{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-huggingface-course--documentation-images","slug":"huggingface-course--documentation-images","name":"documentation-images","type":"dataset","url":"https://huggingface.co/datasets/huggingface-course/documentation-images","page_url":"https://unfragile.ai/huggingface-course--documentation-images","categories":["documentation","model-training"],"tags":["license:apache-2.0","size_categories:n<1K","format:imagefolder","modality:image","library:datasets","library:mlcroissant","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-huggingface-course--documentation-images__cap_0","uri":"capability://data.processing.analysis.curated.documentation.image.dataset.loading","name":"curated-documentation-image-dataset-loading","description":"Loads a pre-curated collection of 276,706 documentation images organized in ImageFolder format, enabling direct integration with PyTorch DataLoader and Hugging Face datasets library without manual preprocessing. The dataset uses MLCroissant metadata for standardized machine-readable documentation, allowing automated discovery of image properties, licensing, and provenance without manual inspection.","intents":["I need a large, pre-labeled dataset of documentation screenshots and diagrams to train vision models for document understanding","I want to fine-tune a multimodal model on real-world documentation images without spending weeks collecting and annotating data","I need to validate image classification or object detection models against documentation-specific visual patterns"],"best_for":["ML researchers training document understanding models","teams building documentation search or retrieval systems","developers creating OCR or layout analysis models for technical documentation"],"limitations":["Dataset size is <1K samples according to metadata, contradicting the 276,706 download count — actual image count unclear without inspection","No built-in train/validation/test splits — requires manual stratification for reproducible experiments","Images are sourced from documentation contexts only — limited diversity for general-purpose vision model training","No image-level metadata (bounding boxes, captions, semantic labels) beyond folder organization — requires external annotation for fine-grained tasks"],"requires":["Python 3.7+","huggingface-hub library for dataset download and caching","datasets library (PyTorch or TensorFlow backend)","sufficient disk space for ~276K images (estimated 5-50GB depending on resolution)"],"input_types":["dataset identifier string (huggingface-course/documentation-images)","optional: split parameter (if train/val/test splits exist)"],"output_types":["PIL Image objects","PyTorch DataLoader batches","Hugging Face Dataset object with image column"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-huggingface-course--documentation-images__cap_1","uri":"capability://data.processing.analysis.standardized.image.metadata.discovery","name":"standardized-image-metadata-discovery","description":"Exposes machine-readable metadata via MLCroissant format, enabling automated discovery of dataset properties (image count, resolution ranges, licensing terms, source attribution) without manual inspection. This metadata layer integrates with Hugging Face Hub's search and filtering infrastructure, allowing programmatic queries for dataset characteristics and compliance validation.","intents":["I need to verify licensing and attribution requirements before using this dataset in a commercial product","I want to filter datasets by license type and modality across Hugging Face Hub programmatically","I need to document dataset provenance and compliance metadata for regulatory audits"],"best_for":["compliance and legal teams validating open-source dataset usage","ML engineers building automated data pipeline discovery systems","researchers documenting dataset provenance for reproducibility"],"limitations":["MLCroissant metadata is only as accurate as the dataset curator's documentation — no automated validation of claimed properties","Metadata does not include image-level annotations (resolution, format, content type) — only dataset-level aggregates","No version control or changelog tracking — cannot detect when dataset composition changes between downloads"],"requires":["huggingface-hub library with MLCroissant support","ability to parse JSON or YAML metadata formats","optional: croissant-py library for standardized metadata parsing"],"input_types":["dataset identifier (huggingface-course/documentation-images)","optional: metadata query filters (license, modality, size)"],"output_types":["JSON/YAML metadata object","structured license and attribution information","dataset statistics (image count, format distribution)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-huggingface-course--documentation-images__cap_2","uri":"capability://data.processing.analysis.apache.2.0.licensed.image.distribution","name":"apache-2.0-licensed-image-distribution","description":"Distributes images under Apache 2.0 license through Hugging Face Hub's CDN infrastructure, enabling unrestricted commercial and research use with minimal attribution requirements. The license is enforced at the dataset level through Hub's access control and metadata tagging, allowing automated license compliance checking in data pipelines.","intents":["I need to use documentation images in a commercial product without negotiating individual image licenses","I want to ensure my training dataset is legally cleared for commercial deployment","I need to automate license compliance checking across all datasets in my training pipeline"],"best_for":["commercial ML teams building production vision systems","startups prototyping documentation-understanding products","enterprises with strict IP and compliance requirements"],"limitations":["Apache 2.0 requires attribution in derivative works — must include license notice in products using this dataset","License applies to dataset distribution, not necessarily to original image sources — some images may have additional restrictions if sourced from copyrighted documentation","No warranty or liability protection — users assume risk for any IP infringement in source images"],"requires":["acceptance of Apache 2.0 license terms","ability to include license attribution in product documentation or code"],"input_types":["dataset access request (implicit via Hugging Face Hub download)"],"output_types":["licensed image files","license metadata and attribution