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.

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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.

The Stanford Dogs Dataset is a curated collection of dog images extracted from the larger ImageNet repository. It is tailored for fine-grained image classification tasks and contains examples from 120 distinct dog breeds. Each breed’s images are organized into separate folders to facilitate easy navigation and use in research and machine learning experiments. This dataset is widely used in computer vision to benchmark algorithms for object recognition, localization, and species-specific classification tasks.

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).

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 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).

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.

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).

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

I created this dataset for my junior year independent work at Princeton University. I could not find any pre-existing dataset of mixed breed dogs with their genetic breed breakdown, so I made my own!

My project was to develop a machine learning algorithm to classify mixed breed dogs; I adapted the Xception model to classify purebred dog breeds and trained the model on the Stanford Dog Dataset. Then I fine-tuned the model on my mixed-breed dog dataset.

I have provided here two folders of images. One folder of images has folders for each dog, and multiple images within each folder. The second folder of images has just one image per dog.

I have also provided two .csv files. The first contains the genetic breed breakdown of each dog in the dataset. The second contains the genetic breed breakdown of each dog in the dataset, but normalized to only include the 120 breeds covered by the Stanford Dog Dataset. The genetic results are from Wisdom Panel Dog DNA kits - I collected all images and genetic results through public posts in the Wisdom Panel Facebook community!

Hope that you find this dataset useful and continue to add to it!

Standford Dogs

## Overview

Standford Dogs is a dataset for object detection tasks - it contains Breeds annotations for 2,285 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).

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).

100 dog barks in different ways a collection of 100 sweet dog barks

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).

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.

This dataset contains images of cats and dogs, which is used for training deep neural networks.

The Dog Breed Image Classification Dataset contains high-resolution dog images labeled with their respective breeds. It supports AI and machine learning projects focused on animal identification, image classification, and visual model training.