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README.md

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+---
+license: cc-by-nc-nd-4.0
+task_categories:
+  - video-classification
+language:
+  - en
+tags:
+  - egocentric
+  - embodied-ai
+  - robotics
+  - imu
+  - real-world
+  - computer-vision
+  - dataset
+  - sample-dataset
+size_categories:
+  - n<1K
+viewer: false
+---
+
+# PIZZA-PREPARE-Dough-Ballforming-sample
+
+## Overview
+
+This dataset provides a high-fidelity, ego-centric capture of professional order preparation tasks within a cold-chain logistics environment. It focuses on the rapid, repetitive, and high-precision picking and packing of multi-unit yogurt packs. This resource is specifically designed to train robotic agents in **bimanual coordination**, **spatial reachability**, and **contact-point optimization** for fragile rigid goods.
+
+## Key Technical Features
+
+**Temporal Protocol (T1-T4)**: Every micro-action is frame-accurately annotated using our proprietary four-point system:
+* **T1 (Contact)**: Initial physical engagement.
+* **T2 (Lift-off)**: Static-to-dynamic transition.
+* **T3 (Placement)**: Target container engagement.
+* **T4 (Release)**: Total tactile disengagement.
+
+## Use Cases for Research
+
+* **Foundation Models & World Models**: Training models to understand the Newtonian physics of rigid payloads and container boundaries.
+* **Bimanual Policy Learning**: Developing algorithms for coordinated two-hand tasks, such as stabilizing a box while inserting a pack.
+* **End-to-End Picking Pipelines**: Optimizing the "approach-to-grasp" phase in unstructured industrial environments where lighting and clutter vary.
+
+## Custom Data Collection Services
+
+Our team specializes in high-fidelity data acquisition within real-world professional settings. We provide on-demand data collection services tailored to specific AI and robotics requirements:
+* Professional Network: Direct access to 100+ professional environments, including professional kitchens, bakeries, mechanical workshops, craft studios, and industrial facilities.
+* Multi-Modal Capture: Expertise in collecting synchronized streams including Third-Person views, Ego-centric (FPV), IMU sensors (motion tracking), and Expert Audio Narration.
+* Domain Expertise: We bridge the gap between technical AI needs and authentic professional "tacit knowledge."
+
+## Commercial Licensing and Contact
+
+* The complete dataset and our custom collection services are available for commercial licensing and large-scale R&D. Whether you need existing data or a custom setup in a specific professional environment, do not hesitate to reach out for more information.
+* Contact: orgn3ai@gmail.com
+
+## License
+
+* This dataset is licensed under cc-by-nc-nd-4.0.
+
+## Dataset Statistics
+
+This section provides detailed statistics extracted from `dataset_metadata.json`:
+
+### Overall Statistics
+
+- **Dataset Name**: PIZZA-PREPARE-Dough-Ballforming-sample
+- **Batch ID**: pizza
+- **Total Clips**: 10
+- **Number of Sequences**: 15
+- **Number of Streams**: 3
+- **Stream Types**: ego, imu, third
+
+### Duration Statistics
+
+- **Total Duration**: 4.82 minutes (289.20 seconds)
+- **Average Clip Duration**: 28.92 seconds (28920.0 ms)
+- **Min Clip Duration**: 27.10 seconds (27100 ms)
+- **Max Clip Duration**: 32.83 seconds (32833 ms)
+
+### Clip Configuration
+
+- **Padding**: 1500 ms
+
+### Statistics by Stream Type
+
+#### Ego
+
+- **Number of clips**: 5
+- **Total duration**: 2.41 minutes (144.60 seconds)
+- **Average clip duration**: 28.92 seconds (28920.0 ms)
+- **Min clip duration**: 27.10 seconds (27100 ms)
+- **Max clip duration**: 32.83 seconds (32833 ms)
+
+#### Third
+
+- **Number of clips**: 5
+- **Total duration**: 2.41 minutes (144.60 seconds)
+- **Average clip duration**: 28.92 seconds (28920.0 ms)
+- **Min clip duration**: 27.10 seconds (27100 ms)
