Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 1,308 Bytes
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---
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 |