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--- |
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license: cc-by-4.0 |
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tags: |
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- ocean |
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- midwater |
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- object-detection |
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--- |
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# MBARI Midwater Object Detector |
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## Model Details |
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- Trained by researchers at [CVisionAI](https://www.cvisionai.com/) and the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI). |
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- [RetinaNet](https://arxiv.org/abs/1708.02002) with [ResNet-50](https://arxiv.org/abs/1512.03385) backbone |
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- Object detection |
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- Fine tuned RetinaNet to detect 16 taxonomic categories of midwater animals in the Greater Monterey Bay Area off the coast of Central California. |
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## Intended Use |
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- Make real time detections on video feed from MBARI Remotely Operated Vehicles. |
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- Post-process video collected in the region by MBARI vehicles. |
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## Factors |
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- Distribution shifts related to sampling platform, camera parameters, illumination, and deployment environment are expected to impact model performance. |
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- Evaluation was performed on an IID subset of available training data. Data to test out of distribution performance not currently available. |
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## Metrics |
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- [Confusion matrix](https://huggingface.co/FathomNet/MBARI-midwater-detector/blob/main/plots/confusion_matrix.png) evaluated at test time. |
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## Training and Evaluation Data |
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- A combination of publicly available [FathomNet](https://fathomnet.org/fathomnet/#/) and internal MBARI data |
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<!-- |
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## Deployment |
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In an environment running [YOLOv5v6.2](https://github.com/ultralytics/yolov5/tree/v6.2): |
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``` |
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python classify/predict.py --weights best.pt --data data/images/ |
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``` |
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--> |
