pytorch-insightface-mxnet2pytorch.rar

# Pytorch InsightFace Pretrained ResNet models from [deepinsight/insightface](https://github.com/deepinsight/insightface) ported to pytorch. | Model | LFW(%) | CFP-FP(%) | AgeDB-30(%) | MegaFace(%) | | ---------- | ------ | --------- | ----------- | ------------- | | iresnet34 | 99.65 | 92.12 | 97.70 | 96.70 | | iresnet50 | 99.80 | 92.74 | 97.76 | 97.64 | | iresnet100 | 99.77 | 98.27 | 98.28 | 98.47 | ## Installation ```bash pip install git+https://github.com/nizhib/pytorch-insightface ``` ## Usage ```python import torch from imageio import imread from torchvision import transforms import insightface embedder = insightface.iresnet100(pretrained=True) embedder.eval() mean = [0.5] * 3 std = [0.5 * 256 / 255] * 3 preprocess = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean, std) ]) face = imread('resource/sample.jpg') tensor = preprocess(face) with torch.no_grad(): features = embedder(tensor.unsqueeze(0))[0] print(features[:5]) ``` ## Recreating the weights locally Download the original [insightface zoo](https://github.com/deepinsight/insightface/wiki/Model-Zoo) weights and place `*.params` and `*.json` files to `resource/{model}`. Run `python scripts/convert.py` to convert and test pytorch weights.