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+# VGG Face 2 Models
+
+- use the VGG `SE-ResNet-50-256D` .caffemodel
+- mean values and size in prototxt
+- training used faces detected with MTCNN using 0.3 scale bbox expansion, thresholded at borders
+- for megapixels, we are using the opencv dnn face detector which has
+- variation for using OpenCV DNN face detector on test set yields
+
+| Detector | Center | Width | Height |
+|:-:|:-:|:-:|:-:|
+| OpenCV DNN | 0.0271% | 3.440% | 3.306% |
+| MTCNN PyTorch | - | - | - |
+| MTCNN TensorFlow | - | - | - |
+| MTCNN Caffe | - | - | - |
+
+### VGG Face 2 TAR@FAR
+
+- From <https://github.com/ox-vgg/vgg_face2>
+- http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/
+- TAR: True Acceptance Rate, FAR: False Acceptance Rate
+
+| Architecture | Feat dim | Pretrain | TAR@FAR = 0.001 | TAR@FAR = 0.01 | Model Link |
+|:-:|:-:|:-:|:-:|:-:|:-:|
+| ResNet-50 | 2048 | N | 0.878 | 0.938 | [Caffe](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/caffe/resnet50_scratch_caffe.tar.gz), [MatConvNet](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/matconvnet/resnet50_scratch_mat.tar.gz), [TF], [PyTorch](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/pytorch/resnet50_scratch_pytorch.tar.gz) |
+| ResNet-50 | 2048 | Y | 0.891 | 0.947 | [Caffe](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/caffe/resnet50_ft_caffe.tar.gz), [MatConvNet](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/matconvnet/resnet50_ft_mat.tar.gz), [TF], [PyTorch](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/pytorch/resnet50_ft_pytorch.tar.gz) |
+| SE-ResNet-50 | 2048 | N | 0.888 | 0.949 | [Caffe](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/caffe/senet50_scratch_caffe.tar.gz), [MatConvNet](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/matconvnet/senet50_scratch_mat.tar.gz), [TF], [PyTorch](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/pytorch/senet50_scratch_pytorch.tar.gz)|
+| SE-ResNet-50 | 2048 | Y | 0.908 | 0.956 | [Caffe](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/caffe/senet50_ft_caffe.tar.gz), [MatConvNet](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/matconvnet/senet50_ft_mat.tar.gz), [TF], [PyTorch](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/pytorch/senet50_ft_pytorch.tar.gz)|
+| ResNet-50-256D | 256 | Y | 0.898 | 0.956 | [Caffe](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/caffe/resnet50_256_caffe.tar.gz), [MatConvNet](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/matconvnet/resnet50_256_mat.tar.gz), [TF], [PyTorch](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/pytorch/resnet50_256_pytorch.tar.gz) |
+| ResNet-50-128D | 128 | Y | 0.904 | 0.956 | [Caffe](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/caffe/resnet50_128_caffe.tar.gz), [MatConvNet](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/matconvnet/resnet50_128_mat.tar.gz), [TF], [PyTorch](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/pytorch/resnet50_128_pytorch.tar.gz) |
+| SE-ResNet-50-256D| 256 | Y | 0.912 | 0.965 | [Caffe](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/caffe/senet50_256_caffe.tar.gz), [MatConvNet](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/matconvnet/senet50_256_mat.tar.gz), [TF], [PyTorch](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/pytorch/senet50_256_pytorch.tar.gz)|
+| SE-ResNet-50-128D| 128 | Y | 0.910 | 0.959 | [Caffe](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/caffe/senet50_128_caffe.tar.gz), [MatConvNet](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/matconvnet/senet50_128_mat.tar.gz), [TF], [PyTorch](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/models/pytorch/senet50_128_pytorch.tar.gz) |