Nails Contour Segmentation Dataset

Project Overview:

Objective

 

To facilitate advancements in health diagnostics, beauty tech, and anthropological studies, a refined dataset dedicated to the segmentation of nail contours is essential. Consequently, this dataset will aid in identifying nail disorders, enabling virtual nail polish try-ons, and utilizing nails as a metric of health and nutrition.

Scope

Assemble diverse collection of images showcasing nails of various shapes, sizes, health conditions, and colors. Each image is meticulously annotated to detail the contour of the nail, distinguishing it from the surrounding skin and cuticles.

Nails Contour Segmentation Dataset
Nails Contour Segmentation Dataset
Nails Contour Segmentation Dataset
Nails Contour Segmentation Dataset

Sources

  • Engaged in collaborative partnerships with dermatologists and nail care specialists, we have meticulously collected and curated an array of resources.
  • Furthermore, we have carefully gathered crowdsourced submissions through dedicated apps targeting diverse demographics.
  • By leveraging health and beauty forums and securing the necessary permissions, we successfully collected and professionally curated a wide assortment of content.
  • Additionally, by establishing connections with nail salons and beauty schools, we have contributed to a thoughtfully collected and curated set of resources.
Nails Contour Segmentation Dataset
Nails Contour Segmentation Dataset

Data Collection Metrics

  • Total Images: 20,000
  • Healthy Nails: 12,000
  • Nail Disorders: 6,000
  • Painted Nails: 2,000

Annotation Process

Stages

  1. Image Pre-processing: First, we refine color accuracy, followed by sharpening and normalization. This sequential approach ensures consistent high-quality throughout the process.
  2. Pixel-wise Segmentation: Annotators then use specialized tools to meticulously trace the precise contours of nails, carefully distinguishing them from the surrounding skin and cuticles.
  3. Validation: After primary annotation, secondary experts thoroughly review each annotation. This crucial step ensures the utmost accuracy and thoroughness in the annotated data.

Annotation Metrics

  • With a total of 20,000 pixel-wise annotations, one for each image, we have ensured comprehensive coverage.
  • Additionally, the average annotation time per image is estimated to be 10 minutes, thereby reflecting the efficiency and precision of the process.
Nails Contour Segmentation Dataset
Nails Contour Segmentation Dataset
Nails Contour Segmentation Dataset
Nails Contour Segmentation Dataset

Quality Assurance

Stages

Automated Model Check: Contour segmentation models are initially utilized to cross-reference the accuracy of annotations. Additionally, these models serve to validate the precision of annotations through a comparative analysis 

Peer Review: Subsequently, seasoned annotators review random subsets to maintain a high-quality benchmark.
Inter-annotator Agreement: Furthermore, a selection of images is re-annotated by different experts to confirm consistency across the board.

QA Metrics

  • Annotations Validated using Models: 10,000 (50% of total images)
  • Furthermore Peer-reviewed Annotations: 6,000 (30% of total images)
  • Additionally, Inconsistencies Identified and Rectified: 300 (1.5% of total images)

Conclusion

The Nails Contour Segmentation Dataset represents a pioneering advancement in the intersection of health and beauty technology within computer vision. With its extensive coverage of various nail types and conditions, this dataset is poised to play a pivotal role in diagnosing nail health, fostering innovation in beauty technology solutions, and facilitating a deeper understanding of human health indicators through nails.

quality dataset

Quality Data Creation

Guaranteed TAT‚Äč

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified‚Äč

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance‚Äč

HIPAA Compliance

GDPR Compliance‚Äč

GDPR Compliance

Compliance and Security‚Äč

Compliance and Security

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