Cat & Dog Segmentation Dataset

Project Overview:

Objective

Our team successfully developed a comprehensive dataset focused on segmenting cats and dogs in images. This dataset serves as a crucial tool in advancing recognition models for various applications including pet identification, veterinary services, and animal welfare initiatives.

Scope

We have compiled an extensive repository of images featuring cats, dogs, or both. Each image in our collection is meticulously annotated to highlight the unique characteristics and boundaries of these animals, ensuring high precision in segmentation.

Sources

Pet Owners: Initiate community engagement drives inviting pet owners to contribute images of their cats and dogs in diverse environments.
Animal Shelters: Partner with animal shelters and rescue organizations, obtaining pictures of various breeds in multiple poses.
Open-source Image Platforms: Curate cat and dog images from existing open image libraries, ensuring diverse representation.

Data Collection Metrics

Pet Owner Contributions: 95,000
Animal Shelter Images: 65,000
Open-source Image Extractions: 50,000

Annotation Process

Stages

Animal Segmentation: Each image underwent detailed segmentation to distinguish the cats and dogs from their surroundings.
Breed Identification: Wherever possible, images were tagged with the specific breed of the cat or dog.
Environmental Context: We also labeled the setting of each image, such as indoor, outdoor, or play area.

Annotation Metrics

Images with Animal Segmentation: 210,000
Breed Identification Tags: 160,000
Environmental Context Labels: 210,000

Quality Assurance

Segmentation Accuracy Checks: Automated tools were utilized alongside manual annotations for precision.
Metadata Verification: Expertise from pet professionals and veterinarians ensured accurate breed identification.Privacy Measures: We strictly ensured that images from pet owners did not contain identifiable human faces or private settings. An opt-out feature was provided for contributors to withdraw their data if desired.

QA Metrics:

Segmentation Validation Cases: 21,000
Breed Authentication Reviews: 32,000
Privacy Audits: 95,000

Conclusion

At GTS, the Cat & Dog Segmentation Dataset Initiative stands as a testament to our capability in compiling and annotating high-quality datasets for AI model training. Our focus on community engagement and stringent quality controls positions this dataset to significantly enhance AI interactions with pet-related visual data, benefiting both technological advancements and animal welfare sectors.