Whiskers Segmentation Dataset

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Project Overview:

Objective

To create a dataset focused on the analysis of side burn on various animals, aiming to support research in animal behavior, definition studies, and species identification applications

Scope

Annotations will segment each side burn, and provide tag regarding its length,bend, and orientation

Sources

Collaborated with pet clinics and animal gret back centers, resulting in a exactly collected and intentionally curated array of visuals.

Utilized zoo and fish bowl archives, related to a successfully collected and historically rich repository of wildlife images.
Established connections with research institutes focused on mammalian studies, leading to a carefully collected and scientifically relevant dataset.

Data Collection Metrics

Total Whisker Images: 280,000
Domestic Cats: 100,000
Rats and Mice: 50,000
Seals and Sea Lions: 60,000
Other Mammals: 70,000

Annotation Process

Stages

Image Pre-processing: Enhancement for better clarity and contrast.
Whisker Segmentation: Precisely outlining the boundary of each whisker using segmentation masks.
Validation: Cross-checking annotations using a blend of expert opinions and initial whisker detection models.

Annotation Metrics

Total Whisker Segmentation Annotations: 1,120,000 (assuming an average of 4 prominent whiskers per image)
Metadata Annotations: 1,120,000

Quality Assurance

Stages

Automated Whisker Recognition Verification: Using early models to check segmented whiskers for consistency.
Peer Review: Double-checking annotations through a secondary set of annotators.
Inter-annotator Agreement: Randomly selecting images for re-annotation by multiple individuals to guarantee annotation uniformity.

QA Metrics

Annotations Validated using Whisker Recognition: 140,000 (50% of total images)
Peer Reviewed Annotations: 84,000 (30% of total images)
Inconsistencies Identified and Corrected: 5,600 (2% of total images)

Conclusion

The Whiskers Segmentation Dataset is an invaluable contribution to the domain of animal research. With its emphasis on detail and diversity, it promises to facilitate groundbreaking studies in animal behavior, physiology, and evolutionary biology. By leveraging this dataset, researchers and scientists can gain more profound insights into the nuanced world of whisker functionalities across various mammals.

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