From 6ec7e48b243051ea59158e71ffb785c7e97ffce6 Mon Sep 17 00:00:00 2001 From: GuoxiaWang Date: Wed, 13 Oct 2021 20:13:04 +0800 Subject: [PATCH] update README.md --- recognition/arcface_paddle/README.md | 107 +++++++++++---------------- 1 file changed, 44 insertions(+), 63 deletions(-) diff --git a/recognition/arcface_paddle/README.md b/recognition/arcface_paddle/README.md index 2e8a5e9..0ef804d 100644 --- a/recognition/arcface_paddle/README.md +++ b/recognition/arcface_paddle/README.md @@ -1,26 +1,22 @@ -# PLSC +[简体中文](README_ch.md) | English + +# Arcface-Paddle ## 1. Introduction -[PLSC](https://github.com/PaddlePaddle/PLSC) is an open source Paddle Large Scale Classification Tools, which supports 60 million classes on 8 NVIDIA V100 (32G). +`Arcface-Paddle` is an open source deep face detection and recognition toolkit, powered by PaddlePaddle. `Arcface-Paddle` provides three related pretrained models now, include `BlazeFace` for face detection, `ArcFace` and `MobileFace` for face recognition. + +- This tutorial is mainly about face recognition. +- For face detection task, please refer to: [Face detection tuturial](../../detection/blazeface_paddle/README_en.md). +- For Whl package inference using PaddleInference, please refer to [whl package inference](https://github.com/littletomatodonkey/insight-face-paddle). ## 2. Environment preparation -### 2.1 Install Paddle from source code +### 2.1 Install Paddle from pypi ```shell -git clone https://github.com/PaddlePaddle/Paddle.git - -cd /path/to/Paddle/ - -mkdir build && cd build - -cmake .. -DWITH_TESTING=ON -DWITH_GPU=ON -DWITH_GOLANG=OFF -DWITH_STYLE_CHECK=ON -DCMAKE_INSTALL_PREFIX=$PWD/output -DWITH_DISTRIBUTE=ON -DCMAKE_BUILD_TYPE=Release -DPY_VERSION=3.7 -DCUDA_ARCH_NAME=All -DPADDLE_VERSION=2.2.0 - -make -j20 && make install -j20 - -pip install output/opt/paddle/share/wheels/paddlepaddle_gpu-2.2.0-cp37-cp37m-linux_x86_64.whl +pip install paddlepaddle-gpu==2.2.0rc0 ``` @@ -139,7 +135,20 @@ sh scripts/inference.sh ## 8. Model performance -### 8.1 Accuracy on Verification Datasets +### 8.1 Performance of Lighting Model + +**Configuration：** + * CPU: Intel(R) Xeon(R) Gold 6184 CPU @ 2.40GHz + * GPU: a single NVIDIA Tesla V100 + +| Model structure | lfw | cfp_fp | agedb30 | CPU time cost | GPU time cost | Inference model | +| ------------------------- | ------ | ------- | ------- | -------| -------- |---- | +| MobileFace-Paddle | 0.9945 | 0.9343 | 0.9613 | 4.3ms | 2.3ms | [download link](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/mobileface_v1.0_infer.tar) | +| MobileFace-mxnet | 0.9950 | 0.8894 | 0.9591 | 7.3ms | 4.7ms | - | + +* Note: MobileFaceNet-Paddle training using MobileFaceNet_128 + +### 8.2 Accuracy on Verification Datasets **Configuration：** * GPU: 8 NVIDIA Tesla V100 32G @@ -154,7 +163,7 @@ sh scripts/inference.sh | Dynamic | MS1MV3 | r50 | 1.0 | 0.98317 | 0.98900| 0.99833 | [log](https://raw.githubusercontent.com/GuoxiaWang/plsc_log/master/dynamic/ms1mv3_r50_dynamic_128_fp16_1.0/training.log) | [checkpoint](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/distributed/ms1mv3_r50_dynamic_128_fp16_1.0_eopch_24.tgz) | -### 8.2 Maximum Number of Identities +### 8.3 Maximum Number of Identities **Configuration：** * GPU: 8 NVIDIA Tesla V100 32G @@ -170,7 +179,7 @@ sh scripts/inference.sh **Note:** config environment variable ``export FLAGS_allocator_strategy=naive_best_fit`` -### 8.3 Throughtput +### 8.4 Throughtput **Configuration：** * BatchSize: 128/1024 @@ -179,73 +188,45 @@ sh scripts/inference.sh ![insightface_throughtput](https://github.com/GuoxiaWang/plsc_log/blob/master/insightface_throughtput.png) -## 9. Demo +For more experimental results see [PLSC](https://github.com/PaddlePaddle/PLSC), which is an open