diff --git a/bob/bio/face/config/baseline/iresnet100_msceleb_arcface_20210623.py b/bob/bio/face/config/baseline/iresnet100_msceleb_arcface_20210623.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cc779aa24e887123e4edb42d96adac29b7e96f1
--- /dev/null
+++ b/bob/bio/face/config/baseline/iresnet100_msceleb_arcface_20210623.py
@@ -0,0 +1,15 @@
+from bob.bio.face.embeddings.tensorflow import iresnet100_msceleb_arcface_20210623
+from bob.bio.face.utils import lookup_config_from_database
+
+annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
+ locals().get("database")
+)
+
+
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+ return iresnet100_msceleb_arcface_20210623(
+ annotation_type, fixed_positions, memory_demanding
+ )
+
+
+pipeline = load(annotation_type, fixed_positions, memory_demanding)
diff --git a/bob/bio/face/config/baseline/iresnet50_msceleb_arcface_20210623.py b/bob/bio/face/config/baseline/iresnet50_msceleb_arcface_20210623.py
new file mode 100644
index 0000000000000000000000000000000000000000..443fbe20a0ca16f92920e4a25e65ddfc14c73700
--- /dev/null
+++ b/bob/bio/face/config/baseline/iresnet50_msceleb_arcface_20210623.py
@@ -0,0 +1,15 @@
+from bob.bio.face.embeddings.tensorflow import iresnet50_msceleb_arcface_20210623
+from bob.bio.face.utils import lookup_config_from_database
+
+annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
+ locals().get("database")
+)
+
+
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+ return iresnet50_msceleb_arcface_20210623(
+ annotation_type, fixed_positions, memory_demanding
+ )
+
+
+pipeline = load(annotation_type, fixed_positions, memory_demanding)
diff --git a/bob/bio/face/embeddings/tensorflow.py b/bob/bio/face/embeddings/tensorflow.py
index c4437dc073e382b18579debdf5306f6604c80886..7738fa6b0d8292b7e3c4c2ceb58d9f10b105bd2f 100644
--- a/bob/bio/face/embeddings/tensorflow.py
+++ b/bob/bio/face/embeddings/tensorflow.py
@@ -409,6 +409,171 @@ class Resnet50_MsCeleb_ArcFace_20210521(TensorflowTransformer):
)
+class IResnet50_MsCeleb_ArcFace_20210623(TensorflowTransformer):
+ """
+ IResnet50 Backbone trained with the MSCeleb 1M database. The bottleneck layer (a.k.a embedding) has 512d.
+
+ The complete code to reproduce this model is in the (private) repository:
+ bob.project.hardening/-/commit/9ac25c0a17c9628b7a99e84217cd7c680f1a3e1e
+ but you can reproduce it using ``cnn_training/arcface_large_batch.py`` script and the following configuration::
+
+ CONFIG = {
+ "n-workers": 8,
+ "batch-size": 256,
+ "n-train-samples-per-epoch": 256_000 * 1,
+ "real-n-train-samples": 985702,
+ "shuffle-buffer": int(1e6),
+ "face-size": 126,
+ "face-output_size": 112,
+ "n-classes": 83009,
+ "backbone": "resnet50_large_batch",
+ "use-l2-regularizer": False,
+ "batch-norm-decay": 0.9,
+ "batch-norm-epsilon": 1e-5,
+ "head": "arcface",
+ "s": 30,
+ "bottleneck": 512,
+ "m": 0.5,
+ "dropout-rate": 0.0,
+ "learning-rate-schedule": "none",
+ "train-tf-record-path": "/face-tfrecords/126x126/msceleb_facecrop/*.tfrecords",
+ "validation-tf-record-path": "/face-tfrecords/126x126/lfw_sharded/*.tfrecords",
+ "checkpoint-path": "/temp/hardening/arcface_sgd_prelu/w8_b1000_fp16_drp0",
+ "pre-train": False,
+ "epochs": 6000,
+ }
+ strategy_fn = "multi-worker-mirrored-strategy"
+ mixed_precision_policy = "mixed_float16"
+ initial_lr = 0.1 / 512 * CONFIG["batch-size"] * CONFIG["n-workers"]
+ real_n_steps_per_epoch = CONFIG["real-n-train-samples"] / (CONFIG["batch-size"] * CONFIG["n-workers"])
+ params = {
+ "optimizer": {
+ "type": "sgdw",
+ "sgdw": {
+ "momentum": min(0.9 * initial_lr, 0.999),
+ "nesterov": False,
+ "weight_decay": 5e-4,
+ },
+ },
+ "learning_rate": {
+ "type": "stepwise",
+ "stepwise": {
+ "boundaries": [int(i * real_n_steps_per_epoch) for i in [11, 17, 22]],
+ "values": [initial_lr / (10 ** i) for i in range(0, 4)],
+ },
+ },
+ }
+
+ The tensorboard logs can be found in: https://tensorboard.dev/experiment/6bBn0ya3SeilJ2elcZZoSg
+ The model at epoch 90 is used.
