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Squeezenext baseline architecture implementation in Pytorch with GPU

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Squeezenext_baseline_Pytorch

Squeezenext baseline architecture implementation in Pytorch with live loss and accuracy update using livelossplot package.

Software used :
Python 3.6
Pytorch 1.0.0
Spyder 3.3.1

Packages needed when running it for first time :
Livelossplot package (https://github.com/stared/livelossplot)
Torch (https://pytorch.org/get-started/locally/)

  • Choose all the options according to your system specification.
  • Recommendations
    OS: Linux
    BUILD : 1.0
    Package : Pip
    Cuda Installation; it depends on your graphic card. Please refer your label

|-----Model-------|Kernel size-|-epochs-|test accuracy||Model Size(MB)||Model speed||Optimizer||Learning rate|
|sqnxt_baseline_23| 7x7 | 200 |--84.69%-----|-----2.617-----|-----22-----|----sgd---|-----0.1------|
|sqnxt_baseline_23| 3x3 | 200 |--87.63%-----|-----2.586-----|-----23-----|----sgd---|-----0.1------|
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