This article concerns the design of effective computer vision programming exercises and presents a novel means of designing these assignments. We describe three recent case studies designed to evaluate the effectiveness of assigning graduate-level computer vision students with publicly available research benchmarks as competitive assignments. This was done rather than assigning more traditional exercises that require students to implement specific algorithms or applications. We allowed our students the freedom of designing or choosing their own methods, with the goal of obtaining the best performance on the benchmark chosen for each assignment. Students, therefore, competed against each other, as well as published state of the art. We detail the design, application, and results of these benchmark exercises. We show that not only are these benchmarks easily adapted for the classroom, but also that in some cases, student assignments matched published state-of-the-art performance. This observation provides strong evidence to support the effectiveness of the proposed exercise design. We conclude by discussing the benefits and drawbacks of these exercises compared to those traditionally employed in computer vision classrooms.