Update Speed Benchmark.

Author: anxiangsir

recognition/arcface_torch/README.md

@@ -45,6 +45,31 @@ CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \
 ```
 More details see [eval.md](docs/eval.md) in docs.
 
+## Speed Benchmark
+![Image text](https://github.com/nttstar/insightface-resources/blob/master/images/partial_fc_speed.png)
+
+ArcFace_torch can train large-scale face recognition training set efficiently and quickly.  
+When the number of classes in training sets is greater than 300K and the training is sufficient, 
+partial fc sampling strategy will get same accuracy with several times faster training performance and smaller GPU memory.
+
+1. Different Parallel Methods Training Speed
+
+| Method                 | bs128_r100_200w identities | bs128_r50_400w identities | bs64_r50_800w identities |
+| :---:                  |    :---                    | :---                      | :---                     |
+| data parallel          |    1                       | 1                         | 1                        |
+| model parallel         |    1362                    | 1600                      | 482                      |
+| fp16 + model parallel  |    2006                    | 2165                      | 767                      | 
+| fp16 + partial fc 0.1  |    3247                    | 4385                      | 3001                     | 
+
+2. Different Parallel Methods GPU Memory 
+
+| Method                 | bs128_r100_200w identities | bs128_r50_400w identities | bs64_r50_800w identities |
+| :---:                  |    :---                    | :---                      | :---                     |
+| data parallel          |    OOM                     | OOM                       | OOM                      |
+| model parallel         |    27382                   | 30322                     | 32182                    |
+| fp16 + model parallel  |    20310                   | 26622                     | 32182                    | 
+| fp16 + partial fc 0.1  |    11987                   | 10890                     | 11108                    | 
+
 
 ## Model Zoo  
 
@@ -68,4 +93,24 @@ All Model Can be found in here.
 | Glint360k-Cosface   |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100/training.log)         |r100                 | 96.19 | 97.39 | 98.52 | 99.26 | 99.83 |
 | Glint360k-Cosface   |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100_fp16_0.1/training.log)|r100-fp16-sample-0.1 | 95.95 | 97.35 | 98.57 | 99.30 | 99.85 |
 
-More details see [eval.md](docs/modelzoo.md) in docs.
+More details see [eval.md](docs/modelzoo.md) in docs.
+
+
+
+## Citation
+```
+@inproceedings{deng2019arcface,
+  title={Arcface: Additive angular margin loss for deep face recognition},
+  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={4690--4699},
+  year={2019}
+}
+@inproceedings{an2020partical_fc,
+  title={Partial FC: Training 10 Million Identities on a Single Machine},
+  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
+  Zhang, Debing and Fu Ying},
+  booktitle={Arxiv 2010.05222},
+  year={2020}
+}
+```