Statements (145)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Model
gptkb:software_framework |
| gptkbp:applies_to |
autonomous driving
transfer learning satellite imagery analysis medical image analysis computer vision tasks multi-scale context |
| gptkbp:based_on |
gptkb:television_channel
gptkb:Deep_Lab_v3 Deep Lab framework |
| gptkbp:can_be |
objects in images
|
| gptkbp:controls |
variable input sizes
|
| gptkbp:deployment |
gptkb:mobile_application
|
| gptkbp:developed_by |
gptkb:Job_Search_Engine
gptkb:Google_Research |
| gptkbp:enhances |
object boundary detection
|
| gptkbp:features |
atrous separable convolution
|
| gptkbp:has |
pre-trained models
open-source code |
| gptkbp:has_achievements |
state-of-the-art results
state-of-the-art performance |
| https://www.w3.org/2000/01/rdf-schema#label |
Deep Lab v3+
|
| gptkbp:improves |
gptkb:Deep_Lab_v3
semantic segmentation semantic segmentation accuracy |
| gptkbp:includes |
gptkb:ASPP_module
|
| gptkbp:input_output |
pixel-wise classification
pixel-wise predictions |
| gptkbp:introduced |
gptkb:2018
atrous convolution |
| gptkbp:is_available_on |
gptkb:archive
|
| gptkbp:is_compatible_with |
cloud platforms
various hardware platforms NVIDIAGP Us TP Us |
| gptkbp:is_considered_as |
a leading model in segmentation tasks
|
| gptkbp:is_designed_for |
dense prediction tasks
|
| gptkbp:is_documented_in |
research papers
technical blogs Git Hub repositories |
| gptkbp:is_evaluated_by |
gptkb:Open_Images_dataset
gptkb:Image_Net gptkb:COCO_dataset gptkb:Cam_Vid_dataset gptkb:Cityscapes_dataset gptkb:KITTI_dataset industry professionals academic researchers pixel accuracy SUN dataset LIP dataset mean Intersection over Union (m Io U) AD E20 K dataset PASCALVOC dataset SBD dataset mean Intersection over Union (m Io U) metric |
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch |
| gptkbp:is_influenced_by |
gptkb:Res_Net
gptkb:Xception gptkb:Res_Net_architecture gptkb:Xception_architecture |
| gptkbp:is_integrated_with |
other neural networks
other AI models |
| gptkbp:is_known_for |
high accuracy
user-friendly interfaces real-time performance efficient computation flexibility in applications high-resolution outputs flexibility in architecture high resolution outputs real-time inference capabilities robustness to occlusions ability to handle varying object scales |
| gptkbp:is_open_source |
gptkb:theorem
|
| gptkbp:is_optimized_for |
GPU acceleration
|
| gptkbp:is_part_of |
gptkb:Deep_Lab_family_of_models
deep learning frameworks AI research initiatives AI research community computer vision research AI toolkits computer vision frameworks |
| gptkbp:is_popular_in |
gptkb:Research_Institute
computer vision community |
| gptkbp:is_recognized_for |
contribution to semantic segmentation.
|
| gptkbp:is_related_to |
gptkb:Artificial_Intelligence
gptkb:software_framework image segmentation |
| gptkbp:is_scalable |
large datasets
large images |
| gptkbp:is_supported_by |
gptkb:Py_Torch_Hub
gptkb:Tensor_Flow_Hub academic institutions community contributions |
| gptkbp:is_tested_for |
real-world applications
benchmark datasets |
| gptkbp:is_used_for |
object detection
image analysis semantic segmentation object detection tasks |
| gptkbp:is_used_in |
gptkb:robot
augmented reality autonomous driving environmental monitoring autonomous driving systems image editing object detection virtual reality applications video analysis smart city projects scene understanding robotics applications security applications augmented reality applications agriculture technology fashion analysis medical image analysis image segmentation tasks image segmentation competitions |
| gptkbp:performance |
image segmentation tasks
|
| gptkbp:released_in |
gptkb:2018
|
| gptkbp:requires |
GPU for training
large datasets for training |
| gptkbp:security_features |
image noise
|
| gptkbp:suitable_for |
gptkb:mobile_application
|
| gptkbp:supports |
real-time processing
multi-scale feature extraction multi-scale context |
| gptkbp:training |
gptkb:COCO_dataset
gptkb:Cityscapes_dataset gptkb:Pascal_VOC_dataset large datasets backpropagation PASCALVOC dataset |
| gptkbp:tuning |
specific tasks
|
| gptkbp:uses |
gptkb:feature_pyramid_networks
encoder-decoder architecture atrous convolution |
| gptkbp:utilizes |
encoder-decoder architecture
atrous convolution |
| gptkbp:bfsParent |
gptkb:Deep_Lab
|
| gptkbp:bfsLayer |
4
|