This is respository is forked from https://github.com/deepinsight/insightface/tree/master/recognition/_evaluation_/ijb

We simply add some functionality to pipe the DVPF models to the evaluation pipelines of IJBC.

For an example of usage please refer to the ./dvpf_sample.sh

Here like before, we are iterating over the all the config files corresponding to different FR backbone and hyperparameters that the DVPF model are being trained upon.

Note the DVPF_ZDIM environmental variable which sets the dimentionality of the bottleneck layer of DVPF model. For example, if the dimentionality of the latent space of DVPF model is 512 you should set the DVPF_ZDIM to 512 and use the configs that corresponds to the 512 dimentionality.

DVPF_ZDIM=512 #Dimensiontlaity of bottleneck layer of DVPF model \ 
python3 ./ijb_evals.py \ 
--model_file /path/to/the/model/files/from/insightfaces_respository # Path to the model file from the insightfaces' repository \
--data_path path/to/data # Path to the mounted ijb_testkit from the insightface using ratarmount. see the `../../ijb_mounth.sh` \
--batch_size 128 \
--dvpf_enable # Flag that enables the pipeing of the DVPF model \
--dvpf_kwargs_path /path/to/config # Path to the config file of DVPF model \

GDrive or Baidu Cloud

Updated Meta data (1:1 and 1:N):

Baidu Cloud (code:7g8o) ; GDrive

Please apply for the IJB-B and IJB-C by yourself and strictly follow their distribution licenses.

Great thanks for Weidi Xie's instruction [2,3,4,5] to evaluate ArcFace [1] on IJB-B[6] and IJB-C[7] (1:1 protocol).

Great thanks for Yuge Huang's code [8] to evaluate ArcFace [1] on IJB-B[6] and IJB-C[7] (1:N protocol).

[1] Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition[J]. arXiv:1801.07698, 2018.

[2] https://github.com/ox-vgg/vgg_face2.

[3] Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, Andrew Zisserman. VGGFace2: A dataset for recognising faces across pose and age. FG, 2018.

[4] Weidi Xie, Andrew Zisserman. Multicolumn Networks for Face Recognition. BMVC 2018.

[5] Weidi Xie, Li Shen, Andrew Zisserman. Comparator Networks. ECCV, 2018.

[6] Whitelam, Cameron, Emma Taborsky, Austin Blanton, Brianna Maze, Jocelyn C. Adams, Tim Miller, Nathan D. Kalka et al. IARPA Janus Benchmark-B Face Dataset. CVPR Workshops, 2017.

[7] Maze, Brianna, Jocelyn Adams, James A. Duncan, Nathan Kalka, Tim Miller, Charles Otto, Anil K. Jain et al. IARPA Janus Benchmark–C: Face Dataset and Protocol. ICB, 2018.

[8] Yuge Huang, Pengcheng Shen, Ying Tai, Shaoxin Li, Xiaoming Liu, Jilin Li, Feiyue Huang, Rongrong Ji. Distribution Distillation Loss: Generic Approach for Improving Face Recognition from Hard Samples. arXiv:2002.03662.