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.

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.

1) Data Introduction • The Stanford Dogs dataset is a high-resolution image dataset consisting of 17 representative dog breeds, including Maltese, Shih Tzu, Afghan Hound, Irish Wolfhound, Saluki, Scottish Deerhound, Sealyham Terrier, Airedale, Tibetan Terrier, Bernese Mountain Dog, Entlebucher, Basenji, Pug, Leonberger, Great Pyrenees, Samoyed, and Pomeranian.

2) Data Utilization (1) Characteristics of the Stanford Dogs Dataset: • The dataset is well-suited for capturing subtle visual differences between dog breeds with similar appearances and can be effectively used for fine-grained breed classification and image recognition experiments.

(2) Applications of the Stanford Dogs Dataset: • Dog Classification Model Development: It can be used to develop artificial intelligence models that automatically classify dog species by learning the visual features of various dog species. • Detailed Image Recognition Study: It can be used for research in the field of fine-grained visual categorization that distinguishes minute differences between dog species with similar appearance.

Stanford Dogs Dataset Dog Breed

## Overview

Stanford Dogs Dataset Dog Breed is a dataset for object detection tasks - it contains Dogs annotations for 20,491 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

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.

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

amaye15/stanford-dogs dataset hosted on Hugging Face and contributed by the HF Datasets community

Detection Of Stray Dogs

## Overview

Detection Of Stray Dogs is a dataset for object detection tasks - it contains Stray Dogs annotations for 305 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).

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. dataset[0]["image"]… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/Cat_and_Dog.

Dataset Card for "Stanford-Dogs"

This is a non-official Stanford-Dogs dataset for fine-grained Image Classification.
If you want to download the official dataset, please refer to the here.

Activity Recognition On Dogs

## Overview

Activity Recognition On Dogs is a dataset for object detection tasks - it contains Dogs annotations for 707 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).

Context

To practice the generation of images using deep learning, a starter dataset like the Stanford dogs could be utilised. For this purpose, this dataset suffers from flaws like unequal dimensions and 'useless' data surrounding the dogs in the images. This dataset is a resized and cropped version of all the original Stanford dogs.

Content

The dataset contains 2 directories: 'annotations' and 'images'. The annotations directory contains the original annotations from the Stanford dogs dataset. Evidently, the dimensions of the image and bbox are no longer valid. The images are the newly resized and cropped images.

New Dogs

## Overview

New Dogs is a dataset for object detection tasks - it contains Dog annotations for 2,520 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).

How many dogs are there in the US? According to a pet owners survey, there were approximately 89.7 million dogs owned in the United States in 2017. This is an increase of over 20 million since the beginning of the survey period in 2000, when around 68 million dogs were owned in the United States.

Why has this figure increased?

The resident population of the United States has also increased significantly within this time period. It is, therefore, no surprise that the number of dogs owned in U.S. households has also increased, especially when considering that the household penetration rate for dog-ownership reached almost 50 percent in recent years.

The dog food market in the United States

The large number of dogs owned by Americans creates a lucrative market for pet food brands and retailers. Pedigree, the leading dry dog food name brand in the U.S., had sales amounting to around 550 million U.S. dollars in 2017. Pedigree also led the pack in the wet dog food category , with sales of around 240 million U.S. dollars in the same year.

A dataset with crowdsourced labels for aggregation and supervised classification.
It contains 400 images of dogs from the Stanford Dogs dataset (http://vision.stanford.edu/aditya86/ImageNetDogs/). Images of dogs that belong to 32 different breeds (classes) are included. Annotators were asked to provide two types of labelling: full labelling (each labeler is allowed to provide a single label for each image) and candidate labelling (each labeler is allowed to provide a set of candidate labels for each image). It includes a total of 61227 annotations (30628 full and 30599 candidate) obtained from a set of 1028 different labelers.

The labels were collected through the online crowdsourcing platform Amazon mTurk thanks to funds provided by the Basque Government through the Elkartek program (KK-2018/00071). The assignments were designed as sequences of 64 images that were given to the annotators. Each image in the sequence was provided together with a specific subset of possible labels (with the number of options ranging from 4 to 32), and a instruction for the annotator to perform a specific type of labelling (full or candidate). Each labeler performed at least one assignment. Not all the labelers completed the 64 annotations in their assignments.

The file 'whichdog.zip' contains a folder ('images') with the 400 images of dogs, a text file ('breed_names.txt') that indicates the names of the different breeds and their assigned label (a number in the interval from 0 to 31) and a CSV file ('whichdog_all_annots.csv') that contains the information about the annotations. Each row of the CSV file represents a single annotation, and each column shows:
- image_id: ID number of the image.
- is_candidate: indicates whether the requested labelling is full (0) or candidate (1).
- labeler_id: ID number of the labeler.
- time: time employed by the labeler to perform the annotation.
- answer: label or set of labels provided by the labeler as annotation.
- options: subset of possible labels shown to the labeler.
- assignment_id: ID number of the assignment
- sequence_point: number that indicates the point of the sequence of images of the assignment in which the annotation was provided.
- class: ground truth label of the image.

The datasets consists of genotype data from dogs in plink format.

ATOPYK2 plink files contains genotype data from five dog breeds with and without atopic dermatitis. The ATOPYK2 datafiles were used for imputation. These are pre-QC:ed with the following plink settings --maf 0.001 --geno 0.05 --mind 0.05

We recommend a second qc before doing the imputation. The imputation was then run per chromosome so for redoing the imputation, as was done in the publication to which this data is connected, use the following plink code:

plink --bfile ATOPYK2 --chr $chrN --make-bed --dog --allow-no-sex --keep-allele-order --maf 0.001 --geno 0.2 --out 'ATOPYK.chr'$chrN

The final datasets after imputation are datasets split breed wise and in plink format. These sets are QC:ed, and relatedness filtered. The "covar"-file includes fixed effects, these are different between depending on what covariates were significantly affecting the trait (atopic dermatitis) in each breed. These datasets are named GR (golden retriever), LR (labrador retriever), GSD (german shepherd) and WHWT (west highland white terrier). (Boxers are in the ATOPYK2 files but not used for final analyses, hence no breed specific datafile for boxer are included).

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

Object Detection Cat And Dogs

## Overview

Object Detection Cat And Dogs is a dataset for object detection tasks - it contains Animals annotations for 659 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).

mdrapha/dogs dataset hosted on Hugging Face and contributed by the HF Datasets community