+- **Max clip duration**: 32.83 seconds (32833 ms)
+
+> **Note**: Complete metadata is available in `dataset_metadata.json` in the dataset root directory.
+
+## Dataset Structure
+
+The dataset uses a **unified structure** where each example contains all synchronized video streams:
+
+```
+dataset/
+├── data-*.arrow         # Dataset files (Arrow format)
+├── dataset_info.json    # Dataset metadata
+├── dataset_metadata.json  # Complete dataset statistics
+├── state.json           # Dataset state
+├── README.md            # This file
+├── medias/              # Media files (mosaics, previews, etc.)
+│   └── mosaic.mp4       # Mosaic preview video
+└── videos/              # All video clips
+    └── ego/      # Ego video clips
+    └── imu/      # Imu video clips
+    └── third/      # Third video clips
+```
+
+### Dataset Format
+
+The dataset contains **10 synchronized scenes** in a single `train` split. Each example includes:
+
+- **Synchronized video columns**: One column per flux type (e.g., `ego`, `imu`, `third`)
+- **Scene metadata**: `scene_id`, `sync_id`, `duration_ms`, `padding_ms`, `fps`
+- **Rich metadata dictionary**: Task, environment, audio info, and synchronization details
+
+All videos in a single example are synchronized and correspond to the same moment in time.
+
+## Usage
+
+### Load and Access Dataset
+
+```python
+import json
+import random
+from pathlib import Path
+import cv2
+from huggingface_hub import snapshot_download
+from datasets import load_from_disk
+
+repo = "orgn3ai/PIZZA-PREPARE-Dough-Ballforming-sample"
+
+# 1) Download snapshot locally
+local_path = snapshot_download(repo_id=repo, repo_type="dataset")
+base_dir = Path(local_path)
+print("Snapshot path:", base_dir)
+
+# 2) Load dataset saved with save_to_disk()
+ds = load_from_disk(str(base_dir))
+train = ds["train"] if isinstance(ds, dict) and "train" in ds else ds
+print("Train rows:", len(train))
+print("Train columns:", train.column_names)
+
+# 3) Read root metadata.json and extract "flux"
+metadata_path = base_dir / "dataset_metadata.json"
+if not metadata_path.exists():
+    raise FileNotFoundError(
+        f"dataset_metadata.json not found at repo root: {metadata_path}\n"
+        "Check your repo tree; maybe it's named dataset_metadata.json instead."
+    )
+
+with metadata_path.open("r", encoding="utf-8") as f:
+    root_meta = json.load(f)
+
+flux = root_meta.get("flux")
+if not isinstance(flux, list) or not flux:
+    raise ValueError(f'Expected metadata.json["flux"] to be a non-empty list, got: {flux}')
+
+print("Flux entries:", flux)
+
+# 4) Pick a random dataset entry
+idx = random.randrange(len(train))
+ex = train[idx]
+
+print("\nRandom example index:", idx)
+print("Example keys:", list(ex.keys()))
+
+def resolve_video_path(video_value) -> Path:
+    """
+    video_value can be:
+    - string path (most common case)
+    - dict like {"path": "...", "bytes": ...} (for backward compatibility)
+    """
+    if isinstance(video_value, dict) and "path" in video_value:
+        rel = video_value["path"]
+    elif isinstance(video_value, str):
+        rel = video_value
+    else:
+        raise TypeError(f"Unsupported video value type: {type(video_value)}; value={video_value}")
+
+    # Normalize to avoid leading "./"
+    rel = str(rel).lstrip("/")
+
+    # Your dataset may store relative paths like "videos/ego/xxx.mp4"
+    # Resolve them inside the snapshot folder.
+    return base_dir / rel
+
+def inspect_video(path: Path):
+    print(f"  Local path: {path}")
+    print(f"  Exists: {path.exists()}")
+    if not path.exists():
+        return {"ok": False, "reason": "file_not_found"}
+
+    cap = cv2.VideoCapture(str(path))
+    if not cap.isOpened():
+        return {"ok": False, "reason": "cannot_open"}
+
+    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = float(cap.get(cv2.CAP_PROP_FPS))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # Some codecs report fps=0; guard it
