Compare
MATH Dataset vs COCO 2017
Side-by-side comparison of MATH Dataset (Dataset) and COCO 2017 (Dataset).
Live Data← All Comparisons
77.3
Composite Score
MATH Dataset
Dataset · UC Berkeley
82.5
Composite Score
COCO 2017
Dataset · Microsoft
Overall Winner
COCO 2017
MATH Dataset wins 1 of 6 categories · COCO 2017 wins 4 of 6 categories
Score Comparison
MATH DatasetvsCOCO 2017
Composite
77.3:82.5
Adoption
88:97
Quality
93:96
Freshness
72:65
Citations
94:98
Engagement
0:0
Details
FieldMATH DatasetCOCO 2017
TypeDatasetDataset
ProviderUC BerkeleyMicrosoft
Version1.02017
Categorybenchmarkscomputer-vision
Pricingopen-sourcefree
LicenseMITCC-BY-4.0
DescriptionA challenging benchmark of 12,500 competition mathematics problems from AMC, AIME, and similar competitions across 5 difficulty levels and 7 subjects. Each problem includes a full step-by-step solution in LaTeX, making it suitable for both evaluation and training of mathematical reasoning.Microsoft COCO (Common Objects in Context) 2017 provides 118K training images with 860K object instances annotated with bounding boxes, segmentation masks, keypoints, and captions across 80 object categories. It remains the primary benchmark for object detection and instance segmentation research.
Capabilities
Only MATH Dataset
math-evaluationadvanced-reasoning-benchmarkstep-by-step-solutions
Shared
None
Only COCO 2017
object-detectioninstance-segmentationkeypoint-detectionimage-captioning
Integrations
Only MATH Dataset
huggingface-datasetslm-eval-harness
Shared
None
Only COCO 2017
PyTorchTensorFlowDetectron2MMDetection
Tags
Only MATH Dataset
competition-mathhard-mathstep-by-steplatex
Shared
benchmark
Only COCO 2017
object-detectionsegmentationkeypointscaptions
Use Cases
MATH Dataset
- ▸model evaluation
- ▸advanced math reasoning
- ▸mathematical training
COCO 2017
- ▸model training
- ▸benchmark
- ▸computer vision research
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