Dataset Summary

A dataset from kaggle with duplicate data removed.

  Data Fields

The data instances have the following fields:

image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"]… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/Cat_and_Dog.

This statistic shows the results of a survey conducted in the United States in 2017 on pets. Some 51 percent of the respondents stated that they prefer dogs.The Survey Data Table for the Statista survey pets in the U.S. 2017 contains the complete tables for the survey including various column headings.

Dogs and Cats Online Data

Among the wide variety of animals kept as pets, cats and dogs are the most common. In Spain, dogs seem to be the most popular pet, with ** percent of Spanish households owing at least one canine in 2023. The share of households that owned a cat experienced an ongoing decrease over the recent years, falling to ** percent in 2019 down from a ** percent in 2010. This figure picked up again in 2022 to reach ** percent. Dogs: Spain’s favorite domestic animal The popularity of dogs in Spain cannot be denied – the number of hounds has only increased in the last years, reaching figures of approximately *** million in 2023. The number of stray and street dogs recused on the Spanish streets saw a drop over time, falling from ******* dogs in 2013 down to *******. This figure, however, increased dramatically in 2019. The situation of pet cats in Spain Spanish homes had around 5.8 million pet cats in 2023, which is about half the number of that of dogs the same year. The number of rescued stray or street felines was also significantly lower compared to dogs, until figures rocketed in 2019. The fate of rescued street and stray pets normally has a happy ending, with nearly half of them adopted by a new family and ** percent returned to their original homes that year.

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).

Context

The Dogs vs. Cats dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or cat. This dataset is provided as a subset of photos from a much larger dataset of 3 million manually annotated photos. The dataset was developed as a partnership between Petfinder.com and Microsoft.

Content

Download Size: 824 MB The data-set follows the following structure: --- kagglecatsanddogs_3367a | |--- readme[1].txt | |--- MSR-LA - 3467.docx | |--- PetImages | | |--- Cat (Contains 12491 images) | | |--- Dog (Contains 12470 images)

Acknowledgements

This data-set has been downloaded from the official Microsoft website: this link

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Dataset Details





  Dataset Description

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

  Dataset Sources [optional]

Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/DanielSongShen/dogs-vs-cats.

Materials and methodsVirusesThe following influenza viral strains were used in this study: H3N2 AIV (A/duck/Guangdong/W12/2011) (Accession Number: JX175250.1); H3N8 AIV (A/Gallinula/Guangzhou/A1/2017) (Accession Number: ON287054.1); H3N2 SIV (A/Swine/Guangdong/FS4/2015) (GISAID isolate-ID: EPI_ISL_249845); H3N2 CIV (A/canine/Guangdong/1/2006) (Accession Number: GU433351.1). The viral titers were evaluated by EID50/ml assay. Virus stocks were propagated in 9- to 12-day-old embryonated specific pathogen-free (SPF) chicken eggs and titrated using the EID50 assay.Animals and groupingSixteen 9- to 11-week-old beagles and sixteen 9- to 12-week-old domestic shorthair cats, all seronegative for influenza A viruses, were used in this study. Animals were randomly divided into five groups for both beagles and shorthair cats separately: three experimental groups and two control groups. Each experimental group consisted of four animals, and each control group consisted of two animals. All animals were anesthetized with propofol (1-2.5 mg/kg) and intranasally inoculated with 10⁶ EID50 of the corresponding virus in 1.0 mL PBS. Control groups were inoculated with 1.0 mL pathogen-free SPF chicken embryo allantoic liquid.Clinical signs and seroconversionClinical signs and rectal temperature were monitored daily for 14 days post-inoculation (dpi). Nasal swabs were collected daily from 1 to 14 dpi and titrated by EID50 assay in SPF chicken eggs. Blood samples were collected at 0, 3, 5, 7, 14, and 21 dpi for serological antibodies assessment, treated with receptor-destroying enzyme (RDE), and subjected to hemagglutination inhibition (HI) assay.Viral replication and pathological examinationAt 4 dpi, one animal from each group was euthanized with an intravenous injection of pentobarbital sodium (150-200 mg/kg). Lung, trachea, nasal turbinate, heart, liver, spleen, kidney, intestine, stomach, and brain tissues were collected. In consideration of animal welfare, euthanasia was performed on only one animal per group in this study, and three tissue samples were collected from each type of tissue. Tissues were fixed in 10% neutral buffered formalin, processed for hematoxylin and eosin (H&E) staining, and immunohistochemistry (IHC). All tissues were weighed and homogenized in 1 mL of PBS per gram, then centrifuged to obtain the supernatant, which was titrated using the EID50 assay to assess viral replication.Ethics statementAll procedures in animal experiments met the requirements of animal welfare and were approved by the Experimental Animal Welfare Ethics Committee of South China Agricultural University (protocol code: 2024c016). All experimental animals were monitored by university-licensed veterinarians. All animal experiments were performed in a level 2 animal biosafety laboratory (A-BSL level 2).

