Use Case and High-Level Description

This is a classical classification network for 1000 classes trained on ImageNet. The difference is that most convolutional layers were replaced by binary once that can be implemented as XNOR+POPCOUN operations. Only input, final and shortcut layers were kept as FP32, all the rest convolutional layers are replaced by BinaryConvolution layers.


Metric Value
Image size 224x224
fp32 conv MFlops960
bin conv MI1ops7218
Source frameworkPyTorch*


The quality metrics calculated on ImageNet validation dataset is 70.69% accuracy

Metric Value
Accuracy top-1 (ImageNet) 70.69%



A blob with a BGR image in the format: [B, C=3, H=224, W=224], where:

It is supposed that input is BGR in 0..255 range


The output is a blob with the shape [B, C=1000].

Legal Information

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