MobileNet
E1300986
UNEXPLORED
MobileNet is a family of lightweight convolutional neural network architectures designed for efficient deployment on mobile and embedded devices.
All labels observed (3)
| Label | Occurrences |
|---|---|
| MobileNet canonical | 1 |
| MobileNetV1 | 1 |
| MobileNetV3 | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T18016298 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MobileNet Context triple: [MobileNetV2, basedOn, MobileNet]
-
A.
MobileNetV2
MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
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B.
SqueezeNet
SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
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C.
NASNet
NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
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D.
ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
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E.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices is a lightweight deep learning architecture designed to deliver high accuracy with very low computational cost, making it well-suited for deployment on mobile and embedded devices.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MobileNet Target entity description: MobileNet is a family of lightweight convolutional neural network architectures designed for efficient deployment on mobile and embedded devices.
-
A.
MobileNetV2
MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
-
B.
SqueezeNet
SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
-
C.
NASNet
NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
-
D.
ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
-
E.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices is a lightweight deep learning architecture designed to deliver high accuracy with very low computational cost, making it well-suited for deployment on mobile and embedded devices.
- F. None of above. chosen
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: MobileNet
linked to: MobileNet