Ehlum/NWSD

🌊 Water Surface Segmentation on Beach Images

Model Overview

This model performs semantic segmentation of water surfaces in beach or coastal images. It’s a fine-tuned version of YOLOv11n, adapted for binary segmentation with a single class: water.

Built for lightweight, real-time deployment, the model achieves strong accuracy while remaining small and efficient.

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🧠 Model Details

• Architecture: YOLOv11n segmentation head (binary)
• Base framework: PyTorch / Ultralytics YOLOv11
• Input size: 640×640 RGB images
• Output: Binary segmentation mask (1 class — water)
• Model file: nwsd-v2.pt (≈6 MB)

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🚀 Key Features

• ⚡ Real-time inference on CPU/GPU
• 🖼 Outputs: Binary masks, overlays, and coverage statistics
• 📊 Evaluation tools included for metrics & visualization
• 🐍 Easy Python integration via a simple API (nwsd_api.py)

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📈 Performance

Metric	Value	Notes
mAP50	> 0.85	On validation set
Inference speed	~50 ms/image	On CPU
GPU memory	< 2 GB	For 640×640 input

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🗂 Dataset

• Type: Binary segmentation
• Classes: water
• Annotations: PNG masks
• Source: Custom-labeled beach dataset

🔗 Dataset on Roboflow

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🧩 Intended Uses

Use cases:

• Coastal or maritime monitoring
• Beach safety & drowning prevention systems
• Environmental analysis (e.g., water coverage estimation)

Limitations:

• Designed for daylight, clear beach imagery
• May underperform in low-visibility or night-time scenes

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🧪 How to Use

Load model from Hub

from huggingface_hub import hf_hub_download
import torch

model_path = hf_hub_download(repo_id="Ehlum-Lucas/NWSD", filename="nwsd-v2.pt")
model = torch.load(model_path, map_location="cpu")
model.eval()

Inference example

from PIL import Image
import torch
from torchvision import transforms

img = Image.open("beachTest.jpg").convert("RGB")
input_tensor = transforms.ToTensor()(img).unsqueeze(0)

with torch.no_grad():
    pred = model(input_tensor)

⚙️ Training

You can fine-tune or retrain the model using YOLOv11 tools:

python train.py --data data.yaml --weights <path_to_weights> --img 640 --batch 16 --epochs 50

Example configuration (data.yaml) defines paths to your datasets and class names.

🧭 Evaluation

python evaluate.py --data data.yaml --weights model/nwsd-v2.pt

Generates:

• Binary mask
• Overlay visualization
• Water coverage stats

License

This model is released under the GPL-3.0 License. See the LICENSE file for details.

Citation

If you use this model in your work, please consider citing:

@misc{nwsd2025,
  title={Water Surface Segmentation on Beach Images},
  author={Lucas Iglesia},
  year={2025},
  howpublished={\url{https://huggingface.co/Ehlum-Lucas/NWSD}}
}