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 in the format: [B, C=3, H=224, W=224], 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
The output is a blob with the shape [B, C=1000].
[*] Other names and brands may be claimed as the property of others.