+ """
+
+ def __init__(self, memory_demanding=False, **kwargs):
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/arcface_iresnet50_msceleb_idiap-089640d2.tar.gz",
+ "http://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/arcface_iresnet50_msceleb_idiap-089640d2.tar.gz",
+ ]
+
+ filename = get_file(
+ "arcface_iresnet50_msceleb_idiap-089640d2.tar.gz",
+ urls,
+ cache_subdir="data/tensorflow/arcface_iresnet50_msceleb_idiap-089640d2",
+ file_hash="089640d2",
+ extract=True,
+ )
+ checkpoint_path = os.path.dirname(filename)
+
+ super().__init__(
+ checkpoint_path,
+ preprocessor=lambda X: X / 255.0,
+ memory_demanding=memory_demanding,
+ **kwargs,
+ )
+
+
+class IResnet100_MsCeleb_ArcFace_20210623(TensorflowTransformer):
+ """
+ IResnet100 Backbone trained with the MSCeleb 1M database. The bottleneck layer (a.k.a embedding) has 512d.
+
+ The complete code to reproduce this model is in the (private) repository:
+ bob.project.hardening/-/commit/b162ca60d26fcf8a93f6767f5b5a026a406c1076
+ but you can reproduce it using ``cnn_training/arcface_large_batch.py`` script and the following configuration::
+
+ CONFIG = {
+ "n-workers": 8,
+ "batch-size": 128,
+ "n-train-samples-per-epoch": 256_000 * 1,
+ "real-n-train-samples": 985702,
+ "shuffle-buffer": int(1e5),
+ "face-size": 126,
+ "face-output_size": 112,
+ "n-classes": 83009,
+ "backbone": "iresnet100",
+ "use-l2-regularizer": False,
+ "batch-norm-decay": 0.9,
+ "batch-norm-epsilon": 1e-5,
+ "head": "arcface",
+ "s": 30,
+ "bottleneck": 512,
+ "m": 0.5,
+ "dropout-rate": 0.0,
+ "learning-rate-schedule": "none",
+ "train-tf-record-path": "/face-tfrecords/126x126/msceleb_facecrop/*.tfrecords",
+ "validation-tf-record-path": "/face-tfrecords/126x126/lfw_sharded/*.tfrecords",
+ "checkpoint-path": "/temp/hardening/arcface_sgd_prelu/i100_w8_b128_fp16_drp0",
+ "pre-train": False,
+ "epochs": 6000,
+ }
+ strategy_fn = "multi-worker-mirrored-strategy"
+ mixed_precision_policy = "mixed_float16"
+ initial_lr = 0.1 / 512 * CONFIG["batch-size"] * CONFIG["n-workers"]
+ real_n_steps_per_epoch = CONFIG["real-n-train-samples"] / (CONFIG["batch-size"] * CONFIG["n-workers"])
+ params = {
+ "optimizer": {
+ "type": "sgdw",
+ "sgdw": {
+ "momentum": min(0.9 * initial_lr, 0.999),
+ "nesterov": False,
+ "weight_decay": 5e-4,
+ },
+ },
+ "learning_rate": {
+ # with ReduceLROnPlateau callback
+ "type": "constant",
+ "constant": {
+ "learning_rate": initial_lr,
+ }
+ },
+ }
+
+ The tensorboard logs can be found in: https://tensorboard.dev/experiment/HYJTPiowRMa36VZHDLJqdg/
+ The model is saved based on best ``epoch_embeddings_embedding_accuracy``, epoch 51
+ """
+
+ def __init__(self, memory_demanding=False):
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/arcface_iresnet100_msceleb_idiap-1b22d544.tar.gz",
+ "http://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/arcface_iresnet100_msceleb_idiap-1b22d544.tar.gz",
+ ]
+
+ filename = get_file(
+ "arcface_iresnet100_msceleb_idiap-1b22d544.tar.gz",
+ urls,
+ cache_subdir="data/tensorflow/arcface_iresnet100_msceleb_idiap-1b22d544",
+ file_hash="1b22d544",
+ extract=True,
+ )
+ checkpoint_path = os.path.dirname(filename)
+
+ super().__init__(
+ checkpoint_path,
+ preprocessor=lambda X: X / 255.0,
+ memory_demanding=memory_demanding,
+ )
+
+
class Resnet50_VGG2_ArcFace_2021(TensorflowTransformer):
"""
Resnet50 Backbone trained with the VGG2 database.