+    duration = (frame_count / fps) if fps and fps > 0 else None
+
+    # Try read first frame
+    ret, frame0 = cap.read()
+    cap.release()
+
+    info = {
+        "ok": True,
+        "width": width,
+        "height": height,
+        "fps": fps,
+        "frame_count": frame_count,
+        "duration_sec": duration,
+        "first_frame_ok": bool(ret),
+        "first_frame_shape": tuple(frame0.shape) if ret and frame0 is not None else None,
+        "first_frame_dtype": str(frame0.dtype) if ret and frame0 is not None else None,
+    }
+    return info
+
+# 5) For each flux key, inspect the associated video
+print("\n=== VIDEO CHECK ===")
+for key in flux:
+    print(f"\nFlux key: {key}")
+    if key not in ex:
+        print(f"  ERROR: key '{key}' not in example. Available keys: {list(ex.keys())}")
+        continue
+
+    try:
+        video_path = resolve_video_path(ex[key])
+    except Exception as e:
+        print(f"  ERROR resolving path: {e}")
+        continue
+
+    info = inspect_video(video_path)
+    if not info["ok"]:
+        print(f"  ERROR: {info['reason']}")
+        continue
+
+    print("  Video properties:")
+    print(f"    - Resolution: {info['width']}x{info['height']}")
+    print(f"    - FPS: {info['fps']:.3f}")
+    print(f"    - Frames: {info['frame_count']}")
+    if info["duration_sec"] is not None:
+        print(f"    - Duration: {info['duration_sec']:.3f}s")
+    else:
+        print("    - Duration: (fps unavailable)")
+    print(f"    - First frame decoded: {info['first_frame_ok']}")
+    if info["first_frame_ok"]:
+        print(f"    - Frame0 shape: {info['first_frame_shape']}")
+        print(f"    - Frame0 dtype: {info['first_frame_dtype']}")
+
+print('\n=== LABELS ===')
+print(f"nbLabels: {len(ex['labels'])}")
+for label in ex['labels']:
+    print(f"    - {label['time_ms']}ms (withoutPadding): {label['label']}")
+
+print("\nDONE.")
+```
+
+### Dataset Features
+
+Each example contains:
+
+- **`scene_id`**: Unique scene identifier (e.g., "01_0000")
+- **`sync_id`**: Synchronization ID linking synchronized clips
+- **`duration_ms`**: Duration of the synchronized clip in milliseconds (includes padding)
+- **`padding_ms`**: Padding applied to clips (added at beginning and end, total padding = padding_ms × 2)
+- **`fps`**: Frames per second (extracted from video)
+- **`batch_id`**: Batch identifier
+- **`dataset_name`**: Dataset name from config
+- **One column per flux**: Each flux name from `metadata['flux_names']` has its own column (e.g., `ego`, `imu`, `third`) - String path to video file (relative to dataset root)
+- **`metadata`**: Dictionary containing:
+  - `task`: Task identifier
+  - `environment`: Environment description
+  - `has_audio`: Whether videos contain audio
+  - `num_fluxes`: Number of synchronized flux types
+  - `flux_names`: List of flux names present
+  - `sequence_ids`: List of original sequence IDs
+  - `sync_offsets_ms`: List of synchronization offsets
+
+## Additional Notes
+
+**Important**: This dataset uses a unified structure where each example contains all synchronized video streams in separate columns. All examples are in the `train` split.
+
+**Synchronization**: Videos in the same example (same index in the `train` split) are automatically synchronized. They share the same `sync_id` and correspond to the same moment in time.
+
+**Flux Keys**: The available flux keys are listed in `dataset_metadata.json` under the `"flux"` key. Use these keys to programmatically access video columns in each example.
+
+**Video Paths**: Video paths are stored as strings (relative to the dataset root directory). Paths can be resolved using the `resolve_video_path` function shown in the usage example above.
+
+## License
+
+This dataset is licensed under **cc-by-nc-nd-4.0**.

