Dataset Card for Stanford Dogs

The Stanford Dogs 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. Contents of this dataset:

Number of categories: 120

Number of images: 20,580

Annotations: Class labels, Bounding boxes (not imported to HF)

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

Paper:… See the full description on the dataset page: https://huggingface.co/datasets/maurice-fp/stanford-dogs.

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.

About the Dataset:

The Cats and Dogs Classification dataset is a widely used benchmark dataset in the field of computer vision. It consists of thousands of images of cats and dogs, with each image labeled as either a cat or a dog. This dataset serves as a fundamental resource for training and evaluating machine learning models for image classification tasks. Researchers and practitioners leverage this dataset to develop and test algorithms that can accurately distinguish between images of cats and dogs, contributing to advancements in computer vision technology.

Dogs vs. Cats Image Classification

The "Cats vs. Dogs" dataset is a comprehensive collection of high-quality images specifically curated for binary image classification tasks, focusing on distinguishing between images of cats and dogs. This dataset is designed to serve as an ideal benchmark for evaluating deep learning and data science models in the domain of image classification.

Dataset Composition: The dataset comprises three main folders, meticulously organized to facilitate model training, validation, and evaluation:

1. Training Set: This folder contains a total of 20,000 images, equally split between 10,000 images of cats and 10,000 images of dogs. These images have been handpicked to cover a wide range of poses, backgrounds, and lighting conditions, ensuring a diverse and representative training sample.

2. Test Set: The test set mirrors the training set in size, comprising 12,461 images, with 6,219 images of dogs and 6,242 images of cats. This set remains completely independent and is intended to assess the generalization ability of trained models on unseen data.

3. Validation Set: Specifically crafted for fine-tuning and hyperparameter tuning, the validation set consists of 5,000 images. It includes 2,500 images of cats and 2,500 images of dogs, providing an unbiased evaluation of model performance during the development phase.

Image Specifications: All images in the dataset adhere to consistent standards to eliminate any bias related to image quality or resolution. The images are stored in popular image formats (e.g., JPEG, PNG) and have been resized to a uniform resolution, enabling seamless input to most deep learning frameworks.

Use Case and Applications: The Cats vs. Dogs dataset is tailored for binary image classification tasks in the domain of computer vision and offers a multitude of practical applications. This dataset can be employed for:

• Training and benchmarking deep learning models for binary image classification.
• Evaluating the effectiveness of various data augmentation techniques.
• Research in transfer learning and fine-tuning pre-trained models.
• Advancing the state-of-the-art in image classification algorithms.
• Classroom education and academic research in machine learning and data science.

Disclaimer: While every effort has been made to ensure the quality and accuracy of the dataset, the creators cannot guarantee absolute perfection or absence of errors. Users are encouraged to verify the dataset's suitability for their specific purposes and report any potential issues to contribute to the dataset's improvement and enrichment.

License: The "Cats vs. Dogs" dataset is made available under an open-source license, fostering collaboration and knowledge sharing within the scientific community. Users are encouraged to adhere to the license terms, which will be detailed in the dataset documentation.

I hope this dataset will facilitate cutting-edge research and innovation in the fascinating field of deep learning and data science, propelling us toward a future where AI-powered computer vision systems bring transformative benefits to society.

Stanford Dogs Dataset

## Overview

Stanford Dogs Dataset is a dataset for object detection tasks - it contains Dogs annotations for 1,137 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).

Puppies

## Overview

Puppies is a dataset for object detection tasks - it contains Puppy annotations for 477 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 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.

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

Cats And Dogs Image Classification

## Overview

Cats And Dogs Image Classification is a dataset for classification tasks - it contains Cats And Dogs annotations for 2,000 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).

Breeds Of Dogs

## Overview

Breeds Of Dogs is a dataset for object detection tasks - it contains Breeds Of Dogs annotations for 997 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).

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).

A massive collection of cats and dogs.

• Mass Instagram Downloads From Popular Cat and Dog Pages
• Public Facing Official Datasets Such As Oxford-IIIT Pet Dataset
• Public Facing Kaggle Datasets (Shoutout if your cats and dogs made it in)
• Google Images Cats And Dogs

This dataset is about book series. It has 1 row and is filtered where the books is Puppies in trouble. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This statistic shows common complaints of Americans who have bought puppies from breeders, pet stores or so-called brokers and realized their new pet is not in good health. The complaints were collected from 2007 to 2011 by the Humane Society of America via e-mail, a website complaint form and a tip line. Overall, 2,479 complaints were registered during these five years, 40 percent of those were complaints about the puppies suffering from an illness.

Dogs

## Overview

Dogs is a dataset for object detection tasks - it contains Dogs annotations for 203 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 [MIT license](https://creativecommons.org/licenses/MIT).

Over a 1000 images of cats and dogs scraped off of google images. The problem statement is to build a model that can classify between a cat and a dog in an image as accurately as possible.

Image sizes range from roughly 100x100 pixels to 2000x1000 pixels.

Image format is jpeg.

Duplicates have been removed.

Dataset

This dataset was created by ABHISHEK DHAKAD

Released under CC0: Public Domain

Contents

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.

Demographic data for dog puppies sampled.

This dataset contains a list of shelter animals that are ready to be adopted from the Montgomery County Animal Services and Adoption Center at 7315 Muncaster Mill Rd., Derwood MD 20855. The 'How To Adopt' details are posted on https://www.montgomerycountymd.gov/animalservices/adoption/howtoadopt.html. Update Frequency : Every two hours