resnet50-binary-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 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

Metric

Value

Accuracy top-1 (ImageNet)

70.69%

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 logits format

Demo usage#

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: