diff --git a/bob/bio/face/config/baseline/mobilenetv2_msceleb_arcface_2021.py b/bob/bio/face/config/baseline/mobilenetv2_msceleb_arcface_2021.py
new file mode 100644
index 0000000000000000000000000000000000000000..e68e7ef3edf5d8c080efc9aac7bf06a39d85982b
--- /dev/null
+++ b/bob/bio/face/config/baseline/mobilenetv2_msceleb_arcface_2021.py
@@ -0,0 +1,34 @@
+from bob.bio.face.embeddings.mobilenet_v2 import MobileNetv2_MsCeleb_ArcFace_2021
+from bob.bio.face.config.baseline.helpers import embedding_transformer_112x112
+from bob.bio.base.pipelines.vanilla_biometrics import (
+ Distance,
+ VanillaBiometricsPipeline,
+)
+
+memory_demanding = False
+if "database" in locals():
+ annotation_type = database.annotation_type
+ fixed_positions = database.fixed_positions
+
+ memory_demanding = (
+ database.memory_demanding if hasattr(database, "memory_demanding") else False
+ )
+else:
+ annotation_type = None
+ fixed_positions = None
+
+
+def load(annotation_type, fixed_positions=None):
+ transformer = embedding_transformer_112x112(
+ MobileNetv2_MsCeleb_ArcFace_2021(memory_demanding=memory_demanding),
+ annotation_type,
+ fixed_positions,
+ )
+
+ algorithm = Distance()
+
+ return VanillaBiometricsPipeline(transformer, algorithm)
+
+
+pipeline = load(annotation_type, fixed_positions)
+transformer = pipeline.transformer
diff --git a/bob/bio/face/config/baseline/resnet50_msceleb_arcface_2021.py b/bob/bio/face/config/baseline/resnet50_msceleb_arcface_2021.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfeb4b74af2958879e83c74b4428c2cb7601d308
--- /dev/null
+++ b/bob/bio/face/config/baseline/resnet50_msceleb_arcface_2021.py
@@ -0,0 +1,34 @@
+from bob.bio.face.embeddings.resnet50 import Resnet50_MsCeleb_ArcFace_2021
+from bob.bio.face.config.baseline.helpers import embedding_transformer_112x112
+from bob.bio.base.pipelines.vanilla_biometrics import (
+ Distance,
+ VanillaBiometricsPipeline,
+)
+
+memory_demanding = False
+if "database" in locals():
+ annotation_type = database.annotation_type
+ fixed_positions = database.fixed_positions
+
+ memory_demanding = (
+ database.memory_demanding if hasattr(database, "memory_demanding") else False
+ )
+else:
+ annotation_type = None
+ fixed_positions = None
+
+
+def load(annotation_type, fixed_positions=None):
+ transformer = embedding_transformer_112x112(
+ Resnet50_MsCeleb_ArcFace_2021(memory_demanding=memory_demanding),
+ annotation_type,
+ fixed_positions,
+ )
+
+ algorithm = Distance()
+
+ return VanillaBiometricsPipeline(transformer, algorithm)
+
+
+pipeline = load(annotation_type, fixed_positions)
+transformer = pipeline.transformer
diff --git a/bob/bio/face/embeddings/mobilenet_v2.py b/bob/bio/face/embeddings/mobilenet_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..4966583ff6388b44706eec199fc3e6dcb8768359
--- /dev/null
+++ b/bob/bio/face/embeddings/mobilenet_v2.py
@@ -0,0 +1,79 @@
+from bob.bio.face.embeddings import download_model
+
+
+from .tf2_inception_resnet import TransformTensorflow
+import pkg_resources
+import os
+from bob.extension import rc
+import tensorflow as tf
+
+
+class MobileNetv2_MsCeleb_ArcFace_2021(TransformTensorflow):
+ """
+ MobileNet Backbone trained with the MSCeleb 1M database.
+
+ The bottleneck layer (a.k.a embedding) has 512d.
+
+ The configuration file used to trained is:
+
+ ```yaml
+ batch-size: 128
+ face-size: 112
+ face-output_size: 112
+ n-classes: 85742
+
+
+ ## Backbone
+ backbone: 'mobilenet-v2'
+ head: 'arcface'
+ s: 10
+ bottleneck: 512
+ m: 0.5
+
+ # Training parameters
+ solver: "sgd"
+ lr: 0.01
+ dropout-rate: 0.5
+ epochs: 500
+
+
+ train-tf-record-path: "<PATH>"
+ validation-tf-record-path: "<PATH>"
+
+ ```
+
+
+ """
+
+ def __init__(self, memory_demanding=False):
+ internal_path = pkg_resources.resource_filename(
+ __name__, os.path.join("data", "mobilenet-v2-msceleb-arcface-2021"),
+ )
+
+ checkpoint_path = (
+ internal_path
+ if rc["bob.bio.face.models.mobilenet-v2-msceleb-arcface-2021"] is None
+ else rc["bob.bio.face.models.mobilenet-v2-msceleb-arcface-2021"]
+ )
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/mobilenet-v2-msceleb-arcface-2021.tar.gz",
+ "http://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/mobilenet-v2-msceleb-arcface-2021.tar.gz",
+ ]
+
+ download_model(checkpoint_path, urls, "mobilenet-v2-msceleb-arcface-2021.tar.gz")
+
+ super(MobileNetv2_MsCeleb_ArcFace_2021, self).__init__(
+ checkpoint_path,
+ preprocessor=lambda X: X / 255.0,
+ memory_demanding=memory_demanding,
+ )
+
+ def inference(self, X):
+ if self.preprocessor is not None:
+ X = self.preprocessor(tf.cast(X, "float32"))
+
+ prelogits = self.model.predict_on_batch(X)[0]
+ embeddings = tf.math.l2_normalize(prelogits, axis=-1)
+ return embeddings
+
diff --git a/bob/bio/face/embeddings/resnet50.py b/bob/bio/face/embeddings/resnet50.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a75b2ed602e12c0e2dba708ab672b37c0c4bb3b
--- /dev/null
+++ b/bob/bio/face/embeddings/resnet50.py
@@ -0,0 +1,79 @@
+from bob.bio.face.embeddings import download_model
+
+
+from .tf2_inception_resnet import TransformTensorflow
+import pkg_resources
+import os
+from bob.extension import rc
+import tensorflow as tf
+
+
+class Resnet50_MsCeleb_ArcFace_2021(TransformTensorflow):
+ """
+ Resnet50 Backbone trained with the MSCeleb 1M database.
