MS COCO (Common Objects in Context) ranks higher at 61/100 vs DecorAI at 42/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | DecorAI | MS COCO (Common Objects in Context) |
|---|---|---|
| Type | Product | Dataset |
| UnfragileRank | 42/100 | 61/100 |
| Adoption | 0 | 1 |
| Quality |
| 1 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 12 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Analyzes uploaded room photographs using computer vision to extract spatial context (dimensions, lighting, existing furniture, architectural features), then conditions a generative image model on these constraints to produce design variations that respect the actual room layout rather than generating abstract designs. The system likely uses object detection and semantic segmentation to identify walls, windows, doors, and existing furnishings, then passes this structured spatial data as conditioning inputs to a diffusion or transformer-based image generation model.
Unique: Combines room photo analysis with conditional image generation to ground design suggestions in actual spatial context, rather than generating isolated design concepts that users must mentally map to their space. Uses detected room features as hard constraints in the generation pipeline.
vs alternatives: More contextually grounded than Pinterest mood boards or generic AI design tools because it conditions generation on the specific room's geometry and lighting rather than treating each design suggestion as context-free.
Generates multiple distinct design interpretations of a single room in rapid succession, allowing users to explore different aesthetic directions (minimalist, maximalist, bohemian, industrial, etc.) without re-uploading photos or re-specifying constraints. Likely implements a sampling-based approach where the same room context is passed to the generative model with different style embeddings or prompt variations, enabling parallel generation of diverse outputs.
Unique: Implements rapid multi-variation generation by reusing room context embeddings and varying only the style/aesthetic conditioning, reducing redundant computation compared to generating each variation from scratch. Likely uses a style-embedding space (e.g., CLIP-based aesthetic embeddings) to systematically explore the design space.
vs alternatives: Faster and more systematic than manual Pinterest curation or hiring a designer for multiple concepts because it generates variations in parallel with consistent room context rather than requiring separate consultations.
Allows users to view generated designs overlaid on their actual room using AR technology (smartphone camera), enabling real-time visualization of how the design would look in their space. Likely uses ARKit/ARCore to track the room and overlay the generated design as a virtual layer, with perspective correction to match the user's viewing angle.
Unique: Enables real-time AR visualization of designs overlaid on the actual room, providing perspective-correct previews from the user's viewpoint. Uses device-based AR tracking (ARKit/ARCore) rather than cloud-based rendering, enabling low-latency interactive exploration.
vs alternatives: More immersive and realistic than 2D renderings because users see designs in their actual room from their perspective, reducing the mental leap between visualization and implementation.
Suggests optimal furniture placement and room layout based on spatial constraints, traffic flow, and design principles (e.g., focal points, balance, ergonomics). Likely uses constraint satisfaction or optimization algorithms to find furniture arrangements that maximize usability and aesthetic appeal while respecting room dimensions and existing fixtures.
Unique: Applies spatial optimization algorithms to suggest furniture arrangements that balance aesthetics with functionality, rather than treating layout as a purely visual design problem. Uses constraint satisfaction to ensure arrangements are practical and usable.
vs alternatives: More functional than purely aesthetic design tools because it optimizes for traffic flow, accessibility, and usability alongside visual appeal, resulting in designs that work better in practice.
Tracks user interactions (which designs users save, like, or request modifications to) and builds a preference profile to bias future generations toward their aesthetic tastes. Likely implements a collaborative filtering or embedding-based preference model that learns style affinities from user feedback, then uses these learned preferences to weight the style conditioning in subsequent generation requests.
Unique: Builds implicit style preference profiles from user interaction history rather than requiring explicit questionnaires, enabling organic preference discovery as users explore designs. Likely uses embedding-based similarity to generalize from saved designs to unseen style combinations.
vs alternatives: More adaptive than static design questionnaires because it learns from actual user choices rather than self-reported preferences, and more scalable than manual designer consultations that require explicit style interviews.
Extracts furniture, decor items, and materials visible in generated designs and maps them to shoppable products with estimated costs, creating a structured shopping list that users can purchase from integrated e-commerce partners. Likely uses object detection to identify items in the generated image, then queries a product database or API (Amazon, Wayfair, etc.) to find matching items with pricing and availability.
Unique: Closes the gap between design inspiration and purchase by automatically extracting shoppable items from generated images and mapping them to real products with pricing, rather than requiring users to manually search for each item. Uses object detection + product matching pipeline to create actionable shopping lists.
vs alternatives: More actionable than design inspiration tools (Pinterest, Houzz) because it directly connects designs to purchasable products with pricing, reducing friction between inspiration and implementation.
