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.

Specification

Metric	Value
Image size	224x224
fp32 conv MFlops	960
bin conv MI1ops	7218
Source framework	PyTorch*

Accuracy

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

Performance

Inputs

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

B – batch size
C – number of channels
H – image height
W – image width

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

Outputs

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

Legal Information

[*] Other names and brands may be claimed as the property of others.