Dogs breeds

https://images.fineartamerica.com/images/artworkimages/mediumlarge/3/cats-and-dogs-together-white-web-banner-good-focused.jpg" alt="aa">

This dataset contains all the information related to 566 dogs breeds. Data scraped from Wikipedia pages order to collect data.

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.

Dog Person

## Overview

Dog Person is a dataset for object detection tasks - it contains Dogs Cats Person annotations for 2,574 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).

All dog owners residing in NYC are required by law to license their dogs. The data is sourced from the DOHMH Dog Licensing System, 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.

This dataset is useful for municipal governments, veterinarians, and researchers who are interested in pet ownership patterns, compliance with local licensing laws, and demographic analysis of pet ownership. It can also aid in public health monitoring, such as tracking rabies vaccinations, which are often required for licensing.

Data scientists and analysts can perform various types of analytics such as:

• Descriptive Statistics: Analyze the distribution of dog breeds, the average age of dogs, and the licensing compliance rates across different zip codes.
• Geospatial Analysis: Map the data to see geographic patterns in pet ownership, breed popularity, or areas with lower licensing compliance, which can help in targeted public health and safety campaigns.
• Time Series Analysis: If longitudinal data is available, trends over time in pet registrations, renewals, and compliance could be evaluated.
• Predictive Modeling: Model the likelihood of license renewal or the factors influencing it, using logistic regression or other suitable predictive models.
• Cluster Analysis: Identify clusters of similar zip codes based on dog population characteristics, which can help in resource allocation for animal control agencies.

National Dog Database: Registration Information - All years presented are a snapshot as at 31 May. The dog registration year is from 1 July - 30 June. - The information presented is a statistical snapshot of what was currently listed on the National Dog Database (NDD) for the sector at the given date. While the NDD contains information about owners that may be viewed by councils, such information is not made publicly available. - The NDD snapshot statistics, some of which have been presented here, do not include any personal identifying information about individual dog owners. - In order to maintain a national view of dog, owner, registration and infringement information from territorial authorities, councils provide an electronic batch file to the NDD on a regular basis that includes any insertions, updates or deletions for dog, owner, registration and infringement information that has occurred in the batch period - The NDD is maintained by Equinox IT on behalf of the Department of Internal Affairs. Neither Equinox nor the Department of Internal Affairs are responsible for the currency or accuracy of this information.

Accident Compensation Corporation - Dog bite claims information - All years presented are as at 30 June. - Fewer than 3 claims in a financial year are not recorded for individual councils; but, may be included in the sector total. - The ACC statistics given to the Department of Internal Affairs, some of which have been presented here, do not include any personal identifying information.

Ministry of Justice - Dog Control Act prosecutions information - All years presented are as at 30 June. - The MoJ statistics given to the Department of Internal Affairs, some of which have been presented here, do not include any personal identifying information.

The Department of Internal Affairs disclaims and excludes all liability for any claim, loss, demand or damages of any kind whatsoever (including for negligence) arising out of, or in connection with, the use of this information.

This dataset is about books. It has 2 rows and is filtered where the book is The people with the dogs. It features 7 columns including author, publication date, language, and book publisher.

