--- 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. ## 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**.