Maltese dogs are the most common dog breed owned in South Korea, according to a survey conducted in 2023, with around 26 percent of respondents answering to own such a dog. The market for pets and pet products in South Korea has continued to grow over the last years in Korea and, according to forecasts, will continue to do so for the next six years. Dog population in South Korea Just as the pet market size has grown, the dog population in South Korea has also experienced an upward trend, with almost 5.5 million dogs owned as pets in 2022. While the number of new dog registrations decreased, it was still an elevated level compared to before the COVID-19 pandemic. Dog registrations became mandatory in 2014 and dog owners have to follow up with multiple veterinarian checks. Reasons for this policy were, among others, to reduce the number of stray dogs in cities, such as Seoul, and simplify the recovery of lost dogs. Pet food market According to a survey among pet owners, the preferred type of dog food was dry food. Dry food can be easily imported from other countries and in 2023, South Korea imported most of its pet food from the U.S. The average monthly spending on other pet related items in South Korea amounted to close to nine thousand South Korean won in 2023.

Breed and breed group sample size (N), sex (F: female, M: male), and mean age (years) of dogs tested in the present study.

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.

Prominent differences in aging among and within species present an evolutionary puzzle. The theories proposed to explain evolutionary differences in aging are based on the axiom that selection maximizes fitness, not necessarily lifespan. This implies trade-offs between investment into self-maintenance and investment into reproduction, where high investment into growth and current reproduction are associated with short lifespans. Fast growth and large adult size are related with shorter lifespans in the domestic dog, a bourgeoning model in aging research, however, whether reproduction influences lifespan in this system remains unknown. Here we test the relationship between reproduction and differences in lifespan among dog breeds, controlling simultaneously for shared ancestry and recent gene flow. We found that shared ancestry explains a higher proportion of the among-breed variation in life history traits, in comparison with recent gene flow. Our results also show that reproductive investment negatively impacts lifespan, and more strongly so in large breeds, an effect that is not merely a correlated response of adult size. These results suggest that basic life history trade-offs are apparent in a domestic animal whose diversity is the result of artificial selection and that among-breed differences in lifespan are due to a combination of size and reproduction.

Body size is an important trait in companion animals. Recently, a primitive Japanese dog breed, the Shiba Inu, has experienced artificial selection for smaller body size, resulting in the “Mame Shiba Inu” breed. To identify loci and genes that might explain the difference in the body size of these Shiba Inu dogs, we applied whole genome sequencing of pooled samples (pool-seq) on both Shiba Inu and Mame Shiba Inu. We identified a total of 13,618,261 unique SNPs in the genomes of these two breeds of dog. Using selective sweep approaches, including FST, Hp and XP-CLR with sliding windows, we identified a total of 12 genomic windows that show signatures of selection that overlap with nine genes (PRDM16, ZNF382, ZNF461, ERGIC2, ENSCAFG00000033351, CCDC61, ALDH3A2, ENSCAFG00000011141, and ENSCAFG00000018533). These results provide candidate genes and specific sites that might be associated with body size in dogs. Some of these genes are associated with body size in other mammals, but 8 of the 9 genes are novel candidate genes that need further study.

A dog segmentation dataset created manually typically involves the following steps:

Image selection: Selecting a set of images that include dogs in various poses and backgrounds.

Image labeling: Manually labeling the dogs in each image using a labeling tool, where each dog is segmented and assigned a unique label.

Image annotation: Annotating the labeled images with the corresponding segmentation masks, where the dog region is assigned a value of 1 and the background region is assigned a value of 0.

Dataset splitting: Splitting the annotated dataset into training, validation, and test sets.

Dataset format: Saving the annotated dataset in a format suitable for use in machine learning frameworks such as TensorFlow or PyTorch.

Dataset characteristics: The dataset may have varying image sizes and resolutions, different dog breeds, backgrounds, lighting conditions, and other variations that are typical of natural images.

Dataset size: The size of the dataset can vary, but it should be large enough to provide a sufficient amount of training data for deep learning models.

Dataset availability: The dataset may be made publicly available for research and educational purposes.

