){kind=link}
MBARI
Collection
Models trained by the Monterey Bay Aquarium Research Institute, including data from FathomNet and the MBARI Video Annotation and Reference System.
•
9 items
•
Updated
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/
- Downloads last month
- 3
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no library tag.