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.… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/Cat_and_Dog.

A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cats_vs_dogs', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/cats_vs_dogs-4.0.1.png" alt="Visualization" width="500px">

This dataset contains de-identified participant responses to a personality measures about their cat's personality (adapted Scottish wildcat personality measure), including information on the age and sex of the cat. The personality measure has 52 items that each contain a personality characteristic and participants were asked to rate the extent their cat demonstrated that characteristic on a seven-point scale. This dataset also indicates if the cat came from Australia or New Zealand.

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

64x64 resiezed rgb images to test your generative models

A dataset containing 5 common cat's breeds: 1. Bengal 2. Domestic Shorthair 3. Maine Coon 4. Ragdoll 5. Siamese

Dataset used for image classification and image detection.

Enjoy Using! 😄 Dog's Breed Dataset Chatbot AI Q&A Countries GDP

This dataset is a modelled dataset, describing an upper estimate 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 upper 95th percentile 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. Attribution statement: ©Crown Copyright, APHA 2016

Dogs vs. Cats is a common binary classification task in the field of computer vision and machine learning. It involves distinguishing between images of dogs and images of cats..

According to a national pet owners survey, there was a total of approximately 95.6 million cats living in households in the United States in 2017. In the same year, some 68 percent of all U.S. households owned at least one pet.

Increasing pet expenditure

Whilst the number of households owning cats, and pets in general, has remained relatively consistent over the last few years, pet industry expenditure has steadily grown. Consumers are expected to spend a record breaking 75.38 billion U.S. dollars on their pets in 2019. The majority of pet market revenue comes from food sales, followed by veterinary care costs.

Shopping location preferences

When it comes to shopping locations, most consumers still purchase their pet products in physical retail stores. However, the number of consumers buying pet products online is on the rise. Dry cat food was the number one pet product bought online by cat owners in the United States in 2018.

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A small audio dataset generated from YouTube videos. The dataset has 566 cat sounds and 484 dog sounds. This was used as a contextual data source for research into contextual machine learning.

All efforts have been made to ensure this dataset was collected in line with copyright legislation regarding fair use. All samples were collected via YouTube and any derivative works of this provided dataset must reference YouTube and the author of this dataset.

This work was completed during a PhD programme at the University Of Greenwich.

The Dutch are cat-loving people. In 2022, the total cat population in the country amounted to almost two million. By comparison: according to the most recent figures, there were about 1.5 million pet dogs in the Netherlands.

 The most popular furry friend

Cats are the most frequently kept pets in the Netherlands. In 2022, one quarter of Dutch households owned a pet cat. This was slightly higher than the share of households owning a dog (21 percent), another beloved four-legged friend. Popular cats include the European Shorthair as well as various mixed breeds.

Cats in Europe

 The number of pet cats amounted to over 127 million in Europe in 2022. Germany had a cat population of roughly 15.2 million that year, or over five times as large as the Netherlands. Other cat-loving nations included France and Italy (14.9 and 10.2 million cats respectively). The share of households owning a cat was highest in Romania though, where nearly half of all households owned at least one furry friend. In Poland and Hungary too, at least three out of ten households were cat owners. On the other hand, in Greece just 13 percent of households were ruled by one or more cats.

Dataset Card for Dataset Name

  Dataset Description

Cite the dataset as: Patrick Fleith. (2023). Controlled Anomalies Time Series (CATS) Dataset (Version 2) [Data set]. Solenix Engineering GmbH. https://doi.org/10.5281/zenodo.8338435

  Dataset Summary

The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies. The CATS Dataset… See the full description on the dataset page: https://huggingface.co/datasets/patrickfleith/controlled-anomalies-time-series-dataset.

nateraw/auto-cats-and-dogs

Image Classification Dataset

  Usage

from PIL import Image from datasets import load_dataset

def pil_loader(path: str): with open(path, 'rb') as f: im = Image.open(f) return im.convert('RGB')

def image_loader(example_batch): example_batch['image'] = [ pil_loader(f) for f in example_batch['file'] ] return example_batch

ds = load_dataset('nateraw/auto-cats-and-dogs') ds =… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/auto-cats-and-dogs.

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.

This is an example project demonstrating image classification via Convolutional Neural Networks. It imports an example dataset containing several thousand test and training images of cats and dogs, with which we can train and evaluate our model.

This project is heavily adapted from its initial publication at: https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8

This statistic presents the estimated number of cats owned by households in Norway in selected years from 2010 to 2022. The cat population in Norway was measured at approximately 783,000 in 2022.

Management Accounts

Number of confirmed cases of FSE (Feline Spongiform Ecephalopathy) in domestic cats by year reported. This dataset includes the following fields: Year reported (year case reported to APHA); Number of cases. Please note: this data is available as part of a wider report on TSE surveillance, published on gov.uk. Attribution statement: ©Crown Copyright, APHA 2016