept a dogpresent_now_dog: respondent keeps dog nowpresent_Ndogs: how many dogs does respondent keep now liking_cats: how much respondent likes cats (scale 1 - 100)present_whenever_cat: respondent has ever kept a catpresent_now_cat: respondent keeps cat nowpresent_Ncats: how many cats does respondent keep now

About This Dataset

The Roboflow Thermal Dogs and People dataset is a collection of 203 thermal infrared images captured at various distances from people and dogs in a park and near a home. Some images are deliberately unannotated as they do not contain a person or dog (see the Dataset Health Check for more). Images were captured both portrait and landscape. (Roboflow auto-orient assures the annotations align regardless of the image orientation.)

Thermal images were captured using the Seek Compact XR Extra Range Thermal Imaging Camera for iPhone. The selected color palette is Spectra.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/h9vhrqB.png" alt="Man and Dog">

Usage

Thermal images have a wide array of applications: monitoring machine performance, seeing in low light conditions, and adding another dimension to standard RGB scenarios. Infrared imaging is useful in security, wildlife detection,and hunting / outdoors recreation.

This dataset serves as a way to experiment with infrared images in Roboflow. (Or, you could build your own night time pet finder!)

Collecting Custom Data

Roboflow is happy to improve your operations with infrared imaging and computer vision. Services range from data collection to building automated monitoring systems leveraging computer vision. Reach out for more.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

All dog owners residing in NYC are required by law to license their dogs. The data is sourced from the DOHMH Dog Licensing System, where owners can apply for and renew dog licenses.

Each record represents a unique dog license that was active during the year, but not necessarily a unique record per dog, since a license that is renewed during the year results in a separate record of an active license period. Each record stands as a unique license period for the dog over the course of the yearlong time frame.

This dataset is useful for municipal governments, veterinarians, and researchers who are interested in pet ownership patterns, compliance with local licensing laws, and demographic analysis of pet ownership. It can also aid in public health monitoring, such as tracking rabies vaccinations, which are often required for licensing.

Data scientists and analysts can perform various types of analytics such as:

• Descriptive Statistics: Analyze the distribution of dog breeds, the average age of dogs, and the licensing compliance rates across different zip codes.
• Geospatial Analysis: Map the data to see geographic patterns in pet ownership, breed popularity, or areas with lower licensing compliance, which can help in targeted public health and safety campaigns.
• Time Series Analysis: If longitudinal data is available, trends over time in pet registrations, renewals, and compliance could be evaluated.
• Predictive Modeling: Model the likelihood of license renewal or the factors influencing it, using logistic regression or other suitable predictive models.
• Cluster Analysis: Identify clusters of similar zip codes based on dog population characteristics, which can help in resource allocation for animal control agencies.

According to the most recent pet population survey in 2024, approximately ** percent of responding households in the United Kingdom (UK) owned at least one dog. Between 2010 and 2020, the percentage of households who own at least one dog remained between ** and ** percent. In 2021, the survey changed its format from face-to-face to online, meaning that data should not be directly compared with previous years. Pet dogs in the United Kingdom A downward trend is also reflected in the number of pet dogs owned in the UK, which was approximately ** million in 2023. This constitutes a decrease of *** million compared to 2022. The majority of British dog owners get their pets from a breeder of one specific breed. Approximately ** percent get their dog from a rescue or rehoming center/shelter based in the UK. Most popular dog breeds in the United Kingdom The most frequently registered dog breeds in the UK are Labrador Retrievers and French Bulldogs. Since 2011, the number of registered French Bulldogs has increased from approximately ***** to over ******. The number of Labrador Retrievers remained relatively stable between 2011 and 2020. Both breeds saw a notable increase in registrations during the pandemic year of 2021. For example, the number of Labrador Retrievers increased from around ****** in 2020 to over ****** in 2021.

