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. There are 20,580 images, out of which 12,000 are used for training and 8580 for testing. Class labels and bounding box annotations are provided for all the 12,000 images.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('stanford_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/stanford_dogs-0.2.0.png" alt="Visualization" width="500px">

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.

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).

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.

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

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.

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.

An 'in the wild' dataset of 20,580 dog images for which 2D joint and silhouette annotations were collected.

Dataset

This dataset was created by Ryan Holbrook

Released under Data files © Original Authors

Contents

Generated Stanford Dogs Dataset

  Description

This repository contains the dataset used for the generative-data-augmentation project. The dataset is organized as follows:

  Dataset Structure

analysis/: This directory contains analysis related to the dataset. metadata/: This directory contains the list of file path used for the Synthetic (Noisy) and Synthetic (Clean) datasets. synthetic/: This directory contains the image files. Each folder represents a class.… See the full description on the dataset page: https://huggingface.co/datasets/czl/generated-stanford-dogs.

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.

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

Dataset

This dataset was created by Chen Lixi

Contents

About

12k labelled instances of dogs in-the-wild with 2D keypoint and segmentations.

This dataset was released with our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Installation

For installation details, please visit our GitHub repo.

Fine grained classification method and a fine grained data set

用于细粒度图像分类的数据集

Approx. 150 images each folder.

Breed list:

African hunting dog Dhole Dingo Mexican hairless Standard poodle Miniature poodle Toy poodle Cardigan Pembroke Brabancon griffon Keeshond Chow Pomeranian Samoyed Great pryenees Newfoundland Leonberg Pug Basenji Affenpinscher Siberian husky Malamute Eskimo dog Saint Bernard Great Dane French Bulldog Tibetan Mastiff Boxer Entlebucher Appenzeller Bernese mountain dog Great Swiss mountain dog Miniature pinscher Doberman German shepherd Rottweiler Bouvier des Flandres Border collie Collie Shetland sheepdog Old english sheepdog Komondor Kelpie Briard Malinois Groenendael Schipperke Kuvasz Irish water spaniel Sussex spaniel Cocker spaniel Welsh springer spaniel English springer Clumber Brittany spaniel Gorden setter Irish setter English setter Vizsla German shorthaired pointer Chesapeake bay retriever Labrador retriever Golden retriever Curly coated retriever Flat coated retriever Lhasa West highland white terrier Soft coated wheaten terrier Silky terrier Tibetan terrier Scotch terrier Standard schnauzer Giant schnauzer Miniature schnauzer Boston bull Dandie Dinmont Australian terrier Cairn Airedale Sealyham terrier Lakeland terrier Wire haired fox terrier Yorkshire terrier Norwich terrier Norfolk terrier Irish terrier Kerry blue terrier Border terrier Bedlington terrier American Staffordshire terrier Staffordshire bull terrier Weimaraner Scottish deerhound Saluki Otterhound Norwegian elkhound Ibizan hound Whippet Italian greyhound Irish wolfhound Borzoi Redbone English foxhound Walker hound Black and tan coonhound Bluetick bloodhound Beagle Basset Afgan hound Rhodesian ridgeback Toy terrier Papillon Blenheim spaniel Shih Tzu Pekinese Maltese dog Japanese spaniel Chihuahua

Keypoint Detection for Dogs Dog photos and annotating from Stanford Dogs

Stanford Dogs, Stanford Cars, Oxford 102 Flowers

![]() The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. We also expect that the higher resolution of this dataset (96x96) will make it a challenging benchmark for developing more scalable unsupervised learning methods. Overview 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck. Images are 96x96 pixels, color. 500 training images (10 pre-defined folds), 800 test images per class. 100000 unlabeled images for uns