FathomNet Megalodon Detector
Model Details
- Trained by researchers at the Monterey Bay Aquarium Research Institute (MBARI).
- Ultralytics YOLO11x
- Object detection model
- Fine-tuned to detect 1 class, called 'object', using all FathomNet localizations
- Updated from the
yolo11x_megalodon_e70_p10 training run
Intended Use
- Post-process video and images collected by marine researchers
- Can be used to build a localized set of training images, when neither training data nor a model exists for the imagery being analyzed
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 as well as out-of-distribution data
Training Details
- Training run:
yolo11x_megalodon_e70_p10
- Max epochs: 70, early stopping patience: 10
- Training stopped at epoch 63
- Best checkpoint: epoch 53 (highest mAP50-95)
- Best mAP50-95: 0.578
- Best mAP50: 0.799
- Full hyperparameters: see
args.yaml
- Per-epoch metrics: see
results.csv
Training and Evaluation Data
Deployment
- Clone this repository
- In an environment with the
ultralytics Python package installed, run:
yolo predict model=mbari-megalodon-yolo11x.pt
