Context

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. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age. I have used only images, so this does not contain any labels .

  Content

Number of… See the full description on the dataset page: https://huggingface.co/datasets/ksaml/Stanford_dogs.

This dataset is a copy of a subset of the full Stanford Dogs Dataset.

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

The original dataset contained 20,580 images of 120 breeds of dogs.

This subset contains 9884 images of 60 breeds of 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.

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.

Dataset

This dataset was created by Moulhanout

Released under Database: Open Database, Contents: © Original Authors

Contents

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.

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… See the full description on the dataset page: https://huggingface.co/datasets/sasha/dog-food.

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.

1750 Active United States Puppies buyers list and United States Puppies importers directory compiled from actual United States import shipments of Puppies.

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.

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 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.

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.

About:

These data were collected to better understand how adoptable dogs in the US are relocated from state to state and imported from outside the US.The findings were published in a visual essay on The Pudding entitled Finding Forever Homes published in October 2019.

These data will not be updated.

Data Collection:

Using the PetFinder API, details about all 58,180 dogs available for adoption in the 50 US states and Washington DC on September 20, 2019 were collected. Since PetFinder does not provide an entry field for an animal’s location before arriving at its current organization, I parsed the text of each pet’s “description”. I started by limiting text to anything that came after the word “from” but before the word “to”, or after “located in”. I then analyzed the remaining text using entity recognition from the spacyR package. I manually checked the data for anything mislabeled.

In all, over 3,000 dogs were described as having originated in places different from where they were listed for adoption. The count discussed in this article (2,460) is lower because we eliminated any listings for animals from a vague region (e.g., “the south”, “the Carolinas”, “LA or TN”) instead of a specific state or country. We also assume that this is an underestimate since not all shelters or rescues will include this information in an animal’s PetFinder description. Any animals that were described as transported by their previous owners instead of by the rescue or shelter were also removed from our data.

Some dogs were listed as being from several places. For example, one was described as “rescued from the euthanasia list at a tiny Alabama shelter and brought to a rescue in Georgia”, but the dog was listed as available for adoption in Massachussetts. In this case, the earliest location is the one reported.

In 238 (9.7% of) cases, the dogs were shown as available for adoption in one state, but they still resided in another. For instance, a dog that was available for adoption in Washington had the disclaimer “Dogs will be transported from Texas upon approved match.” We still considered these to be “imports” since they are listed as available for adoption upon searching PetFinder for dogs in that state.

All data except for description was collected using PetFinder’s V2 API method get-animals as described in their documentation. Since the V2 API doesn’t return the full animal description, I was encouraged by the API maintainers to query the same animal profiles using the V1 API to acquire that information. Thus, I used all of the shelter ID’s returned from the V2 API calls to find all descriptions of dogs in each shelter and combine the two results by the animal’s unique ID.

Caveats:

• This data represents a single day of data. It was all collected on September 20, 2019
• Due to a bug in the PetFinder API, dogs in the state of Nevada had to be accessed by searching for individual zip codes instead of the state abbreviation (as suggested to me by the API maintainers).

License:

Data released under MIT License

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.

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, with the addition of the resize factor and cropped region in the original image. Evidently, the dimensions of the image and bbox are no longer valid. The images are the newly resized and cropped images.

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

A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cats_vs_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/cats_vs_dogs-4.0.1.png" alt="Visualization" width="500px">

42354 United States import shipment records of Puppies from China with prices, volume & current Buyer’s suppliers relationships based on actual United States import trade database.

This dataset includes the official colour and breed lists from the National Dog Database (NDD).