README.md

# Warehouse Object Detection Dataset

## Overview

This is a synthetic warehouse object detection dataset generated using NVIDIA Omniverse Replicator. The dataset contains high-quality RGB images with comprehensive annotations including 2D bounding boxes, instance segmentation masks, depth maps, and 3D primitive paths.

**Version:** 1.0.0
**License:** CC-BY-4.0
**Total Images:** 6,234
**Total Annotations:** 78,441
**Image Resolution:** 512x512
**Number of Classes:** 25

## Dataset Statistics

### Split Distribution
- **Training Set:** 4,363 images (70.0%)
- **Validation Set:** 935 images (15.0%)
- **Test Set:** 936 images (15.0%)

### Annotation Statistics
- **Average Annotations per Image:** 12.6
- **Total Bounding Boxes:** 78,441

### Top 10 Most Common Classes
1. **wall**: 6,234 instances in 6,234 images
2. **floor**: 6,232 instances in 6,232 images
3. **sign**: 5,945 instances in 5,945 images
4. **floor_decal**: 5,945 instances in 5,945 images
5. **pillar**: 5,845 instances in 5,845 images
6. **rack**: 5,824 instances in 5,824 images
7. **box**: 5,789 instances in 5,789 images
8. **pallet**: 5,743 instances in 5,743 images
9. **bracket**: 4,857 instances in 4,857 images
10. **lamp**: 4,451 instances in 4,451 images

## Classes

The dataset includes 25 object classes organized into the following categories:

### Container
- **box**: 5,789 annotations
- **crate**: 1,270 annotations
- **barrel**: 1,229 annotations
- **bottle**: 665 annotations
- **bucket**: 457 annotations

### Storage
- **pallet**: 5,743 annotations
- **rack**: 5,824 annotations

### Infrastructure
- **bracket**: 4,857 annotations
- **pillar**: 5,845 annotations
- **emergency_board**: 569 annotations

### Equipment
- **lamp**: 4,451 annotations
- **sign**: 5,945 annotations
- **wire**: 3,985 annotations
- **fuse_box**: 1,154 annotations
- **fire_extinguisher**: 3,304 annotations
- **forklift**: 343 annotations
- **cart**: 427 annotations
- **cone**: 639 annotations

### Markers
- **floor_decal**: 5,945 annotations
- **barcode**: 1,137 annotations
- **paper_note**: 2,125 annotations
- **paper_shortcut**: 390 annotations

### Background
- **wall**: 6,234 annotations
- **ceiling**: 3,882 annotations
- **floor**: 6,232 annotations

## Dataset Formats

This dataset is provided in three formats to support different use cases:

### 1. Raw Format (`raw/`)
Preserves all original Omniverse data:
- RGB images (PNG)
- 2D bounding boxes (NumPy `.npy`)
- Class labels (JSON)
- 3D primitive paths (JSON)
- Instance segmentation masks (PNG)
- Instance ID mappings (JSON)
- Depth/distance maps (NumPy `.npy`)

**Use for:** Multi-task learning, depth estimation, 3D tasks, research

### 2. YOLO Format (`yolo/`)
Standard YOLO v5/v8 format:
- Images in `images/train/`, `images/val/`, `images/test/`
- Labels in `labels/train/`, `labels/val/`, `labels/test/`
- Each label file contains: `class_id x_center y_center width height` (normalized 0-1)

**Use for:** Object detection with YOLO models (Ultralytics, Darknet)

### 3. COCO Format (`coco/`)
COCO JSON format:
- Images in `images/train/`, `images/val/`, `images/test/`
- Annotations in `annotations/instances_{train,val,test}.json`

**Use for:** Object detection/segmentation with detectron2, MMDetection, etc.

## Directory Structure

```
warehouse_detection_dataset/
├── README.md                    # This file
├── data.yaml                    # YOLO configuration
├── dataset_info.json            # HuggingFace metadata
├── class_mapping.json           # Complete class information
├── raw/                         # Original Omniverse format
│   ├── train/
│   │   ├── images/
│   │   ├── annotations/
│   │   ├── segmentation/
│   │   └── depth/
│   ├── val/
│   └── test/
├── yolo/                        # YOLO format
│   ├── images/
│   │   ├── train/
│   │   ├── val/
│   │   └── test/
│   └── labels/
│       ├── train/
│       ├── val/
│       └── test/
└── coco/                        # COCO format
    ├── images/
    │   ├── train/
    │   ├── val/
    │   └── test/
    └── annotations/
        ├── instances_train.json
        ├── instances_val.json
        └── instances_test.json
```

## Usage Examples

### YOLO (Ultralytics)

```python
from ultralytics import YOLO

# Train a model
model = YOLO('yolov8n.pt')
model.train(data='warehouse_detection_dataset/data.yaml', epochs=100)

# Validate
metrics = model.val()

# Predict
results = model('path/to/image.jpg')
```

### COCO (detectron2)

```python
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.data.datasets import load_coco_json

# Register dataset
DatasetCatalog.register(
    "warehouse_train",
    lambda: load_coco_json(
        "warehouse_detection_dataset/coco/annotations/instances_train.json",
        "warehouse_detection_dataset/coco/images/train"
    )
)

# Train your model
# ... (standard detectron2 training code)
```

### Raw Format (Custom)

```python
import numpy as np
import json
from PIL import Image

# Load image
img = Image.open('raw/train/images/warehouse_000001.png')

# Load bounding boxes
bboxes = np.load('raw/train/annotations/warehouse_000001_bbox.npy')

# Load labels
with open('raw/train/annotations/warehouse_000001_labels.json') as f:
    labels = json.load(f)

# Load segmentation mask
seg_mask = Image.open('raw/train/segmentation/warehouse_000001_seg.png')

# Load depth map
depth = np.load('raw/train/depth/warehouse_000001_depth.npy')
```

## Citiation

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

```bibtex
@dataset{warehouse_detection_2025,
  title={Warehouse Object Detection Dataset},
  author={Howe, McCarthy and Phillips, Cassandra and Lee, Alfred and Sethi, Varun and Hall, Jada},
  year={2025},
  publisher={Clemson University Capstone},
  note={In partnership with Capgemini Supply Chain},
  version={1.0.0},
  license={CC-BY-4.0}
}
```

## License

This dataset is released under the CC-BY-4.0 license.

## Team & Acknowledgments

**Project Context:**
This dataset was created as part of the **Clemson University Capstone 2025** project, in partnership with **Capgemini Supply Chain**.

**Contributors:**
- **McCarthy Howe** (mac@machowe.com)
- **Cassandra Phillips**
- **Alfred Lee**
- **Varun Sethi**
- **Jada Hall**

**Tooling:**
Generated using NVIDIA Omniverse Replicator.

## Contact

For questions, issues, or feedback, please contact **McCarthy Howe** at [mac@machowe.com](mailto:mac@machowe.com).