Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images, basically in proportion to their frequency of occurrence in China, so it significantly increases the diversity for each breed over existing dataset. Furthermore, Tsinghua Dogs annotated bounding boxes of the dog’s whole body and head in each image, which can be used for supervising the training of learning algorithms as well as testing them.

This dataset is a copy of a subset of the full Stanford Dogs Dataset.

Source: http://vision.stanford.edu/aditya86/ImageNetDogs/

The original dataset contained 20,580 images of 120 breeds of dogs.

This subset contains 9884 images of 60 breeds of dogs.

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">

Dataset

This dataset was created by Moulhanout

Released under Database: Open Database, Contents: © Original Authors

Contents

Dataset

This dataset was created by Syed Abdul Qadir

Released under Apache 2.0

Contents

This dataset is for running the code from this site: https://becominghuman.ai/building-an-image-classifier-using-deep-learning-in-python-totally-from-a-beginners-perspective-be8dbaf22dd8.

This is how to show a picture from the training set: display(Image('../input/cat-and-dog/training_set/training_set/dogs/dog.423.jpg'))

From the test set: display(Image('../input/cat-and-dog/test_set/test_set/cats/cat.4453.jpg'))

See an example of using this dataset. https://www.kaggle.com/tongpython/nattawut-5920421014-cat-vs-dog-dl

Dataset Card for "petfinder-dogs"

  Dataset Summary

Contains 700k+ 300px-wide images of 150k+ distinct dogs extracted from the PetFinder API in March 2023. Only those having at least 4 photos are present: Each subject has between 4 and 12 photos. This dataset aims to simplify AI work based on dogs' images and avoid rescraping thousands of them from the PetFinder API again and again.

About:

These data were collected to better understand how adoptable dogs in the US are relocated from state to state and imported from outside the US.The findings were published in a visual essay on The Pudding entitled Finding Forever Homes published in October 2019.

These data will not be updated.

Data Collection:

Using the PetFinder API, details about all 58,180 dogs available for adoption in the 50 US states and Washington DC on September 20, 2019 were collected. Since PetFinder does not provide an entry field for an animal’s location before arriving at its current organization, I parsed the text of each pet’s “description”. I started by limiting text to anything that came after the word “from” but before the word “to”, or after “located in”. I then analyzed the remaining text using entity recognition from the spacyR package. I manually checked the data for anything mislabeled.

In all, over 3,000 dogs were described as having originated in places different from where they were listed for adoption. The count discussed in this article (2,460) is lower because we eliminated any listings for animals from a vague region (e.g., “the south”, “the Carolinas”, “LA or TN”) instead of a specific state or country. We also assume that this is an underestimate since not all shelters or rescues will include this information in an animal’s PetFinder description. Any animals that were described as transported by their previous owners instead of by the rescue or shelter were also removed from our data.

Some dogs were listed as being from several places. For example, one was described as “rescued from the euthanasia list at a tiny Alabama shelter and brought to a rescue in Georgia”, but the dog was listed as available for adoption in Massachussetts. In this case, the earliest location is the one reported.

In 238 (9.7% of) cases, the dogs were shown as available for adoption in one state, but they still resided in another. For instance, a dog that was available for adoption in Washington had the disclaimer “Dogs will be transported from Texas upon approved match.” We still considered these to be “imports” since they are listed as available for adoption upon searching PetFinder for dogs in that state.

All data except for description was collected using PetFinder’s V2 API method get-animals as described in their documentation. Since the V2 API doesn’t return the full animal description, I was encouraged by the API maintainers to query the same animal profiles using the V1 API to acquire that information. Thus, I used all of the shelter ID’s returned from the V2 API calls to find all descriptions of dogs in each shelter and combine the two results by the animal’s unique ID.

Caveats:

• This data represents a single day of data. It was all collected on September 20, 2019
• Due to a bug in the PetFinder API, dogs in the state of Nevada had to be accessed by searching for individual zip codes instead of the state abbreviation (as suggested to me by the API maintainers).

License:

Data released under MIT License

This dataset is a redistribution of the following dataset. https://github.com/suzuki256/dog-dataset The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors.

This dataset displays the name, breed, and approximate location of dogs in Cambridge. It is based on dog license data collected by Cambridge's Animal Commission. All locations listed in this dataset have been obscured to protect privacy. Please see the limitations section below for more information.

To identify genetic changes underlying dog domestication and reconstruct their early evolutionary history, we generated high-quality genome sequences from three gray wolves, one from each of the three putative centers of dog domestication, two basal dog lineages (Basenji and Dingo) and a golden jackal as an outgroup. Analysis of these sequences supports a demographic model in which dogs and wolves diverged through a dynamic process involving population bottlenecks in both lineages and post-divergence gene flow. In dogs, the domestication bottleneck involved at least a 16-fold reduction in population size, a much more severe bottleneck than estimated previously. A sharp bottleneck in wolves occurred soon after their divergence from dogs, implying that the pool of diversity from which dogs arose was substantially larger than represented by modern wolf populations. We narrow the plausible range for the date of initial dog domestication to an interval spanning 11–16 thousand years ago, predating the rise of agriculture. In light of this finding, we expand upon previous work regarding the increase in copy number of the amylase gene (AMY2B) in dogs, which is believed to have aided digestion of starch in agricultural refuse. We find standing variation for amylase copy number variation in wolves and little or no copy number increase in the Dingo and Husky lineages. In conjunction with the estimated timing of dog origins, these results provide additional support to archaeological finds, suggesting the earliest dogs arose alongside hunter-gathers rather than agriculturists. Regarding the geographic origin of dogs, we find that, surprisingly, none of the extant wolf lineages from putative domestication centers is more closely related to dogs, and, instead, the sampled wolves form a sister monophyletic clade. This result, in combination with dog-wolf admixture during the process of domestication, suggests that a re-evaluation of past hypotheses regarding dog origins is necessary.

