Datasets:

bastienp
/

visible-watermark-pita

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   - split: val
     path: "data/val.zip"
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   - 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 [optional]
+- **Repository:** https://github.com/OrdinaryDev83/dnn-watermark
+- **Demo [optional]:** 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.