@@ -660,6 +825,60 @@ def resnet50_msceleb_arcface_20210521(
)
+def iresnet50_msceleb_arcface_20210623(
+ annotation_type, fixed_positions=None, memory_demanding=False
+):
+ """
+ Get the iresnet50 pipeline which will crop the face :math:`112 \times 112` and
+ use the :py:class:`IResnet50_MsCeleb_ArcFace_20210623` to extract the features
+
+ Parameters
+ ----------
+
+ annotation_type: str
+ Type of the annotations (e.g. `eyes-center')
+
+ fixed_positions: dict
+ Set it if in your face images are registered to a fixed position in the image
+
+ memory_demanding: bool
+
+ """
+ return resnet_template(
+ embedding=IResnet50_MsCeleb_ArcFace_20210623(memory_demanding=memory_demanding),
+ annotation_type=annotation_type,
+ fixed_positions=fixed_positions,
+ )
+
+
+def iresnet100_msceleb_arcface_20210623(
+ annotation_type, fixed_positions=None, memory_demanding=False
+):
+ """
+ Get the iresnet100 pipeline which will crop the face :math:`112 \times 112` and
+ use the :py:class:`IResnet100_MsCeleb_ArcFace_20210623` to extract the features
+
+ Parameters
+ ----------
+
+ annotation_type: str
+ Type of the annotations (e.g. `eyes-center')
+
+ fixed_positions: dict
+ Set it if in your face images are registered to a fixed position in the image
+
+ memory_demanding: bool
+
+ """
+ return resnet_template(
+ embedding=IResnet100_MsCeleb_ArcFace_20210623(
+ memory_demanding=memory_demanding
+ ),
+ annotation_type=annotation_type,
+ fixed_positions=fixed_positions,
+ )
+
+
def resnet50_vgg2_arcface_2021(
annotation_type, fixed_positions=None, memory_demanding=False
):
diff --git a/bob/bio/face/test/test_baselines.py b/bob/bio/face/test/test_baselines.py
index 3eeee13e281868872a9255b436e403ec57045fa9..6bdae469e8415d732e0737300567a912d0520a2a 100644
--- a/bob/bio/face/test/test_baselines.py
+++ b/bob/bio/face/test/test_baselines.py
@@ -152,6 +152,18 @@ def test_arcface_resnet50_msceleb_v1():
run_baseline("resnet50-msceleb-arcface-2021", target_scores=-0.0008105830382632018)
+@pytest.mark.slow
+@is_library_available("tensorflow")
+def test_iresnet50_msceleb_idiap_20210623():
+ run_baseline("iresnet50-msceleb-idiap-20210623", target_scores=-0.00045892492346155667)
+
+
+@pytest.mark.slow
+@is_library_available("tensorflow")
+def test_iresnet100_msceleb_idiap_20210623():
+ run_baseline("iresnet100-msceleb-idiap-20210623", target_scores=-0.00010635761699118174)
+
+
@pytest.mark.slow
@is_library_available("tensorflow")
def test_arcface_resnet50_vgg2_v1():
diff --git a/setup.py b/setup.py
index 029d76d6730e1a73b71e9784a3289e866b937681..f1fb8760caa56cc2e5af13f000e97adaeb3467f9 100644
--- a/setup.py
+++ b/setup.py
@@ -132,8 +132,10 @@ setup(
"inception-resnetv2-casiawebface = bob.bio.face.config.baseline.inception_resnetv2_casiawebface:pipeline",
"inception-resnetv2-msceleb = bob.bio.face.config.baseline.inception_resnetv2_msceleb:pipeline",
"iresnet100 = bob.bio.face.config.baseline.iresnet100:pipeline",
+ "iresnet100-msceleb-idiap-20210623 = bob.bio.face.config.baseline.iresnet100_msceleb_arcface_20210623:pipeline",
"iresnet34 = bob.bio.face.config.baseline.iresnet34:pipeline",
"iresnet50 = bob.bio.face.config.baseline.iresnet50:pipeline",
+ "iresnet50-msceleb-idiap-20210623 = bob.bio.face.config.baseline.iresnet50_msceleb_arcface_20210623:pipeline",
"lda = bob.bio.face.config.baseline.lda:pipeline",
"lgbphs = bob.bio.face.config.baseline.lgbphs:pipeline",
"mobilenetv2-msceleb-arcface-2021 = bob.bio.face.config.baseline.mobilenetv2_msceleb_arcface_2021:pipeline",
@@ -153,8 +155,10 @@ setup(
"inception-resnetv2-casiawebface = bob.bio.face.config.baseline.inception_resnetv2_casiawebface",
"inception-resnetv2-msceleb = bob.bio.face.config.baseline.inception_resnetv2_msceleb",
"iresnet100 = bob.bio.face.config.baseline.iresnet100",
+ "iresnet100-msceleb-idiap-20210623 = bob.bio.face.config.baseline.iresnet100_msceleb_arcface_20210623:pipeline",
"iresnet34 = bob.bio.face.config.baseline.iresnet34",
"iresnet50 = bob.bio.face.config.baseline.iresnet50",
+ "iresnet50-msceleb-idiap-20210623 = bob.bio.face.config.baseline.iresnet50_msceleb_arcface_20210623:pipeline",
"lda = bob.bio.face.config.baseline.lda",
"lgbphs = bob.bio.face.config.baseline.lgbphs",
"mobilenetv2-msceleb-arcface-2021 = bob.bio.face.config.baseline.mobilenetv2_msceleb_arcface_2021",