The Stack v2 ranks higher at 59/100 vs Supervisely at 57/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Supervisely | The Stack v2 |
|---|---|---|
| Type | Platform | Dataset |
| UnfragileRank | 57/100 | 59/100 |
| Adoption | 1 | 1 |
| Quality | 1 | 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
Provides collaborative annotation tools for images, videos, point clouds, and DICOM medical data with built-in AI models (YOLOv11, RT-DETRv2, SAM2, ClickSEG) that generate automatic annotations to accelerate manual labeling workflows. Uses smart tool request quotas (500/day community, 5,000/day pro, unlimited for image max tier) to meter AI-assisted suggestions, reducing annotation time while maintaining human quality control through review workflows.
Unique: Integrates multi-modal support (images, video, 3D point clouds, DICOM medical) in a single platform with built-in AI models for auto-annotation, rather than separate tools per data type. Smart tool request quotas provide predictable cost control for AI-assisted labeling at scale.
vs alternatives: Broader multi-modal support (especially 3D point clouds and medical DICOM) than Label Studio or Prodigy, with integrated AI-assisted annotation reducing manual effort vs. purely manual annotation platforms
Enables multiple team members to annotate the same dataset concurrently with role-based permissions (annotator, reviewer, admin), version control for annotation changes, and quality assurance workflows that route annotations through review and approval stages. Tracks annotation history and supports nested ontologies with key-value tags for flexible metadata assignment across team members.
Unique: Implements role-based annotation workflows with version control and QA routing within a single platform, rather than requiring separate tools for collaboration and quality control. Tracks annotation history and supports nested ontologies for flexible team-based labeling.
vs alternatives: Tighter team collaboration and QA workflow integration than Label Studio Community, with built-in role management and audit trails vs. requiring external workflow orchestration tools
Offers managed annotation services where Supervisely's team or certified partners handle annotation work on behalf of customers. Provides consulting services for dataset strategy, annotation workflow design, and ML pipeline optimization. Combines platform capabilities with human expertise to accelerate dataset creation and reduce time-to-model for customers without in-house annotation capacity.
Unique: Combines platform capabilities with managed annotation services and consulting, enabling customers to outsource annotation work while maintaining quality control. Leverages platform expertise for dataset strategy and workflow optimization.
vs alternatives: More integrated than using separate annotation services (e.g., Scale AI, Labelbox Services) with platform, but less specialized than dedicated annotation service providers focused solely on outsourced labeling
Provides an ecosystem index of custom applications and extensions built by Supervisely and partners. Enables discovery and deployment of pre-built applications for specialized annotation tasks, model training, and workflow automation. Marketplace approach allows community and partner contributions, though specific app categories, discovery mechanisms, and installation process not documented in available materials.
Unique: Provides ecosystem index for discovering and sharing custom applications, enabling community contributions and reducing development effort for common tasks. Marketplace approach allows pre-built solutions for specialized workflows.
vs alternatives: Emerging ecosystem feature, less mature than established marketplaces (VS Code Extensions, Hugging Face Models), but enables community-driven extension development
Provides search capabilities across images, annotations, and metadata using both keyword search (filename, class name) and semantic search (find similar images based on visual content). Supports filtering by annotation properties (class, confidence, annotator, date), metadata tags, and custom attributes. Search results can be exported as new datasets or used to create subsets for targeted annotation or analysis. Semantic search uses embeddings (model unknown) to find visually similar images.
Unique: Combines keyword, metadata, and semantic search in a single interface with the ability to export results as new datasets, enabling data exploration and quality analysis without leaving the platform — most annotation tools have basic filtering but lack semantic search or export capabilities
vs alternatives: More powerful than CVAT's filtering because it includes semantic search; more integrated than using Elasticsearch separately because search results can be directly exported as datasets
Enables multiple annotators to work on the same image simultaneously with real-time synchronization of changes. Detects conflicts when two annotators modify the same annotation and flags them for resolution. Supports undo/redo with conflict awareness (undo by one user doesn't affect another user's changes). Annotation state is persisted to the server after each change, ensuring no data loss. Latency and conflict resolution strategy are unknown.
