From 8d183dfb2f7f34c7e8a110605beaa3d09deb5fd2 Mon Sep 17 00:00:00 2001 From: Jia Guo Date: Wed, 24 Jan 2018 18:57:05 +0800 Subject: [PATCH] Update README.md --- README.md | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 09fa6c0..b3b1acf 100644 --- a/README.md +++ b/README.md @@ -31,15 +31,19 @@ In InsightFace, we support several popular network backbones and can be set just in one parameter. Below is the list until today: -- ResNet +* ResNet +* MobiletNet +* InceptionResNetV2 +* DPN +* DenseNet -- MobiletNet - -- InceptionResNetV2 - -- DPN - -- DenseNet + We also support most of popular face recognition algorithms(losses), by specifying loss type: + + * loss-type=0: Softmax + * loss-type=1: SphereFace + * loss-type=2: AMSoftmax/CosineFace + * loss-type=4: Ours(InsightFace) + * loss-type=12: TripletLoss In our paper, we found there're overlap identities between facescrub dataset and Megaface distractors which greatly affects the identification performance. Sometimes more than 10 percent improvement can be achieved after removing these overlaps. This list will be made public soon in this repository. @@ -61,9 +65,10 @@ ``` ### Requirements -      1. Install `MXNet` with GPU support(Python 2.7). +       + 1. Install `MXNet` with GPU support(Python 2.7). - 2. If you want to align dataset by yourself, install tensorflow as we're using the MTCNN writen in TF. + 2. If you want to align dataset by yourself, install tensorflow as we're using the tf-version MTCNN. (Note that any public available MTCNN can be used to align the faces and then transform to 112x112 crop, performance/result should not change.) ### Installation 1. Install MXNet by