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gptkbp:instance_of
|
gptkb:cosmic_ray_detector
|
|
gptkbp:applies_to
|
computer vision
|
|
gptkbp:based_on
|
gptkb:Deep_Lab
|
|
gptkbp:developed_by
|
gptkb:Google
|
|
gptkbp:enhances
|
feature extraction
|
|
gptkbp:has_achieved
|
state-of-the-art performance
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Deep Labv2
|
|
gptkbp:improves
|
semantic segmentation
|
|
gptkbp:is_adopted_by
|
gptkb:academic_research
industry applications
|
|
gptkbp:is_based_on
|
gptkb:Res_Net_architecture
gptkb:Xception_architecture
|
|
gptkbp:is_compatible_with
|
gptkb:Keras
gptkb:Py_Torch
|
|
gptkbp:is_documented_in
|
research papers
Git Hub repositories
|
|
gptkbp:is_evaluated_by
|
gptkb:PASCAL_VOC_dataset
gptkb:COCO_dataset
gptkb:Cam_Vid_dataset
gptkb:Cityscapes_dataset
gptkb:ADE20_K_dataset
LIP dataset
SBD dataset
|
|
gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
|
|
gptkbp:is_influenced_by
|
gptkb:Na'vi
gptkb:FCN_(Fully_Convolutional_Networks)
gptkb:Seg_Net
|
|
gptkbp:is_known_for
|
high accuracy
real-time performance
robustness to noise
flexibility in architecture
adaptability to different tasks
|
|
gptkbp:is_optimized_for
|
GPU acceleration
|
|
gptkbp:is_part_of
|
gptkb:Deep_Lab_family
deep learning frameworks
AI advancements
computer vision research
machine learning innovations
|
|
gptkbp:is_related_to
|
gptkb:Deep_Labv3
gptkb:Deep_Labv3+
|
|
gptkbp:is_supported_by
|
community contributions
research grants
collaborations with universities
|
|
gptkbp:is_trained_in
|
large datasets
|
|
gptkbp:is_used_for
|
object detection
image segmentation tasks
|
|
gptkbp:is_used_in
|
gptkb:virtual_reality
gptkb:medical_imaging
gptkb:robotics
augmented reality
autonomous driving
image analysis
scene understanding
|
|
gptkbp:provides
|
pixel-level classification
|
|
gptkbp:release_year
|
gptkb:2017
|
|
gptkbp:supports
|
multi-scale context
|
|
gptkbp:uses
|
atrous convolution
|
|
gptkbp:utilizes
|
fully convolutional networks
|
|
gptkbp:bfsParent
|
gptkb:Deep_Lab
|
|
gptkbp:bfsLayer
|
5
|