Welcome to the "Comprehensive Dog Dataset," a detailed collection of information on 3,000 dogs covering various breeds, ages, weights, colors, and genders. This dataset is designed to be a rich resource for data analysis, visualization, and machine learning projects related to dogs.

📌 Key Features: 🐕 Canine Characteristics: Breed 🏷️: The dog's breed (e.g., Labrador Retriever, Beagle, Golden Retriever, German Shepherd). Age (Years) 📅: The age of the dog, ranging from 1 to 15 years. Weight (kg) ⚖️: The dog's weight in kilograms, ranging from 5 kg to 60 kg. Color 🎨: The dog's color (e.g., Black, White, Brown, Golden, Spotted). Gender ♂️♀️: The dog's gender (Male or Female). 📈 Use Cases & Applications: 🔹 Breed distribution analysis and visualization 🔹 Exploring weight and age correlations among dog breeds 🔹 Building predictive models for canine classification 🔹 Creating interactive dashboards for dog characteristics 🔹 Training AI models for dog breed recognition

⚠️ Important Note: This dataset is synthetically generated for educational and analytical purposes. It is not real-world data but is designed to simulate realistic dog characteristics.

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.

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.

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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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).

Description

This dataset contains a collection of images for 10 different dog breeds, meticulously gathered and organized to facilitate various computer vision tasks such as image classification and object detection. The dataset includes the following breeds:

• Golden Retriever
• German Shepherd
• Labrador Retriever
• Bulldog
• Beagle
• Poodle
• Rottweiler
• Yorkshire Terrier
• Boxer
• Dachshund

Each breed is represented by 100 images, stored in separate directories named after the respective breed. The images have been curated to ensure diversity and relevance, making this dataset a valuable resource for training and evaluating machine learning models in the field of computer vision.

Cats Vs Dogs

## Overview

Cats Vs Dogs is a dataset for classification tasks - it contains Cat Dog annotations for 312 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).

Dog Identification

## Overview

Dog Identification is a dataset for object detection tasks - it contains Dogs annotations for 930 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).

Here are a few use cases for this project:

1. "Pet Identification App": The model can be used to create an application that helps users identify the breed of their pets or stray dogs. It would be useful for new pet owners, pet shelters, or people considering adoption/rescue.

2. "Dog Breed Study Research": For researchers studying canine genetics, behaviors, or diseases, this model would provide an efficient tool for recognizing different breeds, helping to collect data faster and more accurately.

3. "Virtual Dog Show": In virtual dog shows, this model could be employed to identify and classify the breeds. It could be implemented as part of the pre-judging process to ensure eligibility based on breed.

4. "Lost and Found Assistance": The model could be applied in a lost and found system to identify the breed of lost dogs, helping pet owners and shelters to more rapidly track missing pets.

5. "Pet Service Customization": Businesses offering pet services (like grooming, dog walking, or boarding) could use the model for identifying dog breeds to tailor their services more accurately according to the distinct needs of different breeds.

Object detection dataset featuring various breeds of dogs and domestic cats in diverse indoor and outdoor settings, captured from close-up and eye-level views.

A small audio dataset generated from YouTube videos. The dataset has 566 cat sounds and 484 dog sounds. This was used as a contextual data source for research into contextual machine learning.

All efforts have been made to ensure this dataset was collected in line with copyright legislation regarding fair use. All samples were collected via YouTube and any derivative works of this provided dataset must reference YouTube and the author of this dataset.

This work was completed during a PhD programme at the University Of Greenwich.

Demographic, psychographic, geographic and brand-affinity data for the Sick Puppies audience in Germany, sourced from Rascasse's panel of 12+ social and digital signals.

We investigated if dogs have a cross-modal mental representation of conspecific age class using a cross-modal matching paradigm. In Experiment 1, dogs were presented with images of an adult dog and a puppy projected side-by-side on a wall while vocalization of either an adult dog or a puppy was played back simultaneously. In Experiment 2, we administered the same paradigm within an eye-tracking experiment, to investigate whether the results would be replicated when analyzing the dogs’ gaze behaviour in a more detailed way. Here we provide the data, R code and videos.

Demographic, psychographic, geographic and brand-affinity data for the Puppies audience in Germany, sourced from Rascasse's panel of 12+ social and digital signals.

Dataset containing physical and behavioral characteristics of the Entlebucher Mountain Dog breed.

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).

Demographic, psychographic, geographic and brand-affinity data for the Puppies audience in United States, sourced from Rascasse's panel of 12+ social and digital signals.

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!

Dataset Card for the Dog 🐶 vs. Food 🍔 (a.k.a. Dog Food) Dataset

  Dataset Summary

This is a dataset for binary image classification, between 'dog' and 'food' classes. The 'dog' class contains images of dogs that look like fried chicken and some that look like images of muffins, and the 'food' class contains images of (you guessed it) fried chicken and muffins 😋

  Supported Tasks and Leaderboards

TBC

  Languages

The labels are in English (['dog'… See the full description on the dataset page: https://huggingface.co/datasets/sasha/dog-food.

Summary statistics for species richness (i.e., alpha diversity measures) during pre-weaning development in growing puppies.