Thie repo is modified from insightface

All training face images are aligned by MTCNN and cropped to 112x112:

Please check Dataset-Zoo for detail information and dataset downloading.

• Please check data_process/face2rec2.py on how to build a binary face dataset.

1. Install MXNet with GPU support (Python 3.5).

   pip install mxnet-cu100
   
   

2. Download the training set MS1M-Arcface and place it in $Fairface-Recognition-Solution-ROOT/train/datasets/. Each training dataset includes at least following 6 files:

   faces_emore/
       train.idx
       train.rec
       property
       lfw.bin
       cfp_fp.bin
       agedb_30.bin
   

The first three files are the training dataset while the last three files are verification sets.

3. Train deep face recognition models.

   Edit config file and set you data path and then run

   ./train.sh

4. Multi-step fine-tune the above Softmax model .
   Download the trainging set fairface and then build a binary face dataset from it, then you can run

   ./fairface_finetune.sh

   to get the step 1 finetuned model

   ./fairface_step2_finetune.sh

   to get the step2 finetuned model

   ./fairface_step3_finetune.sh

   to get the step3 finetuned model

   It's a multi step finetuing , we freeze all layers but final fc layer at step1 and then finetune all layers at step2 and then use the most discriminated protected data to finetune the model in step3

5. Hard-Sample finetune
   After we get the finetuned model, we finetune another hard-sample model from the pretrained model (not finetuned) using hard-samples. Hard-samples means samples whose prediction argmax is different from the annotation, and we take all samples from the sub_id of hard-sampels.

   Training scripts are same with section4 but we only need 2 steps at this section.

1. Download the pretrained model from model-zoo and test dataset from fairface , put the model in $Fairface-Recognition-Solution-ROOT/test/final_eval_models and data in $Fairface-Recognition-Solution-ROOT/test/TestData/tmp_data and run

./do.sh