Both cats and dogs fetch, but the likely function(s) of this behavior for each species have not been compared. In this study, we assessed data from online surveys of cat and dog behavior (Fe-BARQ; C-BARQ) completed by cat (N = 8224) and dog owners (N = 73724). We assessed responses to the items "Plays ‘fetch’; likes to retrieve thrown objects or toys" (Fe-BARQ) and “Will ʻfetchʼ or attempt to fetch sticks, balls, or objects” (C-BARQ). Cats and dogs described as "sometimes," "usually" or "always" fetching were categorized as fetchers. Regression models were used to examine which animal-related (e.g., sex, age) and environmental factors best predicted fetching, and chi-square tests were used to explore the effect of breed on fetching behavior.Fetching was reported in 40.9% of cats and 77.8% of dogs. In cats, fetching was correlated with play and activity. In dogs, fetching was correlated with overall trainability. In both cats and dogs, being female, older, living with (other) dogs, and having health problems decreased the likelihood of fetching. Breed effects were observed in both species, with fetching more prominent in cat breeds originating in the Far East and in dog breeds from the Retriever, UK Rural, Poodle, Pointer and Spaniel clades. We discuss the results in the context of domestication history of both cats and dogs and posit several hypotheses about why fetching behavior is observed in both.

This dataset contains the most popular names of licensed dogs and cats living in the City of Toronto.

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...

Here are a few use cases for this project:

1. Animal identification app: Utilize the "cats-dogs-monkeys" model to create a mobile app that helps users identify and learn more about different types of animals, specifically cats, dogs, and monkeys, as well as their behaviors such as eating, walking, and sleeping.

2. Pet adoption platform: Integrate the model into an online pet adoption platform to filter and categorize pets based on species and behavior (such as cats, dogs, and monkeys) to help potential adopters more easily find their preferred pet.

3. Wildlife monitoring and research: Apply the model to analyze video footage or images from wildlife cameras or research studies to automatically categorize and document animal behaviors, helping researchers track and analyze animal populations and habits.

4. Pet care management system: Integrate the model into a smart pet care management tool, such as an automatic pet feeder or home monitoring camera, to identify pets and monitor their behavior, providing data to pet owners to better care for their pets and maintain their well-being.

5. Educational material creation: Utilize the model to generate educational content, such as interactive games, quizzes, or flashcards, to help children and students learn about animals, their behaviors, and other related information, making learning fun and engaging.

The share of households owning a pet in the United Kingdom remained relatively stable between 2012 and 2018, hovering around an estimated percentage of 47 to 45 percent. However, pet ownership levels peaked to an unprecedented high of 62 percent in 2022, likely as a result of the coronavirus pandemic and increased time spent at home. In 2023, this figure shrank to 57 percent.

Pet ownership in the UK With more than half of UK households owning at least one pet in 2021/22, dogs and cats were the most common household pets in that year, with an estimated 13 million dogs and 12 million cats living in homes. As of 2020, the United Kingdom was the second highest-ranking European country in terms of its dog population, preceded only by Germany.

