VGGFace2-pytorch

by cydonia999

cydonia999 / VGGFace2-pytorch

PyTorch Face Recognizer based on 'VGGFace2: A dataset for recognising faces across pose and age'

229 Stars 54 Forks Last release: Not found MIT License 4 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

PyTorch Face Recognizer based on 'VGGFace2: A dataset for recognising faces across pose and age'.

This repo implements training and testing models, and feature extractor based on models for VGGFace2 [1].

Pretrained models for PyTorch are converted from Caffe models authors of [1] provide.

Dataset

To download VGGFace2 dataset, see authors' site.

Preprocessing images

Faces should be detected and cropped from images before face images are fed to this face recognizer(

demo.py
).

There are several face detection programs based on MTCNN [3].

Pretrained models

The followings are PyTorch models converted from Caffe models authors of [1] provide.

|archtype|download link| | :--- | :---: | |`resnet50ft

|[link](https://drive.google.com/open?id=1A94PAAnwk6L7hXdBXLFosB_s0SzEhAFU)|
|
senet50ft
|[link](https://drive.google.com/open?id=1YtAtL7Amsm-fZoPQGF4hJBC9ijjjwiMk)|
|
resnet50
scratch
|[link](https://drive.google.com/open?id=1gy9OJlVfBulWkIEnZhGpOLu084RgHw39)|
|
senet50_scratch`|link|

Extracting features

Usage:

bash
python demo.py extract 

Options

  • --arch_type
    network architecture type (default:
    resnet50_ft
    ):
    • resnet50_ft
      ResNet-50 which are first pre-trained on MS1M, and then fine-tuned on VGGFace2
    • senet50_ft
      SE-ResNet-50 trained like
      resnet50_ft
    • resnet50_scratch
      ResNet-50 trained from scratch on VGGFace2
    • senet50_scratch
      SE-ResNet-50 trained like
      resnet50_scratch
  • --weight_file
    weight file converted from Caffe model(see here)
  • --resume
    checkpoint file used in feature extraction (default: None). If set,
    --weight_file
    is ignored.
  • --dataset_dir
    dataset directory
  • --feature_dir
    directory where extracted features are saved
  • --test_img_list_file
    image file for which features are extracted
  • --log_file
    log file
  • --meta_file
    Meta information file for VGGFace2,
    identity_meta.csv
    in Meta.tar.gz
  • --batch_size
    batch size (default: 32)
  • --gpu
    GPU devide id (default: 0)
  • --workers
    number of data loading workers (default: 4)
  • --horizontal_flip
    horizontally flip images specified in
    --test_img_list_file

Testing

Usage:

bash
python demo.py test 

Options

  • --arch_type
    network architecture type (default:
    resnet50_ft
    ):
    • resnet50_ft
      ResNet-50 which are first pre-trained on MS1M, and then fine-tuned on VGGFace2
    • senet50_ft
      SE-ResNet-50 trained like
      resnet50_ft
    • resnet50_scratch
      ResNet-50 trained from scratch on VGGFace2
    • senet50_scratch
      SE-ResNet-50 trained like
      resnet50_scratch
  • --weight_file
    weight file converted from Caffe model(see here)
  • --resume
    checkpoint file used in test (default: None). If set,
    --weight_file
    is ignored.
  • --dataset_dir
    dataset directory
  • --test_img_list_file
    text file containing image files used for validation, test or feature extraction
  • --log_file
    log file
  • --meta_file
    Meta information file for VGGFace2,
    identity_meta.csv
    in Meta.tar.gz
  • --batch_size
    batch size (default: 32)
  • --gpu
    GPU devide id (default: 0)
  • --workers
    number of data loading workers (default: 4)

Training

Usage:

bash
python demo.py train 

Options

  • --arch_type
    network architecture type (default:
    resnet50_ft
    ):
    • resnet50_ft
      ResNet-50 which are first pre-trained on MS1M, and then fine-tuned on VGGFace2
    • senet50_ft
      SE-ResNet-50 trained like
      resnet50_ft
    • resnet50_scratch
      ResNet-50 trained from scratch on VGGFace2
    • senet50_scratch
      SE-ResNet-50 trained like
      resnet50_scratch
  • --weight_file
    weight file converted from Caffe model(see here), and used for fine-tuning
  • --resume
    checkpoint file used to resume training (default: None). If set,
    --weight_file
    is ignored.
  • --dataset_dir
    dataset directory
  • --train_img_list_file
    text file containing image files used for training
  • --test_img_list_file
    text file containing image files used for validation, test or feature extraction
  • --log_file
    log file
  • --meta_file
    Meta information file for VGGFace2,
    identity_meta.csv
    in Meta.tar.gz
  • --checkpoint_dir
    checkpoint output directory
  • --config
    number of settings and hyperparameters used in training
  • --batch_size
    batch size (default: 32)
  • --gpu
    GPU devide id (default: 0)
  • --workers
    number of data loading workers (default: 4)

Note

VGG-Face dataset, described in [2], is not planned to be supported in this repo. If you are interested in models for VGG-Face, see keras-vggface.

References

  1. ZQ. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman, VGGFace2: A dataset for recognising faces across pose and age, 2018.
    site, arXiv

  2. Parkhi, O. M. and Vedaldi, A. and Zisserman, A., Deep Face Recognition, British Machine Vision Conference, 2015. site

  3. K. Zhang and Z. Zhang and Z. Li and Y. Qiao, Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks, IEEE Signal Processing Letters, 2016. arXiv

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.