| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Extracts and structures semantic relationships between objects in images using scene graph representations where nodes are objects and edges encode spatial/semantic relationships (e.g., 'person sitting on bench', 'cup on table'). The dataset provides pre-annotated scene graphs for 108K images, enabling models to learn structured reasoning about object interactions rather than treating images as flat feature vectors. Each relationship is labeled with predicate types (spatial: 'on', 'under'; semantic: 'wearing', 'holding') and grounded to pixel coordinates.
Unique: Provides densely annotated scene graphs at scale (2.3M relationships across 108K images) with explicit predicate types and pixel-level grounding, enabling structured learning of visual relationships rather than implicit feature-based representations. Uses hierarchical annotation combining object-level, attribute-level, and relationship-level labels in a unified graph structure.
vs alternatives: Richer than COCO (object detection only) and more structured than ImageNet (no relationship annotations); enables training models that reason about object interactions, not just recognition
Provides 5.4 million natural language descriptions grounded to specific image regions (bounding boxes), enabling training of vision-language models that map text to visual regions. Each region description is manually written by annotators and linked to pixel coordinates, creating a dense supervision signal for learning region-text alignment. Descriptions range from simple object names to complex compositional descriptions capturing attributes, actions, and relationships.
Unique: Provides 5.4M region descriptions with pixel-level grounding across 108K images, creating dense supervision for learning fine-grained region-text alignment. Uses multi-annotator consensus for quality control and covers diverse object categories, attributes, and compositional descriptions.
vs alternatives: Denser and more diverse than Flickr30K (158K descriptions) and provides explicit region coordinates unlike raw image-caption pairs; enables training region-grounding models at scale
Contains 1.7 million visual question-answer pairs grounded in scene context, where questions reference objects, relationships, and attributes visible in images. Questions are paired with images and scene graphs, enabling models to learn to answer questions by reasoning over visual structure rather than pattern-matching. Answer types range from simple object names to complex compositional answers requiring multi-step reasoning over relationships.
Unique: Integrates 1.7M QA pairs with scene graph annotations, enabling models to learn reasoning over structured visual knowledge rather than image-level features alone. Questions are grounded in specific objects and relationships, creating a tighter coupling between language and visual structure.
vs alternatives: Larger and more structured than VQA v2 (1.1M questions) and includes scene graph grounding unlike standard VQA datasets; enables training models that reason over visual relationships
Provides 3.8 million annotated object instances with bounding boxes, class labels, and 2.8 million attribute annotations (e.g., color, material, size, state). Each object is labeled with multiple attributes describing its visual properties, enabling training of models that predict not just object categories but fine-grained visual properties. Attributes are structured as key-value pairs (e.g., 'color: red', 'material: wood') and grounded to specific object instances.
Unique: Combines 3.8M object instances with 2.8M attribute annotations in a unified dataset, enabling training of attribute-aware detection models. Attributes are structured as key-value pairs and grounded to specific instances, creating dense supervision for learning visual properties beyond category labels.
vs alternatives: Richer attribute annotations than COCO (which has minimal attributes) and larger scale than fine-grained datasets like CUB-200 (11K images); enables training attribute-aware detection at scale
Integrates images, scene graphs, region descriptions, object attributes, and QA pairs into a unified multimodal dataset, enabling end-to-end training of vision-language models that learn from multiple supervision signals simultaneously. The dataset structure allows models to leverage complementary annotations (e.g., region descriptions for grounding, scene graphs for reasoning, attributes for fine-grained understanding) in a single training pipeline. Supports multi-task learning where models jointly optimize for detection, grounding, VQA, and relationship prediction.
Unique: Provides unified integration of 5 complementary annotation types (scene graphs, region descriptions, object instances, attributes, QA pairs) across 108K images, enabling multi-task learning from diverse supervision signals. Dataset structure supports joint optimization for detection, grounding, reasoning, and attribute prediction in a single training pipeline.
vs alternatives: More comprehensive than single-task datasets (COCO, Flickr30K) and enables multi-task learning unlike datasets with isolated annotation types; supports training unified models that leverage complementary supervision signals
Enables indexing and retrieval of images based on scene graph structure and relationships, allowing queries like 'find images with a person sitting on a bench' or 'images where a dog is next to a car'. Scene graphs are indexed as structured knowledge representations, supporting semantic search over visual relationships rather than keyword matching. Retrieval can be performed by querying for specific objects, relationships, or relationship patterns.
Unique: Provides 2.3M annotated relationships indexed as scene graphs, enabling structured retrieval by visual relationships and spatial configurations. Supports querying by relationship patterns (e.g., 'X on Y') rather than keyword matching, enabling semantic search over visual structure.
vs alternatives: Enables relationship-based retrieval unlike keyword-based image search; supports complex spatial/semantic queries that text-based systems cannot express
Provides statistical analysis and distribution information about visual relationships, objects, and attributes across the dataset, enabling researchers to understand frequency patterns, co-occurrence statistics, and relationship distributions. Includes statistics on predicate frequencies, object co-occurrence patterns, attribute distributions, and relationship types. Enables analysis of visual knowledge biases and patterns in the dataset.
Unique: Provides comprehensive statistical analysis of 2.3M relationships, 3.8M objects, and 2.8M attributes across 108K images, enabling researchers to understand visual knowledge distributions and dataset biases. Includes frequency statistics, co-occurrence patterns, and relationship type distributions.
vs alternatives: Enables large-scale statistical analysis of visual relationships unlike smaller datasets; provides insights into relationship distributions and biases for improving model training
Enables training of compositional visual understanding models by providing structured annotations that decompose images into objects, attributes, and relationships. Models can learn to compose understanding from parts (objects + attributes + relationships) rather than treating images as monolithic wholes. Supports learning of compositional generalization where models understand novel combinations of known objects and relationships.
Unique: Provides explicit decomposition of images into objects, attributes, and relationships, enabling training of compositional models that understand visual scenes through structured components. Scene graphs naturally support compositional learning by representing images as compositions of objects and relationships.
vs alternatives: Enables compositional learning unlike flat image-label datasets; supports training models that generalize to novel combinations of known components
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs Visual Genome at 57/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
+5 more capabilities