{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ms-coco-common-objects-in-context","slug":"ms-coco-common-objects-in-context","name":"MS COCO (Common Objects in Context)","type":"dataset","url":"https://cocodataset.org/","page_url":"https://unfragile.ai/ms-coco-common-objects-in-context","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ms-coco-common-objects-in-context__cap_0","uri":"capability://data.processing.analysis.multi.task.object.instance.annotation.with.polygon.and.rle.encoded.segmentation.masks","name":"multi-task object instance annotation with polygon and rle-encoded segmentation masks","description":"Provides 2.5 million manually-annotated object instances across 330,000 images with dual segmentation encoding: polygon coordinates for precise boundary definition and RLE (run-length encoding) for efficient storage and computation. Each instance includes bounding box coordinates in [x, y, width, height] format, category label from 80 object classes, and instance-level unique identifiers enabling per-object tracking and evaluation. Annotations are structured in JSON format with hierarchical organization linking images to annotations to categories, supporting both dense object scenes and sparse single-object images.","intents":["train object detection models that need to learn both bounding box regression and instance-level classification","develop instance segmentation systems requiring precise pixel-level object boundaries","benchmark detection/segmentation architectures against standardized evaluation metrics","analyze object co-occurrence patterns and spatial relationships in natural images"],"best_for":["computer vision researchers training or evaluating detection/segmentation models","teams building production object detection systems needing large-scale labeled data","benchmark participants competing on standardized leaderboards"],"limitations":["Fixed to 80 object categories — cannot add custom classes without external re-annotation","Segmentation mask quality varies across instances; no per-mask confidence scores provided","Bounding boxes are axis-aligned rectangles only — no rotated or 3D boxes","No temporal continuity — static images only, cannot track objects across frames","Image resolution and size distribution not standardized; resolution range unknown"],"requires":["Python 3.6+ for COCO API","JSON parsing library (built into Python standard library)","10-50GB disk space for full dataset download (exact size depends on image resolution)","Understanding of segmentation mask encoding (polygon vs RLE format)"],"input_types":["JPEG/PNG images","JSON annotation files with hierarchical structure"],"output_types":["Structured annotation objects with instance masks, bboxes, category IDs","RLE-encoded binary masks for efficient computation","Polygon coordinate arrays for precise boundary representation"],"categories":["data-processing-analysis","computer-vision-benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_1","uri":"capability://data.processing.analysis.human.keypoint.detection.annotation.with.standardized.joint.coordinate.system","name":"human keypoint detection annotation with standardized joint coordinate system","description":"Provides keypoint annotations for all people in images using a standardized 17-joint skeleton model (head, shoulders, elbows, wrists, hips, knees, ankles) with (x, y, visibility) tuples per joint. Visibility flag indicates whether keypoint is annotated (1), occluded (0), or outside image bounds (0). Keypoints are linked to parent person instances via instance ID, enabling pose estimation evaluation at both individual and crowd-level scales. Annotations follow COCO Keypoints task specification with consistent coordinate system across all 330K images.","intents":["train human pose estimation models on large-scale diverse pose variations and occlusions","evaluate pose detection accuracy using standardized metrics (OKS — Object Keypoint Similarity)","develop crowd pose understanding systems handling multiple overlapping people","benchmark pose estimation architectures across different body configurations and visibility conditions"],"best_for":["pose estimation researchers and practitioners","teams building human activity recognition or motion capture systems","sports analytics and fitness tracking application developers"],"limitations":["Keypoint annotations limited to human bodies only — no hand/finger keypoints or animal poses","17-joint skeleton is fixed — cannot extend to custom joint definitions without re-annotation","Visibility flag is binary (annotated vs not) — no confidence scores from annotators","Occluded keypoints marked as invisible but not explicitly labeled with occlusion type (self-occlusion vs external)","No temporal keypoint sequences — each image is independent, no motion/trajectory data"],"requires":["Python 3.6+ with COCO API","Understanding of skeleton topology and joint connectivity","Familiarity with OKS (Object Keypoint Similarity) metric for evaluation","Images must contain at least one person for keypoint annotations to be present"],"input_types":["JPEG/PNG images containing people","JSON keypoint annotations with (x, y, visibility) tuples"],"output_types":["Keypoint coordinate arrays (17 joints × 3 values per person)","Visibility masks indicating annotated vs occluded