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">

Small sample of the Kaggle Cats and Dogs dataset (https://www.kaggle.com/c/dogs-vs-cats/data).

Contains 1000 images for the train set (500 cats and 500 dogs), and 400 images for the test set (200 each).

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.

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

Dataset

This dataset was created by chetanimravan

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Syed Abdul Qadir

Released under Apache 2.0

Contents

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.

Dataset

This dataset was created by hongwei cao

Contents

Here are a few use cases for this project:

1. Dog Identification App: "dddog" can be used to build an app that helps users identify various dog breeds from images. This can be useful for pet owners, vets, or enthusiasts who wish to get detailed information about a specific dog breed.

2. Enhanced Security Surveillance: The model can be used in security cameras or surveillance systems, where it can identify and separate movement instances of a dog or a human. The information can be valuable in ensuring security in home or public spaces.

3. Animal Control & Welfare: Animal control agencies can use this model to track and monitor dog populations in various cities. They can identify both stray dogs and dogs with owners, helping with planning and executing animal welfare policies.

4. Augmented Reality Games: Developers can use this model for AR games where users need to identify or interact with virtual dogs or humans in real-world environments.

5. Smart Pet Doors: The model can be used in the development of "smart" pet doors which only operate when detecting a specific type of animal (dogs in this case) approaching, preventing unwanted animals from entering the house.

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.

alexrosen45/dogs dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by alessio sanna

Contents

The Stanford Dogs dataset consists of 20,580 images of different breeds of dogs.

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.

Fine-Grained Visual Classification (FGVC) is an important computer vision prob-lem that involves small diversity within the different classes, and often requires expert annotators to collect data.

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.

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

The Dogs dataset contains 10,400 training and 2,600 test images of dogs.

Dataset

This dataset was created by Chaitanya Sapre

Contents