| ---
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| license: mit
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| ---
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| # megafishdetector
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| Detector for generic "fish" trained on publicly available datasets, currently supporting YOLO-style bounding boxes prediction and training. Can also be used as pre-trained networks for further fine-tuning.
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| Initial experiments to train a generic MegaFishDetector modelled off of the MegaDetector for land animals (https://github.com/microsoft/CameraTraps/blob/main/megadetector.md)
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| Currently based on YOLOv5 (https://github.com/ultralytics/yolov5).
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| This repo contains links to public datasets, code to parse datasets into a common format (currently YOLO darknet only), and a model zoo for people to start with. For instructions to run, see the link above.
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| ## Instructions
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| 1. Install [Yolov5](https://github.com/ultralytics/yolov5)
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| 2. Download desired network [weights](https://github.com/warplab/megafishdetector/blob/main/MODEL_ZOO.md)
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| 3. Usage (from yolov5 root): python detect.py --imgsz 1280 --conf-thres 0.1 --weights [path/to/megafishdetector_v0_yolov5m_1280p] --source [path/to/video/image folder]
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| ## Public Datasets Used in v0:
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| - [AIMs Ozfish](https://github.com/open-AIMS/ozfish)
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| - [FathomNet](https://www.fathomnet.org/)
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| - [VIAME FishTrack](https://viame.kitware.com/#/collection/62afcb66dddafb68c8442126)
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| - [NOAA Puget Sound Nearshore Fish (2017-2018)](https://lila.science/datasets/noaa-puget-sound-nearshore-fish)
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| - [DeepFish](https://alzayats.github.io/DeepFish/)
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| - [NOAA Labelled Fishes in the Wild](https://www.st.nmfs.noaa.gov/aiasi/DataSets.html)
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| ## To Cite:
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| [paper](https://arxiv.org/abs/2305.02330)
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| ```
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| @misc{yang2023biological,
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| title={Biological Hotspot Mapping in Coral Reefs with Robotic Visual Surveys},
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| author={Daniel Yang and Levi Cai and Stewart Jamieson and Yogesh Girdhar},
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| year={2023},
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| eprint={2305.02330},
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| archivePrefix={arXiv},
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| primaryClass={cs.RO}
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| }
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| ```
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| ## TODO:
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| - Train larger models
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| - requirements.txt for things like fathomnet environment
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| - COCO format output
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| |