joints","OKS-based evaluation metrics for pose accuracy"],"categories":["data-processing-analysis","computer-vision-benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_10","uri":"capability://data.processing.analysis.community.driven.dataset.extension.and.variant.creation.with.standardized.evaluation","name":"community-driven dataset extension and variant creation with standardized evaluation","description":"COCO ecosystem includes community-created extensions (COCO-Stuff, COCO DensePose, COCO Panoptic) that extend base dataset with additional annotations while maintaining compatibility with COCO API and evaluation infrastructure. Extensions follow COCO format and evaluation standards, enabling seamless integration into existing pipelines. Community contributions are vetted and published as official COCO variants, ensuring quality and standardization. Variant creation process is documented, enabling researchers to create custom extensions.","intents":["extend COCO with custom annotations (new categories, new tasks) while maintaining compatibility","leverage community extensions (Stuff, DensePose, Panoptic) without creating separate datasets","contribute new annotations or variants to COCO ecosystem for community use","standardize custom extensions using COCO format and evaluation protocols"],"best_for":["researchers creating COCO extensions for new tasks or categories","teams leveraging community-created variants without custom annotation","practitioners standardizing custom datasets using COCO format"],"limitations":["Extension creation requires significant effort and community review process","Not all proposed extensions are accepted — quality and scope requirements are strict","Variant compatibility with base COCO API is not guaranteed — may require custom code","Documentation for extension creation is minimal — must infer process from existing variants","No official process for versioning or maintaining extensions — community-maintained variants may become outdated"],"requires":["Python 3.6+ with COCO API","Understanding of COCO JSON format and evaluation protocols","Large-scale annotation effort (thousands to millions of labels)","Community review and acceptance process"],"input_types":["Base COCO images and annotations","Additional annotations in COCO JSON format"],"output_types":["COCO-compatible dataset variants with new annotations","Evaluation metrics for new tasks","Published dataset on cocodataset.org"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_2","uri":"capability://data.processing.analysis.image.to.text.caption.generation.dataset.with.5.natural.language.descriptions.per.image","name":"image-to-text caption generation dataset with 5 natural language descriptions per image","description":"Provides 1.65 million image-caption pairs (5 captions × 330K images) with natural language descriptions written by human annotators. Each caption is a free-form English sentence describing objects, actions, and scene context without enforced length limits or structured templates. Captions are stored in JSON format linked to image IDs, enabling training of vision-language models for image captioning, visual question answering, and cross-modal retrieval. Multiple captions per image capture linguistic diversity and alternative descriptions of the same visual content.","intents":["train image captioning models that generate natural language descriptions from visual input","develop vision-language models for visual question answering and image-text matching","evaluate caption generation quality using BLEU, METEOR, CIDEr, and SPICE metrics","build cross-modal retrieval systems matching images to textual descriptions"],"best_for":["NLP and computer vision researchers working on vision-language models","teams building image search and visual understanding applications","multimodal AI practitioners training CLIP-style models"],"limitations":["English-language only — no multilingual captions or translations","No structured annotation (no entity tagging, relationship labels, or semantic roles)","Caption length and style vary significantly across annotators — no normalization or quality control metrics provided","No explicit alignment between caption phrases and image regions — cannot determine which caption words refer to which objects","Captions describe only salient objects/actions — background details often omitted"],"requires":["Python 3.6+ with COCO API","Natural language processing libraries (NLTK, spaCy) for caption preprocessing","Vision-language model framework (PyTorch, TensorFlow, or Hugging Face Transformers)","Evaluation metric implementations (BLEU, METEOR, CIDEr, SPICE packages)"],"input_types":["JPEG/PNG images","JSON caption files with image_id and caption text"],"output_types":["Natural language caption strings (variable length, 8-30 words typical)","Caption evaluation scores (BLEU, METEOR, CIDEr, SPICE metrics)","Image-caption embedding pairs for cross-modal retrieval"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_3","uri":"capability://data.processing.analysis.semantic.segmentation.with.171.extended.object.stuff.categories.via.coco.stuff.variant","name":"semantic segmentation with 171 extended object/stuff categories via coco-stuff variant","description":"Extends base 80 object categories with 91 additional 'stuff' categories (background