Overall, a manually created dog segmentation dataset provides a high-quality training data for deep learning models and is essential for developing robust segmentation models.

In 2020, there were approximately 3.19 million large dogs (over 50 lbs or over 23 kg) in Canadian households as pets. In contrast, small dogs (up to 20 lbs or 9 kg) had a total population of around 1.97 million.

Large breed

The top dog breed in the UK in 2022, as measured by number of registrations, was the Labrador Retriever breed. Some 44,311 retrievers were newly registered in the UK in 2022. French Bulldogs and Cocker Spaniels rounded out the top three dog breeds in the UK that year.

Surge in UK dog registrations

In 2022, many dog breeds saw a decrease in registrations after large growth in 2021. Over 17 thousand fewer Labrador Retrievers were registered in 2022 than in 2021. Registrations of French Bulldogs and Cocker Spaniels also saw significant decreases in the UK that year.

UK pet food market

Europe and North America produce the most pet food worldwide. In 2022, Europe produced about 11.8 million metric tons of pet food. Though less pet food is produced in North America overall, the United States has the highest pet food revenue worldwide by far. The UK has the second highest revenue, reaching over 6.8 billion U.S. dollars that year.

Dog Food for Small Breeds Market Outlook

The global market size for dog food catered towards small breeds was estimated at USD 8.2 billion in 2023 and is projected to reach USD 12.5 billion by 2032, growing at a CAGR of 4.6% over the forecast period. This growth is driven by several factors, including increasing pet ownership, rising disposable incomes, and a growing awareness of the dietary needs specific to small breeds.

One of the primary growth factors in this market is the increasing number of pet owners around the world. According to various surveys, millennials are leading the charge in pet adoption, with a significant number of these new pet owners opting for smaller dog breeds due to their suitability for urban living conditions. This surge in adoption rates directly translates into a higher demand for specialized dog food products tailored specifically for small breed dogs, driving market growth.

Another critical factor contributing to the market's expansion is the rising disposable incomes across both developed and developing nations. As pet owners increasingly view their pets as family members, they are more willing to spend on premium and specialized food products. This trend is particularly evident in urban areas where higher disposable incomes allow pet owners to opt for higher-priced, nutritionally optimized dog food options that cater to the specific dietary needs of small breeds.

Growing awareness about the unique dietary requirements of small breed dogs is also propelling market growth. Nutritionists and veterinarians emphasize that small breeds have faster metabolisms and specific health needs that can be addressed through specialized diets. Consequently, pet food manufacturers are focusing on producing dog foods that offer balanced nutrition, improved palatability, and targeted health benefits, further boosting market demand.

Regionally, North America remains a dominant player in the global dog food market for small breeds, driven by high pet ownership rates and significant consumer spending on pet care. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period due to increasing urbanization, rising disposable incomes, and a growing trend of pet adoption. Countries like China and India are particularly noteworthy for their burgeoning pet care markets.

Product Type Analysis

The dog food market for small breeds can be segmented into various product types, including dry dog food, wet dog food, semi-moist dog food, and others. Each of these segments has its unique advantages and market appeal, which contribute to their respective growth trajectories. Dry dog food holds the largest market share due to its convenience, longer shelf life, and cost-effectiveness. Pet owners find dry dog food easier to store and serve, making it a popular choice for those with busy lifestyles.

Wet dog food, on the other hand, is gaining traction for its high palatability and moisture content, which is particularly beneficial for small breeds that may have difficulties in chewing dry kibble. Wet dog food is often enriched with essential nutrients and offers a more varied texture, making it an attractive option for picky eaters. Increased consumer awareness about the importance of hydration in small breeds also drives the growth of this segment.

Semi-moist dog food represents a smaller share of the market but is appreciated for its balance between the convenience of dry food and the palatability of wet food. It is often formulated to be chewy and appealing, which can be particularly beneficial for small breed dogs with dental issues or those transitioning from wet to dry food. This segment is expected to grow steadily as manufacturers innovate to improve the nutritional profile and shelf life of semi-moist products.