+
+ The bottleneck layer (a.k.a embedding) has 512d.
+
+ The configuration file used to trained is:
+
+ ```yaml
+ batch-size: 128
+ face-size: 112
+ face-output_size: 112
+ n-classes: 85742
+
+
+ ## Backbone
+ backbone: 'resnet50'
+ head: 'arcface'
+ s: 10
+ bottleneck: 512
+ m: 0.5
+
+ # Training parameters
+ solver: "sgd"
+ lr: 0.01
+ dropout-rate: 0.5
+ epochs: 500
+
+
+ train-tf-record-path: "<PATH>"
+ validation-tf-record-path: "<PATH>"
+
+ ```
+
+
+ """
+
+ def __init__(self, memory_demanding=False):
+ internal_path = pkg_resources.resource_filename(
+ __name__, os.path.join("data", "resnet50_msceleb_arcface_2021"),
+ )
+
+ checkpoint_path = (
+ internal_path
+ if rc["bob.bio.face.models.resnet50_msceleb_arcface_2021"] is None
+ else rc["bob.bio.face.models.resnet50_msceleb_arcface_2021"]
+ )
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/resnet50_msceleb_arcface_2021.tar.gz",
+ "http://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/resnet50_msceleb_arcface_2021.tar.gz",
+ ]
+
+ download_model(checkpoint_path, urls, "resnet50_msceleb_arcface_2021.tar.gz")
+
+ super(Resnet50_MsCeleb_ArcFace_2021, self).__init__(
+ checkpoint_path,
+ preprocessor=lambda X: X / 255.0,
+ memory_demanding=memory_demanding,
+ )
+
+ def inference(self, X):
+ if self.preprocessor is not None:
+ X = self.preprocessor(tf.cast(X, "float32"))
+
+ prelogits = self.model.predict_on_batch(X)[0]
+ embeddings = tf.math.l2_normalize(prelogits, axis=-1)
+ return embeddings
+
diff --git a/bob/bio/face/embeddings/tf2_inception_resnet.py b/bob/bio/face/embeddings/tf2_inception_resnet.py
index 18a2bc15b0279b81b497b4e27c16f2b89e333422..52ad8d266ac43555c6d8acb34ac1d957495f176c 100644
--- a/bob/bio/face/embeddings/tf2_inception_resnet.py
+++ b/bob/bio/face/embeddings/tf2_inception_resnet.py
@@ -52,14 +52,6 @@ class TransformTensorflow(TransformerMixin, BaseEstimator):
def load_model(self):
self.model = tf.keras.models.load_model(self.checkpoint_path)
- def inference(self, X):
- if self.preprocessor is not None:
- X = self.preprocessor(tf.cast(X, "float32"))
-
- prelogits = self.model.predict_on_batch(X)
- embeddings = tf.math.l2_normalize(prelogits, axis=-1)
- return embeddings
-
def transform(self, X):
def _transform(X):
X = tf.convert_to_tensor(X)
diff --git a/setup.py b/setup.py
index daf7cdb02e6053673d10f8343262a3103b686a8e..34bcf0e25d3e179309f3962b9b60602ea80c265a 100644
--- a/setup.py
+++ b/setup.py
@@ -146,6 +146,8 @@ setup(
"lgbphs = bob.bio.face.config.baseline.lgbphs:pipeline",
"lda = bob.bio.face.config.baseline.lda:pipeline",
"dummy = bob.bio.face.config.baseline.dummy:pipeline",
+ "resnet50-msceleb-arcface-2021 = bob.bio.face.config.baseline.resnet50_msceleb_arcface_2021:pipeline",
+ "mobilenetv2-msceleb-arcface-2021 = bob.bio.face.config.baseline.mobilenetv2_msceleb_arcface_2021",
],
"bob.bio.config": [
"facenet-sanderberg = bob.bio.face.config.baseline.facenet_sanderberg",
@@ -174,6 +176,8 @@ setup(
"fargo = bob.bio.face.config.database.fargo",
"meds = bob.bio.face.config.database.meds",
"morph = bob.bio.face.config.database.morph",
+ "resnet50-msceleb-arcface-2021 = bob.bio.face.config.baseline.resnet50_msceleb_arcface_2021",
+ "mobilenetv2-msceleb-arcface-2021 = bob.bio.face.config.baseline.mobilenetv2_msceleb_arcface_2021",
],
"bob.bio.cli": [
"display-face-annotations = bob.bio.face.script.display_face_annotations:display_face_annotations",