materials, textures, regions like sky, grass, wall) enabling dense semantic segmentation of entire images. Stuff categories are annotated as pixel-level masks without instance boundaries — all sky pixels are labeled 'sky' regardless of continuity. COCO-Stuff combines instance segmentation (80 objects) with semantic segmentation (171 total categories including stuff), stored as single-channel PNG masks where pixel value encodes category ID. Enables panoptic segmentation evaluation combining instance and stuff predictions.","intents":["train semantic segmentation models that classify every pixel into 171 categories including background materials","develop scene understanding systems that recognize both discrete objects and continuous background regions","evaluate panoptic segmentation architectures combining instance and stuff predictions","build dense scene parsing systems for autonomous driving and robotics applications"],"best_for":["semantic segmentation researchers working on dense prediction tasks","autonomous driving and robotics teams needing scene understanding","teams building panoptic segmentation systems"],"limitations":["Stuff categories lack instance boundaries — cannot distinguish between separate sky regions or separate walls","Category overlap between base 80 objects and 91 stuff categories creates ambiguity (e.g., 'person' is object, 'people' might be stuff)","Stuff annotations are coarser than instance masks — no polygon precision, only pixel-level masks","No hierarchical category relationships — 'wall' and 'building' are independent categories despite semantic relationship","Evaluation metrics for stuff categories differ from instance metrics, requiring separate evaluation pipelines"],"requires":["Python 3.6+ with COCO API and COCO-Stuff extensions","Image processing library (PIL, OpenCV) for mask loading and manipulation","Semantic segmentation framework (PyTorch, TensorFlow)","Understanding of panoptic segmentation evaluation (PQ, SQ, RQ metrics)"],"input_types":["JPEG/PNG images","Single-channel PNG masks where pixel value = category ID (0-170)"],"output_types":["Semantic segmentation masks (171 categories)","Panoptic segmentation results combining instance and stuff","Evaluation metrics (mIoU, PQ, SQ, RQ)"],"categories":["data-processing-analysis","computer-vision-benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_4","uri":"capability://data.processing.analysis.panoptic.segmentation.with.unified.instance.and.stuff.prediction.evaluation","name":"panoptic segmentation with unified instance and stuff prediction evaluation","description":"Combines instance segmentation (80 object categories with boundaries) and semantic segmentation (171 stuff categories without boundaries) into single panoptic prediction task. Evaluation uses Panoptic Quality (PQ) metric decomposed into Segmentation Quality (SQ — IoU of matched predictions) and Recognition Quality (RQ — detection rate). Panoptic masks encode both category ID and instance ID, enabling evaluation of both 'what' (category) and 'which' (instance identity) predictions. Standardized evaluation protocol with server-side metric computation ensures consistent benchmarking across submissions.","intents":["train unified panoptic segmentation models that predict both instance objects and stuff regions in single forward pass","evaluate panoptic architectures using standardized PQ metric decomposed into segmentation and recognition components","benchmark scene understanding systems on complete image parsing (objects + background)","develop end-to-end scene understanding for autonomous systems requiring full image interpretation"],"best_for":["panoptic segmentation researchers and practitioners","autonomous driving and robotics teams needing complete scene understanding","teams building unified vision models handling multiple prediction types"],"limitations":["Panoptic metric (PQ) is complex and less interpretable than separate instance/semantic metrics","Instance and stuff predictions require different handling — no unified loss function, requires task-specific heads","Evaluation requires exact category and instance ID matching — no partial credit for near-misses","Stuff categories have no instance boundaries, creating ambiguity in panoptic mask generation (how to assign instance IDs to stuff)","Test set evaluation requires manual submission to leaderboard — no local evaluation API for test set"],"requires":["Python 3.6+ with COCO API and panoptic evaluation tools","Panoptic segmentation framework (Detectron2, MMSegmentation, or custom implementation)","Understanding of PQ metric computation and instance matching algorithm","Ability to generate panoptic predictions in COCO format (JSON with category_id and instance_id)"],"input_types":["JPEG/PNG images","Panoptic segmentation predictions with category and instance IDs"],"output_types":["Panoptic Quality (PQ) metric (0-100 scale)","Segmentation Quality (SQ) — average IoU of matched predictions","Recognition Quality (RQ) — detection rate of instances","Per-category PQ scores for 80 objects and 91 stuff categories"],"categories":["data-processing-analysis","computer-vision-benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_5","uri":"capability://data.processing.analysis.dense.human.surface.correspondence.mapping.via.coco.densepose.variant","name":"dense