FathomNet2023 Baseline Model
Model Details
- Trained by researchers at Monterey Bay Aquarium Research Institute (MBARI) as a baseline for the FathomNet2023 Competition presented with the Fine Grained Visual Categorization workshop at CVPR 2023.
- Ultralytics YOLOv8.0.117
- Object detection
- Fine tuned yolov8m to detect 290 fine grained taxonmic categories of benthic animals in the Greater Monterey Bay Area off the coast of Central California.
Intended Use
- Make detections on images collect on the sea floor in the Monterey Bay Area.
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 can be found on the competition Kaggle page.
Metrics
- Precision-Recall curve and per class accuracy were evaluated at test time.
- mAP@0.5 = 0.33515
- Performance is quite variable depending on the target organism even when testing on in-distribution data.
- Identified out-of-sample images with a binary metric, returning ROC ~= 0.7.
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Training and Evaluation Data
- Training data is the FathomNet2023 competition split and internal MBARI data
- Class labels have a long tail and localizations occur throughout the frame.
Deployment
In an environment running YOLOv8:
python classify/predict.py --weights fathomnet23-comp-baseline.pt --data data/images/