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.
Metric | Value |
---|---|
Image size | 224x224 |
fp32 conv MFlops | 960 |
bin conv MI1ops | 7218 |
Source framework | PyTorch* |
The quality metrics calculated on ImageNet validation dataset is 70.69% accuracy
Metric | Value |
---|---|
Accuracy top-1 (ImageNet) | 70.69% |
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
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
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