README.md

---
task_categories:
- object-detection
tags:
- watermak
- computer-vision
- object-detection
configs:
- config_name: default
  data_files:
  - split: train
    path: "data/train.zip"
  - split: test
    path: "data/test.zip"
  - split: val
    path: "data/val.zip"
---

# Visible watermarks datasets

We have observed that while datasets such as COCO are available for object detection, the availability of datasets
specifically designed for the detection of watermarks added to images is significantly limited. Through our research,
we identified only one such dataset, which originates from the paper Wdnet: Watermark-Decomposition Network for
Visible Watermark Removal [1]. This dataset provides a collection of images along with their corresponding watermark
masks for the purpose of watermark removal. Additionally, we noted that accessing this dataset presented challenges in
terms of data accessibility and regeneration of dataset samples.

The CLWD Dataset, introduced in Wdnet: Watermark-Decomposition Network for Visible Watermark Removal [1],
comprises images sourced from the COCO Dataset (Lin et al., 2014) [ 2] and masks of colored watermarks featuring
random positions and opacities

## Dataset Details (PITA Dataset)

We decided to introduce the Pita dataset, which is based on images from the COCO dataset (Lin et al., 2014) [ 2] and
combines these with logos from the Open Logo Detection Challenge (Su et al., 2018) [3].
The dataset introduces several changes compared to other datasets, with a focus on the task of watermark detection
rather than watermark removal.

The dataset is structured into three splits: a training split, a validation split, and a test split, collectively comprising
approximately 20 000 watermarked images featuring both logos and text.
We decided to incorporate two types of labels:

- Text: The images are watermarked with a random font available on the computer used for generation, and the
text size is also randomized.
- Logos: The logos are sourced from the Open Logo Detection Challenge dataset (Su et al., 2018) and are
characterized by random sizes and opacities.

The position of the logo or text is randomly selected from a set of available positions, specifically corners or the center.
This restriction was introduced based on the observation that watermarks on social media or stock image websites are
predominantly located in these positions.

The dataset is accompanied by command-line interface tools that facilitate reproducibility. These tools support both
YOLO and Hugging Face formats, allowing the download of the dataset and generation with ease

### Dataset Sources

- **Repository:** https://github.com/OrdinaryDev83/dnn-watermark
- **Demo:** https://huggingface.co/spaces/qfisch/watermark-detection

## Uses

- DETR with Hugging face Transformers
- YoloV8 model with ultralytics
- FastRCNN with Pytorch Lighthning

### Source Data

- COCO dataset (Lin et al., 2014) [ 2] and
- Open Logo Detection Challenge (Su et al., 2018) [3].

#### Data Collection and Processing

Generation of the dataset is **reproducible** using the cli tool of this [repository](https://github.com/OrdinaryDev83/dnn-watermark). 

A --help option is available in order to describe how to use the tool.

## Annotation process

Logo were added to COCO images by applying **rotation**, **scaling** and **opacity** changes at a random position on the image.
on the image.