Update README.md

iccv19-challenge/README.md

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 Test Submission Server[TODO]
 
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 **How To Start:**
 
 **Training:**
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 3. Refer to the `retina` dataset config section in `sample_config.py` and copy it to your own`config.py`.
 4. Start training with `CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --dataset retina --network [your-network] --loss arcface`. It will output the accuracy of lfw, cfp_fp and agedb_30 every 2000 batches by default.
 
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 **Testing:**
 
 1. testdata-image from baiducloud or dropbox. These face images are all pre-processed and aligned so no need to do further modification.
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 You can also check the verification performance during training time on LFW,CFP_FP,AgeDB_30 datasets.
 
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 **Evaluation:**
 
 Final ranking is determined by accuracy only, for all valid submissions. For example, score of track-1 will be calculated by ``TAR_glint-light+TAR_iqiyi-light`` while ``TAR_glint-large+TAR_iqiyi-large`` for track-2.
 
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 **Discussion:**
 
 [https://github.com/deepinsight/insightface/issues/632](https://github.com/deepinsight/insightface/issues/632)
 
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 **Baseline:**
 
 1. Network y2(a deeper mobilefacenet): 933M FLOPs. TAR_image: [TODO], TAR_video: [TODO]
 2. Network r100fc(ResNet100FC-IR): 24G FLOPs. TAR_image: [TODO], TAR_video: [TODO]
 
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 **Candidate solutions:**
 
 1. Use slightly deeper or wider mobile-level networks.