ZF/insightface
1
0
Fork 0
mirror of https://github.com/deepinsight/insightface.git synced 2025-12-30 08:02:27 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Jia Guo
2019-01-16 10:52:33 +08:00
committed by GitHub
parent 8dd4161890
commit afec0436d2
1 changed files with 3 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
README.md
View File

@@ -100,7 +100,7 @@ git clone --recursive https://github.com/deepinsight/insightface.git
agedb_30.bin
```
The first three files are the training dataset while the last four files are verification sets.
The first three files are the training dataset while the last three files are verification sets.
4. Train deep face recognition models.
In this part, we assume you are in the directory *`$INSIGHTFACE_ROOT/recognition/`*.
@@ -122,7 +122,7 @@ We give some examples below. Our experiments were conducted on the Tesla P40 GPU
```Shell
CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r100 --loss arcface --dataset emore
```
It will output verification results of *LFW*, *CFP-FP* and *AgeDB-30* every 2000 batches. You can check all command line options in *train\_softmax.py*.
It will output verification results of *LFW*, *CFP-FP* and *AgeDB-30* every 2000 batches. You can check all options in *config.py*.
This model can achieve *LFW 99.80+* and *MegaFace 98.3%+*.
(2). Train CosineFace with LResNet50E-IR.
@@ -146,7 +146,7 @@ CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network m1 --loss triplet --
5. Verification results.
*LResNet100E-IR* network trained on *MS1M* dataset with ArcFace loss:
*LResNet100E-IR* network trained on *MS1M-Arcface* dataset with ArcFace loss:
| Method | LFW(%) | CFP-FP(%) | AgeDB-30(%) |
| ------- | ------ | --------- | ----------- |