The moviesAnalyzed.csv file is a comma-separatede-value file with thedata used in Ghirlanda S, Acerbi A, Herzog H, "Dog movie stars and dogbreed popularity," currently under review at Proceedings of the RoyalSociety of Lomdon, B. The columns in the file have the meaning given below. When a piece ofinformation was not found or cannot be computed, it is given as NA(see paper for possible reasons).

The quantities before[n], after[n], and effect[n] are calculated asgiven in the paper.

dog: name of the dog actor breed: the portrayed dog's breed year: the year of movie release title: the movie title earnings1: movie earnings during the opening weekend (in 2012 USD) earnings: total movie earnings (in 2012 USD) disney: whether the movies has been produced by the Walt Disney Company before[n]: the n-year popularity trend of the considered breed beforemovie release after[n]: the n-year popularity trend of the considered breed aftermovie release popularity[n]: average number of registrations for the consideredbreed in the 2n+1 years around movie release (between n years beforeand n years after) effect[n]: the n-year effect of the movie on the breed's popularity trend excess[n]: registrations of the considered breed attributable to movierelease (actual registrations over the n years after movie releaseminus registrations predicted based on the trend observed n yearsbefore movie release) viewers: estimated number of people who saw the movie viewers1: estimated number of people who saw the movie over itsopening weekend

This dataset is a modelled dataset, describing a lower estimate of dogs 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 lower 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

Periodic canine population studies establish essential frames of reference for analyzing trends in demographics and the prevalence of problematic behaviors. In this study, we hosted a public, online questionnaire to capture up-to-date demographic and behavior problem metrics. Surveyed problematic behaviors include fear/anxiety, aggression, jumping, excessive barking, coprophagia, compulsion, house soiling, rolling in repulsive materials, overactivity/hyperactivity, destructive behavior, running away/escaping, and mounting/humping. A total of 3201 dog owners submitted information about 5018 dogs, spanning mixed and pure breeds. Males and female dogs were equally represented; a majority of which were neutered. The prevalence of canine behavior problems was 85% in the unbiased, filtered results. We found gender, neuter status, origin, and lineage to have a notable effect on the prevalence of behavior problems. We also found age, neutered status, origin, and lineage to have a notable effect on the number of behavior problems per dog. Owners were asked to provide details of any behavior problem they reported such as intensity, frequency and situation in which the behavior problem occurred. We examined the problematic behaviors in terms of their overall prevalence, and characteristics, and computed correlations between the various behavior problems.This dataset includes:- The raw data.- The data dictionary to interpret the raw data.- A link the GitHub repository where analysis was performed.Change Log:- Version 1 (28 Nov 2018) - Initial release.- Version 2 (1 Aug 2019) - Fixed broken link to related software repository.- Version 3 (26 Sep 2019) - Fixed broken link to related software repository.- Version 4 (23 Jan 2022) - Added change log to Mendeley Data description. - Fixed broken link to related software repository.

In 2023, Germany had the highest pet dog population in the European Union, with more than 10 million dogs. Spain ranked second with a dog population of 9.3 million. Other countries, like Greece and Denmark, had comparatively smaller dog populations of approximately 655,000 and 643,000, respectively. Pet dogs in Europe The number of pet dogs in Europe has witnessed a notable increase since 2010, increasing from around 73 million in 2010 to more than 92 million in 2021. This positive trend was accompanied by a similar growth in the number of pet-owning households in Europe, which has increased by an estimated 20 million in the period between 2010 and 2021. Pet food industry in Europe Despite a marked increase in the dog population along with the number of pet-owning households in Europe, this has not been translated in a similarly significant increase in pet food sales in the continent. The annual sales volume of pet food products remained relatively stable in the last decade, until increasing in 2021 to about 10.2 million tons of pet food products. The largest pet food manufacturer in Europe was the Belgian company United Petfood Producers, with an annual revenue stream of approximately 800 million U.S. dollars in 2021.

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.

The gradually growing number of pet dogs owned by Russian families dropped in significantly in 2019, measuring at 16.8 million dogs countrywide. In 2022, the figures recovered up to 17.6 million pet dogs in Russian households.

 Domestic pets in Russia

The love for domestic animals in Russia is well-known. So much so that, the share of households owning at least one cat or dog has been growing year-on-year. When compared, cats are the most favored pets by Russians, with 34 percent of households owning at least one in 2021. Such affinity has been translating into the steadily growing population of domestic cats in the country. 

Domestic animals’ tax in Russia

In 2019, Russian mass media repeatedly voiced legislation related to tax imposition on domestic animals to allegedly be approved during 2020. The main driver behind this measure as sources claim is to improve domestic animals’ wellbeing countrywide. Nonetheless, many argue that such measurements could have the opposite effect and soar the issue of homeless pets in the country. In 2019, nearly every fifth pet owner in Russia picked up their domestic animal on the street .

Dataset

This dataset was created by Arpit Jain

Contents

Active Dog Licenses. 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.