source Paddle Large Scale Classification Tools powered by PaddlePaddle. It supports 60 million classes on 8 NVIDIA V100 (32G). -Combined with face detection model, we can complete the face recognition process. +## 9. Inference using PaddleInference -Firstly, use the fllowing commands to download the models. +### 9.1 Install insightface-paddle ```bash -# Create models directory -mkdir -p models - -# Download blazeface face detection model and extract it -wget https://paddle-model-ecology.bj.bcebos.com/model/insight-face/blazeface_fpn_ssh_1000e_v1.0_infer.tar -P models/ -tar -xzf models/blazeface_fpn_ssh_1000e_v1.0_infer.tar -C models/ -rm -rf models/blazeface_fpn_ssh_1000e_v1.0_infer.tar - -# Download static ResNet50 PartialFC 0.1 model and extract it -wget https://paddle-model-ecology.bj.bcebos.com/model/insight-face/distributed/ms1mv3_r50_static_128_fp16_0.1_epoch_24.tgz -P models/ -tar -xf models/ms1mv3_r50_static_128_fp16_0.1_epoch_24.tgz -C models/ -rm -rf models/ms1mv3_r50_static_128_fp16_0.1_epoch_24.tgz - -# Export static save inference model -python tools/export.py --is_static True --export_type paddle --backbone FresResNet50 --embedding_size 512 --checkpoint_dir models/ms1mv3_r50_static_128_fp16_0.1_epoch_24 --output_dir models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer -rm -rf models/ms1mv3_r50_static_128_fp16_0.1_epoch_24 +# install insightface-paddle +pip install --upgrade insightface-paddle ``` -Then, use the following commands to download the gallery, demo image and font file for visualization. And we generate gallery features. +### 9.2 Download the index gallery, demo image. ```bash -# Download gallery, query and font file -mkdir -p images/ -git clone https://github.com/littletomatodonkey/insight-face-paddle /tmp/insight-face-paddle -cp -r /tmp/insight-face-paddle/demo/friends/gallery/ images/ -cp -r /tmp/insight-face-paddle/demo/friends/query/ images/ -mkdir -p assets -cp /tmp/insight-face-paddle/SourceHanSansCN-Medium.otf assets/ -rm -rf /tmp/insight-face-paddle +mkdir -p images/gallery/ +mkdir -p images/query/ -# Build index file -python tools/test_recognition.py \ - --rec \ - --rec_model_file_path models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdmodel \ - --rec_params_file_path models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdiparams \ - --build_index=images/gallery/index.bin \ - --img_dir=images/gallery \ - --label=images/gallery/label.txt +# Index library for the recognition process +wget https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/demo/friends/index.bin -P images/gallery/ +# Demo image +wget https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/demo/friends/query/friends2.jpg -P images/query/ ``` -Use the following command to run the whole face recognition demo. +### 9.3 Inference using MobileFace ```bash -# detection + recogniotion process -python tools/test_recognition.py \ +# default using MobileFace +insightfacepaddle \ --det \ - --det_model_file_path models/blazeface_fpn_ssh_1000e_v1.0_infer/inference.pdmodel \ - --det_params_file_path models/blazeface_fpn_ssh_1000e_v1.0_infer/inference.pdiparams \ --rec \ - --rec_model_file_path models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdmodel \ - --rec_params_file_path models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdiparams \ --index=images/gallery/index.bin \ --input=images/query/friends2.jpg \ - --cdd_num 10 \ - --rec_thresh 0.4 \ --output="./output" ``` The final result is save in folder `output/`, which is shown as follows.
- +
+ +For more details about parameter explanations, index gallery construction and whl package inference, please refer to [Whl package inference tutorial](https://github.com/littletomatodonkey/insight-face-paddle).