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ImageNet-1K vs COCO 2017

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

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

Score Comparison

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

Details

FieldImageNet-1KCOCO 2017
TypeDatasetDataset
ProviderImageNet / Stanford Vision LabMicrosoft
Version20122017
Categorycomputer-visioncomputer-vision
Pricingfreefree
LicenseCustom (research use)CC-BY-4.0
DescriptionThe 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.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 ImageNet-1K

image-classificationtransfer-learningbenchmark-evaluation

Shared

None

Only COCO 2017

object-detectioninstance-segmentationkeypoint-detectionimage-captioning

Integrations

Only ImageNet-1K

HuggingFace Datasets

Shared

PyTorchTensorFlow

Only COCO 2017

Detectron2MMDetection

Tags

Only ImageNet-1K

image-classificationobject-recognitiondeep-learningsupervised

Shared

benchmark

Only COCO 2017

object-detectionsegmentationkeypointscaptions

Use Cases

ImageNet-1K

  • model training
  • benchmark
  • transfer learning

COCO 2017

  • model training
  • benchmark
  • computer vision research
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https://aaas.blog/compare/imagenet-1k-vs-coco-2017

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