Deep Face Recognition in PyTorch

Related tags

Deep Learning computer-vision deep-learning pytorch face-recognition metric-learning landmark-detection lfw sphereface center-loss focal-loss arcface am-softmax mobilefacenet vggface2 cosface deep-face-recognition sv-softmax
Overview

Face Recognition in PyTorch

License Build Status

By Alexey Gruzdev and Vladislav Sovrasov

Introduction

A repository for different experimental Face Recognition models such as CosFace, ArcFace, SphereFace, SV-Softmax, etc.

Contents

  1. Installation
  2. Preparation
  3. Train/Eval
  4. Models
  5. Face Recognition Demo

Installation

  1. Create and activate virtual python environment
bash init_venv.sh
. venv/bin/activate

Preparation

  1. For Face Recognition training you should download VGGFace2 data. We will refer to this folder as $VGGFace2_ROOT.
  2. For Face Recognition evaluation you need to download LFW data and LFW landmarks. Place everything in one folder, which will be $LFW_ROOT.

Train/Eval

  1. Go to $FR_ROOT folder
cd $FR_ROOT/
  1. To start training FR model:
python train.py --train_data_root $VGGFace2_ROOT/train/ --train_list $VGGFace2_ROOT/meta/train_list.txt
--train_landmarks  $VGGFace2_ROOT/bb_landmark/ --val_data_root  $LFW_ROOT/lfw/ --val_list $LFW_ROOT/pairs.txt  
--val_landmarks $LFW_ROOT/lfw_landmark.txt --train_batch_size 200  --snap_prefix mobilenet_256 --lr 0.35
--embed_size 256 --model mobilenet --device 1
  1. To evaluate FR snapshot (let's say we have MobileNet with 256 embedding size trained for 300k):
 python evaluate_lfw.py --val_data_root $LFW_ROOT/lfw/ --val_list $LFW_ROOT/pairs.txt
 --val_landmarks $LFW_ROOT/lfw_landmark.txt --snap /path/to/snapshot/mobilenet_256_300000.pt --model mobilenet --embed_size 256

Configuration files

Besides passing all the required parameters via command line, the training script allows to read them from a yaml configuration file. Each line of such file should contain a valid description of one parameter in the yaml fromat. Example:

#optimizer parameters
lr: 0.4
train_batch_size: 256
#loss options
margin_type: cos
s: 30
m: 0.35
#model parameters
model: mobilenet
embed_size: 256
#misc
snap_prefix: MobileFaceNet
devices: [0, 1]
#datasets
train_dataset: vgg
train_data_root: $VGGFace2_ROOT/train/
#... and so on

Path to the config file can be passed to the training script via command line. In case if any other arguments were passed before the config, they will be overwritten.

python train.py -m 0.35 @./my_config.yml #here m can be overwritten with the value from my_config.yml

Models

  1. You can download pretrained model from fileshare as well - https://download.01.org/openvinotoolkit/open_model_zoo/training_toolbox_pytorch/models/fr/Mobilenet_se_focal_121000.pt
cd $FR_ROOT
python evaluate_lfw.py --val_data_root $LFW_ROOT/lfw/ --val_list $LFW_ROOT/pairs.txt --val_landmarks $LFW_ROOT/lfw_landmark.txt
--snap /path/to/snapshot/Mobilenet_se_focal_121000.pt --model mobilenet --embed_size 256
  1. You should get the following output:
I1114 09:33:37.846870 10544 evaluate_lfw.py:242] Accuracy/Val_same_accuracy mean: 0.9923
I1114 09:33:37.847019 10544 evaluate_lfw.py:243] Accuracy/Val_diff_accuracy mean: 0.9970
I1114 09:33:37.847069 10544 evaluate_lfw.py:244] Accuracy/Val_accuracy mean: 0.9947
I1114 09:33:37.847179 10544 evaluate_lfw.py:245] Accuracy/Val_accuracy std dev: 0.0035
I1114 09:33:37.847229 10544 evaluate_lfw.py:246] AUC: 0.9995
I1114 09:33:37.847305 10544 evaluate_lfw.py:247] Estimated threshold: 0.7241

Demo

  1. For setting up demo, please go to Face Recognition demo with OpenVINO Toolkit
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Comments
  • I encountered the following problems when running the train.py. What is the reason? I use CASIA datasets to run .

    I encountered the following problems when running the train.py. What is the reason? I use CASIA datasets to run .

    File "D:/PycharmProjects/work/face_recognition.pytorch-develop/train.py", line 123, in train for i, data in enumerate(train_loader,0): File "C:\User\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py", line 435, in next data = self._next_data() File "C:\Users\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py", line 1085, in _next_data return self._process_data(data) File "C:\Users\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py", line 1111, in _process_data data.reraise() File "C:\Users\AppData\Local\Programs\Python\Python37\lib\site-packages\torch_utils.py", line 428, in reraise raise self.exc_type(msg) AssertionError: Caught AssertionError in DataLoader worker process 0. Original Traceback (most recent call last): File "C:\Users\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data_utils\worker.py", line 198, in _worker_loop data = fetcher.fetch(index) File "C:\Users\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\Users\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\PycharmProjects\work\face_recognition.pytorch-develop\datasets\casia.py", line 58, in getitem d_size=(200, 200), normalized=True, show=False) File "D:\PycharmProjects\work\face_recognition.pytorch-develop\utils\face_align.py", line 28, in align assert len(landmarks) == 10 AssertionError

    opened by qqqsd 0
Owner
Alexey Gruzdev
Going Deeper with Deep Learning
Alexey Gruzdev
GitHub
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