Allows users to request modifications to generated designs through natural language feedback (e.g., 'make it brighter', 'add more plants', 'use warmer colors') without re-uploading photos or starting over. Likely implements a prompt-engineering layer that translates user feedback into conditioning adjustments for the generative model, or uses a fine-tuning approach to adapt the model to user-specific modifications.
Unique: Enables conversational design iteration by translating natural language feedback into generative model conditioning, allowing users to refine designs through dialogue rather than re-specifying constraints from scratch. Likely uses prompt engineering or embedding-based feedback interpretation to maintain design coherence across iterations.
vs alternatives: More intuitive than batch re-generation because users can provide incremental feedback without re-uploading photos or rewriting full prompts, reducing friction in the refinement loop.
Converts 2D generated designs into 3D room models that users can explore interactively, walk through, or import into design software (SketchUp, Blender, etc.). Likely uses depth estimation from the original room photo combined with detected furniture dimensions to reconstruct 3D geometry, then maps the generated design onto this 3D model.
Unique: Extends 2D design generation into 3D space by combining monocular depth estimation with detected furniture geometry, enabling interactive exploration and software integration. Bridges the gap between 2D inspiration and 3D implementation by providing exportable models.
vs alternatives: More immersive than 2D renderings because users can explore designs from multiple angles and in 3D software, reducing the mental leap from 2D inspiration to real-world implementation.
+4 more capabilities
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.
Unique: Dual segmentation encoding (polygon + RLE) in single dataset enables both precise boundary analysis and efficient computational workflows; 2.5M instances across 330K images provides scale unmatched by contemporaneous datasets (ImageNet had ~1.2M images, PASCAL VOC had ~11K images)
vs alternatives: Larger and more densely annotated than PASCAL VOC (11K images, ~6 objects/image) and more task-diverse than ImageNet (classification-only); RLE encoding enables 10-100x faster mask loading than polygon-only formats
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.
Unique: Standardized 17-joint skeleton with explicit visibility flags enables robust evaluation of pose estimation under occlusion; linked to instance segmentation masks allows joint-level accuracy analysis within person bounding boxes
MS COCO (Common Objects in Context) scores higher at 61/100 vs DecorAI at 42/100. MS COCO (Common Objects in Context) also has a free tier, making it more accessible.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
vs alternatives: More comprehensive than OpenPose dataset (no visibility flags) and larger scale than Human3.6M (3.6M frames vs 330K images); visibility annotations enable explicit occlusion handling unlike MPII (which lacks visibility metadata)
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.
Unique: Standardized extension process enables community contributions while maintaining compatibility; official variants (Stuff, DensePose, Panoptic) are vetted and published, ensuring quality and discoverability
vs alternatives: More extensible than fixed datasets; community variants enable specialized use cases without forking; standardized format prevents fragmentation unlike ad-hoc dataset variants
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.
Unique: 5 captions per image (vs 1 in most datasets) captures linguistic diversity and enables robust evaluation of caption generation variability; 1.65M caption-image pairs provide scale for training large vision-language models
vs alternatives: 5x more captions per image than Flickr30K (1 caption/image) enabling better linguistic diversity modeling; larger scale than Visual Genome (108K images) while maintaining natural language quality vs automated alt-text
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.
Unique: 171-category taxonomy combining 80 instance objects + 91 stuff categories enables panoptic segmentation in single dataset; pixel-level masks for stuff enable dense scene understanding without instance boundaries
vs alternatives: More comprehensive than ADE20K (150 categories) and larger scale than Cityscapes (5K images); unified instance+stuff annotation enables panoptic evaluation unlike separate semantic/instance datasets
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.
Unique: Panoptic Quality metric with explicit SQ/RQ decomposition enables fine-grained analysis of segmentation vs recognition errors; unified instance+stuff evaluation in single task forces models to handle both prediction types efficiently
vs alternatives: More comprehensive than separate instance/semantic benchmarks; PQ metric better captures real-world scene understanding than independent metrics; standardized evaluation prevents metric gaming unlike custom evaluation scripts
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.
Unique: Dense 2D-to-3D surface correspondence enables pixel-level body understanding beyond skeleton keypoints; UV parameterization allows transfer of appearance and shape across different people and poses
vs alternatives: More detailed than keypoint-only annotations (17 joints vs millions of surface points); enables pose transfer unlike keypoint datasets; larger scale than DensePose-specific datasets
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.
Unique: Server-side metric computation prevents metric gaming and ensures consistency; task-specific metrics (AP, OKS, CIDEr, PQ) are standardized across all submissions enabling fair comparison; public leaderboard provides transparency and reproducibility
vs alternatives: More rigorous than self-reported metrics (prevents cherry-picking); standardized evaluation prevents metric implementation variations unlike custom evaluation scripts; public leaderboard enables community comparison unlike proprietary benchmarks
+3 more capabilities