human surface correspondence mapping via coco densepose variant","description":"Provides dense 2D-to-3D correspondence maps for human bodies, mapping each pixel in a person instance to a 3D human body model surface. Annotations include UV coordinates (parameterization of 3D body surface) and body part indices enabling pixel-level body surface understanding. DensePose enables training of models that predict where each image pixel corresponds to on a canonical 3D human body, useful for pose transfer, virtual try-on, and detailed human understanding. Available from 2020 dataset version onwards, extends keypoint annotations with dense surface coverage.","intents":["train dense pose estimation models that map image pixels to 3D body surface coordinates","develop pose transfer and human shape estimation systems using dense correspondence","build virtual try-on and clothing fitting applications requiring detailed body surface mapping","evaluate dense human understanding beyond skeleton keypoints to full surface correspondence"],"best_for":["pose transfer and human shape analysis researchers","fashion/e-commerce teams building virtual try-on systems","teams developing detailed human body understanding models"],"limitations":["DensePose only available in 2020+ dataset versions — older COCO versions lack this annotation","Annotations limited to visible body surfaces — occluded regions have no correspondence labels","3D body model is generic — does not capture individual body shape variations","UV coordinate system requires understanding of 3D body parameterization — steep learning curve","Annotation quality varies — dense manual annotation is labor-intensive and error-prone"],"requires":["Python 3.6+ with COCO API and DensePose extensions","Understanding of UV parameterization and 3D body surface models","DensePose library (Facebook Research) for coordinate transformation and visualization","COCO dataset version 2020 or later"],"input_types":["JPEG/PNG images containing people","DensePose UV coordinate maps (2-channel images with U and V coordinates)"],"output_types":["Dense correspondence predictions (UV coordinates per pixel)","Body part segmentation (which body part each pixel belongs to)","3D body surface reconstruction from dense predictions"],"categories":["data-processing-analysis","computer-vision-benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_6","uri":"capability://data.processing.analysis.standardized.evaluation.metrics.and.leaderboard.submission.infrastructure","name":"standardized evaluation metrics and leaderboard submission infrastructure","description":"Provides standardized evaluation metrics for each task (Average Precision for detection, IoU for segmentation, OKS for keypoints, BLEU/METEOR/CIDEr for captions, PQ for panoptic) computed server-side on held-out test set. Leaderboard system accepts structured JSON result submissions in COCO format, validates format, computes metrics, and ranks submissions by primary metric. Evaluation infrastructure ensures consistent benchmarking across all submissions and prevents metric gaming through standardized computation. Metrics are task-specific: AP/AP50/AP75 for detection, mIoU for segmentation, OKS for keypoints, CIDEr for captions.","intents":["submit model predictions to official leaderboard for standardized benchmarking","compare architecture performance against published baselines using identical evaluation","validate model improvements using consistent metrics across multiple tasks","track progress on standardized benchmarks over time with reproducible results"],"best_for":["researchers publishing results on COCO benchmark","teams competing on official leaderboards","practitioners validating model improvements against standardized baselines"],"limitations":["Test set evaluation requires manual submission — no local evaluation API for test set, only validation set","Leaderboard submission format is strict JSON — format errors cause rejection without detailed error messages","Evaluation metrics are fixed — cannot customize metrics or evaluation protocol","Results are public on leaderboard — no private evaluation option for proprietary models","Submission rate limits not documented — unclear if there are throttling policies","Metric definitions are complex (AP computation uses 11-point interpolation, OKS uses Gaussian kernel) — implementation details must be matched exactly"],"requires":["Python 3.6+ with COCO API for result formatting","Understanding of task-specific evaluation metrics (AP, IoU, OKS, BLEU, CIDEr, PQ)","Predictions in COCO JSON format matching specification exactly","Account on cocodataset.org for leaderboard submission","Validation set for local metric computation before test submission"],"input_types":["Model predictions in COCO JSON format (category_id, score, bbox/segmentation/keypoints/caption)","Structured result files with image_id and task-specific predictions"],"output_types":["Evaluation metrics (AP, mIoU, OKS, BLEU, CIDEr, PQ depending on task)","Per-category breakdown of metrics","Leaderboard ranking and comparison to baselines","Detailed evaluation report with per-image results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_7","uri":"capability://data.processing.analysis.large.scale.image.collection.with.diverse.object.co.occurrence.and.scene.contexts","name":"large-scale