---
license: cc-by-4.0
tags:
- ocean
- benthic
- object-detection
---

# MBARI Benthic Supercategory Detector

## Model Details
- Trained by researchers at [CVisionAI](https://www.cvisionai.com/) and the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI).
- [Detectron2](https://github.com/facebookresearch/detectron2) with [ResNet](https://arxiv.org/abs/1512.03385) backbone
- Object detection
- Fine tuned to detect 20 morphotaxonomic categories of benthic animals in the Greater Monterey Bay Area off the coast of Central California.

## Intended Use
- Make real time detections on video feed from MBARI Remotely Operated Vehicles.
- Post-process video collected in the region by MBARI vehicles.

## 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. Data to test out of distribution performance not currently available.

## Metrics
- [Confusion matrix](https://huggingface.co./FathomNet/MBARI-benthic-supercategory-detector/blob/main/plots/confusion_matrix_norm.png) evaluated at test time. 

## Training and Evaluation Data
- A combination of publicly available [FathomNet](https://fathomnet.org/fathomnet/#/) and internal MBARI data

<!--
## Deployment
In an environment running [YOLOv5v6.2](https://github.com/ultralytics/yolov5/tree/v6.2):

```
python classify/predict.py --weights best.pt --data data/images/
```
-->