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.

The data provided includes: serial number, year, county and city code, county and city, total estimated number of pet dogs, total estimated number of pet cats, total estimated number of stray dogs, and remarks.

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.

This dataset is a modelled dataset, describing the mean number of cats per square kilometre across GB. The figures are aligned to the British national grid, with a population estimate provided for each 1km square. These data were 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 1km 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.

Cat Dog Person

## Overview

Cat Dog Person is a dataset for object detection tasks - it contains Cat Dog Person annotations for 815 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).

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...

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?

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.

https://www.ethosvet.com/wp-content/uploads/cat-dog-625x375.png" alt="">

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...

Predation by feral cats Felis sylvestris catus is currently one hypothesized cause for the recent dramatic small mammal declines across northern Australia. We conducted a field experiment to measure …Show full descriptionPredation by feral cats Felis sylvestris catus is currently one hypothesized cause for the recent dramatic small mammal declines across northern Australia. We conducted a field experiment to measure the effect of predation by for this areas typically low-density cat populations on the demography of a native small mammal which due to the now natural scarce abundance of small mammals in the wild had to be reintroduced. We established two 12.5-ha enclosures in tropical savanna woodland on Wongalara Sanctuary, south of Arnhem Land in the Northern Territory. Each enclosure was divided in half, with cats allowed access to one half but not the other. We introduced about 20 individuals of Rattus villosissimus, a native rodent, into each of the four compartments (two enclosures x two predator-access treatments) and monitored rat demography by mark-recapture analysis and radio-tracking, and predator incursions by camera surveillance and track and scat searches. The data can be used for the mark-recapture analysis. The radio-tracking data and predator incursions data will be uploaded separately. The Cat and Dingoes camera trap dataset was produced using a heat-in-motion cameras (Reconyx PC800 Hyperfire, Holmen, Wisconsin, USA) around the outside of the perimeter fences to detect predators. At least four (but up to six and always the same number of cameras at a time) cameras were placed as one camera installed at each side on the outside of the fences of each enclosure. Cameras were un-baited, to avoid attracting predators. This one file dataset contains the information on the presence/absence data of cats and dingoes on each day. 'Site' indicates the enclosure the camera was attached to ('Enclosure_I' or Enclosure_II'), 'Camera number' indicates which site the camera was on. Note that between October 2011 and April 2012, Enclosure II had two additional cameras (one facing the front gate and one additional monitoring the lower half of the back fence of the enclosure) which resulted in a total of six cameras for during that time. 'Date' indicates the date the photo(s) was/were taken, 'Photos_recorded' whether the camera was operational or photos were retained (e.g. one SD-cards was lost). And columns 'Dingo' and 'Cat' indicate whether these animals were present that day or not (na = no photos recorded, 0 = not present that day, 1 = present that day).

Non†native predators can cause great harm to natural ecosystems through competition for resources and by directly predating on native species. Domestic cats (Felis catus) predate on wild prey throughout the world and have been implicated in a number of species declines. However, in the UK, long†term, widespread research is lacking. The data provided here relate to prey returned home by pet cats in the UK over a total period of 3.5 years (ranging from one month to 3.5 years per cat). These data were collected by cat owners across the UK, noting details of the prey returned home by their cats monthly. Data were gathered upon registration regarding the age, sex, and body condition of participating cats, allowing for the analysis of the potential influence of such factors. While most cats returned 0–1 prey per month, a small minority (n = 3 cats) returned over 15 individuals monthly. It is important that true predation rates (in addition to the return rates found here) are further exp..., , , # Title of Dataset: Quantifying prey return rates of domestic cats in the UK

[Access this dataset on Dryad](DOI: 10.5061/dryad.31zcrjdv9)

Description of the data and file structure

Data are presented in two files: 'Data1_prey' and 'Data2_cats'.

Data1_prey. This file contains details of all prey returned home by the cats monitored (n=553) over a total period of 3.5 years. Cat_ID is a unique identifier for each cat and Prey_ID is as given by owners or as verified by researchers thanks to photographs provided. Taxonomic group is then given, along with whether prey were dead or alive (or not recorded), what happened to the prey which were returned alive (for example, released), and whether returned whole, part-eaten, or witnessed by owners to be eaten. Age and sex were not required, but some participants gave this information in the related 'notes' section of the data return form. As such, there are many 'NA' datapoints for age and sex fields.

Data2_cats. This file contains data re...,

Dogs and Cats Online Data

Cat Annotation Dataset The CAT dataset includes 10,000 cat images. For each image, we annotate the head of cat with nine points, two for eyes, one for mouth, and six for ears. The detail configuration of the annotation was shown in Figure 6 of the original paper: Weiwei Zhang, Jian Sun, and Xiaoou Tang, "Cat Head Detection - How to Effectively Exploit Shape and Texture Features", Proc. of European Conf. Computer Vision, vol. 4, pp.802-816, 2008. ### Format The annotation data are stored in a file with the name of the corresponding cat image plus ".cat", one annotation file for each cat image. For each annotation file, the annotation data are stored in the following sequence: 1. Number of points (always 9) 2. Left Eye 3. Right Eye 4. Mouth 5. Left Ear-1 6. Left Ear-2 7. Left Ear-3 8. Right Ear-1 9. Right Ear-2 10. Right Ear-3 ### Training, Validation, and Testing We randomly divide the data into three sets: 5,000 images for training, 2,000 images for valid

This statistic presents the estimated number of cats owned by households in Europe in 2010, 2012, 2014, 2016, 2017, 2018, 2019, 2020, 2021, 2022, and 2023. The cat population in Europe was measured at approximately ****** million in 2023.

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!

The complete "CatCam" dataset as described in Betsch BY, Einhäuser W, Körding KP, König P (2004) The World from a Cat's Perspective - Statistics of Natural Videos. Biological Cybernetics, 90(1):41-50.

All frames of each movie are stored as consecutively numbered tiff-files and archived in a tar-file. Each movie will extract to a seperate directory, whose name is starting with "label..." and contains information on the recording parameters.

The data is freely available for academic use only. If you use these data for a publication, the aforementioned article needs to be cited.

Dogs and Cats Online Data 2023-2024

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).

Management Accounts

This dataset contains statistics regarding the impounding of animals. The statistics cover three categories of animals; cats, dogs and livestock. This dataset contains statistics regarding the impounding of animals. The statistics cover three categories of animals; cats, dogs and livestock.

Number of confirmed cases of FSE (Feline Spongiform Ecephalopathy) in domestic cats by year of birth. This dataset includes the following fields: Year of Birth (of the cat); Number of cases (born in that year). Please note: this data is available as part of a wider report on TSE surveillance, published on gov.uk.
Please note: this dataset records no data after 1996, as no confirmed cases of FSE have been reported since then.