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COCO 2017 vs ImageNet-1K

Side-by-side comparison of COCO 2017 (Dataset) and ImageNet-1K (Dataset).

82.5
Composite Score
COCO 2017
Dataset · Microsoft
83.3
Composite Score
ImageNet-1K
Dataset · ImageNet / Stanford Vision Lab
Overall Winner
ImageNet-1K
COCO 2017 wins 2 of 6 categories · ImageNet-1K wins 3 of 6 categories

Score Comparison

COCO 2017vsImageNet-1K
Composite
82.5:83.3
Adoption
97:99
Quality
96:95
Freshness
65:60
Citations
98:99
Engagement
0:0

Details

FieldCOCO 2017ImageNet-1K
TypeDatasetDataset
ProviderMicrosoftImageNet / Stanford Vision Lab
Version20172012
Categorycomputer-visioncomputer-vision
Pricingfreefree
LicenseCC-BY-4.0Custom (research use)
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 canonical large-scale visual recognition benchmark containing 1.28 million training images across 1,000 object categories. ImageNet-1K underpins the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and has driven the majority of deep learning breakthroughs in computer vision since 2012.

Capabilities

Only COCO 2017

object-detectioninstance-segmentationkeypoint-detectionimage-captioning

Shared

None

Only ImageNet-1K

image-classificationtransfer-learningbenchmark-evaluation

Integrations

Only COCO 2017

Detectron2MMDetection

Shared

PyTorchTensorFlow

Only ImageNet-1K

HuggingFace Datasets

Tags

Only COCO 2017

object-detectionsegmentationkeypointscaptions

Shared

benchmark

Only ImageNet-1K

image-classificationobject-recognitiondeep-learningsupervised

Use Cases

COCO 2017

  • model training
  • benchmark
  • computer vision research

ImageNet-1K

  • model training
  • benchmark
  • transfer learning
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https://aaas.blog/compare/coco-2017-vs-imagenet-1k

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