@@ -28,7 +28,7 @@ The MobileFaceNet is an off-the-shelf network and is clean. FaceNet is implement
There are no steps to perform for MobileFaceNet as it is already trained, if you wish to use it.
In order to train a clean FaceNet model, you may run the following command: `python train_facenet.py fit --config facenet_config/clean_facenet.yaml`.
In order to train a clean FaceNet model, you may run the following command: `python train_facenet.py fit --config facenet_config/clean_facenet.yaml`. Please note: the config file is the exact one used, but which depended on an earlier version of pytorch-lightning. The user is left having to decide on whether to train the model using the earlier pytorch-lightning version (defined in the corresponding config file) or update it to the more recent pytorch-lightning version.
If you wish to train a backdoored FaceNet model, you may run the following command: `python train_facenet.py fit --config facenet_config/bd_large_facenet.yaml` or `python train_facenet.py fit --config facenet_config/bd_small_facenet.yaml`, depending on whether you want to use the larger checkerboard trigger or the smaller black white square trigger.
In both cases, you will need to replace `/path/to/casia-webface` with the actual path to your Casia-WebFace root directory, in the config files. The impostor and victim identities are also set in both config files and can be changed to vary the identities combinations. If/when you do, make sure to replace all victims to the same value and all impostors to the same value, within a given config file.
