SqueezeNet
E431007
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.
All labels observed (4)
| Label | Occurrences |
|---|---|
| SqueezeNet canonical | 1 |
| SqueezeNet v1.0 | 1 |
| SqueezeNet v1.1 | 1 |
| SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4326000 — 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: SqueezeNet Context triple: [torchvision, modelFamily, SqueezeNet]
-
A.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
B.
AlexNet
AlexNet is a pioneering deep convolutional neural network architecture that dramatically advanced image recognition performance and helped spark the modern deep learning revolution after winning the 2012 ImageNet competition.
-
C.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
D.
VGG
VGG is a deep convolutional neural network architecture known for its simple, uniform use of small 3×3 filters and great depth, which achieved strong performance in image recognition tasks.
-
E.
ResNeXt
ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition tasks.
- 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: SqueezeNet Target entity description: 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.
-
A.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
B.
AlexNet
AlexNet is a pioneering deep convolutional neural network architecture that dramatically advanced image recognition performance and helped spark the modern deep learning revolution after winning the 2012 ImageNet competition.
-
C.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
D.
VGG
VGG is a deep convolutional neural network architecture known for its simple, uniform use of small 3×3 filters and great depth, which achieved strong performance in image recognition tasks.
-
E.
ResNeXt
ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition tasks.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
convolutional neural network architecture
ⓘ
deep learning model ⓘ image classification model ⓘ |
| accuracyComparableTo | AlexNet ⓘ |
| benchmarkedOn | ImageNet ⓘ |
| designedBy |
Forrest N. Iandola
ⓘ
Khalid Ashraf ⓘ Kurt Keutzer ⓘ Matthew W. Moskewicz ⓘ Song Han ⓘ William J. Dally ⓘ |
| developedAt |
DeepScale
ⓘ
Stanford University ⓘ University of California, Berkeley ⓘ |
| hasComponent |
Fire module
ⓘ
expand layer with 1x1 and 3x3 filters ⓘ squeeze layer with 1x1 filters ⓘ |
| hasGoal |
achieve AlexNet-level accuracy with dramatically fewer parameters
ⓘ
enable deployment on resource-constrained devices ⓘ |
| hasVersion |
SqueezeNet v1.0
ⓘ
linked to:
SqueezeNet
SqueezeNet v1.1 ⓘ
linked to:
SqueezeNet
|
| implementedIn |
Caffe
ⓘ
PyTorch ⓘ TensorFlow ⓘ |
| influencedBy | AlexNet ⓘ |
| keyIdea |
decrease number of input channels to 3x3 filters
ⓘ
downsample late in the network to maintain large activation maps ⓘ replace many 3x3 filters with 1x1 filters ⓘ |
| license | permissive open-source license (via reference implementations) ⓘ |
| modelSize | less than 0.5 MB (for some configurations) ⓘ |
| notableProperty |
amenable to further compression techniques such as pruning and quantization
ⓘ
suitable for deployment over low-bandwidth networks ⓘ very small model size compared to AlexNet ⓘ |
| openSource | true ⓘ |
| optimizedFor |
embedded devices
ⓘ
mobile devices ⓘ resource-constrained hardware ⓘ |
| parameterCountRelativeTo | 50x fewer parameters than AlexNet ⓘ |
| publicationTitle |
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
ⓘ
linked to:
SqueezeNet
|
| publicationYear | 2016 ⓘ |
| supportsOperation |
distributed training
ⓘ
model compression ⓘ |
| targetTask | image classification ⓘ |
| usesLayerType |
1x1 convolution
ⓘ
3x3 convolution ⓘ Fire module ⓘ convolutional layer ⓘ expand layer ⓘ max pooling layer ⓘ squeeze layer ⓘ |
| v1.1Characteristics | faster with similar accuracy compared to SqueezeNet v1.0 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: SqueezeNet Description of subject: 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.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject linked to:
torchvision (ecosystem)
SqueezeNet
→
publicationTitle
→
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
ⓘ
linked to: SqueezeNet
linked to: SqueezeNet
linked to: SqueezeNet