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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
+---
+
+# MEAT-CUT-sample
+
+## 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** (meat, sausage stuffing, and casings), a significant challenge in current robotics and computer vision research.
+
+
+<video controls width="100%">
+  <source src="medias/mosaic.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
+
+## Key Technical Features
+
+* **Synchronized Multi-View FPV & 3rd Person:** Includes perfectly aligned ego-centric (First-Person View) and multiple third-person perspectives.
+* **Expert Human Narration:** Each task is accompanied by a human voice-over explaining the **intent, tactile feedback, and professional heuristics** behind every gesture.
+* **Non-Rigid Physics:** Captures complex material behaviors such as plasticity, elasticity, and shear during the sausage-making process.
+* **Multimodal Grounding:** Provides a direct link between visual actions and expert verbal instructions, ideal for training **Vision-Language Models (VLM)**.
+* **High-Quality, Multi-View Synchronization:** All views are precisely time-aligned to ensure seamless cross-modal understanding.
+
+## Use Cases for Research
+
+* **Embodied AI & World Models:** Training agents to predict the physical consequences of interacting with deformable organic matter.
+* **Procedural Task Learning:** Modeling long-form sequential actions where step order and expert intent are critical.
+* **Tactile-Visual Inference:** Learning to estimate force and material resistance through visual observation and expert narration.
+
+## Full Dataset Specifications
+
+This Hugging Face repository contains a **5-minute preview sample**. The full professional corpus includes:
+* **Total Duration:** 50+ hours of continuous expert operations.
+* **Tasks:** Full-cycle sausage production, precise meat cutting, and tool maintenance.
+* **Data Quality:** 4K resolution, studio-grade audio, and temporal action annotations.
+
+## Dataset Statistics
+
+This section provides detailed statistics extracted from `dataset_metadata.json`:
+
+### Overall Statistics
+
+- **Dataset Name**: MEAT-CUT-sample
+- **Batch ID**: 02
+- **Total Clips**: 214
+- **Number of Sequences**: 2
+- **Number of Streams**: 2
+- **Stream Types**: ego, third
+
+### Duration Statistics
+
+- **Total Duration**: 6.42 minutes (385.20 seconds)
+- **Average Clip Duration**: 1.80 seconds
+- **Min Clip Duration**: 1.80 seconds
+- **Max Clip Duration**: 1.80 seconds
+
+### Clip Configuration
+
+- **Base Clip Duration**: 1.00 seconds
+- **Clip Duration with Padding**: 1.80 seconds
+- **Padding**: 400 ms
+
+### Statistics by Stream Type
+
+#### Ego
+
+- **Number of clips**: 107
+- **Total duration**: 3.21 minutes (192.60 seconds)
+- **Average clip duration**: 1.80 seconds
+- **Min clip duration**: 1.80 seconds
+- **Max clip duration**: 1.80 seconds
+
+#### Third
+
+- **Number of clips**: 107
+- **Total duration**: 3.21 minutes (192.60 seconds)
+- **Average clip duration**: 1.80 seconds
+- **Min clip duration**: 1.80 seconds
+- **Max clip duration**: 1.80 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 **214 synchronized scenes** in a single `train` split. Each example includes:
+
+- **Synchronized video columns**: One column per flux type (e.g., `ego_video`, `third_video`, `top_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/MEAT-CUT-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/02/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
+
+### Access Video Files
+
+```python
+import cv2
+from pathlib import Path
+
+# Get an example
+example = dataset['train'][0]
+
+# Access video paths (stored as relative paths)
+ego_video_path = example.get('ego_video')  # e.g., 'videos/ego/0000.mp4'
+
+# Resolve full path 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
+
+# Load video using OpenCV or other library
+cap = cv2.VideoCapture(str(ego_video_full_path))
+
+# Or use a helper function
+def get_video_path(dataset, example, flux_name):
+    """Get the full path to a video file from a dataset example."""
+    cache_dir = Path(dataset['train'].cache_files[0]['filename']).parent.parent
+    video_relative_path = example[f'{flux_name}_video']
+    return cache_dir / video_relative_path
+
+# Usage
+ego_path = get_video_path(dataset, example, 'ego')
+cap = cv2.VideoCapture(str(ego_path))
+```
+
+## 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
+
+## Commercial Licensing & Contact
+
+The complete dataset is available for commercial licensing and large-scale industrial or academic research. It offers deep insights into "tacit knowledge" that is otherwise unavailable in public video repositories.
+**To discuss full access or custom data collection, please contact : lain@gmail.com **
+
+## 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": "MEAT-CUT-sample",
+  "batch_id": "02",
+  "total_clips": 214,
+  "num_sequences": 2,
+  "num_streams": 2,
+  "stream_types": [
+    "ego",
+    "third"
+  ],
+  "padding_ms": 400,
+  "default_duration_ms": 1000,
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+  },
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+    "total": 385.2,
+    "min": 1.8,
+    "max": 1.8
+  },
+  "flux_stats": {
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+      "duration_ms": {
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+        "min": 1800,
+        "max": 1800
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+        "min": 1.8,
+        "max": 1.8
+      }
+    },
+    "third": {
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+      "duration_ms": {
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+        "min": 1800,
+        "max": 1800
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+        "min": 1.8,
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+    }
+  },
+  "sequences": [
+    {
+      "id": "01_ego",
+      "flux_name": "ego",
+      "num_clips": 107,
+      "duration_ms": 1000
+    },
+    {
+      "id": "01_third",
+      "flux_name": "third",
+      "num_clips": 107,
+      "duration_ms": 1000
+    }
+  ]
+}

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train/dataset_info.json

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

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+  "_split": null
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