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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
+  - real-world
+  - computer-vision
+  - dataset
+  - sample-dataset
+size_categories:
+  - n<1K
+---
+
+# SAUSAGE-CRAFTING-sample: Fine Manipulation of Deformable Sausage Casings
+
+## Overview
+
+This dataset provides a high-quality, multi-view synchronized capture of expert procedural tasks in a professional butchery environment. It specifically focuses on the complex manipulation of non-rigid and deformable objects such as sausage casings and stuffing. This resource addresses current challenges in robotics and computer vision regarding physical interaction with elastic and organic materials.
+
+
+<video controls loop width="100%">
+  <source src="https://huggingface.co/datasets/orgn3ai/SAUSAGE-CRAFTING-sample/resolve/main/medias/mosaic.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
+
+## Key Technical Features
+
+* Synchronized Dual-View: Includes perfectly aligned ego-centric (First-Person View) and third-person perspectives.
+* Non-Rigid Physics: Captures complex material behaviors such as plasticity and elasticity during the sausage-making process.
+* High-Quality Synchronization: All views are precisely time-aligned using a unified sync_id to ensure seamless cross-modal understanding.
+* Expert Craftsmanship: Focused on the specific task of rolling and measuring sausage casings with professional dexterity.
+
+## Use Cases for Research
+
+* Embodied AI and World Models: Training agents to predict the physical consequences of interacting with deformable organic matter.
+* Procedural Task Learning: Modeling long-form sequential actions where expert intent is critical.
+* Tactile-Visual Inference: Learning to estimate force and material resistance through visual observation of fine manipulation.
+
+## 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."
+
+## Full Dataset Specifications
+
+* Expert Audio Narration: Live commentary explaining intent, tactile feedback, and professional heuristics.
+* Total Duration: 50+ hours of continuous professional expert operations.
+* Extended Tasks: Includes stuffing preparation, casing filling, and specialized tool maintenance.
+* Data Quality: Comprehensive temporal action annotations.
+
+## 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**: SAUSAGE-CRAFTING-sample: Fine Manipulation of Deformable Sausage Casings
+- **Batch ID**: 01
+- **Total Clips**: 120
+- **Number of Sequences**: 6
+- **Number of Streams**: 2
+- **Stream Types**: ego, third
+
+### Duration Statistics
+
+- **Total Duration**: 8.00 minutes (480.00 seconds)
+- **Average Clip Duration**: 4.00 seconds
+- **Min Clip Duration**: 4.00 seconds
+- **Max Clip Duration**: 4.00 seconds
+
+### Clip Configuration
+
+- **Base Clip Duration**: 3.00 seconds
+- **Clip Duration with Padding**: 4.00 seconds
+- **Padding**: 500 ms
+
+### Statistics by Stream Type
+
+#### Ego
+
+- **Number of clips**: 60
+- **Total duration**: 4.00 minutes (240.00 seconds)
+- **Average clip duration**: 4.00 seconds
+- **Min clip duration**: 4.00 seconds
+- **Max clip duration**: 4.00 seconds
+
+#### Third
+
+- **Number of clips**: 60
+- **Total duration**: 4.00 minutes (240.00 seconds)
+- **Average clip duration**: 4.00 seconds
+- **Min clip duration**: 4.00 seconds
+- **Max clip duration**: 4.00 seconds
+
+> **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
+    └── third/      # Third video clips
+```
+
+### Dataset Format
+
+The dataset contains **120 synchronized scenes** in a single `train` split. Each example includes:
+
+- **Synchronized video columns**: One column per flux type (e.g., `ego_video`, `third_video`)
+- **Scene metadata**: `scene_id`, `sync_id`, `duration_sec`, `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 Dataset
+
+```python
+from datasets import load_dataset
+
+# Load from Hugging Face Hub
+dataset = load_dataset('orgn3ai/SAUSAGE-CRAFTING-sample')
+
+# IMPORTANT: The dataset has a 'train' split
+# Check available splits
+print(f"Available splits: {list(dataset.keys())}")  # Should show: ['train']
+
+# Or load from local directory
+# from datasets import load_from_disk
+# dataset = load_from_disk('outputs/01/dataset')
+
+# Access the 'train' split
+train_data = dataset['train']
+
+# Access synchronized scenes from the train split
+example = train_data[0]  # First synchronized scene
+
+# Or directly:
+example = dataset['train'][0]  # First synchronized scene
+
+# Access all synchronized videos
+ego_video = example['ego_video']  # Ego-centric view
+third_video = example['third_video']  # Third-person view
+
+# Access metadata
+print(f"Scene ID: {example['scene_id']}")
+print(f"Duration: {example['duration_sec']}s")
+print(f"FPS: {example['fps']}")
+print(f"Metadata: {example['metadata']}")
+
+# Get dataset info
+print(f"Number of examples in train split: {len(dataset['train'])}")
+```
+
+### Access Synchronized Videos
+
+Each example contains all synchronized video streams. Access them directly:
+
+```python
+import cv2
+from pathlib import Path
+
+# IMPORTANT: Always access the 'train' split
+# Get a synchronized scene from the train split
+example = dataset['train'][0]
+
+# Access video objects (Video type stores path in 'path' attribute or as dict)
+ego_video_obj = example.get('ego_video')
+third_video_obj = example.get('third_video')
+
