If you are interested to see how a Megalodon AI model can be trained, check out the Monterey Bay Aquarium Megalodon. The trained AI model provides an excellent example of the megalodon AI use in science and is trained by researchers at the Monterey Bay Aquarium Research Institute (MBARI).
Please see the full article at: https://huggingface.co/FathomNet/megalodon.
Excerpts are provided below for megalodon.com domain valuation research.
FathomNet Megalodon Detector
Model Details
- Trained by researchers at the Monterey Bay Aquarium Research Institute (MBARI).
- Ultralytics YOLOv8x
- Object detection model
- Fine-tuned to detect 1 class, called ‘object’, using all FathomNet localizations
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
Metrics
- Normalized confusion matrix, precision-recall curve, and F1-confidence curve were evaluated at test time
- mAP@0.5 = 0.782
Training and Evaluation Data
- All publicly-available data on FathomNet
Deployment
- Clone this repository
- In an environment with the
ultralyticsPython package installed, run:
yolo predict model=best.pt