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gptkbp:instance_of
|
gptkb:Model
|
|
gptkbp:adapted_into
|
edge devices
|
|
gptkbp:applies_to
|
image classification
|
|
gptkbp:collaborated_with
|
research institutions
technology companies
|
|
gptkbp:competes_with
|
CNN models
|
|
gptkbp:developed_by
|
gptkb:Facebook_AI_Research
collaborative tools
|
|
gptkbp:has_achieved
|
state-of-the-art performance
|
|
gptkbp:has_variants
|
gptkb:Dei_T-Base
gptkb:Dei_T-Small
gptkb:Dei_T-Tiny
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Dei T
|
|
gptkbp:introduced_in
|
gptkb:2020
|
|
gptkbp:is_adopted_by
|
industry applications
|
|
gptkbp:is_based_on
|
gptkb:Transformers
|
|
gptkbp:is_cited_in
|
academic literature
|
|
gptkbp:is_compared_to
|
SOTA models
|
|
gptkbp:is_designed_for
|
efficient training
|
|
gptkbp:is_documented_in
|
technical reports
Git Hub repositories
|
|
gptkbp:is_enhanced_by
|
hyperparameter tuning
data augmentation techniques
|
|
gptkbp:is_evaluated_by
|
gptkb:CIFAR-10
gptkb:historical_memory
gptkb:CIFAR-100
F1 score
confusion matrix
precision
top-5 accuracy
top-1 accuracy
mean average precision
standard benchmarks
cross-validation.
Oxford Pets
ROC AUC score
|
|
gptkbp:is_implemented_in
|
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:BERT
gptkb:Vi_T
|
|
gptkbp:is_integrated_with
|
gptkb:cloud_services
software libraries
|
|
gptkbp:is_open_source
|
gptkb:true
|
|
gptkbp:is_optimized_for
|
GPU training
|
|
gptkbp:is_part_of
|
deep learning frameworks
AI research advancements
AI toolkits
AI model zoo
transformer family
AI model comparisons
|
|
gptkbp:is_popular_in
|
computer vision community
|
|
gptkbp:is_promoted_by
|
online courses
AI conferences
|
|
gptkbp:is_supported_by
|
gptkb:NVIDIA_GPUs
community contributions
open-source community
research grants
|
|
gptkbp:is_tested_for
|
robustness
various datasets
adversarial examples
|
|
gptkbp:is_trained_in
|
gptkb:Image_Net_dataset
|
|
gptkbp:is_used_for
|
transfer learning
|
|
gptkbp:is_used_in
|
research papers
|
|
gptkbp:is_utilized_for
|
feature extraction
semantic segmentation
|
|
gptkbp:is_utilized_in
|
automated systems
real-time applications
image recognition tasks
|
|
gptkbp:performance
|
gptkb:Res_Net
|
|
gptkbp:supports
|
multi-class classification
|
|
gptkbp:type
|
gptkb:Transformers
|
|
gptkbp:uses
|
knowledge distillation
|
|
gptkbp:bfsParent
|
gptkb:Transformers
|
|
gptkbp:bfsLayer
|
4
|