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 sizeC
– number of channelsH
– image heightW
– 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 sizeC
- 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:
Legal Information#
[*] Other names and brands may be claimed as the property of others.