{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-allenai--objaverse","slug":"allenai--objaverse","name":"objaverse","type":"dataset","url":"https://huggingface.co/datasets/allenai/objaverse","page_url":"https://unfragile.ai/allenai--objaverse","categories":["model-training"],"tags":["language:en","license:odc-by","arxiv:2212.08051","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-allenai--objaverse__cap_0","uri":"capability://data.processing.analysis.large.scale.3d.object.dataset.curation.and.indexing","name":"large-scale 3d object dataset curation and indexing","description":"Objaverse aggregates 800K+ 3D models from diverse sources (Sketchfab, TurboSquid, etc.) into a unified, searchable dataset with standardized metadata, canonical naming, and hierarchical object categorization. The dataset uses a multi-source ingestion pipeline that normalizes heterogeneous 3D formats (GLB, OBJ, USD) into a common representation, applies deduplication via perceptual hashing and geometric similarity metrics, and indexes objects by semantic category, license, and source provenance for efficient retrieval and filtering.","intents":["Train 3D vision models on diverse object geometry without manually collecting and licensing thousands of models","Build 3D scene understanding systems with representative coverage across object categories and real-world variations","Benchmark 3D reconstruction, shape completion, or object detection algorithms against a standardized, large-scale corpus","Create synthetic training data for downstream vision tasks by rendering objects from the dataset with varied camera angles and lighting"],"best_for":["ML researchers training 3D generative models (NeRF, diffusion, mesh generation)","Computer vision teams building object recognition systems that need diverse 3D geometry","Synthetic data generation pipelines requiring large object libraries for scene composition"],"limitations":["License heterogeneity — not all 800K models have identical usage rights; requires per-model license verification for commercial use","Format normalization may lose source-specific metadata or high-fidelity details from specialized formats","No built-in rendering pipeline — users must implement their own 3D-to-2D rendering for vision model training","Geometric quality varies significantly across sources; some models have topology issues or missing textures","Dataset size (~1TB+ when fully downloaded) requires substantial storage and bandwidth"],"requires":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","3D processing libraries (trimesh, pyvista, or similar) for model manipulation","~1TB disk space for full dataset download","Network bandwidth for streaming or bulk download"],"input_types":["3D model files (GLB, OBJ, USD, FBX)","Metadata queries (category, license, source)","Semantic search terms (object class, material type)"],"output_types":["3D model tensors/arrays","Mesh geometry (vertices, faces, normals)","Texture maps and material properties","Metadata JSON (category, source, license, bounding box)","Rendered 2D images (via external rendering)"],"categories":["data-processing-analysis","3d-vision-datasets"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-allenai--objaverse__cap_1","uri":"capability://data.processing.analysis.semantic.object.category.filtering.and.hierarchical.retrieval","name":"semantic object category filtering and hierarchical retrieval","description":"Objaverse indexes all 800K models with multi-level semantic categories (e.g., furniture → chair → office chair) derived from source metadata and automated tagging. Users can filter and retrieve subsets by category, enabling efficient dataset slicing without downloading the full corpus. The retrieval system supports both exact category matching and hierarchical traversal, allowing queries like 'all furniture' or 'all chairs' to return relevant subsets with consistent filtering semantics across heterogeneous source taxonomies.","intents":["Train specialized 3D models on specific object classes (e.g., only furniture) without downloading irrelevant geometry","Build category-balanced training splits for fair model evaluation across object types","Explore dataset composition by object class to understand coverage gaps or biases","Create domain-specific synthetic datasets (e.g., indoor scene generation) by filtering to relevant categories"],"best_for":["Researchers training category-specific 3D models (furniture recognition, vehicle detection, etc.)","Data scientists building balanced training sets with stratified sampling by object type","Teams analyzing dataset composition and coverage for bias auditing"],"limitations":["Category taxonomy is derived from source metadata and automated tagging — inconsistencies exist across sources (e.g., 'chair' vs 'seat' vs 'seating')","Hierarchical depth varies; some categories are deeply nested while others are flat","Automated tagging may misclassify