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.
| Metric | Value |
|---|---|
| Image size | 224x224 |
| Source framework | PyTorch* |
The quality metrics calculated on ImageNet validation dataset is 61.71% accuracy
| Metric | Value |
|---|---|
| Accuracy top-1 (ImageNet) | 61.71% |
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
[*] Other names and brands may be claimed as the property of others.