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.

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.

add this dataset alongside @jessicali9530/stanford-dogs-dataset as a helper

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.

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!

Dogs Intelligence and Size

An Exploration of Breed-Based Characteristics

By len fishman [source]

About this dataset

This dataset provides valuable insights into the potential relationship between size and intelligence in different breeds of dogs. It includes data from a research conducted by Stanley Coren, a professor of canine psychology at the University of British Columbia, as well as breed size data from the American Kennel Club (AKC). With this dataset, users will be able to explore how larger and smaller breeds compare when it comes to obedience and intelligence. The columns present in this dataset include Breed, Classification, Obey (probability that the breed obeys the first command), Repetitions Lower/Upper Limits (for understanding new commands). From examining this data, users may gain further insight on our furry friends and their behaviors. Dive deeper into these intricate relationships with this powerful dataset!

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How to use the dataset

This dataset provides insight into how intelligence and size may be connected in dogs. It includes information on dog breeds, including their size, how well they obey commands, and the number of repetitions required for them to understand new commands. This can help pet owners who are looking for a dog that fits their lifestyle and residential requirements.

To get started using this dataset, begin by exploring the different attributes included: Breed (the type of breed), Classification (the size classification of the dog - small, medium or large), height_low_inches & height_high_inches (these are the lower limit and upper limit in inches when it comes to the height of the breed), weight_low_lbs & weight_high lbs (these are the lower limit and upper limit in pounds when it comes to the weight of a breed). Also included is obey (the probability that a particular breed obeys a given command) as well as reps_lower & reps_upper which represent respectively lower and upper repetitions required for a given breed to understand new commands

Once you have an understanding of what each attribute represents you can start exploring specific questions such as 'how many breeds fit in within certain size categories?', 'what type of 'obey' score do large breeds tend to achieve?', or you could try comparing size with intelligence by plotting out obey against both reps_lower & reps_upper . If higher obedience scores correlate with smaller numbers on either attributes this might suggest that smaller breeds tend require fewer repetitions when attempting learn something new.

By combining these attributes with other datasets such as those focusing on energy levels it’s possible create even more specific metrics based questions regarding which types of dogs might suit certain lifestyles better than others!

Research Ideas

• Examining the correlation between obedience and intelligence in different dog breeds.
• Investigating how size is related to other traits such as energy level, sociability and trainability in a particular breed of dog.
• Analyzing which sizes are associated with specific behavior patterns or medical issues for dogs of various breeds

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: AKC Breed Info.csv | Column name | Description | |:-----------------------|:--------------------------------------------------------------| | Breed | The breed of the dog. (String) | | height_low_inches | The lower range of the height of the dog in inches. (Integer) | | height_high_inches | The upper range of the height of the dog in inches. (Integer) | | weight_low_lbs | The lower range of the weight of the dog in pounds. (Integer) | | weight_high_lbs | The upper range of the weight of the dog in pounds. (Integer) |

File: dog_intelligence.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------------------------------| | Breed | The breed of the dog. (String) | | Classification | The size classification of the dog according to the American Kennel Club. (String) | | obey | The probability that the breed obeys the first command. (Float) | | reps_lower | The lower limit of repetitions to understand new commands. (Integer) | | reps_upper | The upper limit of repetitions to understand new commands. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit len fishman.

Information about a dog including breed, colour, address, classification and registration status Column_InfoDog_Number, int : Animal record numberDog_Name, char : Dogs nameKept_At_Suburb, varchar : Suburb dog lives inKept_At_Post_Code, varchar : Hamilton City post codeKept_At_Town, varchar : Town/city dog lives inPrimary_Breed_Code, varchar : Primary breed National Dog Database codePrimary_Breed, varchar : Primary breedSecondary_Breed_Code, varchar : Secondary NDD breed codeSecondary_Breed, varchar : Secondary breedPrimary_Colour_Code, varchar : Primary colour NDD codPrimary_Colour, varchar : Primary colourSecondary_Colour_Code, varchar : Secondary colour NDD codeSecondary_Colour, varchar : secondary colourDisabled_Assist_Flag, varchar : Disability assist dogDate_Of_Birth, datetime : Dogs date of birthDate_Of_Death, datetime : Dogs date of deathDate_Of_Departure, datetime : Date dog departed from HCCAnimal_Sex, char : Dog sex/genderDesexed, varchar : Is dog de-sexedWorker, varchar : Is it a working dogExternal_Tag_Source, varchar : Dog moved to HCC from...Current_Tag_Set, char : Current registration yearCurrent_Tag_Effective_Date, datetime : Date current registration effective fromCurrent_Tag_Receipt_Date, datetime : Date current registration was receiptedCurrent_Tag_Reg_Til_Date, datetime : Current registration valid untilSpecial_Category_Code, varchar : Special category codesSpecial_Category_Description, varchar : Special category dogs e.g. guide dogs, hearing assist, police dogsClassification, varchar : Dogs classification staus (N/A, dangerous, menacing)Classification_Date, datetime : Date dog was classifiedClassification_Reason_Code, varchar : Reason for classification codeClassification_Reason_Description, varchar : Reason for classificationDeactivated, varchar : Date animal record deactivatedDeactivated_Description, varchar : Reason for deactivationAnimal_Description, varchar : Description of dogFee_Code, smallint : Fee codeFee_Description, varchar : Fee descriptionMicrochip_Flag, varchar : Microchip statusMicrochip_Brand, varchar : Microchip brandAnimal_Features, varchar : Any distinguishing features on dogOffence_Free_Flag, varchar : Offence free statusActive_Dog_Record, varchar : Dogs currently registered in HCC Relationship This table is referenced by Dog_Complaint_To_DogThis table is referenced by Dog_ImpoundingThis table is referenced by Dog_InfringementThis table is referenced by Dog_RegistrationThis table is referenced by Excess_Dog_Register_To_DogThis table is referenced by Owner_Classification_Event_To_Dog Disclaimer Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works. Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data. While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data: ‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

A large set of images of cats and dogs.

Homepage: https://www.microsoft.com/en-us/download/details.aspx?id=54765

Source code: tfds.image_classification.CatsVsDogs

Versions:

4.0.0 (default): New split API (https://tensorflow.org/datasets/splits) Download size: 786.68 MiB

Source: https://www.tensorflow.org/datasets/catalog/cats_vs_dogs

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

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 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.

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.

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.

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

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

Limited information is available describing the development of the neonatal fecal microbiome in dogs. Feces from puppies were collected at 2, 21, 42, and 56 days after birth. Feces were also collected from the puppies’ mothers at a single time point within 24 hours after parturition. DNA was extracted from fecal samples and 454-pyrosequencing was used to profile 16S rRNA genes. Species richness continued to increase significantly from 2 days of age until 42 days of age in puppies. Furthermore, microbial communities clustered separately from each other at 2, 21, and 42 days of age. The microbial communities belonging to dams clustered separately from that of puppies at any given time point. Major phylogenetic changes were noted at all taxonomic levels with the most profound changes being a shift from primarily Firmicutes in puppies at 2 days of age to a co-dominance of Bacteroidetes, Fusobacteria, and Firmicutes by 21 days of age. Further studies are needed to elucidate the relationship between puppy microbiota development, physiological growth, neonatal survival, and morbidity.

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.

Dataset Summary

A dataset from kaggle with duplicate data removed.

  Data Fields

The data instances have the following fields:

image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e.… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/Cat_and_Dog.

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.