# NVR Entity Recognition Dataset (Standardized)

## Dataset Structure

This dataset follows the COCO format for object detection training:

```
dataset/
├── images/
│   ├── train/          # Training images (68 images)
│   ├── val/            # Validation images (19 images)
│   └── test/           # Test images (11 images)
├── annotations/
│   ├── train.json      # Training annotations (COCO format)
│   ├── val.json        # Validation annotations (COCO format)
│   └── test.json       # Test annotations (COCO format)
└── dataset_config.json # Dataset configuration and metadata

```

## Categories

19 categories identified from folder structure:

- back-sleeping (ID: 1)
- bedroom (ID: 2)
- blanket-in-bassinet (ID: 3)
- face-down (ID: 4)
- pacifier_pacifier-fell-out (ID: 5)
- pacifier_pacifier-in-mouth (ID: 6)
- reference_subject (ID: 7)
- side-sleeping (ID: 8)
- smothering_baby-under-blanket (ID: 9)
- smothering_smothering (ID: 10)
- smothering_smothering-high-risk (ID: 11)
- subject-obscured (ID: 12)
- swaddling_normal-swaddle (ID: 13)
- swaddling_swaddle-breakout (ID: 14)
- unsafe-object-presence_button-battery (ID: 15)
- unsafe-object-presence_pill (ID: 16)
- unsafe-object-presence_screw (ID: 17)
- unsafe-object-presence_sim-opener (ID: 18)
- zone-empty (ID: 19)

## Usage

This dataset can be loaded directly with popular ML frameworks:

### PyTorch/Detectron2
```python
from detectron2.data.datasets import register_coco_instances

register_coco_instances("nvr_train", {}, "annotations/train.json", "images/train")
register_coco_instances("nvr_val", {}, "annotations/val.json", "images/val")
```

### TensorFlow/Keras
```python
import tensorflow_datasets as tfds

# Load COCO format dataset
dataset = tfds.load('coco', data_dir='path/to/dataset')
```

## Original Folder Structure Preserved

All original folder classification information is preserved in the annotation metadata:
- `camera_location`: Which camera captured the image
- `primary_category`: Main classification (sleeping_position, event, reference_subject)
- `subcategory`: Specific category within primary
- `specific_event`: Detailed event classification where applicable