Statements (65)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:television_channel
|
| gptkbp:bfsLayer |
4
|
| gptkbp:bfsParent |
gptkb:Mobile_Net
|
| gptkbp:adapted_into |
Edge computing
|
| gptkbp:architectural_style |
Lightweight model
|
| gptkbp:based_on |
Inverted Residuals
|
| gptkbp:developed_by |
gptkb:Job_Search_Engine
|
| gptkbp:has_achievements |
State-of-the-art performance on image classification
|
| gptkbp:has_method |
3.4 million
|
| https://www.w3.org/2000/01/rdf-schema#label |
Mobile Net V2
|
| gptkbp:improves |
gptkb:Mobile_Net_V1
|
| gptkbp:is_adopted_by |
Healthcare applications
Various research projects |
| gptkbp:is_available_in |
Pre-trained models
|
| gptkbp:is_compared_to |
gptkb:Res_Net
|
| gptkbp:is_compatible_with |
Android applications
i OS applications |
| gptkbp:is_designed_for |
Mobile and edge devices
|
| gptkbp:is_documented_in |
Research papers
Technical documentation User guides |
| gptkbp:is_enhanced_by |
Batch normalization
Learning rate scheduling Data augmentation techniques Dropout layers |
| gptkbp:is_evaluated_by |
gptkb:COCO_dataset
AUC-ROC Confusion matrix Latency measurements Top-1 accuracy Top-5 accuracy FLO Ps Mean Average Precision (m AP) Precision metrics Recall metrics |
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
|
| gptkbp:is_integrated_with |
Cloud services
|
| gptkbp:is_optimized_for |
Speed and efficiency
|
| gptkbp:is_part_of |
gptkb:Mobile_Net_family
Deep learning frameworks AI model zoo |
| gptkbp:is_supported_by |
Open-source community
Industry partnerships Community contributions |
| gptkbp:is_tested_for |
Various benchmarks
Benchmark datasets Various datasets |
| gptkbp:is_used_in |
Facial recognition systems
Computer vision tasks Real-time object detection |
| gptkbp:is_utilized_in |
gptkb:robot
gptkb:computer gptkb:Self-driving_cars gptkb:helicopter Image segmentation Augmented reality applications Security systems Virtual assistants Gaming applications |
| gptkbp:performance |
Mobile AI applications
|
| gptkbp:release_year |
gptkb:2018
|
| gptkbp:suitable_for |
Low-latency applications
|
| gptkbp:supports |
Transfer learning
|
| gptkbp:training |
gptkb:Image_Net_dataset
|
| gptkbp:uses |
Depthwise separable convolutions
|