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 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.

Brief

The data consist of 45.5K images of dogs and cats well distributed into 3 different sets. This is the first Dataset uploaded by me at Kaggle.

Download Api

> kaggle datasets download -d kunalgupta2616/dog-vs-cat-images-data

Content

Train Data - consist of 25000 images divided into equal half (12500 * 2) of cats and dogs images in separate directories.

Validation Data - consist of 8000 images divided into equal half (4000 * 2) of cats and dogs images in separate directories.

Test Data - consist of 12500 images of cats and dogs images.

sampleSubmission.csv - The submission file for the test data which can be found at link given under Acknowledgements.

Acknowledgements

This is an improvement to the dataset from the competition that can be found at this link. in terms of addition of more data and hierarchical distribution.

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

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.

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).

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...

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

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.

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.

PET_ASR001_CN is a cat and dog barking dataset. Cats and dogs each need to cover more than 10 breeds, including large, medium, and small dog breeds, with no less than 5 dogs per breed. Age 10% at 0-3 months. 20% at 3-10 months. 60% at 10 months-10 years old. 10% above 10 years old. Yap_Barking, full frequency band, high energy, continuous multiple Snarl_Show your teeth and roar, with lasting longer than Yap Growl_Low pitched roar, low pitched sound, no barking, energy in low frequency band… See the full description on the dataset page: https://huggingface.co/datasets/Appenlimited/pet_asr_001_cn.

This report analyses the number of pet cats and dogs in the United Kingdom. Cats and dogs are by far the most popular pets in the United Kingdom and as such, the total number of pet cats and dogs is a good proxy for the total number of pets. Due to poor and conflicting data availability, some of the historical data has been estimated by IBISWorld. The data is sourced from the Pet Food Manufacturers' Association (PFMA).

Description:

👉 Download the dataset here

This dataset consists of a diverse collection of images, tailored specifically for the task of Animal Image Classification Dataset in the domain of animal species. It contains 15 distinct folders, each corresponding to a unique animal class, with each folder representing the name of the animal species. The dataset is composed of a variety of images that have been preprocessed and prepared for use in machine learning applications.

Dataset Details:

Image Size: Each image in the dataset has been resized to dimensions of 224x224 pixels with 3 color channels (RGB), making them suitable for immediate use in neural networks.

Data Source: Images were sourced from publicly available databases on the web. They encompass various environments, lighting conditions, and angles, ensuring a rich and diverse representation of each animal class.

Classes: The dataset includes 15 animal classes such as cats, dogs, birds, elephants, lions, and more, with each class represented by images stored in its respective folder.

Download Dataset

Preprocessing and Augmentation:

The dataset underwent extensive preprocessing using OpenCV libraries, ensuring that all images were standardized to the same size. In addition to resizing, multiple augmentation techniques were applied to diversify the dataset and improve model generalization. These augmentations include:

Rotation: Random rotations applied to simulate different perspectives.

Flipping: Horizontal flips to account for variations in animal orientation.

Cropping: Random cropping to focus on various parts of the animal subjects.

Scaling: Minor scaling adjustments to simulate different zoom levels.

All preprocessing and augmentation were carried out to enhance the robustness of any model trained on this data, without the need for further augmentation steps. Therefore, the dataset is ready for immediate use in training deep learning models such as CNNs (Convolutional Neural Networks) or transfer learning models.

Applications:

This dataset is ideal for:

Image Classification: Train models to accurately classify different animal species.

Transfer Learning: Utilize pre-trained models to fine-tune performance on this dataset.

Computer Vision Research: Explore various computer vision tasks, such as animal identification, object detection, and species recognition.

Wildlife and Conservation Studies: Use the dataset to build Al systems capable of identifying animals in the wild for tracking and conservation efforts.

Potential Use Cases:

Education: For students and researchers to learn and experiment with animal classification using computer vision techniques.

Al and Machine Learning Competitions: A challenging dataset for machine learning competitions centered around image classification.

Mobile Applications: Can be used to develop apps for real-time animal identification using image recognition technology.

Dataset Format:

The dataset is structured for ease of use, with each folder containing images pertaining to a specific class. The file format is as follows:

Folder Structure: dataset/{class_name}/{image_files.jpg}

Image Type: JPEG/PNG

Annotations: No specific annotations are included, but each folder name serves as the label for the images within it.

This dataset is sourced from Kaggle.

Dataset Card for "dogs-cats-openimages"

More Information needed

Xogta Qaybinta Bisadda & Eeyga waxa loo habeeyey warbaahinta & madadaalada iyo warshadaha dalxiiska, oo ay ku jiraan sawirro badan oo internet-ka laga soo ururiyey oo leh qaraaro kala duwan oo u dhexeeya 367 x 288 ilaa 3456 x 4608 pixels. Xog-ururintan waxay diiradda saartaa kala-soocidda koontaroolada oo ay ku jiraan sharraxaadyo kala duwan sida aadanaha, bisadaha, eyda, iyo walxaha deegaanka sida derbiyada, miisaska, cawska, iyo biyaha oogooyinka, iyo kuwo kale.

Successfully perceiving risk and reward is fundamental to the fitness of an animal, and can be achieved through a variety of perception tactics. For example, mesopredators may ‘directly’ perceive risk by visually observing apex predators, or may ‘indirectly’ perceive risk by observing habitats used by predators. Direct assessments should more accurately characterize the arrangement of risk and reward; however, indirect assessments are used more frequently in studies concerning the response of GPS-marked animals to spatiotemporally variable sources of risk and reward. We investigated the response of a mesopredator to the presence of risk and reward created by an apex predator, where risk and reward likely vary in relative perceptibility (i.e., degree of being perceptible). First, we tested whether coyotes (Canis latrans) use direct or indirect assessments to navigate the presence of mountain lions (Puma concolor; risk) and kills made by mountain lions (reward) in an area where coyotes we...

Map shows all stray cats and dogs that are currently listed in AAC's database for no longer than a week. Most will be located at AAC, but some will be held by citizens, which will be indicated on the "At AAC" column. Please check http://www.austintexas.gov/department/lost-found-pet for more information.

O le Cat & Dog Segmentation Dataset ua mamanuina mo le aufaasālalau & fa'afiafiaga ma pisinisi tau turisi, e fa'aalia ai se aofa'iga lautele o ata e aoina i luga ole laiga ma fa'ai'uga e ese mai le 367 x 288 i le 3456 x 4608 pixels. O lenei fa'amaumauga o lo'o taula'i i le fa'avasegaina o fa'asologa ma e aofia ai fa'amatalaga eseese e pei o tagata, pusi, maile, ma elemene o le si'osi'omaga e pei o puipui, laulau, mutia, ma vai, ma isi.