dataset_info.json

{
  "dataset_name": "warehouse-object-detection",
  "description": "Synthetic warehouse object detection dataset with 6,234 images generated using NVIDIA Omniverse Replicator. Includes 2D bounding boxes, instance segmentation masks, depth maps, and 3D primitive paths for 18 object classes.",
  "version": "1.0.0",
  "license": "CC-BY-4.0",
  "homepage": "",
  "splits": {
    "train": 4363,
    "validation": 935,
    "test": 936
  },
  "task_categories": [
    "object-detection",
    "instance-segmentation",
    "depth-estimation"
  ],
  "task_ids": [
    "object-detection",
    "instance-segmentation",
    "monocular-depth-estimation"
  ],
  "features": {
    "image": "PIL.Image",
    "bboxes": "List[List[float]]",
    "labels": "List[int]",
    "segmentation_mask": "PIL.Image (optional)",
    "depth_map": "numpy.ndarray (optional)"
  },
  "num_classes": 25,
  "classes": [
    "box",
    "crate",
    "barrel",
    "bottle",
    "pallet",
    "rack",
    "bracket",
    "lamp",
    "sign",
    "wire",
    "fuse_box",
    "floor_decal",
    "fire_extinguisher",
    "barcode",
    "wall",
    "ceiling",
    "floor",
    "pillar",
    "forklift",
    "bucket",
    "cone",
    "cart",
    "emergency_board",
    "paper_note",
    "paper_shortcut"
  ],
  "image_size": [
    512,
    512
  ],
  "citation": "@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}, version={1.0.0}}",
  "tags": [
    "synthetic-data",
    "warehouse",
    "logistics",
    "omniverse",
    "object-detection",
    "instance-segmentation",
    "depth-estimation"
  ]
}