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COCO 2017 vs Protein Data Bank

Side-by-side comparison of COCO 2017 (Dataset) and Protein Data Bank (Dataset).

82.5
Composite Score
COCO 2017
Dataset · Microsoft
81.9
Composite Score
Protein Data Bank
Dataset · RCSB PDB / wwPDB Consortium
Overall Winner
COCO 2017
COCO 2017 wins 2 of 6 categories · Protein Data Bank wins 2 of 6 categories

Score Comparison

COCO 2017vsProtein Data Bank
Composite
82.5:81.9
Adoption
97:95
Quality
96:97
Freshness
65:91
Citations
98:98
Engagement
0:0

Details

FieldCOCO 2017Protein Data Bank
TypeDatasetDataset
ProviderMicrosoftRCSB PDB / wwPDB Consortium
Version20172026
Categorycomputer-visionscientific
Pricingfreefree
LicenseCC-BY-4.0CC0 1.0
DescriptionMicrosoft 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.The RCSB Protein Data Bank (PDB) is the single worldwide archive of experimentally determined 3D structures of proteins, nucleic acids, and complex assemblies, currently containing over 220,000 biological macromolecular structures determined by X-ray crystallography, NMR, and cryo-EM. It is the foundational structural dataset for computational biology and was used to train and validate AlphaFold2 and other structure-prediction models.

Capabilities

Only COCO 2017

object-detectioninstance-segmentationkeypoint-detectionimage-captioning

Shared

None

Only Protein Data Bank

3d-structure-searchsequence-structure-mappingligand-binding-analysis

Integrations

Only COCO 2017

PyTorchTensorFlowDetectron2MMDetection

Shared

None

Only Protein Data Bank

biopythonpymolalphafold

Tags

Only COCO 2017

object-detectionsegmentationkeypointscaptionsbenchmark

Shared

None

Only Protein Data Bank

proteinsstructuresbiologycrystallographyalphafold

Use Cases

COCO 2017

  • model training
  • benchmark
  • computer vision research

Protein Data Bank

  • protein structure prediction training
  • drug design
  • structural biology research
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https://aaas.blog/compare/coco-2017-vs-protein-data-bank

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