NVIDIA Cosmos: Lab to Field

A hands-on demo showing how to solve real-world data scarcity and domain shift using NVIDIA Cosmos-Transfer2.5 and FiftyOne. We begin by extracting rare moth samples from the BioTrove biodiversity dataset using BioCLIP embeddings, converting them into controllable video inputs, and generating edge-based control signals. NVIDIA Cosmos-Transfer2.5 then performs world-model–driven domain translation, transforming sterile lab images into realistic field environments. The outputs are validated and analyzed in FiftyOne using grouped datasets, embeddings, and semantic similarity search. This generative workflow yields 20–40% improvements on downstream classifiers by expanding rare classes and correcting dataset bias.

This demo directly addresses three core challenges in real-world computer vision: (1) severe class imbalance and ecological domain shift, (2) absence of realistic field imagery for rare species, and (3) scalable validation of generative outputs. The session walks through the pipeline end-to-end: selecting scarce samples with BioCLIP, preparing video and edge-control inputs, running Cosmos-Transfer2.5 for domain transfer, and validating the generated outputs in FiftyOne using embeddings, grouped datasets, and semantic search.