Create README.md

README.md

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+---
+annotations_creators:
+- manual
+language_creators:
+- none
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: Lpipe Dataset
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- underwater
+- semantic-segmentation
+- pipelines
+- robotics
+- marine
+- oil-and-gas
+- computer-vision
+task_categories:
+- image-segmentation
+task_ids:
+- semantic-segmentation
+---
+
+# Lpipe Dataset
+
+[📄 **DualMatch: A Dual EMA Teacher for Underwater Semi-Supervised Pipeline Segmentation**](http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2025/09.15.00.38/doc/LawsonSIBGRAPI_2025-1.pdf)  
+
+The **Lpipe dataset** is an underwater image dataset designed for **semantic segmentation** tasks involving subsea environments.  
+It includes manually annotated RGB images containing **pipelines, humans, animals, and robots**.  
+The dataset aims to support research in **underwater computer vision**, **autonomous robotics**, and **oil and gas inspection systems**.
+
+---
+
+## Dataset Summary
+
+The Lpipe dataset provides **716 underwater images** paired with **716 pixel-level segmentation masks**.  
+Each mask is color-coded in RGB, where each color corresponds to one of the four semantic classes:  
+
+| Class ID | Class Name | Description |
+|-----------|-------------|--------------|
+| 0 | Background | Underwater background and seabed |
+| 1 | Pipeline | Subsea pipeline or tubular structures |
+| 2 | Human | Diver or human presence |
+| 3 | Animal | Marine animals (fish, crustaceans, etc.) |
+| 4 | Robot | Underwater inspection or maintenance robot |
+
+This dataset was created to assist in the development and evaluation of **semantic segmentation** models for complex underwater scenarios.
+
+---
+
+## Supported Tasks and Benchmarks
+
+The dataset can be used for:
+- **Semantic Segmentation**  
+- **Semi-supervised Segmentation** (splits available on GitHub)  
+- **Object detection and domain adaptation** in underwater imagery  
+
+---
+
+## Dataset Structure
+
+Inside the dataset, two folders are provided:
+
+Lpipe/
+
+- images/ # RGB underwater images (.jpg)
+- masks/ # Segmentation masks (.png, RGB-coded)
+
+
+Although the dataset on Hugging Face includes only images and masks,  
+**official training/validation/test splits** (for semi-supervised learning) are available in the associated GitHub repository:  
+🔗 [https://github.com/EduardoLawson1/DualMatch](https://github.com/EduardoLawson1/DualMatch)
+
+---
+
+## Data Fields
+
+| Field | Type | Description |
+|--------|------|-------------|
+| image | `RGB Image (.jpg)` | The original underwater image |
+| mask | `RGB Image (.png)` | Pixel-level color-coded segmentation mask |
+
+---
+
+## Data Splits
+
+There are 716 total samples.  
+The dataset is not pre-split in the Hugging Face version, but the following splits are defined in the GitHub repository:
+- `train.txt`
+- `val.txt`
+- `test.txt`
+- `unlabeled.txt` (for semi-supervised training)
+
+---
+
+## Licenses
+
+This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.  
+You are free to share, copy, redistribute, adapt, and build upon the material for any purpose, provided that you give proper credit to the original author.  
+
+---
+
+## Citation
+
+If you use this dataset, please cite the related publication:
+
+bibtex
+@article{silvadualmatch,
+  title={DualMatch: A Dual EMA Teacher for Underwater Semi-Supervised Pipeline Segmentation},
+  author={Silva, Eduardo L and Schein, Tatiana T and Briao, Stephanie L and Anastacio, Gabriel L and Oliveira, Felipe G and Drews-Jr, Paulo LJ}
+}