objects, especially for ambiguous or multi-functional items","No fine-grained attribute filtering (e.g., 'red chairs' or 'chairs with wheels') — only categorical filtering"],"requires":["HuggingFace Datasets library","Python 3.8+","Knowledge of Objaverse category taxonomy"],"input_types":["Category name (string)","Hierarchical category path (e.g., 'furniture/chair')","Multiple category filters (OR/AND logic)"],"output_types":["Filtered dataset subset (metadata + model references)","Category statistics (count, distribution)","Hierarchical category tree (JSON)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-allenai--objaverse__cap_2","uri":"capability://data.processing.analysis.license.aware.model.access.and.commercial.use.filtering","name":"license-aware model access and commercial-use filtering","description":"Objaverse tracks license metadata for all 800K models (CC-BY, CC-0, proprietary, etc.) and enables filtering by license type and commercial-use permissions. The system maintains a license registry that maps source-specific license strings to standardized SPDX identifiers, allowing users to query 'all CC-BY models' or 'all models with commercial-use rights' without manual license verification. This enables compliant dataset construction for commercial applications and research with clear legal provenance.","intents":["Build training datasets with guaranteed commercial-use rights for production ML systems","Filter to open-license models (CC-0, CC-BY) for research without licensing friction","Audit dataset composition for license compliance before deployment","Identify proprietary models that require explicit licensing agreements"],"best_for":["Commercial teams building 3D AI products who need verified commercial-use rights","Academic researchers prioritizing open-license data for reproducibility","Legal/compliance teams auditing ML training data provenance"],"limitations":["License metadata is sourced from original platforms and may be incomplete or outdated","License interpretation varies by jurisdiction — filtering by license type does not constitute legal advice","Some models have ambiguous or missing license information; requires manual verification for high-stakes use","Commercial-use rights may depend on attribution or other conditions not captured in simple license tags","No built-in license compliance checking — users must independently verify compliance for their use case"],"requires":["HuggingFace Datasets library","Python 3.8+","Understanding of SPDX license identifiers"],"input_types":["License type (CC-BY, CC-0, proprietary, etc.)","Commercial-use flag (boolean)","Attribution requirement flag (boolean)"],"output_types":["Filtered dataset subset with license metadata","License distribution statistics","License compliance report (JSON)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-allenai--objaverse__cap_3","uri":"capability://data.processing.analysis.multi.source.model.deduplication.and.canonical.naming","name":"multi-source model deduplication and canonical naming","description":"Objaverse applies perceptual hashing, geometric similarity metrics, and metadata cross-referencing to identify and deduplicate models that appear across multiple sources (e.g., same model uploaded to both Sketchfab and TurboSquid). The system assigns canonical identifiers and names to deduplicated model groups, tracks source provenance for each variant, and enables users to retrieve all variants of a model or filter to a single canonical version. This prevents training data contamination and ensures fair representation across sources.","intents":["Avoid training data leakage by identifying and deduplicating models that appear in multiple sources","Retrieve all variants of a model (different resolutions, formats, textures) for comprehensive coverage","Understand source overlap and model popularity across platforms","Build deduplicated training sets with consistent model representation"],"best_for":["ML researchers training models where data leakage between train/test splits is a concern","Teams building 3D datasets with strict deduplication requirements","Researchers analyzing source overlap and model provenance"],"limitations":["Deduplication heuristics are imperfect — some duplicates may be missed (especially heavily modified variants) and some unique models may be incorrectly flagged as duplicates","Geometric similarity metrics are sensitive to mesh resolution and topology; low-poly and high-poly versions of the same model may not be detected as duplicates","Canonical naming is deterministic but may not match user expectations or source-specific naming conventions","Deduplication is one-way — no mechanism to 'un-deduplicate' if users disagree with the grouping"],"requires":["HuggingFace Datasets library","Python 3.8+","3D processing libraries (trimesh) for geometric analysis"],"input_types":["Model identifier or canonical name","Deduplication query (retrieve all