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.

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 Stanford Dogs dataset contains 20,580 images of 120 classes of dogs from around the world, which are divided into 12,000 images for training and 8,580 images for testing.

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/cats_vs_dogs.

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

add this dataset alongside @jessicali9530/stanford-dogs-dataset as a helper

Dogs Intelligence and Size

An Exploration of Breed-Based Characteristics

By len fishman [source]

About this dataset

This dataset provides valuable insights into the potential relationship between size and intelligence in different breeds of dogs. It includes data from a research conducted by Stanley Coren, a professor of canine psychology at the University of British Columbia, as well as breed size data from the American Kennel Club (AKC). With this dataset, users will be able to explore how larger and smaller breeds compare when it comes to obedience and intelligence. The columns present in this dataset include Breed, Classification, Obey (probability that the breed obeys the first command), Repetitions Lower/Upper Limits (for understanding new commands). From examining this data, users may gain further insight on our furry friends and their behaviors. Dive deeper into these intricate relationships with this powerful dataset!

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How to use the dataset

This dataset provides insight into how intelligence and size may be connected in dogs. It includes information on dog breeds, including their size, how well they obey commands, and the number of repetitions required for them to understand new commands. This can help pet owners who are looking for a dog that fits their lifestyle and residential requirements.

To get started using this dataset, begin by exploring the different attributes included: Breed (the type of breed), Classification (the size classification of the dog - small, medium or large), height_low_inches & height_high_inches (these are the lower limit and upper limit in inches when it comes to the height of the breed), weight_low_lbs & weight_high lbs (these are the lower limit and upper limit in pounds when it comes to the weight of a breed). Also included is obey (the probability that a particular breed obeys a given command) as well as reps_lower & reps_upper which represent respectively lower and upper repetitions required for a given breed to understand new commands

Once you have an understanding of what each attribute represents you can start exploring specific questions such as 'how many breeds fit in within certain size categories?', 'what type of 'obey' score do large breeds tend to achieve?', or you could try comparing size with intelligence by plotting out obey against both reps_lower & reps_upper . If higher obedience scores correlate with smaller numbers on either attributes this might suggest that smaller breeds tend require fewer repetitions when attempting learn something new.

By combining these attributes with other datasets such as those focusing on energy levels it’s possible create even more specific metrics based questions regarding which types of dogs might suit certain lifestyles better than others!

Research Ideas

• Examining the correlation between obedience and intelligence in different dog breeds.
• Investigating how size is related to other traits such as energy level, sociability and trainability in a particular breed of dog.
• Analyzing which sizes are associated with specific behavior patterns or medical issues for dogs of various breeds

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: AKC Breed Info.csv | Column name | Description | |:-----------------------|:--------------------------------------------------------------| | Breed | The breed of the dog. (String) | | height_low_inches | The lower range of the height of the dog in inches. (Integer) | | height_high_inches | The upper range of the height of the dog in inches. (Integer) | | weight_low_lbs | The lower range of the weight of the dog in pounds. (Integer) | | weight_high_lbs | The upper range of the weight of the dog in pounds. (Integer) |

File: dog_intelligence.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------------------------------| | Breed | The breed of the dog. (String) | | Classification | The size classification of the dog according to the American Kennel Club. (String) | | obey | The probability that the breed obeys the first command. (Float) | | reps_lower | The lower limit of repetitions to understand new commands. (Integer) | | reps_upper | The upper limit of repetitions to understand new commands. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit len fishman.

JbIPS/stanford-dogs dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

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.

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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

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

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.

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.

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..

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.

Humans possess intuitive associations linking certain non-redundant features of stimuli – e.g., high-pitched sounds with small object-size (or similarly, low-pitched sounds with large object-size). This phenomenon, known as crossmodal correspondence, has been identified in humans across multiple different senses. There is some evidence that non-human animals also form crossmodal correspondences, but the known examples are mostly limited to the associations between the pitch of vocalisations and the size of callers. To investigate whether domestic dogs, like humans, show abstract pitch-size association, we first trained dogs to approach and touch an object after hearing a sound emanating from it. Subsequently, we repeated the task but presented dogs with two objects differing in size, only one of which was playing a sound. The sound was either high- or low-pitched, thereby creating trials that were either congruent (high-pitch from small object; low-pitch from large objects) or incongruent (the reverse). We found that dogs reacted faster on congruent versus incongruent trials. Moreover, their accuracy was at chance on incongruent trials, but significantly above chance for congruent trials. Our results suggest that non-human animals show abstract pitch-sound correspondences, indicating these correspondences may not be uniquely human but rather a sensory processing feature shared by other species.

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.