Community Fish Detector

Open single-class fish detector built by Filippo Varini and a group of contributors, trained on seventeen open datasets pooled into one; finds fish in underwater images and video from any camera or habitat.

The Community Fish Detector is a set of pretrained object-detection models that recognise one class, fish, in underwater imagery. It was made by a group led by Filippo Varini, with Dan Morris, Sonny Burniston, Oceane Boulais and others; each contributor converted one open dataset to a common format, and the pooled Community Fish Detection Dataset holds about two million images and 935,000 fish bounding boxes from seventeen sources, including baited video, ROVs, deep-sea cameras, coral reefs, aquaculture pens, lab tanks and rivers. It does not identify species.

The models are released as open weights: RF-DETR variants at three sizes under Apache 2.0, and a YOLOv12x model under AGPL-3.0, which the project has deprecated because of that licence. Inference runs from Python scripts on images or video (one frame per second by default) and writes MegaDetector-format output; a Hugging Face Space runs the model in a browser. The dataset is hosted on LILA BC.

Status: the makers call the released models an initial training effort, with model versions dated February to July 2026, and are gathering contributors and annotated imagery for a second version.