What is recorded here?
Designed squeeze/expand Fire modules and channel/filter allocation strategies to reduce parameters while maintaining measured ImageNet classification performance.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits convolutional networks and compression. Sub-megabyte claims additionally use model compression and are not raw full-precision architecture storage; parameter count alone does not imply lower end-to-end latency everywhere.
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People and systems
Forrest N. Iandola; Matthew W. Moskewicz; Khalid Ashraf; Song Han; William J. Dally; Kurt Keutzer
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
SqueezeNet: Fire-module small-model architecture
Designed squeeze/expand Fire modules and channel/filter allocation strategies to reduce parameters while maintaining measured ImageNet classification performance.
Scientific scope
Inherits convolutional networks and compression. Sub-megabyte claims additionally use model compression and are not raw full-precision architecture storage; parameter count alone does not imply lower end-to-end latency everywhere.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Forrest N. Iandola; Matthew W. Moskewicz; Khalid Ashraf; Song Han; William J. Dally; Kurt Keutzer
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.