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%

## Inputs

A blob with a BGR image and the shape 1, 3, 224, 224 in the format B, C, H, W, 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 1, 1000 in the format B, C, where:

• B - batch size
• C - predicted probabilities for each class in [0, 1] range

## Legal Information

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