|
gptkbp:instance_of
|
gptkb:neural_networks
|
|
gptkbp:application
|
Mobile devices
|
|
gptkbp:architecture
|
Lightweight model
|
|
gptkbp:based_on
|
Depthwise separable convolutions
|
|
gptkbp:community_support
|
Open source
|
|
gptkbp:designed_for
|
mobile and edge devices
|
|
gptkbp:developed_by
|
gptkb:Google
|
|
gptkbp:evaluates
|
FLOPs
Inference time
|
|
gptkbp:feature
|
Low latency
Efficient computation
Reduced model size
|
|
gptkbp:has_variants
|
gptkb:Mobile_Net_V1
gptkb:Mobile_Net_V2
gptkb:Mobile_Net_V3
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Mobile Net
|
|
gptkbp:input_output
|
varies
224x224
class probabilities
|
|
gptkbp:introduced_in
|
gptkb:2017
|
|
gptkbp:is_a_framework_for
|
gptkb:Tensor_Flow
|
|
gptkbp:is_optimized_for
|
mobile and edge devices
|
|
gptkbp:latest_version
|
gptkb:Mobile_Net_V1
gptkb:Mobile_Net_V2
gptkb:Mobile_Net_V3
|
|
gptkbp:license
|
Apache License 2.0
|
|
gptkbp:orbital_period
|
4.2 million
|
|
gptkbp:performance
|
Top-1 accuracy
Top-5 accuracy
|
|
gptkbp:provides
|
efficient computation
|
|
gptkbp:provides_information_on
|
gptkb:Image_Net
|
|
gptkbp:related_to
|
gptkb:Artificial_Intelligence
Deep learning
Computer vision
|
|
gptkbp:release_year
|
gptkb:2017
|
|
gptkbp:successor
|
gptkb:Mobile_Net_V2
gptkb:Mobile_Net_V3
|
|
gptkbp:supports
|
transfer learning
|
|
gptkbp:targets
|
Edge devices
|
|
gptkbp:training
|
Transfer learning
|
|
gptkbp:tuning
|
Quantization
Pruning
|
|
gptkbp:use_case
|
gptkb:wearable_technology
gptkb:virtual_reality
gptkb:Io_T_devices
gptkb:smartphone
gptkb:drones
gptkb:robotics
Medical imaging
Smart TVs
Tablets
Autonomous vehicles
Augmented reality
Video analysis
Object detection
Face recognition
Style transfer
Smart cameras
Semantic segmentation
Pose estimation
|
|
gptkbp:used_for
|
Image classification
|
|
gptkbp:uses
|
depthwise separable convolutions
|
|
gptkbp:bfsParent
|
gptkb:Transformers
gptkb:neural_networks
|
|
gptkbp:bfsLayer
|
4
|