Swin-T

GPTKB entity

Statements (65)
Predicate Object
gptkbp:instance_of gptkb:neural_networks
gptkbp:applies_to image classification
object detection
semantic segmentation
gptkbp:based_on gptkb:Transformers
gptkbp:competes_with gptkb:Vision_Transformers
gptkbp:developed_by gptkb:Microsoft_Research
gptkbp:has attention mechanism
layer normalization
hierarchical feature maps
multi-scale feature representation
gptkbp:has_achieved state-of-the-art performance
https://www.w3.org/2000/01/rdf-schema#label Swin-T
gptkbp:improves computational efficiency
gptkbp:introduced_in gptkb:2021
gptkbp:is_adopted_by research institutions
startups
tech companies
gptkbp:is_compatible_with gptkb:NVIDIA_GPUs
gptkb:TPUs
gptkbp:is_documented_in research papers
technical reports
gptkbp:is_enhanced_by data augmentation techniques
regularization methods
gptkbp:is_evaluated_by gptkb:Open_Images_dataset
gptkb:PASCAL_VOC_dataset
gptkb:COCO_dataset
gptkb:Cityscapes_dataset
gptkb:ADE20_K_dataset
gptkb:LSUN_dataset
Kaggle competitions
Image Net-21 K dataset
gptkbp:is_implemented_in gptkb:Tensor_Flow
gptkb:Py_Torch
gptkbp:is_influenced_by gptkb:neural_networks
gptkb:Vision_Transformers
gptkbp:is_known_for high accuracy
modular design
scalability
robustness to noise
layer-wise training
flexibility in various tasks
gptkbp:is_optimized_for GPU acceleration
gptkbp:is_part_of deep learning frameworks
AI research community
computer vision benchmarks
AI model zoo
image processing pipeline
Swin Transformer family
gptkbp:is_popular_in gptkb:academic_research
industry applications
gptkbp:is_supported_by tutorials
community contributions
open-source libraries
gptkbp:is_trained_in gptkb:Image_Net_dataset
large-scale datasets
gptkbp:is_used_by gptkb:developers
gptkb:researchers
gptkbp:is_used_in semi-supervised learning
self-supervised learning
gptkbp:supports transfer learning
gptkbp:used_for computer vision tasks
gptkbp:utilizes shifted windows
gptkbp:bfsParent gptkb:Swin_Transformer
gptkbp:bfsLayer 5