--- 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: Fine Manipulation of Deformable Organic Matter ## 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 meat, sausage stuffing, and organic tissues. This resource addresses significant challenges in current robotics and computer vision regarding physical interaction, plasticity, and shear during expert handling. ## Key Technical Features * Synchronized Multi-View: Includes perfectly aligned ego-centric (First-Person View) and third-person perspectives captured simultaneously. * Non-Rigid Physics: Specifically designed to capture material behaviors such as plasticity, elasticity, and shear during professional butchery tasks. * Unified Dataset Structure: Features a modern unified format where each example contains all synchronized video streams in a single row for seamless cross-modal training. * Expert Precision: High-density micro-clips (1.80s) focusing on the exact moment of contact and manipulation between the expert and the organic material. ## 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 short-burst, high-precision actions where step order and expert intent are critical. * Tactile-Visual Inference: Learning to estimate force, grip, and material resistance through visual observation of fine meat-cutting and handling. ## 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: Full-cycle sausage production, precise meat cutting, and specialized tool maintenance. * Data Quality: Studio-grade audio, and 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**: MEAT-CUT-sample: Fine Manipulation of Deformable Organic Matter - **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`) - **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 ## 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**.