---
title: beluga-seeker
canonical_url: "https://www.modelscope.cn/models/ML4Sustain/beluga-seeker"
md_url: "https://www.modelscope.cn/models/ML4Sustain/beluga-seeker.md"
repository: ML4Sustain/beluga-seeker
last_updated: 2026-06-04
license: gpl-3.0
base_model:
  - Ultralytics/YOLOv8
base_model_relation: finetune
library_name:
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 1
stars: 0
tags:
  - earth-science
  - remote-sensing
  - marine-biology
  - object-detection
  - beluga-whale
  - yolo
  - satellite-imagery
  - computer-vision
---

# beluga-seeker

> beluga-seeker - ML4Sustain 在 ModelScope 开源的模型。Beluga Whale Detection from Satellite Imagery with Point Labels

ML4Sustain/beluga-seeker 是 ModelScope 魔搭社区上的机器学习模型，采用 gpl-3.0 许可，基于 Ultralytics/YOLOv8 构建。

- **Repository**: ML4Sustain/beluga-seeker
- **License**: gpl-3.0
- **Base model**: Ultralytics/YOLOv8
- **Tags**: earth-science, remote-sensing, marine-biology, object-detection, beluga-whale, yolo, satellite-imagery, computer-vision
- **Downloads**: 1
- **Stars**: 0
- **Last updated**: 2026-06-04

Source: https://www.modelscope.cn/models/ML4Sustain/beluga-seeker

---

# Beluga Whale Detection from Satellite Imagery with Point Labels

<p align="center">
<a href="https://www.modelscope.cn/models/ML4Sustain/beluga-seeker"><img src="https://img.shields.io/badge/Model-ModelScope-624aff?logo=modelscope" /></a>
<a href="https://huggingface.co/ML4Sustain/beluga-seeker"><img src="https://img.shields.io/badge/Model-HuggingFace-ffd21e?logo=huggingface" alt="Dataset"></a>
<a href="https://github.com/VoyagerXvoyagerx/beluga-seeker"><img src="https://img.shields.io/badge/GitHub-Repo-blue?logo=github" alt="GitHub"></a>
</p>

This repository hosts the model checkpoints for **Beluga-Seeker**, a project for detecting beluga whales in Arctic satellite imagery using point-label supervision and automated bounding-box generation via Segment Anything Model (SAM).

## Model Description

Beluga-Seeker provides YOLOv8s-based object detectors trained on very-high-resolution satellite imagery. Two variants are released:

| Checkpoint | Description | Classes |
|------------|-------------|---------|
| `YOLO-SAM.pth` | Trained on SAM-generated bounding boxes (recommended) | certain whale, uncertain whale, harp seal |
| `YOLO-Buffer.pth` | Trained on point-label buffer bounding boxes | certain whale, uncertain whale, harp seal |

Both models are trained with the [MMYOLO](https://github.com/open-mmlab/mmyolo) framework and can be loaded via `mmengine`.

## Paper

> Yijie Zheng, Jinxuan Yang, Yu Chen, Yaxuan Wang, Yihang Lu, Guoqing Li.  
> **Beluga Whale Detection from Satellite Imagery with Point Labels.**  
> Accepted for oral presentation at **IGARSS 2025**.  
> [arXiv:2505.12066](https://arxiv.org/abs/2505.12066)

## Installation

```bash
# Recommended environment
conda create -n belugaSeeker python=3.10
conda activate belugaSeeker
pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118

# Install MMYOLO and dependencies
pip install openmim
mim install "mmengine>=0.6.0" "mmcv>=2.0.0rc4,<2.1.0" "mmdet>=3.0.0rc6,<3.1.0" "mmyolo"
```

## Usage

```python
from mmengine.runner import Runner
from mmyolo.utils import register_all_modules

register_all_modules()

# Load checkpoint
cfg = 'configs_beluga/yolov8_s_b24-100e.py'
runner = Runner.from_cfg(cfg)
runner.load_checkpoint('YOLO-SAM.pth')
```

For a complete inference demo, see [`inference_demo.ipynb`](https://github.com/VoyagerXvoyagerx/beluga-seeker/blob/main/inference_demo.ipynb) in the source repository.

## Model Checkpoints

| File | Size | Training Data |
|------|------|---------------|
| `YOLO-SAM.pth` | ~87 MB | SAM-generated bounding boxes |
| `YOLO-Buffer.pth` | ~87 MB | Point-label buffer boxes |

## Dataset & Labels

- **Classes:** `certain whale`, `uncertain whale`, `harp seal`
- **Palette:** `[(250, 165, 30), (255, 255, 0), (0, 255, 0)]`
- Labels were originally created as point annotations and converted to bounding boxes using SAM to improve annotation efficiency.

## Citation

```bibtex
@inproceedings{zheng2025beluga,
  title={Beluga Whale Detection from Satellite Imagery with Point Labels},
  author={Zheng, Yijie and Yang, Jinxuan and Chen, Yu and Wang, Yaxuan and Lu, Yihang and Li, Guoqing},
  booktitle={IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year={2025}
}
```

## Acknowledgements

This work is supported by the [4th IEEE GRSS Student Challenge](https://www.grss-ieee.org/community/groups-initiatives/ieee-grss-student-grand-challenge).

The authors gratefully acknowledge Amou for assistance with ArcGIS Pro and Zori for delivering the data.

## License

This project is licensed under the [GPL-3.0 License](https://github.com/VoyagerXvoyagerx/beluga-seeker/blob/main/LICENSE).