|
gptkbp:instance_of
|
gptkb:microprocessor
|
|
gptkbp:bfsLayer
|
7
|
|
gptkbp:bfsParent
|
gptkb:Squeeze_Net_1.1
|
|
gptkbp:based_on
|
convolutional neural networks
|
|
gptkbp:competes_with
|
gptkb:Alex_Net
|
|
gptkbp:developed_by
|
gptkb:Deep_Scale
resource-constrained environments
|
|
gptkbp:has_achievements
|
high accuracy
|
|
gptkbp:has_variants
|
gptkb:Squeeze_Net_1.0
gptkb:Squeeze_Net_1.1
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Squeeze Net family
|
|
gptkbp:introduced
|
gptkb:2016
|
|
gptkbp:is_adopted_by
|
gptkb:Research_Institute
|
|
gptkbp:is_compared_to
|
gptkb:Mobile_Net
gptkb:Res_Net
|
|
gptkbp:is_designed_for
|
image classification
|
|
gptkbp:is_evaluated_by
|
gptkb:CIFAR-10_dataset
gptkb:COCO_dataset
gptkb:Image_Net_dataset
MNIST dataset
fashion MNIST dataset
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:Goog_Le_Net
VGG Net
|
|
gptkbp:is_known_for
|
low latency
high throughput
real-time inference
low computational cost
efficient parameter usage
|
|
gptkbp:is_optimized_for
|
gptkb:smartphone
|
|
gptkbp:is_part_of
|
gptkb:Artificial_Intelligence
gptkb:computer_science
gptkb:software_framework
gptkb:Research_Institute
deep learning frameworks
|
|
gptkbp:is_supported_by
|
gptkb:Google_Cloud
gptkb:AWS
NVIDIAGP Us
|
|
gptkbp:is_used_in
|
gptkb:Io_T_devices
gptkb:smartphone
gptkb:Photographer
gptkb:robot
gptkb:engine
gptkb:helicopter
augmented reality
object detection
image segmentation
face recognition
smart cameras
computer vision tasks
|
|
gptkbp:notable_for
|
small model size
|
|
gptkbp:suitable_for
|
real-time applications
|
|
gptkbp:supports
|
transfer learning
|
|
gptkbp:uses
|
fire modules
|