Add files using upload-large-folder tool

LICENSE

@@ -0,0 +1,15 @@
+Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+Copyright (c) 2025 McCarthy Howe, Cassandra Phillips, Alfred Lee, Varun Sethi, Jada Hall
+
+This work is licensed under the Creative Commons Attribution 4.0 International License.
+To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
+or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+You are free to:
+- Share: copy and redistribute the material in any medium or format
+- Adapt: remix, transform, and build upon the material for any purpose, even commercially
+
+Under the following terms:
+- Attribution: You must give appropriate credit, provide a link to the license,
+  and indicate if changes were made.

README.md

@@ -1,3 +1,243 @@
----
-license: cc-by-4.0
----
+# 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).

class_mapping.json

@@ -0,0 +1,222 @@
+{
+  "version": "1.0.0",
+  "num_classes": 25,
+  "classes": [
+    {
+      "id": 0,
+      "name": "box",
+      "annotation_count": 5789,
+      "image_count": 5789,
+      "category": "container"
+    },
+    {
+      "id": 1,
+      "name": "crate",
+      "annotation_count": 1270,
+      "image_count": 1270,
+      "category": "container"
+    },
+    {
+      "id": 2,
+      "name": "barrel",
+      "annotation_count": 1229,
+      "image_count": 1229,
+      "category": "container"
+    },
+    {
+      "id": 3,
+      "name": "bottle",
+      "annotation_count": 665,
+      "image_count": 665,
+      "category": "container"
+    },
+    {
+      "id": 4,
+      "name": "pallet",
+      "annotation_count": 5743,
+      "image_count": 5743,
+      "category": "storage"
+    },
+    {
+      "id": 5,
+      "name": "rack",
+      "annotation_count": 5824,
+      "image_count": 5824,
+      "category": "storage"
+    },
+    {
+      "id": 6,
+      "name": "bracket",
+      "annotation_count": 4857,
+      "image_count": 4857,
+      "category": "infrastructure"
+    },
+    {
+      "id": 7,
+      "name": "lamp",
+      "annotation_count": 4451,
+      "image_count": 4451,
+      "category": "equipment"
+    },
+    {
+      "id": 8,
+      "name": "sign",
+      "annotation_count": 5945,
+      "image_count": 5945,
+      "category": "equipment"
+    },
+    {
+      "id": 9,
+      "name": "wire",
+      "annotation_count": 3985,
+      "image_count": 3985,
+      "category": "equipment"
+    },
+    {
+      "id": 10,
+      "name": "fuse_box",
+      "annotation_count": 1154,
+      "image_count": 1154,
+      "category": "equipment"
+    },
+    {
+      "id": 11,
+      "name": "floor_decal",
+      "annotation_count": 5945,
+      "image_count": 5945,
+      "category": "markers"
+    },
+    {
+      "id": 12,
+      "name": "fire_extinguisher",
+      "annotation_count": 3304,
+      "image_count": 3304,
+      "category": "equipment"
+    },
+    {
+      "id": 13,
+      "name": "barcode",
+      "annotation_count": 1137,
+      "image_count": 1137,
+      "category": "markers"
+    },
+    {
+      "id": 14,
+      "name": "wall",
+      "annotation_count": 6234,
+      "image_count": 6234,
+      "category": "background"
+    },
+    {
+      "id": 15,
+      "name": "ceiling",
+      "annotation_count": 3882,
+      "image_count": 3882,
+      "category": "background"
+    },
+    {
+      "id": 16,
+      "name": "floor",
+      "annotation_count": 6232,
+      "image_count": 6232,
+      "category": "background"
+    },
+    {
+      "id": 17,
+      "name": "pillar",
+      "annotation_count": 5845,
+      "image_count": 5845,
+      "category": "infrastructure"
+    },
+    {
+      "id": 18,
+      "name": "forklift",
+      "annotation_count": 343,
+      "image_count": 343,
+      "category": "equipment"
+    },
+    {
+      "id": 19,
+      "name": "bucket",
+      "annotation_count": 457,
+      "image_count": 457,
+      "category": "container"
+    },
+    {
+      "id": 20,
+      "name": "cone",
+      "annotation_count": 639,
+      "image_count": 639,
+      "category": "equipment"
+    },
+    {
+      "id": 21,
+      "name": "cart",
+      "annotation_count": 427,
+      "image_count": 427,
+      "category": "equipment"
+    },
+    {
+      "id": 22,
+      "name": "emergency_board",
+      "annotation_count": 569,
+      "image_count": 569,
+      "category": "infrastructure"
+    },
+    {
+      "id": 23,
+      "name": "paper_note",
+      "annotation_count": 2125,
+      "image_count": 2125,
+      "category": "markers"
+    },
+    {
+      "id": 24,
+      "name": "paper_shortcut",
+      "annotation_count": 390,
+      "image_count": 390,
+      "category": "markers"
+    }
+  ],
+  "class_hierarchy": {
+    "container": [
+      "box",
+      "crate",
+      "barrel",
+      "bottle",
+      "bucket"
+    ],
+    "storage": [
+      "pallet",
+      "rack"
+    ],
+    "infrastructure": [
+      "bracket",
+      "pillar",
+      "emergency_board"
+    ],
+    "equipment": [
+      "lamp",
+      "sign",
+      "wire",
+      "fuse_box",
+      "fire_extinguisher",
+      "forklift",
+      "cart",
+      "cone"
+    ],
+    "markers": [
+      "floor_decal",
+      "barcode",
+      "paper_note",
+      "paper_shortcut"
+    ],
+    "background": [
+      "wall",
+      "ceiling",
+      "floor"
+    ]
+  },
+  "total_annotations": 78441,
+  "total_images": 6234
+}

data.yaml

@@ -0,0 +1,40 @@
+# Warehouse Object Detection Dataset - YOLO Configuration
+
+path: /Users/machowe/School/Capstone/data/warehouse_detection_dataset/yolo  # dataset root dir
+train: images/train  # train images (relative to 'path')
+val: images/val      # val images (relative to 'path')
+test: images/test    # test images (optional)
+
+# Classes
+nc: 25  # number of classes
+names:
+  0: box
+  1: crate
+  2: barrel
+  3: bottle
+  4: pallet
+  5: rack
+  6: bracket
+  7: lamp
+  8: sign
+  9: wire
+  10: fuse_box
+  11: floor_decal
+  12: fire_extinguisher
+  13: barcode
+  14: wall
+  15: ceiling
+  16: floor
+  17: pillar
+  18: forklift
+  19: bucket
+  20: cone
+  21: cart
+  22: emergency_board
+  23: paper_note
+  24: paper_shortcut
+
+# Dataset Info
+description: Synthetic warehouse object detection dataset with 6,272 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

dataset_info.json

@@ -0,0 +1,71 @@
+{
+  "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"
+  ]
+}

yolo/data.yaml

@@ -0,0 +1,31 @@
+train: images/train
+val: images/val
+test: images/test
+
+nc: 25
+names:
+  0: box
+  1: crate
+  2: barrel
+  3: bottle
+  4: pallet
+  5: rack
+  6: bracket
+  7: lamp
+  8: sign
+  9: wire
+  10: fuse_box
+  11: floor_decal
+  12: fire_extinguisher
+  13: barcode
+  14: wall
+  15: ceiling
+  16: floor
+  17: pillar
+  18: forklift
+  19: bucket
+  20: cone
+  21: cart
+  22: emergency_board
+  23: paper_note
+  24: paper_shortcut