---
license: cc-by-4.0
tags:
  - ocean
  - object-detection
  - object-localization
  - single-class
---

# FathomNet Megalodon Detector

## Model Details

- Trained by researchers at the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI).
- Ultralytics [YOLOv8x](https://github.com/ultralytics/ultralytics)
- 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](plots/confusion_matrix_normalized.png), [precision-recall curve](plots/PR_curve.png), and [F1-confidence curve](plots/F1_curve.png) were evaluated at test time
- mAP@0.5 = 0.782

## Training and Evaluation Data

- All publicly-available data on [FathomNet](https://fathomnet.org/)

## Deployment

1. Clone this repository
2. In an environment with the [`ultralytics` Python package](https://github.com/ultralytics/ultralytics) installed, run:

```bash
yolo predict model=best.pt
```