ProcedureWe have conducted two surveys in Germany, both were developed by Jesko Wilke, a freelancer journalist of the German ‘Dogs’ magazine. The data were collected online by the magazine’s own website (www.dogs-magazin.de). The surveys were described in detail in Kubinyi et al., 2009; Turcsán et al., 211 and Turcsán et al., 2017. Both surveys comprised two parts. The first part collected information about the demographic characteristics of the owners and dogs, as well as about the dog keeping practices. Twelve of these questions were the same in both surveys, eight were present in only one. The second part was different in the two surveys. The Survey 1 aimed at measuring the dogs’ general behaviour tendencies (personality) and was developed based on a human Big Five Inventory. This questionnaire contained 24 items (e.g. „My dog is calm, even in ambiguous situations”), for each item the owners were asked to indicate the level of agreement on a 3-point scale (true, partly true, not true). Our previous results using principal component analysis have revealed that 17 items out of the 24 belonged to four components, labelled as calmness, trainability, dog sociability, and boldness, all traits with middle or high internal consistency.The Survey 2 listed 12 examples of typical behaviour problems like „ My dog most often does not even attend me when I call him/her back”. Again, the owners indicated for each statement how far they agree with it using a 3-point scale. The questions were designed to assess not (only) the frequency of behaviour problems of the dogs but (also) the owners’ attitude towards these behaviour; i.e. if he/she considers them as problematic. In the current dataset, we recoded responses into a binary (yes/no) format: responses of "agree" or "partly agree" were categorized as "yes", while "disagree" was categorized as "no".SubjectsOn total, we collected responses from N = 14,004 dog owners in the first survey and N = 10,240 in the second. In the current dataset, we excluded reports with- missing data- duplicate entries (i.e., cases where owners submitted multiple reports for the same dog)- reports on mixed-breed dogs- reports on breeds where the cephalic index of the breed was unknown- reports when the cephalic index of the breed fell between 50 and 53, and between 62 and 65.Finally, to prevent a few highly popular breeds from disproportionately influencing group values, we capped the number of individuals per breed at 100. If a breed exceeded this threshold, we randomly selected 100 individuals for the final dataset.Kubinyi, E., Turcsán, B. & Miklósi, Á. Dog and owner demographic characteristics and dog personality trait associations. Behavioural Processes 81, 392–401 (2009).Turcsán, B., Kubinyi, E. & Miklósi, Á. Trainability and boldness traits differ between dog breed clusters based on conventional breed categories and genetic relatedness. Applied Animal Behaviour Science 132, 61–70 (2011).Turcsán, B., Miklósi, Á. & Kubinyi, E. Owner perceived differences between mixed-breed and purebred dogs. PLoS ONE 12, (2017).

Dataset used in the article "Does Visual Stimulation by Photographs of Cats and Dogs Make People Happier and More Optimistic?"ColumnsIDis_preview: true - response by the researcher to check the questionnaire, it should be removedremove: respondent checked that his/her responses are not valid and should not be used in future analysisfinished_proc: percentage of the questionnaire finisheddate_time: filing of the questionnaire started at this timeduration_formatted: duration of the filling of the questionnairebrowserbrowser_versionOS: operating systempriming: true - primed group, false - control groupcat_dog: objects on photos showngenderage: in yerssex_o: attraction to people of the opposite sex (scale 1 - 7)sex_s: attraction to people of the same sex (scale 1 - 7) orientation: computed as the difference of previous twomood: actual mood (scale 0 - 5)condition_phys: physical condition (scale 0 - 5)condition_psych: mental condition (scale 0 - 5)life_quality: life quality (scale 0 - 5)optimism: mean of previous threeoptimism_zskore: z-score of the previous children_own: how many children does respondent havewanted_sons: total number of sons which respondent would like to havewanted_daughters: total number of daughters which respondent would like to havewanted_children: a sum of previous twoliking_dogs: how much respondent likes dogs (scale 1 - 100)present_whenever_dog: respondent has ever kept a dogpresent_now_dog: respondent keeps dog nowpresent_Ndogs: how many dogs does respondent keep now liking_cats: how much respondent likes cats (scale 1 - 100)present_whenever_cat: respondent has ever kept a catpresent_now_cat: respondent keeps cat nowpresent_Ncats: how many cats does respondent keep now

Context

This dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.

Content

• Number of categories: 120
• Number of images: 20,580
• Annotations: Class labels, Bounding boxes

Acknowledgements

The original data source is found on http://vision.stanford.edu/aditya86/ImageNetDogs/ and contains additional information on the train/test splits and baseline results. If you use this dataset in a publication, please cite the dataset on the following papers: Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. Secondary: J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009.

Inspiration

• Can you correctly identify dog breeds that have similar features, such as the basset hound and bloodhound?
• Is this chihuahua young or old?

