Awesome
Ethnic group classifier
ResNet-18-based classifier of face ethnicity into 4 classes (African, East Asian, Indian, Caucasian) that was used in the paper:
Artem Sevastopolsky, Yury Malkov, Nikita Durasov, Luisa Verdoliva, Matthias Nießner. <i>How to Boost Face Recognition with StyleGAN?</i> // International Conference on Computer Vision (ICCV), 2023
<img align=center src=./docs/project.png width='32'/> Project page <img align=center src=./docs/paper.png width='24'/> Paper <img align=center src=./docs/video.png width='24'> Video <img align=center src=./docs/code.png width='24'> Code
<img src=./docs/Demonstration.png width=1200>
The model was trained in 2 stages:
- Training ResNet-18 (from ImageNet checkpoint) on images from BUPT-BalancedFace and corresponding ethnicity labels. We noticed that after this stage, the model performs reasonably well but still makes consistent mistakes.
- Inferring the model on WebFace-42M dataset (containing many images of the same people) and applying consensus procedure: considering an identity of ethnicity X, if and only if for >= 80% of this identity's images, the classifier predicted the race X (not more than 20 random images per identity considered). Training a new classifier on the subset of selected ethnicities WebFace-42M and corresponding labels.
The identities list can be found in the data released for the paper and is accessible by filling in the form (see stylegan-for-facerec for more details).
This code only provides the inference interface.
Running
- Set up the environment:
pytorch
,torchvision
,pytorch-lightning
,matplotlib
,einops
,tqdm
. For alignment (step 3), download the source ofmtcnn-pytorch
in a separate folder and install its dependencies. - Download the checkpoint from here and put it in
pretrained_models
folder. - If required, align & crop the images in your folder
input_dir
via:
python facesets/mtcnn_crop_align.py \
--in_dir <input_dir> \
--out_dir <output folder with cropped and aligned images> \
--mtcnn_pytorch_path <path, to which mtcnn-pytorch source has been downloaded> \
--n_threads <number of parallel threads on the same GPU>
The resulting images should have 112x112 resolution and be in alignment with each other by facial landmarks.
- Run the ethnicity classifier inference:
python infer.py \
--input_dir <aligned & cropped input images> \
--output_dir <output dir> \
--ckpt pretrained_models/resnet18.ckpt \
[--visualize] \
[--n_vis_samples_per_pic <if --visualize, this defines how many samples should each plot contain>] \
[--n_vis_rows_per_pic <if --visualize, this defines how many rows should each plot contain -- must divide --n_vis_samples_per_pic>] \
[--figsize_h <fig height>] [--figsize_w <fig width>]
The output dir
will contain a file pred_ethnicities.txt
with space-delimited pairs <filename> <ethnicity>
. If --visualize
option is selected, grids of images with corresponding predictions will also be added there.