Other product types in this market include specialty formulas designed for specific health conditions or dietary preferences, such as grain-free or hypoallergenic options. These niche products cater to the growing segment of health-conscious pet owners who are willing to invest in premium dog food to ensure the well-being of their small breed pets. As awareness of pet health continues to rise, the demand for these specialized products is expected to grow.

Report Scope

The dataset you have consists of a collection of images of different dog breeds that were obtained from the web. The images showcase a variety of dog breeds with different physical characteristics such as size, shape, color, and fur type. The images were gathered from various online sources, and they may vary in quality, resolution, and orientation.

The dataset provides a valuable resource for researchers and developers interested in studying and developing computer vision and machine learning algorithms for image recognition, object detection, and classification. The images can be used to train and test machine learning models to accurately identify and classify different dog breeds.

The dataset includes a diverse range of dog breeds, including but not limited to Labrador Retriever, German Shepherd, Bulldog, Beagle, Rottweiler, and many more. Each breed is represented by multiple images, taken from different angles and in different poses, to capture as much information about the breed's physical features as possible.

Domestication is a well-known example of the relaxation of environmentally-based cognitive selection that leads to reductions in brain size. However, little is known about how brain size evolves after domestication and whether subsequent directional/artificial selection can compensate for domestication effects. The first animal to be domesticated was the dog, and recent directional breeding generated the extensive phenotypic variation among breeds we observe today. Here we use a novel endocranial dataset based on high-resolution CT scans to estimate brain size in 159 dog breeds and analyze how relative brain size varies across breeds in relation to functional selection, longevity, and litter size. In our analyses, we controlled for potential confounding factors such as common descent, gene flow, body size, and skull shape. We found that dogs have consistently smaller relative brain size than wolves supporting the domestication effect, but breeds that are more distantly related to wolves have relatively larger brains than breeds that are more closely related to wolves. Neither functional category, skull shape, longevity, nor litter size was associated with relative brain size, which implies that selection for performing specific tasks, morphology, and life history do not necessarily influence brain size evolution in domesticated species. Methods We processed the collection of dog skulls that is maintained at the Department of Anatomy, Cell and Developmental Biology, Eötvös Loránd University (Budapest, Hungary). This private collection (owned by TC) is composed of specimens that have been obtained mostly in the last 10 years by the appropriate preparation of the heads of deceased dogs (which were donated post-mortem), from which the soft materials have been removed a priori. TC systematically collected the prepared skulls with the aim of having both male and female samples from as many breeds as possible. Breed identity was usually verified upon the collection of cadavers/skulls, given that these materials originate from known dog breeders. Alternatively, we checked the appropriate breed certificates/chips for pedigree. Currently, the collection consists of 383 individual skulls (including males, females and unknown sexes) from 146 breeds. We selected 172 skulls (38 females, 83 males and 50 unknown sexes) across all breeds represented in the collection for subsequent CT scan analysis (see Supplementary Material, Table S1). Skulls were selected from adult individuals, which we verified using morphological characteristics (i.e., the presence of permanent teeth, as dogs should replace all baby teeth before 6-7 months of age). The selected skulls were transferred to the Diagnostic and Oncoradiology Centre in Kaposvár (Hungary) for CT scanning. We used a Siemens Somatom Definition AS+ CT machine (Siemens, Erlangen, Germany) to digitalize the skulls with high resolution (170 mAs, 140 kV, pixel size 0.323 × 0.322 mm, slice thickness 0.6 mm, with a v80u bone kernel). The resulting DICOM image series were imported into the 3D Slicer software (freeware, www.slicer.org), and using its segmentation and modelling tools, the endocranial volumes (=endocast) were reconstructed (see details in Czeibert et al. 2020). These endocasts reflect the surface morphology of the brain in such detail that external blood vessels and differences in gyrification can be observed (Figure 1). In parallel, we calculated the volume of the endocasts for the analysis (Czeibert et al. 2020) in this study. We also extracted additional data on brain volumes from the literature for some dog breeds.

