---
license: etalab-2.0

# Responsible AI (RAI) Metadata
rai:
  dataLimitations: "TiBuDB contains images from different gas-liquid mass transfer experiment but not the attached explotary parameter set (e.g. pressure or gas/liquid flow rate).
  Our strategy is annotating based on smaller patches then infer on complete image for reducing annotating effort. Consequently, bubbles located at the image edges may appear 
  as standard horizontal bounding boxes rather than oriented bounding boxes in case of oriented detection. Because the rotation angles are difficult to determine when the bubble is truncated by the frame when patching.
  Also, some regions with low contrast or strong overlap may lead to less accurate annotations."

  dataBiases: "TiBuDB focus on tiny bubble detection, because the larger scale bubble now is not integrate in our set it may lead to unrecognizable when face to large scale bubble.
  Furthermore, in term of mask annotation, under the assumption that all bubble has a elliptical shape in 2D, so if some anomal form the masks because a coarse map than pixel-wise ground truth.
  For the overlap annoation, the labels will be assign approximated relatively base on visible part."

  personalSensitiveInformation: "TiBuDB does not contain any personal or sensitive information. The images are from physical gas-liquid experiments and do not involve human subjects, identity, or private data."

  dataUseCases: "TiBuDB is used for bubble deep learning vision-based detector which use for Bubble Size Distribution and Sauter Mean Diameter in gas-liquid mass transfer analysis"

  dataSocialImpact: "TiBuDB is mainly used for scientific research in fluid mechanics and chemical engineering. It can help improving understanding of gas-liquid mass transfer and industrial process efficiency.
  There is no direct negative social impact expected because no human-related data is involved. However, using this dataset outside of its intended experimental context may lead to incorrect interpretation or unreliable model performance."


task_categories:
- object-detection
- image-segmentation
language:
- en
tags:
- oriented-bounding-box
- obb
- multi-task
- computer-vision
size_categories:
- n<1K

configs:
- config_name: yolo_obb
  data_dir: yolo_obb
  default: true
  
- config_name: yolo_det
  data_dir: yolo_det

- config_name: yolo_seg
  data_dir: yolo_seg

- config_name: dota
  data_dir: dota

- config_name: roboflow
  data_dir: roboflow
  data_files:
    - split: train
      path: 
        - "images/train/_annotations.coco.json"
        - "images/train/*.png" 
    - split: validation
      path: 
        - "images/valid/_annotations.coco.json"
        - "images/valid/*.png"
      
- config_name: coco_standard
  data_dir: coco
  data_files:
    - split: train
      path: 
        - "images/train/1.0_train_coco.json"
        - "images/train/*.png"
    - split: validation
      path: 
        - "images/val/1.0_val_coco.json"
        - "images/val/*.png"

- config_name: coco_obb
  data_dir: coco
  data_files:
    - split: train
      path: 
        - "images/train/1.0_train_coco_obb.json"
        - "images/train/*.png"
    - split: validation
      path: 
        - "images/val/1.0_val_coco_obb.json"
        - "images/val/*.png"
      
- config_name: large_image_test
  data_dir: large_image_test
  description: "Metrological reference set. Includes raw images, binary masks, and ImageJ measurements (mm) for physical accuracy assessment."

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: string
---

# 🫧 TiBuDB Dataset

TiBuDB is a multi-task benchmark for bubble flow detection. 
It supports 5+ annotation formats including HBB, OBB, and Segmentation.

## 🛠️ Toolkit & Scripts
To facilitate multi-format training, we provide a specialized toolkit in the `/tools` directory:
*   **Format Converters**: `obb2coco.py`, `yolo2coco.py`, and `yolo2dota.py` for seamless transitions between annotation standards.
*   **Data Integrity**: `check_duplicated_yolo.py` to ensure dataset uniqueness and `correct_cocorf.py` for Roboflow-specific COCO fixes.
*   **Quick Instance and Image Counts**: `count_instances.py` to count images and instance quantity in train and val splits.
*   **Visualization**: `viz_samples.py`,`viz_yolo_det.py`,`viz_yolo_obb.py`,`viz_yolo_seg.py`, `viz_yolo_together.py`,`viz_dota.py` scripts to overlay OBB and Segmentation masks for manual verification.

### ⚠️ Annotation Notes: Edge Cases
*   **Boundary Bubbles**: Bubbles located at the image edges may appear as standard **HBB** (Horizontal Bounding Boxes) rather than **OBB**.
*   **Reason**: Rotation angles are difficult to determine when the bubble is truncated by the frame. 
*   **Impact**: When training OBB models, these instances provide a 'neutral' rotation signal.

## Dataset Structure
The dataset is organized into subfolders for each format. 
Select the configuration in the Hugging Face viewer to preview specific formats.