Can you upload your model?

[LaviLiu]

Hi @MuggleWang ，thank you for your work about the project. I don`t have sufficient resource to train a model. Can you share your pytorch model woth us ? Thank you any way!

[MuggleWang]

The model is too big. I don't know how to upload it. If you still have this need, leave your email. I will send you a model. Or teach me how to upload a big file please~~

[chenyyx]

@MuggleWang hi~, you can upload the model via BaiduNetDisk ,and then send us the link and the password~ I think this will be okay ~ :) thanks a lot!

[MuggleWang]

Link: https://pan.baidu.com/s/1evtAUy3zUG46DbZdsXVTnQ Password: 6ezs Trained with vggface2. Acc is 99.40%. New version of Network(see details in net.py)

[chenyyx]

@MuggleWang thank you so much~

[chenyyx]

@MuggleWang Sorry to bother you, can you send me your vggface2 code? Or update net.py? Thank you anyway.

[MuggleWang]

VGG-Face2 is not code but a database. The model above was trained with this database but not WebFace. The network is still sphereface20 which is already in net.py. I'm not ready to upload network LResnet50E-IR. I rewrite official version insightface. There may be some mistakes, I don't want to mislead people. You can try to make a new version

[chenyyx]

@MuggleWang Oh，thanks！！I don't even know that vgg-face2 is a dataset，I even thought it was a network structure.Thank you again for your reply and wish you all the best.

[DawnHH]

