1 min readfrom Machine Learning

Dataset: Starfield Fauna - 20,000 images in 50 species categories. [P]

Our take

Explore the Starfield Fauna dataset, a curated collection of 20,000 images spanning 50 distinct species from Bethesda’s immersive video game. Extracted from approximately two minutes of gameplay footage, this dataset prioritizes species identification through close-up, centered imagery. A robust PowerShell script ensures consistent frame extraction and quality control, with normalization applied to balance biome representation across training, validation, and test sets. For those interested in scalable attention mechanisms, consider our recent work on SSOG-Attention, a promising alternative to traditional methods.
Dataset: Starfield Fauna - 20,000 images in 50 species categories. [P]
Dataset: Starfield Fauna - 20,000 images in 50 species categories. [P]

Repo with dataset links: https://github.com/tesselwait/Starfield_Fauna

Image classification dataset: 20,000 images from 50 fauna species in the video game Starfield. Images were extracted from video capture. About 2 minutes of footage was shot in all or most of the species biomes. One minute of daytime and nighttime footage respectively, usually in two 30-second takes to vary the background. A PowerShell script is used to establish a frame extract rate and extract the 400 frames plus some extra to replace images that were obstructed/blurry or contained other fauna species ignoring birds/critters. The shots are for the most part close-up and centered to keep the task focused on discerning between 50 species rather than finding the creature in the image. The images are initially randomized however some normalization was done if the ratio of images from some biomes was heavily skewed between the training, validation, and test sets.

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