In the framework of early prevention of problems in the owner-dog relationship, it is important to have a broad perspective on the development of this relation over time, starting even before people actually acquire the dog. People who currently (or previously) own(ed) a dog can rely on their experiences when considering a new dog while this knowledge is unavailable to first time dog-owners. In this study we explored how self-efficacy and perceptions on the benefits and costs (the social cognitive factors), and canine problem behaviors, perceived costs and satisfaction with the dog, changed over time from the motivational phase of relationship development (before acquiring the dog) to the experience phase (six and twelve month after acquiring a dog) in experienced (previous (n=75) and current (n=86)) versus unexperienced (first time (n=32) dog owners: Respondents filled in online questionnaires before and twice after acquisition of their dog. From T0 (before acquiring a dog) to T1 (having a dog for six months) especially participants with no experience had to adjust their beliefs about having a dog. Experiencing the relationship for an additional year (from T1 to T2) hardly changed much in the social cognitive factors and small (non-significant) changes occurred in canine problem behaviors, perceived costs, and satisfaction with the dog. To conclude, perceptions of dog ownership change over time, but after calibrating these perceptions with reality, perceptions become stable.

This dataset is a modelled dataset, describing the predicted population of dogs per postcode district (e.g. YO41). This dataset gives the mean estimate for population for each district, and was generated as part of the delivery of commissioned research. The data contained within this dataset are modelled figures, based on national estimates for pet population, and available information on Veterinary activity across GB. The data are accurate as of 01/01/2015. The data provided are summarised to the postcode district level. Further information on this research is available in a research publication by James Aegerter, David Fouracre & Graham C. Smith, discussing the structure and density of pet cat and dog populations across Great Britain.

As of 2025, approximately 42 percent of consumers in the United States with over 50k$ household income considered it important for the food to have natural ingredients. A high percentage of pet owners also found the price important factors to keep in mind when making a purchasing decision.

Dogs and cats have become the most important and successful pets through long-term domestication. People keep them for various reasons, such as their functional roles or for physical or psychological support. However, why humans are so attached to dogs and cats remains unclear. A comprehensive understanding of the current state of human preferences for dogs and cats and the potential influential factors behind it is required. Here, we investigate this question using two independent online datasets and anonymous questionnaires in China. We find that current human preferences for dog and cat videos are relatively higher than for most other interests, with video plays ranking among the top three out of fifteen interests. We also find genetic variations, gender, age, and economic development levels notably influence human preferences for dogs and cats. Specifically, dog and cat ownership are significantly associated with parents’ pet ownership of dogs and cats (Spearman’s rank correlation c..., , , # Human preferences for dogs and cats in China: the current situation and influencing factors of watching online videos and pet ownership

https://doi.org/10.5061/dryad.qfttdz0rr

This dataset contains three CSV data files, each corresponding to one of the three parts described in the study.

Description of the data and file structure

**“1, bilibili.csv†**: contains data extracted from the Bilibili website. Each row in the dataset represents yearly data for each popular channel. Missing data are indicated with NA.

• ID:Â The serial number for each video, ranging from 1 to 167368.
• year: The year the video was published on the website, from 2009 to 2021.
• Videourl:Â The URL of the video.
• plays:Â The total number of plays for the video.
• likes: The total number of likes for the video.
• sort: The ranking of the video in terms of play count among all popular videos in its channel for that year.
• channelID: The I...

