add README from github.com/warplab/megafishdetector

README.md

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 license: mit
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+
+# megafishdetector
+
+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.
+
+Initial experiments to train a generic MegaFishDetector modelled off of the MegaDetector for land animals (https://github.com/microsoft/CameraTraps/blob/main/megadetector.md)
+
+Currently based on YOLOv5 (https://github.com/ultralytics/yolov5).
+
+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.
+
+## Instructions
+1. Install [Yolov5](https://github.com/ultralytics/yolov5)
+2. Download desired network [weights](https://github.com/warplab/megafishdetector/blob/main/MODEL_ZOO.md)
+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]
+
+## Public Datasets Used in v0:
+
+- [AIMs Ozfish](https://github.com/open-AIMS/ozfish) 
+- [FathomNet](https://www.fathomnet.org/)
+- [VIAME FishTrack](https://viame.kitware.com/#/collection/62afcb66dddafb68c8442126)
+- [NOAA Puget Sound Nearshore Fish (2017-2018)](https://lila.science/datasets/noaa-puget-sound-nearshore-fish)
+- [DeepFish](https://alzayats.github.io/DeepFish/)
+- [NOAA Labelled Fishes in the Wild](https://www.st.nmfs.noaa.gov/aiasi/DataSets.html)
+
+## To Cite:
+
+[paper](https://arxiv.org/abs/2305.02330)
+```
+@misc{yang2023biological,
+      title={Biological Hotspot Mapping in Coral Reefs with Robotic Visual Surveys}, 
+      author={Daniel Yang and Levi Cai and Stewart Jamieson and Yogesh Girdhar},
+      year={2023},
+      eprint={2305.02330},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO}
+}
+```
+## TODO:
+- Train larger models
+- requirements.txt for things like fathomnet environment
+- COCO format output