FathomNet/MBARI-midwater-supercategory-detector

MBARI Midwater Supercategory Detector

Model Details

• Trained by researchers at CVisionAI and the Monterey Bay Aquarium Research Institute (MBARI).
• YOLOv5v6.2
• 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 and per class accuracy were evaluated at test time.
• mAP@0.5 = 0.866
• Indicates reasonably good performance for target task.

Training and Evaluation Data

• A combination of publicly available FathomNet and internal MBARI data
• Class labels have a long tail and localizations occur throughout the frame.

Deployment

In an environment running YOLOv5v6.2:

python classify/predict.py --weights best.pt --data data/images/