Duplicate from sayantan47/POLAR-Posture-Level-Action-Recognition-Dataset

Co-authored-by: synthwave <sayantan47@users.noreply.huggingface.co>

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POLAR.zip

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README.md

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+---
+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*  

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