Dogs and cats have become the most important and successful pets through long-term domestication. People keep them for various reasons, such as their functional roles or for physical or psychological support. However, why humans are so attached to dogs and cats remains unclear. A comprehensive understanding of the current state of human preferences for dogs and cats and the potential influential factors behind it is required. Here, we investigate this question using two independent online datasets and anonymous questionnaires in China. We find that current human preferences for dog and cat videos are relatively higher than for most other interests, with video plays ranking among the top three out of fifteen interests. We also find genetic variations, gender, age, and economic development levels notably influence human preferences for dogs and cats. Specifically, dog and cat ownership are significantly associated with parents’ pet ownership of dogs and cats (Spearman’s rank correlation c..., , , # Human preferences for dogs and cats in China: the current situation and influencing factors of watching online videos and pet ownership

https://doi.org/10.5061/dryad.qfttdz0rr

This dataset contains three CSV data files, each corresponding to one of the three parts described in the study.

Description of the data and file structure

**“1, bilibili.csv†**: contains data extracted from the Bilibili website. Each row in the dataset represents yearly data for each popular channel. Missing data are indicated with NA.

• ID:Â The serial number for each video, ranging from 1 to 167368.
• year: The year the video was published on the website, from 2009 to 2021.
• Videourl:Â The URL of the video.
• plays:Â The total number of plays for the video.
• likes: The total number of likes for the video.
• sort: The ranking of the video in terms of play count among all popular videos in its channel for that year.
• channelID: The I...

This dataset is a modelled dataset, describing the predicted population of dogs per postcode district (e.g. YO41). This dataset gives the upper estimate for population for each district, and was 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 postcode district 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

Thermal People And Dogs

## Overview

Thermal People And Dogs is a dataset for object detection tasks - it contains Thermal annotations for 619 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).

About this dataset

Do you want to help a dog in need? This dataset contains information on over 3,000 adoptable dogs across the United States. By understanding patterns of dog movement and relocation, we can help these animals find their forever homes.

The data includes information on the origin of each dog, as well as the state they are currently listed for adoption in. This can be used to understand patterns of dog movement across the country, and how different states rely on imported dogs for adoption.

There are several things to keep in mind when using this dataset: - The data represents a single day of data. It is possible that patterns have changed since then. - The data only includes adoptable dogs that were listed on PetFinder.com

How to use the dataset

This dataset of adoptable dogs in the US was collected to better understand how animals are relocated from state to state and imported from outside the US. The data includes information on over 3,000 dogs that were described as having originated in places different from where they were listed for adoption. The findings were published in a visual essay on The Pudding entitled Finding Forever Homes published in October 2019.

This dataset is a snapshot of data collected on a single day and does not include all adoptable dogs in the US. However, it provides valuable insights into the whereabouts of these animals and the journey they take to find their forever homes

So, how should you use it?

This dataset is a great resource for understanding how adoptable dogs are relocated from state to state and imported into the US. The data provides information on the origin of each dog, as well as the state they are currently listed for adoption in. This can be used to understand patterns of dog movement across the country, and how different states rely on imported dogs for adoption.

Research Ideas

• Finding out how many of each type and breed of dog are brought into shelters across the USA in a given year.
• Seeing which states have the most imports of dogs and what breeds/types those are.
• Determining if there are any trends in the types/breeds of dogs being brought into shelters (e.g. more pit bulls than golden retrievers)

Columns

File: dogTravel.csv | Column name | Description | |:------------------|:---------------------------------------------------------------------| | contact_city | The city where the animal is located. (String) | | contact_city | The city where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | description | A description of the animal. (String) | | description | A description of the animal. (String) | | found | The date the animal was found. (Date) | | found | The date the animal was found. (Date) | | manual | A manual override for the animal's location. (String) | | manual | A manual override for the animal's location. (String) | | remove | The date the animal was removed from the dataset. (Date) | | remove | The date the animal was removed from the dataset. (Date) | | still_there | Whether or not the animal is still available for adoption. (Boolean) | | still_there | Whether or not the animal is still available for adoption. (Boolean) |

File: allDogDescriptions.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | contact_city | The city where the animal is located. (String) | | contact_city | The city where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | contact_state | The state where the animal is located. (String) | | description | A description of the animal. (String) | | description | A description of the animal. (String) | | url | The URL of the animal's profile on PetFinder. (String) | | url | The URL of the animal's profile on PetFinder. (String) | | type.x | The type of animal. (String) | | type.x | The type of animal. (String) | | species | The species of the animal. (S...

