|
gptkbp:instance_of
|
gptkb:television_channel
|
|
gptkbp:bfsLayer
|
4
|
|
gptkbp:bfsParent
|
gptkb:tf.keras.applications
gptkb:Transformers_character
|
|
gptkbp:based_on
|
Mobile Net architecture
|
|
gptkbp:consists_of
|
multiple variants
|
|
gptkbp:developed_by
|
gptkb:Google_AI
|
|
gptkbp:has_achievements
|
state-of-the-art accuracy on Image Net
|
|
gptkbp:has_variants
|
gptkb:Efficient_Net_B0
gptkb:Efficient_Net_B1
gptkb:Efficient_Net_B2
gptkb:Efficient_Net_B3
gptkb:Efficient_Net_B4
gptkb:Efficient_Net_B5
gptkb:Efficient_Net_B6
gptkb:Efficient_Net_B7
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Efficient Net
|
|
gptkbp:improves
|
model efficiency
|
|
gptkbp:introduced
|
gptkb:2019
|
|
gptkbp:is_available_in
|
Tensor Flow and Py Torch
|
|
gptkbp:is_cited_in
|
research papers
|
|
gptkbp:is_compared_to
|
gptkb:Inception
gptkb:VGG
gptkb:Dense_Net
gptkb:Res_Net
|
|
gptkbp:is_evaluated_by
|
gptkb:CIFAR-10
gptkb:CIFAR-100
Kaggle competitions
Oxford Pets
Image Net validation set
Flowers 102
|
|
gptkbp:is_known_for
|
scalability
flexibility in architecture design
high accuracy with fewer parameters
|
|
gptkbp:is_optimized_for
|
resource efficiency
|
|
gptkbp:is_part_of
|
transfer learning models
AI model zoo
|
|
gptkbp:is_popular_in
|
computer vision community
|
|
gptkbp:is_supported_by
|
NVIDIAGP Us
TP Us
|
|
gptkbp:is_used_for
|
feature extraction
fine-tuning
|
|
gptkbp:is_used_in
|
gptkb:mobile_application
real-time applications
edge computing
cloud-based services
image classification tasks
object detection tasks
medical image analysis
semantic segmentation tasks
video classification tasks
|
|
gptkbp:performance
|
previous models on Image Net
|
|
gptkbp:training
|
large datasets
|
|
gptkbp:uses
|
compound scaling method
|