I replaced 'class sphere20' with 'class sphere' in lfw_eval.py in order to load the model in new version of network, but it still can't be loaded correctly. The error says: Error(s) in loading state_dict for sphere: Missing key(s) in state_dict: "layer1.0.weight", "layer1.0.bias", "layer1.1.weight", "layer1.2.prelu2.weight", "layer2.0.weight", "layer2.0.bias", "layer2.1.weight", "layer2.2.prelu2.weight", "layer2.3.prelu2.weight", "layer3.0.weight", "layer3.0.bias", "layer3.1.weight", "layer3.2.prelu2.weight", "layer3.3.prelu2.weight", "layer3.4.prelu2.weight", "layer3.5.prelu2.weight", "layer4.0.weight", "layer4.0.bias", "layer4.1.weight", "layer4.2.prelu2.weight", "fc.weight", "fc.bias". Unexpected key(s) in state_dict: "conv1.weight", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "prelu1.weight", "layer1.0.bn1.weight", "layer1.0.bn1.bias", "layer1.0.bn1.running_mean", "layer1.0.bn1.running_var", "layer1.0.conv1.weight", "layer1.0.bn2.weight", "layer1.0.bn2.bias", "layer1.0.bn2.running_mean", "layer1.0.bn2.running_var", "layer1.0.prelu1.weight", "layer1.0.conv2.weight", "layer1.0.bn3.weight", "layer1.0.bn3.bias", "layer1.0.bn3.running_mean", "layer1.0.bn3.running_var", "layer1.0.downsample.0.weight", "layer1.0.downsample.1.weight", "layer1.0.downsample.1.bias", "layer1.0.downsample.1.running_mean", "layer1.0.downsample.1.running_var", "layer1.1.bn1.weight", "layer1.1.bn1.bias", "layer1.1.bn1.running_mean", "layer1.1.bn1.running_var", "layer1.1.conv1.weight", "layer1.1.bn2.weight", "layer1.1.bn2.bias", "layer1.1.bn2.running_mean", "layer1.1.bn2.running_var", "layer1.1.prelu1.weight", "layer1.1.conv2.weight", "layer1.1.bn3.weight", "layer1.1.bn3.bias", "layer1.1.bn3.running_mean", "layer1.1.bn3.running_var", "layer1.2.bn1.weight", "layer1.2.bn1.bias", "layer1.2.bn1.running_mean", "layer1.2.bn1.running_var", "layer1.2.bn2.weight", "layer1.2.bn2.bias", "layer1.2.bn2.running_mean", "layer1.2.bn2.running_var", "layer1.2.bn3.weight", "layer1.2.bn3.bias", "layer1.2.bn3.running_mean", "layer1.2.bn3.running_var", "layer2.0.bn1.weight", "layer2.0.bn1.bias", "layer2.0.bn1.running_mean", "layer2.0.bn1.running_var", "layer2.0.conv1.weight", "layer2.0.bn2.weight", "layer2.0.bn2.bias", "layer2.0.bn2.running_mean", "layer2.0.bn2.running_var", "layer2.0.prelu1.weight", "layer2.0.conv2.weight", "layer2.0.bn3.weight", "layer2.0.bn3.bias", "layer2.0.bn3.running_mean", "layer2.0.bn3.running_var", "layer2.0.downsample.0.weight", "layer2.0.downsample.1.weight", "layer2.0.downsample.1.bias", "layer2.0.downsample.1.running_mean", "layer2.0.downsample.1.running_var", "layer2.1.bn1.weight", "layer2.1.bn1.bias", "layer2.1.bn1.running_mean", "layer2.1.bn1.running_var", "layer2.1.conv1.weight", "layer2.1.bn2.weight", "layer2.1.bn2.bias", "layer2.1.bn2.running_mean", "layer2.1.bn2.running_var", "layer2.1.prelu1.weight", "layer2.1.conv2.weight", "layer2.1.bn3.weight", "layer2.1.bn3.bias", "layer2.1.bn3.running_mean", "layer2.1.bn3.running_var", "layer2.2.bn1.weight", "layer2.2.bn1.bias", "layer2.2.bn1.running_mean", "layer2.2.bn1.running_var", "layer2.2.bn2.weight", "layer2.2.bn2.bias", "layer2.2.bn2.running_mean", "layer2.2.bn2.running_var", "layer2.2.bn3.weight", "layer2.2.bn3.bias", "layer2.2.bn3.running_mean", "layer2.2.bn3.running_var", "layer2.3.bn1.weight", "layer2.3.bn1.bias", "layer2.3.bn1.running_mean", "layer2.3.bn1.running_var", "layer2.3.bn2.weight", "layer2.3.bn2.bias", "layer2.3.bn2.running_mean", "layer2.3.bn2.running_var", "layer2.3.bn3.weight", "layer2.3.bn3.bias", "layer2.3.bn3.running_mean