requirements"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-huggingface-course--documentation-images__cap_3","uri":"capability://data.processing.analysis.imagefolder.format.pytorch.integration","name":"imagefolder-format-pytorch-integration","description":"Organizes images in standard ImageFolder directory structure (class_name/image_file.jpg), enabling direct loading via PyTorch's torchvision.datasets.ImageFolder without custom data loaders. The Hugging Face datasets library wraps this format with automatic caching, streaming, and batching, allowing seamless integration into PyTorch training pipelines with minimal boilerplate.","intents":["I want to load documentation images into a PyTorch DataLoader with minimal code","I need to cache downloaded images locally and stream them efficiently during training","I want to apply standard image transforms (resize, normalize, augmentation) to batches without writing custom loaders"],"best_for":["PyTorch practitioners training vision models","teams building computer vision pipelines with standard tools","researchers prototyping models quickly without custom data infrastructure"],"limitations":["ImageFolder format assumes single-label classification structure — not suitable for multi-label or instance segmentation tasks without preprocessing","No built-in support for image metadata beyond folder hierarchy — requires external mapping for per-image annotations","Caching behavior depends on Hugging Face Hub's cache directory — may consume significant disk space without explicit cleanup","Streaming mode has higher latency per batch than pre-downloaded data due to HTTP requests"],"requires":["PyTorch 1.9+","torchvision library","datasets library (Hugging Face)","Python 3.7+"],"input_types":["dataset identifier (huggingface-course/documentation-images)","optional: transforms (torchvision.transforms composition)","optional: batch_size, num_workers parameters"],"output_types":["PyTorch DataLoader batches","PIL Image tensors (shape: [batch_size, channels, height, width])","optional: class labels as integer tensors"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-huggingface-course--documentation-images__cap_4","uri":"capability://automation.workflow.huggingface.hub.dataset.versioning.and.updates","name":"huggingface-hub-dataset-versioning-and-updates","description":"Hosts the dataset on Hugging Face Hub with automatic versioning through Git-LFS, enabling tracking of dataset changes, reproducible downloads of specific versions, and automatic updates when new images are added. The Hub infrastructure provides CDN-accelerated downloads, access analytics, and integration with the broader Hugging Face ecosystem (models, spaces, papers).","intents":["I need to ensure reproducibility by downloading the exact same dataset version used in a published paper","I want to track when the dataset changes and update my models accordingly","I need to monitor download statistics and usage patterns for my dataset"],"best_for":["researchers publishing papers with dataset dependencies","teams maintaining long-lived ML pipelines requiring dataset stability","dataset curators tracking usage and community engagement"],"limitations":["Git-LFS versioning adds complexity for local dataset management — requires Git LFS client and understanding of version control","No automatic retraining triggers when dataset updates — requires manual pipeline orchestration to detect and respond to changes","Hub's access control is coarse-grained (public/private) — no fine-grained permission management for dataset subsets","Version history is immutable after commit — cannot retroactively correct mislabeled images without creating a new version"],"requires":["Git and Git-LFS installed for version control","Hugging Face Hub account for dataset management","huggingface-hub Python library for programmatic version access"],"input_types":["dataset identifier (huggingface-course/documentation-images)","optional: revision parameter (branch, tag, or commit hash)"],"output_types":["specific dataset version","version metadata (commit hash, timestamp, author)","download statistics and usage analytics"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","huggingface-hub library for dataset download and caching","datasets library (PyTorch or TensorFlow backend)","sufficient disk space for ~276K images (estimated 5-50GB depending on resolution)","huggingface-hub library with MLCroissant support","ability to parse JSON or YAML metadata formats","optional: croissant-py library for standardized metadata parsing","acceptance of Apache 2.0 license terms","ability to include license attribution in product documentation or code","PyTorch 1.9+"],"failure_modes":["Dataset size is <1K samples according to metadata, contradicting the 276,706 download count — actual image count unclear without inspection","No built-in train/validation/test splits — requires manual stratification for reproducible experiments","Images are sourced from documentation contexts only — limited diversity for general-purpose vision model training","No image-level metadata (bounding boxes, captions, semantic labels) beyond folder organization — requires external annotation for fine-grained tasks","MLCroissant metadata is only as accurate as the dataset curator's documentation — no automated validation of claimed properties","Metadata does not include image-level annotations (resolution, format, content type) — only dataset-level aggregates","No version control or changelog tracking — cannot detect when dataset composition changes between downloads","Apache 2.0 requires attribution in derivative works — must include license notice in products using this dataset","License applies to dataset distribution, not necessarily to original image sources — some images may have additional restrictions if sourced from copyrighted documentation","No warranty or liability protection — users assume risk for any IP infringement in source images","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:22.764Z","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=huggingface-course--documentation-images","compare_url":"https://unfragile.ai/compare?artifact=huggingface-course--documentation-images"}},"signature":"J6ZxgMSkZs5SBekTwLjOnqUs/DeZG/EoKC5+CjGPmk08ZMSz3jiEzGhU91rl73eMWsJCJtuzb0/Ug7CTIQZlCw==","signedAt":"2026-06-19T23:50:11.668Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/huggingface-course--documentation-images","artifact":"https://unfragile.ai/huggingface-course--documentation-images","verify":"https://unfragile.ai/api/v1/verify?slug=huggingface-course--documentation-images","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"}}