Computer-Vision Data Pipeline

From imaging variability to ML-ready data

I built an image-acquisition pipeline integrating programmatic camera control, standardized illumination, structured metadata, quality control, and dataset curation. Controlling capture variance at the source improves input consistency before model training.

The pipeline produced 5,000+ curated images across species with consistent capture conditions and traceable metadata. It provides a reproducible input specification for downstream computer-vision training, validation, and error analysis.