Borcherding/Hed2CoralReef_Annotations

Hed2CoralReef_Annotation Dataset

Overview

This dataset contains 801 SDXL-generated coral reef images and their associated Holistically-Nested Edge Detections (HED). The reef images were created using SDXL and the Underwater Environment v01 lora by PrivateHindsight on CivitAi: PrivateHindsight/Underwater_Environment_v01

Dataset Contents

• 801 coral reef images generated with SDXL with the PrivateHindsight/Underwater_Environment_v01 LoRA
• 801 corresponding Holistically-Nested Edge Detection maps created with this great implementation with pytorch by sniklaus: sniklaus/pytorch-hed
• Paired data suitable for image-to-image translation tasks

Use Cases

This dataset is designed for:

• Training HED 2 Image cycleGAN models with my dataset annotators, training notebooks, & inference tools at Leoleojames1/CycleGANControlNet2Anything

Citation

If you use this dataset in your research, please cite:

@misc{borcherding2025depth2anything,
  title={Hed2CoralReef Annotations},
  author={Borcherding},
  year={2025},
  howpublished={\url{https://huggingface.co/datasets/Borcherding/Hed2CoralReef_Annotation}}
}

Model Attributions

When using this dataset, please also cite the original models:

SDXL:

@misc{SDXL,
  title={stable-diffusion-xl-base-1.0},
  author={StabilityAI},
  year={2025},
  howpublished={\url{https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0}}
}

Underwater Environment v01 LoRA:

@misc{Underwater-Environment-v01,
  title={Underwater Environment v01},
  author={PrivateHindsight},
  year={2025},
  howpublished={\url{https://civitai.com/models/561874?modelVersionId=625879}}
}

pytorch-hed:

@misc{pytorch-hed,
     author = {Simon Niklaus},
     title = {A Reimplementation of {HED} Using {PyTorch}},
     year = {2018},
     howpublished = {\url{https://github.com/sniklaus/pytorch-hed}}
}

Holistically-Nested Edge Detection:

@inproceedings{Xie_ICCV_2015,
   author = {Saining Xie and Zhuowen Tu},
   title = {Holistically-Nested Edge Detection},
   booktitle = {IEEE International Conference on Computer Vision},
   year = {2015}
}