", "layer2.3.bn3.running_var", "layer3.6.bn1.weight", "layer3.6.bn1.bias", "layer3.6.bn1.running_mean", "layer3.6.bn1.running_var", "layer3.6.conv1.weight", "layer3.6.bn2.weight", "layer3.6.bn2.bias", "layer3.6.bn2.running_mean", "layer3.6.bn2.running_var", "layer3.6.prelu1.weight", "layer3.6.conv2.weight", "layer3.6.bn3.weight", "layer3.6.bn3.bias", "layer3.6.bn3.running_mean", "layer3.6.bn3.running_var", "layer3.7.bn1.weight", "layer3.7.bn1.bias", "layer3.7.bn1.running_mean", "layer3.7.bn1.running_var", "layer3.7.conv1.weight", "layer3.7.bn2.weight", "layer3.7.bn2.bias", "layer3.7.bn2.running_mean", "layer3.7.bn2.running_var", "layer3.7.prelu1.weight", "layer3.7.conv2.weight", "layer3.7.bn3.weight", "layer3.7.bn3.bias", "layer3.7.bn3.running_mean", "layer3.7.bn3.running_var", "layer3.8.bn1.weight", "layer3.8.bn1.bias", "layer3.8.bn1.running_mean", "layer3.8.bn1.running_var", "layer3.8.conv1.weight", "layer3.8.bn2.weight", "layer3.8.bn2.bias", "layer3.8.bn2.running_mean", "layer3.8.bn2.running_var", "layer3.8.prelu1.weight", "layer3.8.conv2.weight", "layer3.8.bn3.weight", "layer3.8.bn3.bias", "layer3.8.bn3.running_mean", "layer3.8.bn3.running_var", "layer3.9.bn1.weight", "layer3.9.bn1.bias", "layer3.9.bn1.running_mean", "layer3.9.bn1.running_var", "layer3.9.conv1.weight", "layer3.9.bn2.weight", "layer3.9.bn2.bias", "layer3.9.bn2.running_mean", "layer3.9.bn2.running_var", "layer3.9.prelu1.weight", "layer3.9.conv2.weight", "layer3.9.bn3.weight", "layer3.9.bn3.bias", "layer3.9.bn3.running_mean", "layer3.9.bn3.running_var", "layer3.10.bn1.weight", "layer3.10.bn1.bias", "layer3.10.bn1.running_mean", "layer3.10.bn1.running_var", "layer3.10.conv1.weight", "layer3.10.bn2.weight", "layer3.10.bn2.bias", "layer3.10.bn2.running_mean", "layer3.10.bn2.running_var", "layer3.10.prelu1.weight", "layer3.10.conv2.weight", "layer3.10.bn3.weight", "layer3.10.bn3.bias", "layer3.10.bn3.running_mean", "layer3.10.bn3.running_var", "layer3.11.bn1.weight", "layer3.11.bn1.bias", "layer3.11.bn1.running_mean", "layer3.11.bn1.running_var", "layer3.11.conv1.weight", "layer3.11.bn2.weight", "layer3.11.bn2.bias", "layer3.11.bn2.running_mean", "layer3.11.bn2.running_var", "layer3.11.prelu1.weight", "layer3.11.conv2.weight", "layer3.11.bn3.weight", "layer3.11.bn3.bias", "layer3.11.bn3.running_mean", "layer3.11.bn3.running_var", "layer3.12.bn1.weight", "layer3.12.bn1.bias", "layer3.12.bn1.running_mean", "layer3.12.bn1.running_var", "layer3.12.conv1.weight", "layer3.12.bn2.weight", "layer3.12.bn2.bias", "layer3.12.bn2.running_mean", "layer3.12.bn2.running_var", "layer3.12.prelu1.weight", "layer3.12.conv2.weight", "layer3.12.bn3.weight", "layer3.12.bn3.bias", "layer3.12.bn3.running_mean", "layer3.12.bn3.running_var", "layer3.13.bn1.weight", "layer3.13.bn1.bias", "layer3.13.bn1.running_mean", "layer3.13.bn1.running_var", "layer3.13.conv1.weight", "layer3.13.bn2.weight", "layer3.13.bn2.bias", "layer3.13.bn2.running_mean", "layer3.13.bn2.running_var", "layer3.13.prelu1.weight", "layer3.13.conv2.weight", "layer3.13.bn3.weight", "layer3.13.bn3.bias", "layer3.13.bn3.running_mean", "layer3.13.bn3.running_var", "layer3.0.bn1.weight", "layer3.0.bn1.bias", "layer3.0.bn1.running_mean", "layer3.0.bn1.running_var", "layer3.0.conv1.weight", "layer3.0.bn2.weight", "layer3.0.bn2.bias", "layer3.0.bn2.running_mean", "layer3.0.bn2.running_var", "layer3.0.prelu1.weight", "layer3.0.conv2.weight", "layer3.0.bn3.weight", "layer3.0.bn3.bias", "layer3.0.bn3.running_mean", "layer3.0.bn3.running_var", "layer3.0.downsample.0.weight", "layer3.0