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eloise54/cots_yolo_dataset

πŸͺΈ CSIRO Crown-of-Thorns Starfish (COTS) Detection Dataset β€” YOLO Format This dataset is a modified version of the CSIRO COTS and COTS Scars Dataset, originally released under the Creative Commons Attribution 4.0 License (CC BY 4.0). The original dataset contains images and annotations for Crown-of-Thorns Starfish (COTS) and COTS scars, collected to support coral reef monitoring and control efforts on the Great Barrier Reef (GBR). These starfish are coral predators, and their… See the full description on the dataset page: https://huggingface.co/datasets/eloise54/cots_yolo_dataset.

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πŸͺΈ CSIRO Crown-of-Thorns Starfish (COTS) Detection Dataset β€” YOLO Format

![Hugging Face Spaces](https://huggingface.co/spaces/eloise54/cotsdetectionproject)

This dataset is a modified version of the CSIRO COTS and COTS Scars Dataset, originally released under the Creative Commons Attribution 4.0 License (CC BY 4.0).

The original dataset contains images and annotations for Crown-of-Thorns Starfish (COTS) and COTS scars, collected to support coral reef monitoring and control efforts on the Great Barrier Reef (GBR).

These starfish are coral predators, and their outbreaks can severely damage reef ecosystems.

Please cite the original dataset authors if you use this dataset in your work.

πŸ™ Citation

bibtex
@dataset{csiro_cots_2024,
  author = {Armin, Ali and Bainbridge, Scott and Page, Geoff and Tychsen-Smith, Lachlan and Coleman, Greg and Oorloff, Jeremy and Harvey, De'vereux and Do, Brendan and Marsh, Benjamin and Lawrence, Emma and Kusy, Brano and Hayder, Zeeshan and Bonin, Mary},
  title = {COTS and COTS scar dataset},
  year = {2024},
  publisher = {CSIRO},
  version = {v1},
  doi = {10.25919/03a7-hn83},
  url = {https://data.csiro.au/collection/csiro:64235}
}

πŸ”§ How to use this dataset ?

Training
git clone https://huggingface.co/datasets/eloise54/cots_yolo_dataset
pip install ultralytics

In a python file:

from ultralytics import YOLO

# Dowload pretrained model
import urllib.request
url = 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt'
urllib.request.urlretrieve(url, 'yolo11m.pt')

# Load a pretrain model
model = YOLO('yolo11m.pt')
results = model.train(data='data/cots_yolo_dataset/cots_yolo_dataset.yaml', epochs=20, task='detect')

Example of inference without downloading everything : notebook link

πŸ”§ Modifications

Here is the list of the modification made on the original CSIRO COTS and COTS Scars Dataset:

  • β€”Annotations converted to YOLO format
  • β€”Created train/val split using only images with COTS labels
  • β€”Retained the original test set
  • β€”Removed all COTS scars annotations
  • β€”Organized directory structure as follows:
cots_yolo_dataset/
β”œβ”€β”€ images/
β”‚   β”œβ”€β”€ train/
β”‚   β”œβ”€β”€ val/
β”‚   └── test/
β”œβ”€β”€ labels/
β”‚   β”œβ”€β”€ train/
β”‚   β”œβ”€β”€ val/
β”‚   └── test/
└── cots_yolo_dataset.yaml

πŸ€— Example of use with finetuned yolo11m

Demo on Hugging Face Spaces:

![Hugging Face Spaces](https://huggingface.co/spaces/eloise54/cotsdetectionproject)

Gitlab repository containing full training pipeline:

  • β€”original CSIRO COTS and COTS Scars Dataset download
  • β€”modifications done to make this current dataset
  • β€”yolov11m finetuning
  • β€”gradio app and inference using this current dataset

![GitLab Repo](https://gitlab.com/robotics2ai/cotsdetectionproject)