image collection with diverse object co-occurrence and scene contexts","description":"Dataset of 330,000 images collected from Flickr with natural object co-occurrence patterns and diverse scene contexts (indoor, outdoor, crowded, sparse). Images are not filtered for specific objects or scenes — they represent natural distribution of visual content including rare objects and complex multi-object scenes. Diversity in image resolution, lighting, viewpoint, and object scale enables training of robust models. Image collection methodology prioritizes diversity over balance — some object categories appear more frequently than others reflecting real-world distribution.","intents":["train object detection models on naturally-distributed object co-occurrence patterns","develop robust vision models that handle diverse image resolutions, lighting, and viewpoints","analyze object relationships and scene composition in natural images","evaluate model robustness across diverse visual conditions and rare object instances"],"best_for":["computer vision researchers training robust detection/segmentation models","teams building production vision systems requiring diverse training data","practitioners studying object co-occurrence and scene composition"],"limitations":["Image resolution and size distribution not standardized — ranges from small thumbnails to high-resolution images","No explicit metadata about image properties (resolution, aspect ratio, lighting conditions)","Object category distribution is imbalanced — some categories appear in <100 images while others appear in >10K images","Geographic and demographic bias in Flickr collection — overrepresents Western countries and certain demographics","Temporal coverage unknown — images collected over unknown time period, may contain outdated objects/scenes","No explicit scene type labels (indoor/outdoor, crowded/sparse) — must be inferred from annotations"],"requires":["Python 3.6+ with image processing libraries (PIL, OpenCV)","Sufficient storage (10-50GB depending on image resolution)","Understanding of class imbalance and sampling strategies for training","Ability to handle variable image resolutions in model pipeline"],"input_types":["JPEG/PNG images from Flickr with variable resolution and aspect ratio"],"output_types":["Raw image data with associated annotations","Image metadata (implicit: resolution, aspect ratio, object counts)"],"categories":["data-processing-analysis","computer-vision-benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_8","uri":"capability://data.processing.analysis.json.based.hierarchical.annotation.format.with.image.annotation.category.linking","name":"json-based hierarchical annotation format with image-annotation-category linking","description":"Annotations stored in JSON format with hierarchical structure: images array (image metadata), annotations array (instance-level labels), categories array (category definitions with names and IDs). Each annotation links to image via image_id and category via category_id, enabling efficient querying and filtering. JSON structure supports multiple annotation types (bboxes, segmentation masks, keypoints, captions) in unified format. COCO API provides Python interface to load and query annotations without manual JSON parsing, handling coordinate transformations and mask decoding.","intents":["load and parse COCO annotations programmatically without manual JSON handling","filter annotations by image, category, or annotation type for task-specific training","transform annotations between formats (polygon to RLE, image to annotation coordinates)","integrate COCO data into training pipelines with minimal preprocessing"],"best_for":["computer vision practitioners building training pipelines","researchers integrating COCO data into custom frameworks","teams automating annotation loading and preprocessing"],"limitations":["JSON schema not formally documented — must infer structure from examples","COCO API is Python-only — no official support for other languages (R, MATLAB, JavaScript)","RLE mask decoding requires understanding of run-length encoding format","Polygon coordinates use image pixel coordinates — require transformation for model input","Large JSON files (>1GB for full annotations) can be slow to parse and load into memory"],"requires":["Python 3.6+ with COCO API (pip install pycocotools)","JSON parsing library (built into Python)","Understanding of COCO JSON schema (images, annotations, categories)","Image processing library (PIL, OpenCV) for coordinate transformation"],"input_types":["JSON annotation files with hierarchical structure","Image files (JPEG/PNG) referenced by image_id"],"output_types":["Parsed annotation objects with image metadata, instance labels, and category info","Transformed coordinates and masks in model-ready format","Filtered annotation subsets for task-specific training"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__cap_9","uri":"capability://data.processing.analysis.multi.task.dataset.enabling.transfer.learning.across.detection.segmentation.captioning.and.pose.tasks","name":"multi-task dataset enabling transfer learning across detection, segmentation, captioning, and pose tasks","description":"Single dataset with annotations for multiple vision tasks (object detection, instance segmentation, semantic