With an estimated 12. 5 million dogs in the UK alone, many people acquire a dog at some point during their lives. However, there are gaps in understanding about why UK owners decide to get dogs. Using a mixed-methods convergent design, this study identified the reasoning behind dog acquisition in a sample of UK current and prospective owners. An online survey of current (n = 8,050) and potential (n = 2,884) dog owners collected quantitative and qualitative data. Current owners were asked about the acquisition of their most recently acquired dog, whilst potential owners were asked about their dog ownership aspirations. Additional qualitative data were collected through semi-structured interviews with current (n = 166) and potential (n = 10) dog owners. Interviews focused on the factors that affected why and how people acquire dogs. Of survey responses, companionship for the respondent was the most common reason for wanting to get a dog, reported by 79.4 and 87.8% of current and potential owners, respectively. Facilitating exercise was reported as a reason for wanting to get a dog by 48.2 and 69.7% of current and potential owners, respectively. There were significant differences between current and potential owners in their likelihood of reporting pre-defined reasons, factors and influences involved in their decision to get a dog. Compared to current owners, potential owners were significantly more likely to report being motivated by most of the survey response options offered (including companionship for themselves or other adults in the household, helping a dog in need, lifestyle changes and previous experiences of meeting dogs), suggesting that current ownership status may affect experience and/or reporting expectations around dog ownership. Reflexive thematic analysis of qualitative data confirmed the importance of these motivations and identified additional reasons and factors that drive dog acquisition. These were organized into three overarching themes: Self-Related Motivation, Social-Based Motivation, and Dog-Related Positive Affect-Based Motivation. These findings provide insights into owners' expectations of ownership which may inform the development of interventions to support potential owners' decision-making around acquisition to maximize both dog and human welfare.

Started out as a pumpkin detector to test training YOLOv5. Now suffering from extensive feature creep and probably ending up as a cat/dog/spider/pumpkin/randomobjects-detector. Or as a desaster.

The dataset does not fit https://docs.ultralytics.com/tutorials/training-tips-best-results/ well. There are no background images and the labeling is often only partial. Especially in the humans and pumpkin category where there are often lots of objects in one photo people apparently (and understandably) got bored and did not labe everything. And of course the images from the cat-category don't have the humans in it labeled since they come from a cat-identification model which ignored humans. It will need a lot of time to fixt that.

Dataset used: - Cat and Dog Data: Cat / Dog Tutorial NVIDIA Jetson https://github.com/dusty-nv/jetson-inference/blob/master/docs/pytorch-cat-dog.md © 2016-2019 NVIDIA according to bottom of linked page - Spider Data: Kaggle Animal 10 image set https://www.kaggle.com/datasets/alessiocorrado99/animals10 Animal pictures of 10 different categories taken from google images Kaggle project licensed GPL 2 - Pumpkin Data: Kaggle "Vegetable Images" https://www.researchgate.net/publication/352846889_DCNN-Based_Vegetable_Image_Classification_Using_Transfer_Learning_A_Comparative_Study https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset Kaggle project licensed CC BY-SA 4.0 - Some pumpkin images manually copied from google image search - https://universe.roboflow.com/chess-project/chess-sample-rzbmc Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/steve-pamer-cvmbg/pumpkins-gfjw5 Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/nbduy/pumpkin-ryavl Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/homeworktest-wbx8v/cat_test-1x0bl/dataset/2 - https://universe.roboflow.com/220616nishikura/catdetector - https://universe.roboflow.com/atoany/cats-s4d4i/dataset/2 - https://universe.roboflow.com/personal-vruc2/agricultured-ioth22 - https://universe.roboflow.com/sreyoshiworkspace-radu9/pet_detection - https://universe.roboflow.com/artyom-hystt/my-dogs-lcpqe - license: Public Domain url: https://universe.roboflow.com/dolazy7-gmail-com-3vj05/sweetpumpkin/dataset/2 - https://universe.roboflow.com/tristram-dacayan/social-distancing-g4pbu - https://universe.roboflow.com/fyp-3edkl/social-distancing-2ygx5 License MIT - Spiders: https://universe.roboflow.com/lucas-lins-souza/animals-train-yruka

Currently I can't guarantee it's all correctly licenced. Checks are in progress. Inform me if you see one of your pictures and want it to be removed!

Context

This dataset consists of 2000 transformed images (1000 each of cat and dog). It can be directly used with CNN models without the need for any transformation

Content

As this is beginner's competition, we do lot of trial and error to understand how computer vision problems are solved. Hence, I feel the training on 25000 images would be time consuming. I have reduced it to 2000 images (1000 per category) by randomly shuffling from the original. Following transforms are applied: data_transform = transforms.Compose([ transforms.Resize(256), transforms.ColorJitter(), transforms.RandomCrop(224), transforms.RandomHorizontalFlip(), # transforms.Resize(128), transforms.ToTensor() ])

Inspiration

Save some time when learning on this dataset

Context

I have taken this dataset from the NYC Open Data Website: https://data.cityofnewyork.us

I wanted to use the cleaned version of this dataset and I thought people might like to use this version. The original dataset was last updated on 10th September 2018.

