Abstract
Advances in high-throughput imaging and experimental automation have dramatically increased the scale of biological datasets, creating a growing need for tools that can efficiently identify and localize features in complex image data. Although deep learning has transformed image analysis, methods such as region-based convolutional neural networks remain underutilized in biology due to technical barriers such as coding requirements and reliance on cloud infrastructure. We present EZ-FRCNN, a locally hosted, user-friendly package that enables the accessible and scalable application of object detection to biological datasets. Through graphical and scriptable interfaces, users can annotate data, train models, and perform inference entirely offline. We demonstrate its utility in detecting cell phenotypes for large-scale screening, enabling the first label-free tracking of grinder motion in freely moving C. elegans to quantify feeding dynamics, and identifying animals in naturalistic environments for ecological field studies. These once-infeasible analyses now enable rapid screening of cell therapies, investigation of internal state-behavior coupling without immobilization or genetic modification, and efficient wildlife tracking with minimal computational cost. Together, these examples demonstrate how accessible tools like EZ-FRCNN can drive new biological discoveries in both laboratory and field environments.