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---
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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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