resnet18-xnor-binary-onnx-0001

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 ones that can be implemented as XNOR+POPCOUNT operations. Only input, final and shortcut layers were kept as FP32, all the rest convolutional layers are replaced by binary convolution layers.

Specification

Metric Value
Image size 224x224
Source framework PyTorch*

Accuracy

The quality metrics calculated on ImageNet validation dataset is 61.71% accuracy

Metric Value
Accuracy top-1 (ImageNet) 61.71%

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.