---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- Watermark-or-Not
- Experimental
size_categories:
- 10K<n<100K
---

# 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