Description: All dog owners residing in NYC are required by law to license their dogs. The data is sourced from the DOHMH Dog Licensing System (https://a816-healthpsi.nyc.gov/DogLicense), where owners can apply for and renew dog licenses. Each record represents a unique dog license that was active during the year, but not necessarily a unique record per dog, since a license that is renewed during the year results in a separate record of an active license period. Each record stands as a unique license period for the dog over the course of the yearlong time frame.

Content

The original dataset contained 122K rows and 15 columns. After cleaning the data, the count has reduced to 121862 rows.

Acknowledgements

Thank you to the city of new york for collecting and providing this data! As well as the NYC Department of Health who acquired this data from owners who registered their dogs for the dog license.

Inspiration

I'll let you guys get creative and explore the dataset.

The present dataset is based on a questionnaire which is also part of this package. The enclose questionnaire includes identifiable and relevant variables names (yellow highlighted).

Participants were recruited by Norstat, a European-based survey company, with the aim of gaining a representative sample of Austrian, Danish and UK citizens, including pet owners. The survey company administers and hosts online panels comprising citizens from many European countries. We aimed for a sample that is representative in terms of age, gender, and region. Therefore, a stratified sampling principle was set up where individuals within each stratum were randomly invited to participate. The invitations were issued through e-mail that contained a link to the online questionnaire. Data was collected from 11-25^th of March 2022 in Austria, from 11-24^th of March 2022 in Denmark and from 8-23^rd of March 2022 in the UK. The invitation provided information about the background of the study, the participating universities, ethical approval, estimated time for questionnaire completion and further, participants were informed that the completion of the questionnaire was voluntary and anonymous, and that they could exit the survey at any point. Before participants were directed to the survey, they ensured informed consent by confirming that they are over 17 years old, and consent to participate in this survey.

Besides the questionnaire the dataset includes a csv and an Excel file consisting of the data that is used in the ms. and an rtf and a pdf file with data variable names/labels, and value labels.

ProcedureWe have conducted two surveys in Germany, both were developed by Jesko Wilke, a freelancer journalist of the German ‘Dogs’ magazine. The data were collected online by the magazine’s own website (www.dogs-magazin.de). The surveys were described in detail in Kubinyi et al., 2009; Turcsán et al., 211 and Turcsán et al., 2017. Both surveys comprised two parts. The first part collected information about the demographic characteristics of the owners and dogs, as well as about the dog keeping practices. Twelve of these questions were the same in both surveys, eight were present in only one. The second part was different in the two surveys. The Survey 1 aimed at measuring the dogs’ general behaviour tendencies (personality) and was developed based on a human Big Five Inventory. This questionnaire contained 24 items (e.g. „My dog is calm, even in ambiguous situations”), for each item the owners were asked to indicate the level of agreement on a 3-point scale (true, partly true, not true). Our previous results using principal component analysis have revealed that 17 items out of the 24 belonged to four components, labelled as calmness, trainability, dog sociability, and boldness, all traits with middle or high internal consistency.The Survey 2 listed 12 examples of typical behaviour problems like „ My dog most often does not even attend me when I call him/her back”. Again, the owners indicated for each statement how far they agree with it using a 3-point scale. The questions were designed to assess not (only) the frequency of behaviour problems of the dogs but (also) the owners’ attitude towards these behaviour; i.e. if he/she considers them as problematic. In the current dataset, we recoded responses into a binary (yes/no) format: responses of "agree" or "partly agree" were categorized as "yes", while "disagree" was categorized as "no".SubjectsOn total, we collected responses from N = 14,004 dog owners in the first survey and N = 10,240 in the second. In the current dataset, we excluded reports with- missing data- duplicate entries (i.e., cases where owners submitted multiple reports for the same dog)- reports on mixed-breed dogs- reports on breeds where the cephalic index of the breed was unknown- reports when the cephalic index of the breed fell between 50 and 53, and between 62 and 65.Finally, to prevent a few highly popular breeds from disproportionately influencing group values, we capped the number of individuals per breed at 100. If a breed exceeded this threshold, we randomly selected 100 individuals for the final dataset.Kubinyi, E., Turcsán, B. & Miklósi, Á. Dog and owner demographic characteristics and dog personality trait associations. Behavioural Processes 81, 392–401 (2009).Turcsán, B., Kubinyi, E. & Miklósi, Á. Trainability and boldness traits differ between dog breed clusters based on conventional breed categories and genetic relatedness. Applied Animal Behaviour Science 132, 61–70 (2011).Turcsán, B., Miklósi, Á. & Kubinyi, E. Owner perceived differences between mixed-breed and purebred dogs. PLoS ONE 12, (2017).

