Animal Species Image Dataset
Project Overview:
Objective
Scope
The dataset encompasses a wide range of animal species, captured in diverse environments and poses, to ensure the generalization of models across various scenarios.
Sources
- Real-world Animal Encounters: Data is sourced from real encounters with animals, including wildlife, domestic pets, and zoo specimens.
- Controlled Environments: Additionally, images are collected in controlled environments, such as studios, to ensure consistent lighting and backgrounds.
Data Collection Metrics
- Total Data Collected: 100,000 images.
- Data Annotated for ML Training: 120,000 images annotated with detailed species labels for machine learning purposes.
Annotation Process
Stages
- Species Classification: Each image is annotated with the correct species label, ensuring precise classification.
- Object Localization: Annotators carefully outline each animal in the image to facilitate object detection tasks.
- Pose and Environment Annotation: Additional annotations include animal poses and environmental context, aiding in understanding real-world scenarios.
Annotation Metrics
- Species Labels: 120,000 images annotated with accurate species labels.
- Object Localization: 110,000 images annotated with precise object boundaries.
- Additional Context: 100,000 images annotated with contextual information, such as poses and environments.
Quality Assurance
Stages
Regular Model Evaluation: Continuous assessment of the dataset ensures its reliability and relevance to computer vision tasks.
Privacy Measures: Strict adherence to privacy regulations ensures anonymity and responsible data collection practices.
Feedback Integration: Feedback from users and evaluations is used to iteratively improve the dataset’s quality and usefulness.
QA Metrics
- Species Identification Accuracy: Achieved 97% accuracy in correctly identifying animal species.
- Object Localization Accuracy: Attained 95% accuracy in accurately localizing animals within images.
- Privacy Compliance: Maintained 100% compliance with international data protection and privacy regulations.
Conclusion
The creation of the Animal Species Image Dataset based on the Oxford-IIIT Pet Dataset represents a significant advancement in computer vision research, particularly in the domain of animal species recognition. By providing a diverse and meticulously annotated collection of images, the dataset serves as a valuable resource for developing and benchmarking computer vision algorithms aimed at understanding and interacting with the animal kingdom effectively.
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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