Consumer spending on pets in the UK As the pet population in the United Kingdom increased in size, so did consumer spending on pet food and pet-related products and services. Spending on pets and related products reached almost eight billion British pounds in 2020, a notable increase from a mere 2.9 billion British pounds in 2005. Among the most expensive pet-related expenditures are veterinary and pet services, which constituted almost four billion British pounds in 2020.

The Cat & Dog Body Segmentation Supplementary Dataset is tailored for the visual entertainment industry, comprising a variety of internet-collected images with resolutions exceeding 440 x 440 pixels. This dataset focuses on contour segmentation, specifically delineating the outlines of cats and dogs of various breeds, providing detailed data for applications requiring precise pet representations.

A small dataset that contains dogs' barking sounds and cats' meowing sounds. Used for 'hello-world' binary audio classification using machine learning and deep learning.

Snapshot img

Wet Pet Food Market Size 2024-2028

The wet pet food market size is forecast to increase by USD 11.01 billion at a CAGR of 6.99% between 2023 and 2028.

The market is experiencing significant growth, driven by several key factors. Firstly, the increasing trend of pet ownership, particularly in urban areas, is fueling demand for premium and convenient pet food options. Secondly, the growing popularity of customized pet foods catering to specific dietary needs and preferences is gaining traction among pet owners. However, challenges persist In the form of increasing product recalls due to contamination issues, which can negatively impact market growth and consumer trust. To mitigate these challenges, market players are focusing on implementing stringent quality control measures and adhering to regulatory standards to ensure the safety and health of pets. The high protein content in wet pet food makes it an attractive option for pets, particularly for those with active lifestyles. Wet pet food also contains essential minerals and vitamins that contribute to a balanced diet. Overall, the market is expected to continue its growth trajectory, driven by these trends and the evolving needs of pet owners.

What will be the Size of the Wet Pet Food Market During the Forecast Period?

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Wet pet food has become a popular choice among pet owners for dogs and cats, offering various benefits over dry pet food. The humanization of pets has driven the demand for premium wet pet food offerings. Pet owners are increasingly focusing on providing eco-friendly and sustainable options for their pets, leading to an increase In the popularity of wet pet food made from plant-based protein sources. However, for non-vegan pet owners, wet pet food comes in various protein sources like beef and lamb. Dental problems are a common concern for pet owners, and it can help address this issue as it promotes better oral hygiene.
The carbohydrate content is also an essential consideration, with grains being a common ingredient. Pet adoption rates have also contributed to the growth of the market. Veterinarians and diet suppliers recommend it for pets with specific dietary requirements. Dog and cat ownership continues to rise, further fueling the demand. Overall, the market is expected to grow as pet owners prioritize their pets' health and wellbeing.

How is this Wet Pet Food Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Cat food
  Dog food
  Others


Distribution Channel

  Pet-specialty stores and vet clinics
  Supermarkets and hypermarkets
  Convenience stores
  Others


Geography

  North America

    US


  Europe

    Germany
    UK
    France


  APAC

    Japan


  South America



  Middle East and Africa

By Product Insights

The cat food segment is estimated to witness significant growth during the forecast period. Wet pet food, particularly for cats, holds a significant market share due to its ability to provide essential hydration and cater to the preference for palatable and aromatic meals. As pets, specifically dogs and cats, are increasingly considered family members, the humanization of pets has led to an increase in demand for premium offerings. Wet pet food, with its high protein content and varied sources, including minerals, vitamins, and grains, meets the nutritional needs of these cherished companions. However, sustainability and environmental concerns are emerging factors influencing the market. Eco-friendly practices, such as reducing single-use plastics and promoting biodegradable packaging, are becoming essential for brands seeking customer loyalty and satisfaction.
Digestibility and nutrient absorption are crucial aspects of pet health, with it often outperforming dry food In these areas. Gastrointestinal health is a significant concern for pet owners, making the taste and aroma an essential selling point. Price sensitivity and the convenience of e-commerce and digital avenues have led to a rise in online sales. Pet specialty stores continue to dominate the market due to their expertise and personalized services. Urbanization and pet ownership rates have contributed to the growth of the market, with birds and other pets also benefiting from these nutritious offerings. Natural pet food startups are gaining popularity, offering organic and ethically sourced ingredients, further expanding the market landscape. Despite dental problems being more prevalent in dry pet food, the benefits, such as improved hydration and digestion, make it a preferred choice for many pet owners.

