--- license: cc-by-4.0 tags: - ocean - midwater - object-detection --- # MBARI Midwater Supercategory Detector ## Model Details - Trained by researchers at [CVisionAI](https://www.cvisionai.com/) and the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI). - [YOLOv5v6.2](https://github.com/ultralytics/yolov5/tree/v6.2) - Object detection - Fine tuned yolov5l to detect 22 morhpotaxonmic categories of midwater animals in the Greater Monterey Bay Area off the coast of Central California. ## Intended Use - Make real time detections on video feed from MBARI Remotely Operated Vehicles. - Post-process video collected in the region by MBARI vehicles. ## Factors - Distribution shifts related to sampling platform, camera parameters, illumination, and deployment environment are expected to impact model performance. - Evaluation was performed on an IID subset of available training data. Data to test out of distribution performance not currently available. ## Metrics - Precision, Recall, and per class accuracy were evaluated at test time. - mAP@0.5 = 0.866 - Indicates reasonably good performance for target task. ## Training Data - A combination of publicly available [FathomNet](https://fathomnet.org/fathomnet/#/) and internal MBARI data ## Deployment In an environment running [YOLOv5v6.2](https://github.com/ultralytics/yolov5/tree/v6.2): ``` python classify/predict.py --weights best.pt --data data/images/ ```