Seagulls on Frozen Lake Surface – Winter Wildlife
Source Type: Real world Photography
Processing Status: Unprocessed
Authenticity: Natural lighting and color profile preserved
Optical Status: Zero Adjustment / No Perspective Correction
Sequence Type: Multi-Angle Morphological Sequence
Format: Native RAW / CR2
Camera: Canon EOS 70D
Maximum Resolution: 5472 x 3648
Capture Location: Alanya – Turkiye
Capture Date: January 2015
Dataset Size: 1.07 GB
Photo Count: 50
Dataset Specifications & Overview
This dataset features a image sequence of the Vanessa cardui (Painted Lady) butterfly, captured in its natural Mediterranean habitat. Focusing on the intricate wing patterns and micro-movements of this migratory species, the collection provides essential data for entomological classification and AI-driven morphological studies.
Painted Lady Butterfly
Full CollectIon PrevIew
Use Cases & Visual ASSETS References
AI Training: AI Training & Research: High-quality, multi-angle datasets optimized for object detection, image segmentation, and style transfer models.
Editorial & Publishing: Professional-grade stock content tailored for botanical magazines, educational publishing, and high-end digital web projects.
Digital & Concept Arts: Ultra-high-resolution assets ideal for matte painting, photo manipulation, and concept art, providing authentic textures and lighting.
Explore More Image CollectIons
FAQ
In what formats are Pixamin datasets offered?
All datasets are provided in high-resolution JPG or RAW formats, organized in a structured ZIP file. This organization is specifically designed for easy integration into machine learning workflows such as object detection and style transfer.
Can I use these datasets for commercial AI model training?
Yes. The datasets are compiled to support commercial AI development and digital arts. Each purchase includes a license that permits model training, editorial use, and high-level digital manipulation, subject to our standard terms.
Are technical metadata and EXIF data preserved in the images?
To provide environmental context for computer vision tasks, all original metadata, including ISO, Aperture, and Shutter Speed, is preserved. This allows AI models to learn from the technical specifications of each visual asset.