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YOLO-Fish

A Robust Fish Detection Model to Detect Fish in Realistic Underwater Environment.

Accepted At Ecological Informatics [Paper]

Model Architecture Detection on DeepFish Detection on OzFish

Dataset

Download the annotated DeepFish dataset from here
To download OzFish dataset, visit the link

Working Procedure

This projects was implemented using darknet framework for detection model. To know the setup requirements, how to compile and train on different OS, how to use command line everything you will get by visiting AlexeyAB/darknet github repository.

Evaluation Model

  1. Download the test dataset and unzip.
    test dataset of deepfish
    test dataset of ozfish
    test dataset of merge dataset
  2. Download the cfg file of a chosen model.
    cfg of different models
  3. Download the Model's weights.(google-drive mirror)
    models trained on deepfish
    models trained on ozfish
    models trained on merge dataset
  4. Content of the file obj.data should be
classes=1
train=data/train.txt
valid=data/test.txt
names=data/obj.names
backup=backup/
  1. obj.names files should contain just a 'Fish' word.
  2. Keep wieghts files in a backup directory and cfg files in cfg directory.
  3. Run the command(linux) to evaluate map for a YOLO-Fish models.
./darknet detector map data/obj.data cfg/yolo-fish-2.cfg backup/merge_yolo-fish-2.weights  

Run the command to test on video for real-time detection

./darknet detector demo data/obj.data cfg/yolo-fish-2.cfg backup/merge_yolo-fish-2.weights input.mp4 -dont_show -ext_output -out_filename output.avi

For using network video-camera mjpeg-stream with any Android smartphone, check here

yolo-fish's People

Contributors

tamim662 avatar

Stargazers

 avatar  avatar  avatar Chahit Uppal avatar Camille Pagniello avatar Alexandre Rosa avatar  avatar Ikaris avatar Alexander Bunn avatar DrLee avatar fun_dl avatar yeshu avatar  avatar  avatar Timo Klerx avatar  avatar  avatar  avatar sean_zhang avatar Karthik Dani avatar  avatar Smettus avatar  avatar  avatar Kefflen Moreno Ramos avatar Yakup avatar  avatar  avatar kurisu_u avatar Robin Sandfort avatar Dan Morris avatar  avatar  avatar  avatar William E Hahn avatar baudneo avatar Sungjoo(Dennis) Hwang avatar  avatar David Humphrey avatar Kanyakorn Jumangmor avatar suixinio avatar OliverHawk avatar Hedula avatar  avatar  avatar Adam S. Grodek avatar  avatar  avatar Aarush Aggarwal avatar Alteox.com avatar HAOJIE CHANG avatar Ajinkya avatar Muhib Al Hasan avatar  avatar Nasif Ishtiaque Islam avatar Talha Kavuncu avatar

Watchers

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yolo-fish's Issues

Improving runtime performance (esp. on Apple Silicon)

I've been working with YOLO-fish to detect target species of minnows in videos, and I'm really pleased that this is possible. This is amazing work, thank you for sharing it!

However, I have to run it on M1 Macs, and I'm finding it extremely slow for working with video. I wonder if anyone has thoughts on getting this to run faster? For example, are there any plans to update this to use yolov8 (which can use Apple's GPUs)? I also tried using this OpenCL version of Darknet, and it's better, but still slow.

Thanks for any insight or tips.

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