Lessons learned in deploying CV for ecology

Good benchmark performance is the first step to impact, but is only a small piece of the complex system necessary to enable computer vision models to be deployed and trusted in sustainability and conservation applications – a system that requires human and computational infrastructure, iterative development, software support and maintenance, and continual quality control. I will speak about lessons learned in deployed computer vision systems for applications in ecology, discussing differences in what is needed for end users with unequal access to resources and expertise, different priorities and risks of failure, and different operational needs from real-time decision support to post-hoc analysis.