Get a glance at the market report of share of various segments Request Free

This dataset is about book subjects. It has 3 rows and is filtered where the books is Raining cats & dogs. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

In March 2020, Americans began experiencing numerous lifestyle changes due to the COVID-19 pandemic. Some reports have suggested that pet acquisition and ownership increased during this period, and some have suggested shelters and rescues will be overwhelmed once pandemic-related restrictions are lifted and lifestyles shift yet again. In May 2021, the ASPCA hired the global market research company Ipsos to conduct a general population survey that would provide a more comprehensive picture of pet ownership and acquisition during the pandemic. Although pet owners care for a number of species, the term pet owner in this study specifically refers to those who had dogs and/or cats. One goal of the survey was to determine whether data from a sample of adults residing in the United States would corroborate findings from national shelter databases indicating that animals were not being surrendered to shelters in large numbers. Furthermore, this survey gauged individuals' concerns related to the lifting of COVID-19 restrictions, and analyses examined factors associated with pet owners indicating they were considering rehoming an animal within the next 3 months. The data showed that pet ownership did not increase during the pandemic and that pets may have been rehomed in greater numbers than occurs during more stable times. Importantly, rehomed animals were placed with friends, family members, and neighbors more frequently than they were relinquished to animal shelters and rescues. Findings associated with those who rehomed an animal during the pandemic, or were considering rehoming, suggest that animal welfare organizations have opportunities to increase pet retention by providing resources regarding pet-friendly housing and affordable veterinary options and by helping pet owners strategize how to incorporate their animals into their post-pandemic lifestyles.

The association between pet ownership and the development of allergic and respiratory diseases has been the aim of several studies, however, the effects of exposure in adults remain uncertain. The aims of the present study were to investigate the prevalence of asthma and lung function status among dog and cat owners. This cross-sectional study was performed at two universities with students and workers who were allocated into 3 groups according to pet ownership in the previous year: cat owners, dog owners, and no pets (control group). Subjects underwent spirometry, bronchial challenge test with mannitol, skin prick tests, and questionnaires about animal exposures and respiratory symptoms. Control group comprised 125 subjects; cat owner group, 51 subjects; and dog owner group, 140 subjects. Cat owners had increased asthma prevalence (defined by symptoms and positive bronchial challenge test), but no changes in lung function compared to the control group. The dog owner group had lower spirometry values (forced expiratory volume in one second and lower forced vital capacity), but similar asthma prevalence, compared to the control group. In the cat owner group, excess of asthma may have an immunological basis, since we found an association with atopy. Although we did not have endotoxin data from volunteers' households, we postulated that low values of lung function were associated to exposure to endotoxins present in environments exposed to dogs.

Dataset Summary

The Animal Image Classification Dataset is a comprehensive collection of images tailored for the development and evaluation of machine learning models in the field of computer vision. It contains 3,000 JPG images, carefully segmented into three classes representing common pets and wildlife: cats, dogs, and snakes.

Dataset Contents

cats/: A set of 1,000 JPG images of cats, showcasing a wide array of breeds, environments, and postures.

dogs/: A diverse compilation of 1,000 dog images, capturing a multitude of breeds in various activities and settings.

snakes/: An assortment of 1,000 images of snakes, depicting numerous species in both natural and controlled habitats. Image Details:

Resolution: Each image maintains a uniform resolution of 256x256 pixels, providing clarity and consistency for model training.

File Format: JPG Color Space: RGB

Intended Applications

This dataset is primed for use in developing and testing AI models specialized in multi-class animal recognition. It offers valuable resources for researchers and hobbyists in fields such as zoology, pet technology, and biodiversity conservation.

Acknowledgements and Licensing

This dataset is a collective effort of various photographers and organizations. All images are distributed with permissions for academic and non-commercial usage, provided that proper attribution is given to the original sources.

Source

https://huggingface.co/datasets/AlvaroVasquezAI/Animal_Image_Classification_Dataset