Compare
COCO 2017 vs ImageNet-1K
Side-by-side comparison of COCO 2017 (Dataset) and ImageNet-1K (Dataset).
Live Data← All Comparisons
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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