segmentation, keypoint detection, image captioning, panoptic segmentation, dense pose) enables training of multi-task models and transfer learning across tasks. Shared image set (330K images) with task-specific annotations allows models to learn shared visual representations and transfer knowledge between tasks. Multi-task training can improve performance on individual tasks through shared feature learning and regularization.","intents":["train multi-task vision models that jointly predict detection, segmentation, and keypoints","develop transfer learning approaches leveraging annotations from multiple tasks","study task relationships and shared visual representations across detection/segmentation/pose","build unified vision models handling multiple prediction types in single forward pass"],"best_for":["multi-task learning researchers studying task relationships","teams building unified vision models for multiple tasks","practitioners leveraging transfer learning across vision tasks"],"limitations":["Task annotations are independent — no explicit task relationships or shared labels","Multi-task training requires careful loss weighting and task balancing — no guidance provided","Not all images have annotations for all tasks — some images have only detection, others only captions","Task-specific evaluation metrics are different — cannot use single metric to evaluate multi-task performance","Multi-task models are more complex and harder to debug than single-task models"],"requires":["Python 3.6+ with COCO API","Multi-task learning framework (PyTorch, TensorFlow) with support for multiple loss functions","Understanding of task weighting and loss balancing strategies","Ability to handle variable annotation availability across images"],"input_types":["Images with variable annotation types (detection, segmentation, keypoints, captions)"],"output_types":["Multi-task predictions (bboxes, masks, keypoints, captions)","Task-specific evaluation metrics","Shared feature representations learned across tasks"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ms-coco-common-objects-in-context__headline","uri":"capability://image.visual.standard.dataset.for.computer.vision.tasks","name":"standard dataset for computer vision tasks","description":"MS COCO is the foundational dataset for computer vision, featuring 330,000 images with 2.5 million labeled objects, ideal for tasks like object detection, segmentation, and image captioning.","intents":["best computer vision dataset","dataset for object detection","dataset for image captioning","benchmark dataset for segmentation tasks","top dataset for visual question answering"],"best_for":["researchers","machine learning practitioners"],"limitations":["limited object categories","potential annotation errors"],"requires":["basic understanding of computer vision","familiarity with ML frameworks"],"input_types":["images"],"output_types":["annotations","segmentation masks","captions"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"low","permissions":["Python 3.6+ for COCO API","JSON parsing library (built into Python standard library)","10-50GB disk space for full dataset download (exact size depends on image resolution)","Understanding of segmentation mask encoding (polygon vs RLE format)","Python 3.6+ with COCO API","Understanding of skeleton topology and joint connectivity","Familiarity with OKS (Object Keypoint Similarity) metric for evaluation","Images must contain at least one person for keypoint annotations to be present","Understanding of COCO JSON format and evaluation protocols","Large-scale annotation effort (thousands to millions of labels)"],"failure_modes":["Fixed to 80 object categories — cannot add custom classes without external re-annotation","Segmentation mask quality varies across instances; no per-mask confidence scores provided","Bounding boxes are axis-aligned rectangles only — no rotated or 3D boxes","No temporal continuity — static images only, cannot track objects across frames","Image resolution and size distribution not standardized; resolution range unknown","Keypoint annotations limited to human bodies only — no hand/finger keypoints or animal poses","17-joint skeleton is fixed — cannot extend to custom joint definitions without re-annotation","Visibility flag is binary (annotated vs not) — no confidence scores from annotators","Occluded keypoints marked as invisible but not explicitly labeled with occlusion type (self-occlusion vs external)","No temporal keypoint sequences — each image is independent, no motion/trajectory data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"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":"2026-05-24T12:16:23.328Z","last_scraped_at":null,"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=ms-coco-common-objects-in-context","compare_url":"https://unfragile.ai/compare?artifact=ms-coco-common-objects-in-context"}},"signature":"PrzKToPy8hR3aOO1vCdGGM7nY5FS0T4Td7EesPs2IXSM510SOVprgcff7JTSTIpj4jiUcGnevTXpScAwMQ0VBA==","signedAt":"2026-06-21T02:29:26.333Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ms-coco-common-objects-in-context","artifact":"https://unfragile.ai/ms-coco-common-objects-in-context","verify":"https://unfragile.ai/api/v1/verify?slug=ms-coco-common-objects-in-context","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"}}