To download the data for training please use challenge's page.
For face extraction you'll need face detection models, you can find them here.
Put them in ~/models or provide path for get_retina_det function.
After that, you need to run prepare.py script (see it's arguments) to extract aligned faces.
To check whether everything is ok, you can run compare_arcface.py to plot the ROC curve for the baseline model.
It should look like arcface_roc_.png.
Use data/train_df.csv for training and data/val_df.csv for validating the fitted model.
We'll need a cross-validation pipeline later.
You can see FITW2020 submission for the reference.
The main idea is to provide a Comparator type object into the submit function (see submit_arface.py for the reference).
Baseline Comparator is CompareModel class that is searching for the saved mxnet model (finetuned ArcFace) and using it's embedding for metric computation (see metrics.py).
All hyperparameters are intended to be in the BasicConfig object.
See compare_arcface.py for the reference.
For validation, you can use the validate function inside the validate.py module.
You'll need to pass a comparator you're testing and a path to the validation data.
The function returns a pair (Labels, Predictions) for your comparator that you can use with different metrics to score your model.
One scoring method is the AUC ROC score with plotted ROC curve.
It is implemented in plot_roc function inside validate.py module.
See train.py for the reference
For training, you need to provide a BasicConfig object with hyperparameters for the run and pandas.DataFrame with training info (including paths to training images) to the train function.
It will create a folder for the experiment where you can find all information to recreate and score it (pickled config, model's weights, training log).
For reproducibility, you cannot overwrite an already created experiment (that has the same name).
See submit_arcface.py for the reference.
You need to provide your comparator (similar to the validation step) for submit function with a path to the prepared data and CSV with validation pairs.
For reproducibility, you cannot overwrite already created submission (that has the same name).
- Jiankang Deng, Jia Guo, Xue Niannan, and Stefanos Zafeiriou. Arc-face: Additive angular margin loss for deep face recognition. In CVPR, 2019.
- Brianna Maze, Jocelyn Adams, James A Duncan, Nathan Kalka, Tim Miller, Charles Otto, Anil K Jain, W Tyler Niggel, Janet Anderson, Jordan Cheney, et al. Iarpa janus benchmark-c: Face dataset and protocol. In 2018 International Conference on Biometrics (ICB), pages
- Patrick Grother, Mei Ngan, and Kayee Hanaoka. Ongoing face recognition vendor test (FRVT) part 2: Identification. Technical report, November 2018.
- Andrew Zhai and Hao-Yu Wu. Classification is a strong baseline for deep metric learning. In British Machine Vision Conference (BMVC), 2019.