Watermark-or-Not-20K

# Watermark-or-Not-20K Dataset

## Overview

The **Watermark-or-Not-20K** dataset consists of 20,000 images annotated with binary labels indicating the presence or absence of a watermark. It is designed to support training and evaluation of models focused on watermark detection, which is useful for content filtering, copyright protection, and image moderation tasks.

## Dataset Structure

- **Split:** `train`
- **Number of samples:** 20,000
- **Label Type:** Categorical (2 classes)
- **Image Resolution:** Ranges from 158 pixels to 4.93k pixels in width
- **Storage Format:** Auto-converted to Parquet for efficient access

## Label Classes

The dataset contains the following classes:

- `0` - No Watermark
- `1` - Watermark

## Usage

The dataset can be accessed using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Watermark-or-Not-20K")
````

## Applications

This dataset is suitable for:

* Training computer vision models to detect watermarks
* Fine-tuning transformer-based vision models on binary classification tasks
* Building AI-based content moderation pipelines

# Watermark-or-Not-20K 数据集

## 概述

**Watermark-or-Not-20K 数据集**包含20000张带有二元标注标签的图像，标签用于指示图像是否含水印。本数据集旨在为专注于水印检测的模型训练与评估提供支持，该技术可应用于内容过滤、版权保护及图像审核等任务。

## 数据集结构

- **划分方式:** `训练集`
- **样本数量:** 20000
- **标签类型:** 分类标签（共2类）
- **图像分辨率:** 宽度范围为158像素至4.93千像素
- **存储格式:** 自动转换为Parquet格式以实现高效访问

## 标签类别

本数据集包含以下类别：
- `0` - 无水印
- `1` - 有水印

## 使用方法

可通过Hugging Face的`datasets`库访问本数据集：
python
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Watermark-or-Not-20K")

## 应用场景

本数据集适用于以下场景：
* 训练用于水印检测的计算机视觉模型
* 在二元分类任务上微调基于Transformer的视觉模型
* 构建基于AI的内容审核流水线