+# Extract path from Video object (Video type stores: {{'path': 'videos/ego/0000.mp4', 'bytes': ...}})
+def get_video_path(video_obj):
+    if isinstance(video_obj, dict) and 'path' in video_obj:
+        return video_obj['path']
+    elif isinstance(video_obj, str):
+        return video_obj
+    else:
+        return getattr(video_obj, 'path', str(video_obj))
+
+ego_video_path = get_video_path(ego_video_obj)
+third_video_path = get_video_path(third_video_obj)
+
+# Resolve full paths from dataset cache (when loading from Hub)
+cache_dir = Path(dataset['train'].cache_files[0]['filename']).parent.parent
+ego_video_full_path = cache_dir / ego_video_path
+third_video_full_path = cache_dir / third_video_path
+
+# Process all synchronized videos together
+# IMPORTANT: Iterate over the 'train' split
+for example in dataset['train']:
+    scene_id = example['scene_id']
+    sync_id = example['sync_id']
+    metadata = example['metadata']
+    
+    print(f"Scene {{scene_id}}: {{metadata['num_fluxes']}} synchronized fluxes")
+    print(f"Flux names: {{metadata['flux_names']}}")
+    
+    # Access video paths and resolve them
+    ego_video_path = example.get('ego_video')
+    third_video_path = example.get('third_video')
+    
+    # Resolve full paths
+    ego_video_full = cache_dir / ego_video_path
+    third_video_full = cache_dir / third_video_path
+    
+    # Process synchronized videos...
+```
+
+### Filter and Process
+
+```python
+# IMPORTANT: Always work with the 'train' split
+# Filter by sync_id
+scene = dataset['train'].filter(lambda x: x['sync_id'] == 0)[0]
+
+# Filter by metadata
+scenes_with_audio = dataset['train'].filter(lambda x: x['metadata']['has_audio'])
+
+# Access metadata fields
+# Iterate over the 'train' split
+for example in dataset['train']:
+    task = example['metadata']['task']
+    environment = example['metadata']['environment']
+    has_audio = example['metadata']['has_audio']
+    flux_names = example['metadata']['flux_names']
+    sync_offsets = example['metadata']['sync_offsets_ms']
+```
+
+### Dataset Features
+
+Each example contains:
+
+- **`scene_id`**: Unique scene identifier (e.g., "01_0000")
+- **`sync_id`**: Synchronization ID linking synchronized clips
+- **`duration_sec`**: Duration of the synchronized clip in seconds
+- **`fps`**: Frames per second (default: 30.0)
+- **`batch_id`**: Batch identifier
+- **`dataset_name`**: Dataset name from config
+- **`ego_video`**: Video object for ego-centric view (Hugging Face `Video` type with `decode=False`, stores path)
+- **`third_video`**: Video object for third-person view (Hugging Face `Video` type with `decode=False`, stores path)
+- **`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.
+
+**Video Paths**: Video paths are stored using Hugging Face's `Video` type with `decode=False`. To access the actual file path, extract the `path` attribute from the Video object (see examples above).
+
+- `clip_index`: Clip index within the flux folder
+- `duration_sec`: Clip duration in seconds
+- `start_time_sec`: Start time in source video
+- `batch_id`, `dataset_name`, `source_video`, `sync_offset_ms`: Additional metadata
+
+## 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": "SAUSAGE-CRAFTING-sample: Fine Manipulation of Deformable Sausage Casings",
+  "batch_id": "01",
+  "total_clips": 120,
+  "num_sequences": 6,
+  "num_streams": 2,
+  "stream_types": [
+    "ego",
+    "third"
+  ],
+  "padding_ms": 500,
+  "default_duration_ms": 3000,
+  "clip_duration_ms": {
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+    "with_padding": 4000
+  },
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+    "total": 480000,
+    "min": 4000,
+    "max": 4000
+  },
+  "duration_sec": {
+    "average": 4.0,
+    "total": 480.0,
+    "min": 4.0,
+    "max": 4.0
+  },
+  "flux_stats": {
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+      "duration_ms": {
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+        "min": 4000,
+        "max": 4000
+      },
+      "duration_sec": {
+        "average": 4.0,
+        "total": 240.0,
+        "min": 4.0,
+        "max": 4.0
+      }
+    },
+    "third": {
+      "num_clips": 60,
+      "duration_ms": {
+        "average": 4000.0,
+        "total": 240000,
+        "min": 4000,
+        "max": 4000
+      },
+      "duration_sec": {
+        "average": 4.0,
+        "total": 240.0,
+        "min": 4.0,
+        "max": 4.0
+      }
+    }
+  },
+  "sequences": [
+    {
+      "id": "02_ego",
+      "flux_name": "ego",
+      "num_clips": 14,
+      "duration_ms": 3000
+    },
+    {
+      "id": "02_third",
+      "flux_name": "third",
+      "num_clips": 14,
+      "duration_ms": 3000
+    },
+    {
+      "id": "03_ego",
+      "flux_name": "ego",
+      "num_clips": 42,
+      "duration_ms": 3000
+    },
+    {
+      "id": "03_third",
+      "flux_name": "third",
+      "num_clips": 42,
+      "duration_ms": 3000
+    },
+    {
+      "id": "05_ego",
+      "flux_name": "ego",
+      "num_clips": 4,
+      "duration_ms": 3000
+    },
+    {
+      "id": "05_third",
+      "flux_name": "third",
+      "num_clips": 4,
+      "duration_ms": 3000
+    }
+  ]
+}

medias/mosaic.mp4

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train/dataset_info.json

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+    }
+  },
+  "homepage": "",
+  "license": ""
+}

train/state.json

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