NWSD / model_card.yaml
Ehlum-Lucas
fix: fix model card file name
ce9666a
# model_card_template.yaml
# =====================================================
# ๐ŸŒŠ Water Surface Segmentation on Beach Images
# =====================================================
# Hugging Face model metadata file
# =====================================================
language:
- en
license: gpl-3.0
library_name: pytorch
tags:
- segmentation
- computer-vision
- yolo
- beach
- water
- open-source
task_categories:
- image-segmentation
model-index:
- name: Water Surface Segmentation (NWSD)
results:
- task:
type: image-segmentation
name: Image Segmentation
metrics:
- type: mAP50
name: Mean Average Precision @ 0.5
value: 0.85
- type: inference_speed
name: Inference Speed (CPU)
value: 50
unit: "ms/image"
model_details:
description: >
A YOLOv11n-based segmentation model fine-tuned for detecting and segmenting
water surfaces in coastal or beach images. Trained on a custom-labeled dataset
containing a single class: "water".
developed_by: Lucas Iglesia
repo: https://huggingface.co/Lucas-Iglesia/NWSD
license: GPL-3.0
framework: PyTorch
model_size: 6.07 MB
input_size: "640x640"
num_classes: 1
class_labels: ["water"]
release_date: "2025-11-07"
inference:
parameters:
device: "cpu or cuda"
conf_threshold: 0.5
example_inputs:
- beachTest.jpg
example_outputs:
- binary_mask.png
- overlay.png
usage_snippet: |
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()
recommended_use:
- Coastal monitoring
- Beach safety and drowning prevention
- Environmental water coverage analysis
limitations:
- Optimized for daylight beach scenes
- May underperform in low-visibility or night images
citation:
- type: misc
title: "Water Surface Segmentation on Beach Images"
author: "Lucas Iglesia"
year: 2025
url: "https://huggingface.co/Ehlum-Lucas/NWSD"