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# 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.
<div align="center">
<img src="https://github.com/GuoxiaWang/plsc_log/blob/master/friends2.jpg" width = "800" />
<img src="https://raw.githubusercontent.com/littletomatodonkey/insight-face-paddle/main/demo/friends/output/friends2.jpg" width = "800" />
</div>
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).