dataset_dict.json

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+{"splits": ["train"]}

dataset_metadata.json

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+{
+  "dataset_name": "PIZZA-PREPARE-Dough-Ballforming-sample",
+  "batch_id": "pizza",
+  "total_clips": 10,
+  "num_sequences": 15,
+  "num_streams": 3,
+  "stream_types": [
+    "ego",
+    "imu",
+    "third"
+  ],
+  "flux": [
+    "ego",
+    "imu",
+    "third"
+  ],
+  "padding_ms": 1500,
+  "default_duration_ms": 500,
+  "clip_duration_ms": {
+    "base": 500,
+    "with_padding": 3500
+  },
+  "duration_ms": {
+    "average": 28920.0,
+    "total": 289200,
+    "min": 27100,
+    "max": 32833
+  },
+  "flux_stats": {
+    "ego": {
+      "num_clips": 5,
+      "duration_ms": {
+        "average": 28920.0,
+        "total": 144600,
+        "min": 27100,
+        "max": 32833
+      }
+    },
+    "third": {
+      "num_clips": 5,
+      "duration_ms": {
+        "average": 28920.0,
+        "total": 144600,
+        "min": 27100,
+        "max": 32833
+      }
+    }
+  },
+  "sequences": [
+    {
+      "id": "pizza_dough_ballforming_01_ego_25000ms",
+      "flux_name": "ego",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 25000
+    },
+    {
+      "id": "pizza_dough_ballforming_01_ego_24100ms",
+      "flux_name": "ego",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 24100
+    },
+    {
+      "id": "pizza_dough_ballforming_01_ego_26500ms",
+      "flux_name": "ego",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 26500
+    },
+    {
+      "id": "pizza_dough_ballforming_01_ego_24167ms",
+      "flux_name": "ego",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 24167
+    },
+    {
+      "id": "pizza_dough_ballforming_01_ego_29833ms",
+      "flux_name": "ego",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 29833
+    },
+    {
+      "id": "pizza_dough_ballforming_01_third_25000ms",
+      "flux_name": "third",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 25000
+    },
+    {
+      "id": "pizza_dough_ballforming_01_third_24100ms",
+      "flux_name": "third",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 24100
+    },
+    {
+      "id": "pizza_dough_ballforming_01_third_26500ms",
+      "flux_name": "third",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 26500
+    },
+    {
+      "id": "pizza_dough_ballforming_01_third_24167ms",
+      "flux_name": "third",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 24167
+    },
+    {
+      "id": "pizza_dough_ballforming_01_third_29833ms",
+      "flux_name": "third",
+      "type": "video",
+      "num_clips": 1,
+      "duration_ms": 29833
+    },
+    {
+      "id": "pizza_dough_ballforming_01_imu_25000ms",
+      "flux_name": "imu",
+      "type": "imu",
+      "num_clips": 1,
+      "duration_ms": 25000
+    },
+    {
+      "id": "pizza_dough_ballforming_01_imu_24100ms",
+      "flux_name": "imu",
+      "type": "imu",
+      "num_clips": 1,
+      "duration_ms": 24100
+    },
+    {
+      "id": "pizza_dough_ballforming_01_imu_26500ms",
+      "flux_name": "imu",
+      "type": "imu",
+      "num_clips": 1,
+      "duration_ms": 26500
+    },
+    {
+      "id": "pizza_dough_ballforming_01_imu_24167ms",
+      "flux_name": "imu",
+      "type": "imu",
+      "num_clips": 1,
+      "duration_ms": 24167
+    },
+    {
+      "id": "pizza_dough_ballforming_01_imu_29833ms",
+      "flux_name": "imu",
+      "type": "imu",
+      "num_clips": 1,
+      "duration_ms": 29833
+    }
+  ]
+}

train/data-00000-of-00001.arrow

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train/dataset_info.json

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+  "description": "",
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+        }
+      },
+      "_type": "List"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

train/state.json

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+  "_format_kwargs": {},
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+  "_output_all_columns": false,
+  "_split": null
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