Context:

This dataset contains survey responses to a survey that people could complete when they signed up for the 5-Day Data Challenge.

On December 12, 2017 survey responses for the second 5-Day Data Challenge were added. For this version of the challenge, participants could sign up for either an intro version or a more in-depth regression challenge.

Content:

The optional survey included four multiple-choice questions:

1. Have you ever taken a course in statistics?

• Yep
• Yes, but I've forgotten everything
• Nope

1. Do you have any previous experience with programming?

• Nope
• I have a little bit of experience
• I have quite a bit of experience
• I have a whole lot of experience

1. What's your interest in data science?

• Just curious
• It will help me in my current job
• I want to get a job where I use data science
• Other

1. Just for fun, do you prefer dogs or cat?

• Dogs 🐶
• Cats 🐱
• Both 🐱🐶
• Neither 🙅

In order to protect privacy, the data has been shuffled (so there’s no temporal order to the responses) and a random 2% of the data has been removed (so even if you know that someone completed the survey, you cannot be sure that their responses are included in this dataset). In addition, all incomplete responses have been removed, and any text entered in the “other” free response field has been replaced with the text “other”.

Acknowledgements:

Thanks to everyone who completed the survey! :)

Inspiration:

• Is there a relationship between how much programming experience someone has and why they’re interested in data science?
• Are more experienced programmers more likely to have taken statistics?
• Do people tend to prefer dogs, cats, both or neither? Is there a relationship between what people prefer and why they’re interested in data science?

Thermal Dogs And People

## Overview

Thermal Dogs And People is a dataset for object detection tasks - it contains Thermal annotations for 994 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

Person & Dog Thermal Images Dataset For YOLO V3-V4 Darkent Dataset It includes 489 images. Dogs-person are annotated in YOLO v3 Darknet format.

Content

Train Folder - Containing 429 Images With Annotations.txt Test Folder - Containing 18 Images With Annotations.txt Valid Folder - For Validating the Darknet YOLO V4 Model

The following augmentation was applied to create 3 versions of each source image: * 50% probability of horizontal flip * Randomly crop between 0 and 12 percent of the image * Random brigthness adjustment of between -18 and +18 percent

Acknowledgements

Thanks To @Roboflow To Provide This Dataset To Us

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered

Cat&dog&people

## Overview

Cat&dog&people is a dataset for object detection tasks - it contains Cats annotations for 612 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

This dataset was created when I practiced webscraping.

Content

The data is a compilation of information on dogs who were available for adoption on December 12, 2019 in the Hungarian Database of Homeless Pets. In total, there were 2,937 dogs in the database. It contains information on dogs' names, breed, color, age, sex, the date they were found, and some characteristics of their personalities.

Inspiration

I thought it would be interesting to have a dataset that looks at adoptable dogs' characteristics. It is not really well-suited for prediction, but could be a good practice dataset for data visualization and working with categorical data.