variants or canonical version only)"],"output_types":["Deduplicated model group (all variants with source provenance)","Canonical model identifier and metadata","Deduplication statistics (duplicate count, source overlap)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-allenai--objaverse__cap_4","uri":"capability://data.processing.analysis.rendering.agnostic.3d.model.access.with.format.standardization","name":"rendering-agnostic 3d model access with format standardization","description":"Objaverse stores all 800K models in standardized GLB (glTF binary) format with normalized geometry, materials, and metadata, enabling consistent programmatic access regardless of source format (OBJ, FBX, USD, etc.). The system provides APIs to load models as mesh tensors, extract geometry (vertices, faces, normals), access material properties (textures, PBR parameters), and query bounding boxes and scale information. This abstraction eliminates format-specific parsing and enables downstream systems to work with a uniform 3D representation.","intents":["Load 3D models into ML pipelines without format-specific parsing or conversion","Extract geometry and material properties for 3D vision tasks (reconstruction, segmentation, etc.)","Access normalized bounding boxes and scale information for consistent model preprocessing","Build rendering pipelines that work with any model in the dataset without format-specific code"],"best_for":["ML engineers building 3D vision pipelines that need consistent model access","Researchers training models on diverse 3D geometry without format-specific preprocessing","Teams building 3D rendering or simulation systems that need standardized model input"],"limitations":["GLB normalization may lose source-specific metadata or high-fidelity details (e.g., complex material nodes in USD)","Geometry normalization (vertex/face count, scale) may not preserve original model intent or artistic details","Material properties are simplified to PBR (physically-based rendering) standard — specialized materials or shaders are lost","No support for animation or rigging data — only static geometry and materials are preserved","Texture resolution is capped to reduce dataset size; high-resolution textures from source models may be downsampled"],"requires":["HuggingFace Datasets library","Python 3.8+","3D processing library (trimesh, pyvista, or similar) for mesh manipulation","PyTorch or NumPy for tensor operations"],"input_types":["Model identifier (string)","Format specification (GLB, mesh tensor, etc.)"],"output_types":["GLB file (binary)","Mesh tensor (vertices, faces, normals as NumPy/PyTorch arrays)","Material properties (textures, PBR parameters as JSON/images)","Metadata (bounding box, scale, category)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-allenai--objaverse__cap_5","uri":"capability://data.processing.analysis.synthetic.training.data.generation.via.model.rendering.and.augmentation","name":"synthetic training data generation via model rendering and augmentation","description":"Objaverse enables synthetic training data generation by providing APIs to render models with configurable camera angles, lighting, backgrounds, and material variations. The system supports batch rendering of multiple models with randomized parameters, enabling efficient generation of large synthetic datasets for 3D vision tasks (object detection, pose estimation, etc.). Rendering can be integrated with external engines (Blender, PyRender, etc.) or used with built-in lightweight rendering for rapid iteration.","intents":["Generate large-scale synthetic training data for 3D object detection without manual annotation","Create pose estimation datasets with known camera parameters and object poses","Build domain randomization datasets with varied lighting, backgrounds, and material properties","Generate multi-view datasets for 3D reconstruction training"],"best_for":["Computer vision teams training object detection or pose estimation models with synthetic data","Researchers exploring domain randomization and sim-to-real transfer","Teams building 3D reconstruction systems that need multi-view training data"],"limitations":["Rendering quality depends on external engine (Blender, PyRender) — no built-in high-quality rendering","Synthetic data may not transfer well to real images due to domain gap (lighting, material properties, occlusion patterns)","Batch rendering is computationally expensive — generating large datasets requires significant GPU/CPU resources","Camera parameter randomization may not match real-world camera distributions","No built-in annotation generation — users must implement their own annotation pipeline (bounding boxes, segmentation masks, etc.)"],"requires":["HuggingFace Datasets library","Python 3.8+","External rendering engine (Blender, PyRender, or similar)","GPU for efficient batch rendering (optional but recommended)","PyTorch or NumPy for tensor operations"],"input_types":["Model