arcface_torch/docs/eval.md

Eval on ICCV2021-MFR

coming soon.

Eval IJBC

You can eval ijbc with pytorch or onnx.

1. Eval IJBC With Onnx

CUDA_VISIBLE_DEVICES=0 python onnx_ijbc.py --model-root ms1mv3_arcface_r50 --image-path IJB_release/IJBC --result-dir ms1mv3_arcface_r50

2. Eval IJBC With Pytorch

CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \
--model-prefix ms1mv3_arcface_r50/backbone.pth \
--image-path IJB_release/IJBC \
--result-dir ms1mv3_arcface_r50 \
--batch-size 128 \
--job ms1mv3_arcface_r50 \
--target IJBC \
--network iresnet50

Inference

python inference.py --weight ms1mv3_arcface_r50/backbone.pth --network r50

Result

Datasets	Backbone	MFR-ALL	IJB-C(1E-4)	IJB-C(1E-5)
WF12M-PFC-0.05	r100	94.05	97.51	95.75
WF12M-PFC-0.1	r100	94.49	97.56	95.92
WF12M-PFC-0.2	r100	94.75	97.60	95.90
WF12M-PFC-0.3	r100	94.71	97.64	96.01
WF12M	r100	94.69	97.59	95.97