Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:neural_networks
|
| gptkbp:achieved_accuracy |
Top-1 accuracy of 82.7% on Image Net
|
| gptkbp:composed_of |
fully connected layers
convolutional layers pooling layers |
| gptkbp:designed_for |
image classification
|
| gptkbp:developed_by |
gptkb:Google
|
| gptkbp:has_variants |
NASNet-A Large
NASNet-A Mobile |
| https://www.w3.org/2000/01/rdf-schema#label |
NASNet-A
|
| gptkbp:introduced_in |
gptkb:2017
|
| gptkbp:is_evaluated_by |
gptkb:Image_Net
gptkb:CIFAR-10 gptkb:Pascal_VOC gptkb:COCO_dataset gptkb:CIFAR-100 |
| gptkbp:is_implemented_in |
gptkb:Tensor_Flow
gptkb:Keras |
| gptkbp:is_known_for |
high accuracy
low latency high throughput state-of-the-art performance flexibility in architecture design automated architecture search |
| gptkbp:is_optimized_for |
GPU performance
|
| gptkbp:is_part_of |
NAS (Neural Architecture Search) framework
|
| gptkbp:is_related_to |
gptkb:Artificial_Intelligence
deep learning |
| gptkbp:is_scalable |
cloud environments
edge devices different hardware |
| gptkbp:is_trained_in |
large datasets
|
| gptkbp:is_used_in |
gptkb:Telecommunications
gptkb:drones gptkb:robotics gptkb:smart_home_devices augmented reality financial forecasting natural language processing energy management surveillance systems weather prediction object detection self-driving cars video analysis facial recognition retail analytics medical image analysis computer vision tasks gaming AI |
| gptkbp:performance |
gptkb:Image_Net_competition
other architectures |
| gptkbp:predecessor |
gptkb:Inception-v4
|
| gptkbp:successor |
gptkb:NASNet-B
|
| gptkbp:uses |
reinforcement learning
|
| gptkbp:utilizes |
transfer learning
|
| gptkbp:bfsParent |
gptkb:NASNet
|
| gptkbp:bfsLayer |
6
|