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.

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.

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 dog breed image classification dataset is a collection of images of dogs, each labeled with their respective breeds. These datasets are commonly used in the field of computer vision and machine learning for tasks such as image classification and object recognition..

Who doesn't love dogs? 🐕 Those wonderful creatures make our life so much better! So if you love dogs as much as I do, you'd be probably interested in this dataset.

It includes the name of the breed, country of origin, longevity, height, color of fur and eyes, their character traits and typical health problems for each breed.

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.

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.

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.

This dataset is a redistribution of the following dataset. https://github.com/suzuki256/dog-dataset The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors.

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.

The DogBreedImageClassificationDataset is a comprehensive collection of dog images, sourced from the Dog CEO's Dog API, intended for use in training machine learning models for the task of dog breed identification. The dataset contains 6 breeds, providing a diverse range of data.

The dataset has pictures of many types of dogs. Each picture file is in JPEG format. The name of each file tells you the breed of the dog in the picture. The pictures are stored in different folders based on the breed of the dog.

The licensing of the dataset depends on the original source of the images. Please refer to the Dog CEO's Dog API for detailed information.

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.

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

karma5207/puppies dataset hosted on Hugging Face and contributed by the HF Datasets community

Puppies is a book. It was written by Fiona Watt and published by Usborne in 2010.

Domestic dogs are particularly skilled at using human visual signals to locate hidden food. This is, to our knowledge, the first series of studies that investigates the ability of dogs to use only auditory communicative acts to locate hidden food. In a first study, from behind a barrier, a human expressed excitement towards a baited box on either the right or left side, while sitting closer to the unbaited box. Dogs were successful in following the human's voice direction and locating the food. In the two following control studies, we excluded the possibility that dogs could locate the box containing food just by relying on smell, and we showed that they would interpret a human's voice direction in a referential manner only when they could locate a possible referent (i.e. one of the boxes) in the environment. Finally, in a fourth study, we tested 8–14-week-old puppies in the main experimental test and found that those with a reasonable amount of human experience performed overall even better than the adult dogs. These results suggest that domestic dogs' skills in comprehending human communication are not based on visual cues alone, but are instead multi-modal and highly flexible. Moreover, the similarity between young and adult dogs' performances has important implications for the domestication hypothesis.

This dataset is about stocks and is filtered where the company is Dog breeders with puppies for sale in the U.S. & Canada. It has 8 columns such as stock, stock name, exchange, exchange symbol, and timezone. The data is ordered by stock.

This dataset is proposed for the Re-ID study, which contains a total of 1657 images of 192 dogs. On average, there are 9 images per dog. When we took the data, we collected as many images as possible with different postures and different perspectives.

I1it and I2it are Indicator or Dummy variables indicating the stimulus category (I1it: 1 = African infants, 0 = Caucasian infants; I1it: 1 = dog puppies, 0 = Caucasian infants). Hit reflects the health state (0 = mean assessment of perceived health across all stimuli and participants, a positive value indicates perceived above-average illness frequency). St represents participants’ sex (0 = male, 1 = female). At indicates participants’ age (0 = mean age of the sample). For interpreting the coefficients all other predictor variables have to be held constant.An unstructured covariance structure was used for the random part at Level 2. Hence, the variances and covariances of Level 2 residuals were estimated without any constraints. Robust estimators were used for statistical inference with respect to fixed effects and variance components to account for possible violations of model assumptions, such as normality of Level-2 residuals. Degrees of freedom were computed based on the Satterthwaite’s Approximation to account for the moderate sample size at Level 2 [46]. Therefore, the degrees of freedom were not necessarily integers and could vary across tests independent of the number of parameters.Results of Study 2.

This dataset is about books and is filtered where the book series is Number puppies. It has 9 columns such as book, author, ISBN, BNB id, and language. The data is ordered by publication date.