Unique: Implements real-time collaborative annotation with automatic conflict detection and per-user undo/redo, allowing multiple annotators to work on the same image without stepping on each other's changes — most annotation tools are single-user or require manual conflict resolution
vs alternatives: More collaborative than CVAT because it supports simultaneous editing with conflict detection; more user-friendly than Google Docs-style conflict resolution because it's domain-specific to annotation conflicts
Provides integrated neural network training capabilities using built-in models (YOLOv11, RT-DETRv2, MM Segmentation, SAM2, ClickSEG) with support for custom model integration via SDK. Abstracts training infrastructure and hyperparameter configuration, allowing users to train models directly on annotated datasets without managing compute resources or writing training code. Custom models can be integrated for auto-labeling workflows, enabling iterative dataset improvement.
Unique: Integrates model training directly into the annotation platform with built-in model zoo and custom model support via SDK, enabling closed-loop annotation-training-labeling workflows without switching tools. Abstracts training infrastructure and hyperparameter tuning, reducing friction for non-ML teams.
vs alternatives: Tighter integration of training and annotation than separate tools (e.g., Label Studio + PyTorch), but lacks experiment tracking and model versioning features of dedicated ML platforms (MLflow, Weights & Biases)
Manages annotation projects with version control, data retention policies, and export capabilities. Community tier archives inactive projects after 30 days (available as download), while pro/enterprise tiers offer unlimited retention. Supports downloading archived projects and exporting datasets in standard formats, though export completeness and supported formats not fully documented. Provides storage quotas (5GB community, 50GB pro, expandable at €40/100GB) with file limits (10,000 community, 50,000 pro, expandable via add-ons).
Unique: Provides tiered storage and retention policies (30-day archival for community, unlimited for pro/enterprise) with per-tier file limits and expandable add-ons, creating predictable cost scaling. Version control for annotation projects enables tracking changes over time.
vs alternatives: Clearer storage/retention pricing model than Label Studio (which requires external storage), but less flexible than cloud-agnostic platforms (e.g., DVC) for multi-cloud data management
+6 more capabilities
Aggregates 67 TB of source code from the Software Heritage archive, filtering for permissively licensed repositories (MIT, Apache 2.0, BSD, etc.) across 600+ programming languages. Uses automated license detection and validation to ensure legal compliance for model training. Implements a rigorous deduplication pipeline at file and repository levels to eliminate redundant training data and reduce dataset bloat.
Unique: Largest open-source code dataset at 67 TB with automated opt-out governance allowing repository owners to request removal, combined with rigorous deduplication and PII removal pipeline — no other public dataset offers this scale with legal compliance and community control mechanisms
vs alternatives: Larger and more legally compliant than GitHub's CodeSearchNet (14M files) or Google's BigQuery public datasets, with explicit opt-out governance vs. implicit inclusion, and covers 600+ languages vs. Codex training data's undisclosed language distribution
Implements a community-driven opt-out system where repository owners can request removal of their code from the dataset without legal takedown notices. Maintains a registry of excluded repositories and re-applies exclusions during dataset updates. Provides transparent governance documentation and a clear submission process for removal requests, balancing open access with creator rights.
Unique: First large-scale code dataset to implement opt-out governance at dataset level rather than relying solely on license compliance, with transparent registry and community submission process — shifts power from dataset creators to code contributors
vs alternatives: More respectful of creator autonomy than GitHub Copilot's training approach (no opt-out) or academic datasets (one-time snapshot), and more scalable than individual DMCA takedowns
The Stack v2 scores higher at 59/100 vs Supervisely at 57/100.
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Automated pipeline that scans source code for personally identifiable information (email addresses, API keys, SSH keys, credit card patterns, phone numbers) and removes or redacts them before dataset release. Uses regex patterns, entropy-based detection for secrets, and heuristic rules to identify sensitive data. Operates at file level with configurable sensitivity thresholds to balance data utility against privacy risk.
