# ClimbInst: Climbing Hold Instance Segmentation Dataset

ClimbInst is an instance segmentation dataset for climbing holds and volumes on indoor climbing walls. It was developed to support research in climbing-hold instance segmentation and related computer vision applications.

The dataset contains **1,509 images** with a total of **89,020 annotated instances**. Climbing holds and volumes are represented using a single unified class.

## Dataset Structure

The dataset is divided into training, validation, and test splits:

```text
ClimbInst/
├── train/
│   ├── images/
│   └── annotations/
├── validation/
│   ├── images/
│   └── annotations/
└── test/
    ├── images/
    └── annotations/
```
### Dataset Statistics

| Split    | Images   | Annotated Instances |
| -------- | -------- | -------- |
| Train    | 1,169    | 71,319   |
| Validation   | 290      | 14,268   | 
| Test    | 50       | 3,433    | 
| **Total**    | **1,509**    | **89,020**   | 

The test set is independent of the training and validation data.

## Dataset Construction

Images used to construct ClimbInst were collected from multiple sources, including publicly available climbing-wall datasets and separately collected climbing-wall imagery.

Initial instance masks were generated using **SAM3** as an annotation-assistance mechanism. The generated masks were subsequently manually inspected and corrected using **Labelme**. Incorrect masks were refined or removed, and climbing holds missed during the initial annotation process were manually added.

Both climbing holds and climbing volumes are annotated as a single unified class.

## Annotations

The dataset provides instance-level segmentation annotations for climbing holds and volumes.

Each training, validation, and test split contains its corresponding images and annotations.

## Intended Use

ClimbInst is intended to support research and development involving:

- Climbing-hold instance segmentation

- Climbing-hold detection

- Computer vision for indoor climbing

- Digital representation of climbing walls

- Climbing route selection and analysis

- Related computer vision applications

## Dataset Sources

ClimbInst incorporates climbing-wall imagery collected from multiple sources, including publicly available datasets and separately collected images.

The publicly available datasets used during dataset construction include:

- **Indoor Climbing Gym Hold and Route Segmentation** — T. Sláma, Kaggle  

  https://www.kaggle.com/datasets/tomasslama/indoor-climbing-gym-hold-segmentation

- **Climbing Holds and Volumes** — Roboflow  

  https://app.roboflow.com/weedlabelling/climbing-holds-and-volumes-zdgn3/browse

## License

License information is provided according to the applicable usage and redistribution terms of the constituent image sources.