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---
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
``` |