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96 lines
3.1 KiB
Markdown
96 lines
3.1 KiB
Markdown
## ArcFace with Parallel Acceleration on both Features and Centers
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### Memory Consumption and Training Speed
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Parallel acceleration on both feature x and centre W. Setting: ResNet 50, batch size 8 * 64, feature dimension 512, float point 32, GPU 8 * P40 (24GB).
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### Illustration of Main Steps
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Parallel calculation by simple matrix partition. Setting: ResNet 50, batch size 8 * 64, feature dimension 512, float point 32, identity number 1 Million, GPU 8 * 1080ti (11GB). Communication cost: 1MB (feature x). Training speed: 800 samples/second.
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**Note:** Replace ``train.py`` with ``train_parall.py`` in following examples if you want to use parallel acceleration.
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### Model Training
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1. Install `MXNet` with GPU support.
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```
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pip install mxnet-cu100 # mxnet-cu102
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```
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2. Clone the InsightFace repository. We call the directory insightface as *`INSIGHTFACE_ROOT`*.
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```
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git clone --recursive https://github.com/deepinsight/insightface.git
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```
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3. Download the training set (`MS1MV2-Arcface`) and place it in *`$INSIGHTFACE_ROOT/recognition/datasets/`*. Each training dataset includes the following 6 files:
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```Shell
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faces_emore/
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train.idx
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train.rec
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property
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lfw.bin
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cfp_fp.bin
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agedb_30.bin
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```
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The first three files are the training dataset while the last three files are verification sets.
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4. Train deep face recognition models.
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In this part, we assume you are in the directory *`$INSIGHTFACE_ROOT/recognition/ArcFace`*.
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Place and edit config file:
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```Shell
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cp sample_config.py config.py
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vim config.py # edit dataset path etc..
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```
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We give some examples below. Our experiments were conducted on the Tesla P40 GPU.
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(1). Train ArcFace with LResNet100E-IR.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r100 --loss arcface --dataset emore
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```
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It will output verification results of *LFW*, *CFP-FP* and *AgeDB-30* every 2000 batches. You can check all options in *config.py*.
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This model can achieve *LFW 99.80+* and *MegaFace 98.3%+*.
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(2). Train CosineFace with LResNet50E-IR.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r50 --loss cosface --dataset emore
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```
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(3). Train Softmax with MobileFaceNet.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network y1 --loss softmax --dataset emore
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```
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(4). Fine-turn the above Softmax model with Triplet loss.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network mnas05 --loss triplet --lr 0.005 --pretrained ./models/y1-softmax-emore,1
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```
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### Citation
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If you find *ArcFace* useful in your research, please consider to cite the following related papers:
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```
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@inproceedings{deng2019arcface,
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title={Arcface: Additive angular margin loss for deep face recognition},
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author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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pages={4690--4699},
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year={2019}
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}
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```
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