MBARI-315k-yolov5

MBARI Monterey Bay 315k YOLOv5

<!-- TODO: Fill out the model card ## 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 curve](https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/PR_curve.png) and [per class accuracy]((https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/confusion_matrix.png)) were evaluated at test time. - [email protected] = 0.866 - Indicates reasonably good performance for target task. ## Training and Evaluation Data - A combination of publicly available [FathomNet](https://fathomnet.org/fathomnet/#/) and internal MBARI data - Class labels have a [long tail and localizations occur throughout the frame](https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/labels.jpg). ## 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/ ``` -->