Update README.md

README.md

@@ -27,4 +27,108 @@ license: cc-by-4.0
 language:
 - en
 pretty_name: QA Patches Task Dataset
----
+---
+# Dataset Card for Patch-Based Visual Question Answering Dataset
+
+## Dataset Details
+
+### Dataset Description
+
+This dataset contains approximately 160,000 triplets of `question`, `answer`, and `image` designed for patch-based visual reasoning tasks.  
+
+A standard question in this dataset is formatted as follows:
+
+> Image Grid: The image is divided into a 4x4 grid of 16 equal-sized patches. Patches are numbered sequentially from the top-left corner and moving right, then down to the next row.  
+> Task: Identify the patch number(s) that contain a potted plant.  
+> Response Format: Provide only the relevant patch number(s) as a list (e.g., [3], [5, 12], or [] if none are found).
+
+The dataset is built on top of **COCO-2017**, from which object bounding boxes (bboxes) are used to generate questions and answers.
+
+- **Curated by:** Yurii Potapov
+- **Language(s) :** English
+- **License:** Annotations and code: CC BY 4.0 (COCO), Images: Flickr Terms of Use
+
+### Dataset Sources
+
+- **Repository:** [Not yet published]
+- **Paper:** [Not yet published]
+- **Demo:** [More Information Needed]
+
+## Uses
+
+### Direct Use
+
+- Training and evaluating **visual-language models (VLMs)** or other multimodal models.  
+- Patch-based object detection and reasoning.  
+- Research in **image question answering**, **visual reasoning**, and **multimodal representation learning**.
+
+### Out-of-Scope Use
+
+- Direct commercial redistribution of original COCO images without following Flickr Terms of Use.  
+- Use cases where original images are required to be displayed in full, due to copyright restrictions.  
+
+## Dataset Structure
+
+- **question**: A textual description of the task referring to a 4x4 patch grid.  
+- **answer**: List of integers representing the patch indices containing the target object(s).  
+- **image**: Corresponding COCO-2017 image (PIL Image object or file path).  
+
+The dataset contains no explicit splits; users can generate their own train/validation/test splits as needed.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to facilitate **patch-level visual question answering** and to improve the training of visual-language models using real-world images with structured spatial queries.
+
+### Source Data
+
+The dataset is based on COCO-2017 images and annotations. Bounding boxes from COCO are used to determine which patches contain specific objects (e.g., potted plants).
+
+#### Data Collection and Processing
+
+- Images are sourced from COCO-2017 (Flickr) respecting their Terms of Use.  
+- Bounding boxes from COCO are used to automatically generate 4x4 grid questions.  
+- Each question asks which patch(es) contain a specific object.  
+- Answers are stored as lists of patch indices.  
+
+#### Who are the source data producers?
+
+Original images were contributed by Flickr users and annotated by the COCO Consortium.
+
+### Annotations
+
+#### Annotation process
+
+Annotations (bounding boxes) are sourced from COCO-2017. Patch assignments and questions were automatically generated programmatically based on bounding box locations.
+
+#### Who are the annotators?
+
+Annotations are from COCO annotators; patch-level questions are generated automatically.
+
+#### Personal and Sensitive Information
+
+The dataset does **not contain personal or sensitive information**.
+
+## Bias, Risks, and Limitations
+
+- Images reflect the distribution and biases present in COCO-2017.  
+- Models trained on this dataset may inherit biases from the original dataset.  
+- Limited to the objects annotated in COCO-2017.  
+
+### Recommendations
+
+Users should be aware of the **copyright limitations of the original images** and provide attribution for COCO annotations. Use transformed or model-generated outputs rather than raw images for publication if possible.
+
+## Glossary
+
+- **Patch**: One of 16 equally sized blocks in a 4x4 grid over an image.  
+- **VLM (Visual-Language Model)**: A model that learns joint representations of images and text.  
+
+## Dataset Card Authors
+
+Yurii Potapov
+
+## Dataset Card Contact
+
+yurii.a.potapov@gmail.com