The basic tenets of the evolutionary theories of senescence are well supported. However, there has been little progress in determining the relative influences of mutation accumulation and life history optimisation. The causes of the well-established inverse relationship between lifespan and body size across dog breeds are used here to test these two classes of theories. The lifespan-body size relationship is confirmed for the first time after controlling for breed phylogeny. The lifespan-body size relationship cannot be explained by evolutionary responses to differences in extrinsic mortality, either of contemporary breeds or of breeds at their establishment. The development of breeds larger and smaller than ancestral grey wolves has occurred through changes in early growth rate. This may explain the increase in the minimum age-dependent mortality rate with breed body size and thus higher age-dependent mortality throughout adult life. The main cause of this mortality is cancer. These patterns are consistent with the optimisation of life history as described by the disposable soma theory of the evolution of ageing. The dog breed lifespan-body size relationship may be the result of the evolution of greater defence against cancer lagging behind the rapid increase in body size during recent breed establishment.

Over the five years to 2024, increases in both competition and public disapproval have threatened the Dog and Pet Breeders industry. This industry includes various operations, from small independent home breeders to large USDA-certified breeding facilities. However, the industry also comprises unlicensed puppy mills, often using inhumane practices to reduce costs and maximize profit. Recent attention to these operations has hurt the overall industry's reputation. In 2017, the "Adopt, Don't Shop" campaign emphasized the ethical benefits of getting pets from shelters and adoption groups rather than pet stores. This campaign has led many pet stores to no longer work with breeders and instead host adoption events with local shelters. Through the end of 2024, industry revenue is expected to grow at an annualized rate of 2.6% to $3.1 billion, including an estimated 0.9% decline in 2024. The breeding industry's trajectory has been mixed with challenges. Stringent regulations like those mandated by the Animal Welfare Act have kept most breeders operating on a small scale, with only a tiny fraction exceeding the threshold for requiring USDA licensing. The public's growing scrutiny of puppy mills and other inhumane conditions has pressured breeders to maintain higher quality standards. Some medium-sized breeders will likely seek USDA certification to improve their reputation and gain legitimacy. Higher standards and requirements will enable these breeders to increase their profits. Heightening veterinary services and pet essentials costs will likely strain household budgets, leading to more careful spending on pet purchases. Demographic trends suggest older populations might shy away from new pets, while younger generations lean towards pet adoption, driven by ethical considerations. Legislative pressures and higher public awareness are expected to push the industry towards more stringent welfare standards. While adoption campaigns and regulatory constraints present formidable challenges, purebred breeders may still find sustained demand from consumers looking for specific traits and appearance. Through the end of 2029, industry revenue is projected to fall at an annualized rate of 0.4% to $3.0 billion.

This dataset contains images of 3 different Dog Breeds (Golden Retriever, Bulldog, and Chihuahua). The images are sized to be 256x256 dimensions. The task for this dataset is to create a model that can classify different dog breeds.

This dataset is perfect for some beginners who want to get their hands dirty with computer vision and learn how to deal with images and apply augmentation techniques. 💯

.txt files

Here are a few use cases for this project:

1. Pet Adoption Agencies: Agencies could use the "TIP dddggg" model to automatically identify and categorize images of dogs in their database, speeding up the adoption process by allowing potential adopters to quickly find and view images of available dogs.

2. Animal Rescue Center: This model could be used in animal rescue centers to identify images of dogs under their care, especially dogs being held in cages. These identifications could help in ensuring each dog's proper care and health condition monitoring.

3. Dog Breed Identification: If the model can identify different breeds of dogs, it could be used by dog enthusiasts or dog breeders to categorize and identify specific breeds in various scenarios and environments.

4. Security and Monitoring: Surveillance systems could use this model to detect the presence of dogs in restricted areas or monitor animals in boarding kennels, ensuring their well-being and safety.

5. Online Pet Shop: E-commerce platforms selling pet accessories could use this model to identify dogs in customer-submitted images, helping to recommend the correct size or type of product like cages, collars, etc., based on the dog's breed and size.

The size and share of the market is categorized based on Type (Wet Food, Dry Food, Frozen Food) and Application (Supermarket / Hypermarket, E-commerce, Retail Stores, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).