Data gathered under the Control of Dogs Act 1986 in order to enforce legislation. Current data being held in order to initiate legal proceedings. Historical data is held in order to generate requested reports and statistics.

People, Dogs And Monkeys

## Overview

People, Dogs And Monkeys is a dataset for object detection tasks - it contains People annotations for 3,248 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).

The Cat and Dog Classification dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or a cat. This dataset is provided as a subset of photos from a much larger dataset of approximately 25 thousands.

The dataset contains 24,998 images, split into 12,499 Cat images and 12,499 Dog images. The training images are divided equally between cat and dog images, while the test images are not labeled. This allows users to evaluate their models on unseen data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7367057%2F498b0fc0a7a8cf40ac4337da82a4ebc5%2Fhow-to-introduce-a-dog-to-a-cat-blog-cover.webp?generation=1696702214010539&alt=media" alt="">

About This Dataset

The Roboflow Thermal Dogs and People dataset is a collection of 203 thermal infrared images captured at various distances from people and dogs in a park and near a home. Some images are deliberately unannotated as they do not contain a person or dog (see the Dataset Health Check for more). Images were captured both portrait and landscape. (Roboflow auto-orient assures the annotations align regardless of the image orientation.)

Thermal images were captured using the Seek Compact XR Extra Range Thermal Imaging Camera for iPhone. The selected color palette is Spectra.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/h9vhrqB.png" alt="Man and Dog">

Usage

Thermal images have a wide array of applications: monitoring machine performance, seeing in low light conditions, and adding another dimension to standard RGB scenarios. Infrared imaging is useful in security, wildlife detection,and hunting / outdoors recreation.

This dataset serves as a way to experiment with infrared images in Roboflow. (Or, you could build your own night time pet finder!)

Collecting Custom Data

Roboflow is happy to improve your operations with infrared imaging and computer vision. Services range from data collection to building automated monitoring systems leveraging computer vision. Reach out for more.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Animals Dataset

  Dataset Description

This dataset contains images of three animal categories: cats, dogs, and pandas.

  Dataset Structure

The dataset is organized into training and testing splits: Animals_dataset/ ├── train/ │ ├── cats/ │ ├── dogs/ │ └── panda/ └── test/ ├── cats/ ├── dogs/ └── panda/

  Dataset Statistics

Total Images: 600 Training Images: 480 (80.0%) Testing Images: 120 (20.0%)

  Class Distribution

Training… See the full description on the dataset page: https://huggingface.co/datasets/Melisa13/Animals_dataset.

Dogs and Cats Online Data 2023-2024

Additional resource. SNP genotypes from the Moroccan dogs used to reconstruct the genealogy. Files in Plink format (.ped, .map) for 163,594 autosomal SNPs for 196 Moroccan dogs.Dataset S1. Reconstructed pedigrees including all the individuals sampled in the three populations. The name of the father and mother is indicated as well as the number of offspring and mates. For Morocco and Italy, three different offspring numbers for each individual correspond to (i) the total number of offspring, (ii) a corrected measure considering only one offspring per parent pair in litters, and (iii) a measure excluding individuals sampled as pups.Dataset S2. Social network data for the Moroccan population. These individual social network measures are the average of measures collected from 2017 to 2020. For each year and every individual, values were z-transformed. The dataset also includes information about sex of individuals and the reproductive success data inferred from the genealogy (the number of offspring and the number of reproductive mates).Dataset S3. R scripts used for the data analysis. R markdown file shortly describing and compiling the different analyses performed on pedigrees: data extraction, computation and comparison of the number of offspring and mates, tree parentage figures, identification of close inbreeding cases, sex-biased dispersal in Ukraine, simulation of random mating, social network analysis, computation of the average number of full and halfsiblings, code to run pedigree simulation, and analyses of simulation outputs (close inbreeding cases, pedigree statistics, subsampling).