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