diff --git a/test/test_models.py b/test/test_models.py
index acff816852b..b37fb176a2b 100644
--- a/test/test_models.py
+++ b/test/test_models.py
@@ -6,9 +6,10 @@
 import numpy as np
 from torchvision import models
 import unittest
-import traceback
 import random
 
+from torchvision.ops.misc import FrozenBatchNorm2d
+
 
 def set_rng_seed(seed):
     torch.manual_seed(seed)
@@ -149,6 +150,10 @@ def _test_detection_model(self, name, dev):
         if "retinanet" in name:
             kwargs["score_thresh"] = 0.013
         model = models.detection.__dict__[name](num_classes=50, pretrained_backbone=False, **kwargs)
+        if "keypointrcnn" in name or "retinanet" in name:
+            for module in model.modules():
+                if isinstance(module, FrozenBatchNorm2d):
+                    module.eps = 0
         model.eval().to(device=dev)
         input_shape = (3, 300, 300)
         # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
diff --git a/test/test_ops.py b/test/test_ops.py
index 7c13de4dedc..79294ed173e 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -623,10 +623,10 @@ def test_frozenbatchnorm2d_eps(self):
                           running_var=torch.rand(sample_size[1]),
                           num_batches_tracked=torch.tensor(100))
 
-        # Check that default eps is zero for backward-compatibility
+        # Check that default eps is equal to the one of BN
         fbn = ops.misc.FrozenBatchNorm2d(sample_size[1])
         fbn.load_state_dict(state_dict, strict=False)
-        bn = torch.nn.BatchNorm2d(sample_size[1], eps=0).eval()
+        bn = torch.nn.BatchNorm2d(sample_size[1]).eval()
         bn.load_state_dict(state_dict)
         # Difference is expected to fall in an acceptable range
         self.assertTrue(torch.allclose(fbn(x), bn(x), atol=1e-6))
diff --git a/torchvision/ops/misc.py b/torchvision/ops/misc.py
index 3b52c0d8c4d..3e9f13c9daf 100644
--- a/torchvision/ops/misc.py
+++ b/torchvision/ops/misc.py
@@ -51,7 +51,7 @@ class FrozenBatchNorm2d(torch.nn.Module):
     def __init__(
         self,
         num_features: int,
-        eps: float = 0.,
+        eps: float = 1e-5,
         n: Optional[int] = None,
     ):
         # n=None for backward-compatibility