README.md

---
language:
  - en
license: other  # Original dataset license not explicitly stated; refer to Mendeley terms at https://data.mendeley.com/datasets/hvnsh7rwz7/1
pretty_name: "POLAR: Posture-Level Action Recognition Dataset"
size_categories: "10K<n<100K"
tags:
  - computer-vision
  - image-classification
  - object-detection
  - action-recognition
  - human-pose-estimation
dataset_info:
  features:
    - name: image
      dtype: image
    - name: objects
      list_of:
        - name: id
          dtype: int64
        - name: bbox
          list_of:
            dtype: float64
        - name: category
          dtype: int64
  splits:
    - name: train
      # num_examples: 28259  # Approximate; adjust based on your splits/train.txt count
    - name: val
      # num_examples: 3532   # Approximate (10% of total)
    - name: test
      # num_examples: 3533   # Approximate (10% of total)
  supervised_keys:
    - image
    - objects
  task_templates:
    - task: image-object-detection
citations:
  - title: "POLAR: Posture-level Action Recognition Dataset"
    authors:
      - Wentao Ma
      - Shuang Liang
    year: 2021
    doi: 10.17632/hvnsh7rwz7.1
    url: https://data.mendeley.com/datasets/hvnsh7rwz7/1
---

# POLAR: Posture-Level Action Recognition Dataset

## Disclaimer

This dataset is a restructured and YOLO-formatted version of the original **POsture-Level Action Recognition (POLAR)** dataset. I do not claim ownership or licensing rights over this dataset. For full details, including original licensing and usage terms, please refer to the [original dataset on Mendeley Data](https://data.mendeley.com/datasets/hvnsh7rwz7/1).

## Motivation

The original POLAR dataset, while comprehensive, has a somewhat complex structure that can make it challenging to navigate and integrate with modern object detection frameworks like YOLO. To address this, I reorganized the dataset into a clean, split-based format and converted the annotations to YOLO-compatible labels. This makes it easier to use for training action recognition models directly.

## Description

The **POLAR (POsture-Level Action Recognition)** dataset focuses on nine categories of human actions directly tied to posture: **bending**, **jumping**, **lying**, **running**, **sitting**, **squatting**, **standing**, **stretching**, and **walking**. It contains a total of **35,324 images** and covers approximately **99% of posture-level human actions** in daily life, based on the authors' analysis of the PASCAL VOC dataset.

This dataset is suitable for tasks such as:
- **Image Classification**
- **Action Recognition**
- **Object Detection** (with YOLO-formatted bounding boxes around persons)

Each image features a single or multiple persons with bounding box annotations labeled by their primary action/pose.

## Dataset Structure

The dataset is pre-split into **train**, **val**, and **test** sets. The directory structure is as follows:

```
POLAR/
├── Annotations/          # Original JSON annotation files (for reference)
│   ├── test/
│   ├── train/
│   └── val/
├── images/               # Original images (.jpg)
│   ├── test/
│   ├── train/
│   └── val/
├── labels/               # YOLO-formatted .txt label files
│   ├── test/
│   ├── train/
│   └── val/
├── splits/               # Split definition files
│   ├── test.txt
│   ├── train.txt
│   └── val.txt
└── dataset.yaml          # YOLO configuration file (for training)
```

- **splits/**: Text files listing image filenames (one per line, without extensions) for each split.
- **labels/**: For each image (e.g., `images/train/p1_00001.jpg`), there is a corresponding `labels/train/p1_00001.txt` with YOLO-format annotations (class ID + normalized bounding box coordinates).
- **dataset.yaml**: Pre-configured for Ultralytics YOLO training (see [YOLO Dataset Format](https://docs.ultralytics.com/datasets/detect/#ultralytics-yolo-format) for details).

## Changes Made

Compared to the original dataset, the following modifications were applied:

1. **Restructured Splits**:
   - Organized images and annotations into explicit **train**, **val**, and **test** subfolders.
   - Used the original split definitions from the provided `.txt` files in `splits/` to ensure consistency.

2. **YOLO Formatting**:
   - Converted JSON annotations to YOLO `.txt` files in the `labels/` folder.
   - Each line in a `.txt` file follows the format: `<class_id> <center_x> <center_y> <norm_width> <norm_height>` (normalized to [0,1]).
   - Class IDs map to actions as follows (0-8):
     - 0: bending
     - 1: jumping
     - 2: lying
     - 3: running
     - 4: sitting
     - 5: squatting
     - 6: standing
     - 7: stretching
     - 8: walking
   - Included a ready-to-use `dataset.yaml` for YOLOv8+ training.

These changes simplify setup while preserving the original data integrity.

## Usage

### Training with YOLO (Ultralytics)
1. Clone or download this dataset to your working directory.
2. Install Ultralytics: `pip install ultralytics`.
3. Train a model (e.g., using YOLOv8 nano):
   ```
   yolo detect train data=dataset.yaml model=yolov8n.pt epochs=100 imgsz=640
   ```
   - This assumes the YAML is in the root (`POLAR/`).
   - Adjust `epochs`, `imgsz`, or other hyperparameters as needed.
   - YOLO will automatically pair images with labels based on filenames.

For more details on YOLO integration, see the [Ultralytics documentation](https://docs.ultralytics.com/).

## Citation

If you use this dataset in your research, please cite the original work:

> Ma, Wentao; Liang, Shuang (2021), “POLAR: Posture-level Action Recognition Dataset”, Mendeley Data, V1, doi: [10.17632/hvnsh7rwz7.1](https://doi.org/10.17632/hvnsh7rwz7.1).

---

*Last updated: October 20, 2025*