identifiers (list of strings)","Rendering parameters (camera angles, lighting, background, material variations)","Batch size and output format (images, depth maps, segmentation masks)"],"output_types":["Rendered images (RGB, depth, normal maps, segmentation masks)","Camera parameters (intrinsics, extrinsics, pose)","Metadata (model ID, lighting conditions, material properties)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-allenai--objaverse__cap_6","uri":"capability://search.retrieval.3d.model.search.and.discovery.via.semantic.and.geometric.similarity","name":"3d model search and discovery via semantic and geometric similarity","description":"Objaverse provides semantic search capabilities that enable users to find models by natural language queries (e.g., 'red wooden chair') or by geometric similarity to a reference model. The system uses pre-computed embeddings (semantic and geometric) to enable fast similarity search across the 800K model corpus. Users can query by category, text description, or by uploading a reference 3D model to find similar objects, enabling efficient dataset exploration and model discovery.","intents":["Find models similar to a reference object for data augmentation or comparison","Discover models by natural language description without knowing exact category names","Explore dataset diversity by finding all variations of a concept (e.g., 'different types of chairs')","Build training sets with semantic coherence by retrieving related models"],"best_for":["Researchers exploring dataset composition and finding relevant models for specific tasks","Teams building 3D datasets with semantic coherence","Users discovering models without prior knowledge of Objaverse taxonomy"],"limitations":["Semantic search relies on pre-computed embeddings — may not capture fine-grained attributes or recent additions to the dataset","Geometric similarity is based on mesh-level features — may not capture functional or semantic similarity","Natural language search quality depends on embedding model quality — ambiguous queries may return irrelevant results","Search is read-only — no mechanism to provide feedback or improve embeddings","Embedding updates require dataset recomputation — may lag behind new model additions"],"requires":["HuggingFace Datasets library","Python 3.8+","Pre-computed embeddings (provided by Objaverse)"],"input_types":["Natural language query (string)","Reference 3D model (GLB file or model ID)","Similarity metric (semantic or geometric)"],"output_types":["Ranked list of similar models (with similarity scores)","Model metadata (category, source, license)","Embedding vectors (optional)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","3D processing libraries (trimesh, pyvista, or similar) for model manipulation","~1TB disk space for full dataset download","Network bandwidth for streaming or bulk download","HuggingFace Datasets library","Knowledge of Objaverse category taxonomy","Understanding of SPDX license identifiers","3D processing libraries (trimesh) for geometric analysis","3D processing library (trimesh, pyvista, or similar) for mesh manipulation"],"failure_modes":["License heterogeneity — not all 800K models have identical usage rights; requires per-model license verification for commercial use","Format normalization may lose source-specific metadata or high-fidelity details from specialized formats","No built-in rendering pipeline — users must implement their own 3D-to-2D rendering for vision model training","Geometric quality varies significantly across sources; some models have topology issues or missing textures","Dataset size (~1TB+ when fully downloaded) requires substantial storage and bandwidth","Category taxonomy is derived from source metadata and automated tagging — inconsistencies exist across sources (e.g., 'chair' vs 'seat' vs 'seating')","Hierarchical depth varies; some categories are deeply nested while others are flat","Automated tagging may misclassify objects, especially for ambiguous or multi-functional items","No fine-grained attribute filtering (e.g., 'red chairs' or 'chairs with wheels') — only categorical filtering","License metadata is sourced from original platforms and may be incomplete or outdated","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.42,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.066Z","last_scraped_at":"2026-05-03T14:22:48.064Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=allenai--objaverse","compare_url":"https://unfragile.ai/compare?artifact=allenai--objaverse"}},"signature":"DEZZ5TSaBtfJo28zOnABwEzDYJI7wmhBVECtUkZ5VatuMsq55qv+8n0F/hA0IO/cEbB1qx9lycEPY0ex/pfwCA==","signedAt":"2026-06-22T07:02:00.728Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/allenai--objaverse","artifact":"https://unfragile.ai/allenai--objaverse","verify":"https://unfragile.ai/api/v1/verify?slug=allenai--objaverse","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}