Unique: Combines regex pattern matching, entropy-based secret detection, and heuristic rules in a unified pipeline with configurable sensitivity — more comprehensive than simple regex-only approaches, but trades off false positive rate against security coverage
vs alternatives: More thorough than GitHub's secret scanning (which only flags known patterns) because it includes entropy-based detection for unknown secret formats, but less accurate than specialized tools like TruffleHog due to language-agnostic approach
Indexes 67 TB of source code across 600+ programming languages with language-aware metadata (syntax, file extension, language family). Enables retrieval by language, license, repository, or code patterns. Uses Software Heritage's existing indexing infrastructure as foundation, augmented with language detection and classification. Supports both bulk download and filtered queries for specific language subsets.
Unique: Leverages Software Heritage's existing language detection and indexing infrastructure, then augments with BigCode-specific language classification and filtering — avoids reinventing language detection while providing dataset-specific query capabilities
vs alternatives: More comprehensive language coverage (600+ languages) than GitHub's Linguist (500+ languages) and more accessible than Software Heritage's raw API because it's pre-filtered for permissive licenses and deduplicated
Removes duplicate code files and repositories using content hashing (SHA-256 or similar) and fuzzy matching for near-duplicates. Operates in two stages: exact deduplication via hash matching, then fuzzy matching (e.g., Jaccard similarity or MinHash) to catch semantically identical code with minor formatting differences. Preserves one canonical copy of each unique code pattern while removing redundant training examples.
Unique: Two-stage deduplication combining exact hash matching with fuzzy similarity matching (likely MinHash or Jaccard) to catch both identical and near-identical code — more thorough than single-stage approaches but computationally expensive
vs alternatives: More aggressive deduplication than CodeSearchNet (which uses simple hash matching) because it catches near-duplicates, but less semantic than clone detection tools (which understand code structure) because it's content-based
Integrates with Software Heritage's comprehensive archive of 200+ million repositories and their full version control history. Extracts source code snapshots from Software Heritage's Git/Mercurial/SVN repositories, preserving repository metadata (commit history, author info, timestamps). Provides access to code at specific points in time, enabling historical analysis or training on code evolution patterns.
Unique: Leverages Software Heritage's universal code archive (200M+ repositories) as data source, providing access to code that would be impossible to collect via GitHub API alone — enables training on archived/deleted repositories and non-GitHub platforms (GitLab, Gitea, etc.)
vs alternatives: More comprehensive than GitHub-only datasets because it includes code from GitLab, Gitea, SourceForge, and other platforms archived by Software Heritage; more legally defensible than web scraping because it uses an established, community-maintained archive
Tracks and validates SPDX license identifiers for each repository, ensuring only permissively licensed code (MIT, Apache 2.0, BSD, etc.) is included. Maintains license metadata alongside code files, enabling downstream users to verify legal compliance. Implements license hierarchy and compatibility checking to handle dual-licensed or complex licensing scenarios.
Unique: Combines automated SPDX detection with manual review and maintains license metadata alongside code, enabling downstream users to verify compliance — more transparent than datasets that simply claim 'permissive licenses' without proof
vs alternatives: More legally rigorous than GitHub's CodeSearchNet (which doesn't validate licenses) and more transparent than Codex training data (which doesn't disclose license filtering at all)
Maintains versioned snapshots of the dataset (e.g., v2.0, v2.1) with documented changes between versions (new repositories added, deduplication improvements, PII removal updates). Provides checksums and manifests for reproducibility, enabling researchers to cite specific dataset versions and reproduce results. Tracks dataset lineage and transformation history.
Unique: Maintains semantic versioning and detailed changelogs for dataset releases, enabling researchers to cite specific versions and understand dataset evolution — more rigorous than one-off dataset releases without versioning
vs alternatives: More reproducible than academic datasets that are released once without versioning, and more transparent than commercial datasets (Codex) that don't disclose version history or changes
+2 more capabilities