.downsample.1.weight", "layer3.0.downsample.1.bias", "layer3.0.downsample.1.running_mean", "layer3.0.downsample.1.running_var", "layer3.1.bn1.weight", "layer3.1.bn1.bias", "layer3.1.bn1.running_mean", "layer3.1.bn1.running_var", "layer3.1.conv1.weight", "layer3.1.bn2.weight", "layer3.1.bn2.bias", "layer3.1.bn2.running_mean", "layer3.1.bn2.running_var", "layer3.1.prelu1.weight", "layer3.1.conv2.weight", "layer3.1.bn3.weight", "layer3.1.bn3.bias", "layer3.1.bn3.running_mean", "layer3.1.bn3.running_var", "layer3.2.bn1.weight", "layer3.2.bn1.bias", "layer3.2.bn1.running_mean", "layer3.2.bn1.running_var", "layer3.2.bn2.weight", "layer3.2.bn2.bias", "layer3.2.bn2.running_mean", "layer3.2.bn2.running_var", "layer3.2.bn3.weight", "layer3.2.bn3.bias", "layer3.2.bn3.running_mean", "layer3.2.bn3.running_var", "layer3.3.bn1.weight", "layer3.3.bn1.bias", "layer3.3.bn1.running_mean", "layer3.3.bn1.running_var", "layer3.3.bn2.weight", "layer3.3.bn2.bias", "layer3.3.bn2.running_mean", "layer3.3.bn2.running_var", "layer3.3.bn3.weight", "layer3.3.bn3.bias", "layer3.3.bn3.running_mean", "layer3.3.bn3.running_var", "layer3.4.bn1.weight", "layer3.4.bn1.bias", "layer3.4.bn1.running_mean", "layer3.4.bn1.running_var", "layer3.4.bn2.weight", "layer3.4.bn2.bias", "layer3.4.bn2.running_mean", "layer3.4.bn2.running_var", "layer3.4.bn3.weight", "layer3.4.bn3.bias", "layer3.4.bn3.running_mean", "layer3.4.bn3.running_var", "layer3.5.bn1.weight", "layer3.5.bn1.bias", "layer3.5.bn1.running_mean", "layer3.5.bn1.running_var", "layer3.5.bn2.weight", "layer3.5.bn2.bias", "layer3.5.bn2.running_mean", "layer3.5.bn2.running_var", "layer3.5.bn3.weight", "layer3.5.bn3.bias", "layer3.5.bn3.running_mean", "layer3.5.bn3.running_var", "layer4.0.bn1.weight", "layer4.0.bn1.bias", "layer4.0.bn1.running_mean", "layer4.0.bn1.running_var", "layer4.0.conv1.weight", "layer4.0.bn2.weight", "layer4.0.bn2.bias", "layer4.0.bn2.running_mean", "layer4.0.bn2.running_var", "layer4.0.prelu1.weight", "layer4.0.conv2.weight", "layer4.0.bn3.weight", "layer4.0.bn3.bias", "layer4.0.bn3.running_mean", "layer4.0.bn3.running_var", "layer4.0.downsample.0.weight", "layer4.0.downsample.1.weight", "layer4.0.downsample.1.bias", "layer4.0.downsample.1.running_mean", "layer4.0.downsample.1.running_var", "layer4.1.bn1.weight", "layer4.1.bn1.bias", "layer4.1.bn1.running_mean", "layer4.1.bn1.running_var", "layer4.1.conv1.weight", "layer4.1.bn2.weight", "layer4.1.bn2.bias", "layer4.1.bn2.running_mean", "layer4.1.bn2.running_var", "layer4.1.prelu1.weight", "layer4.1.conv2.weight", "layer4.1.bn3.weight", "layer4.1.bn3.bias", "layer4.1.bn3.running_mean", "layer4.1.bn3.running_var", "layer4.2.bn1.weight", "layer4.2.bn1.bias", "layer4.2.bn1.running_mean", "layer4.2.bn1.running_var", "layer4.2.bn2.weight", "layer4.2.bn2.bias", "layer4.2.bn2.running_mean", "layer4.2.bn2.running_var", "layer4.2.bn3.weight", "layer4.2.bn3.bias", "layer4.2.bn3.running_mean", "layer4.2.bn3.running_var", "fc.0.weight", "fc.0.bias", "fc.0.running_mean", "fc.0.running_var", "fc.2.weight", "fc.2.bias", "fc.3.weight", "fc.3.bias", "fc.3.running_mean", "fc.3.running_var".

Am I missed the right network? what should I do to load the model and test it? It would be a lot of thanks if you could tell me.

[MuggleWang]

Sorry, the model is wrong. I will re-upload later.

[MuggleWang]

Link: https://pan.baidu.com/s/1uOBATynzBTzZwrIKC4kcAA Password: 69e6

[ghost]

@MuggleWang Thank you for sharing, could you share your pretrained model (with only one GPU) to Googledrive? I cannot create a Baidu account.