| 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 | |
| ## 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 | |
| ``` |