Accurate and efficient stock assessment methods of commercially relevant fish species are extremely important toward sustainable fisheries management. Currently used manual techniques are highly inefficient, time-consuming, and not incredibly accurate. In partnership with the MIT Sea Grant group, students worked to automate the detection and counting of herring fish species in image and video data for efficient fishery management, working toward an end-to-end platform that takes video inputs, applies state-of-the-art computer vision techniques, and outputs count of herring fishes moving upstream.