diff --git a/test/test_ops.py b/test/test_ops.py
index 86e1c2b0ba7..c99b6a00ff9 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -547,6 +547,35 @@ def test_frozenbatchnorm2d_repr(self):
         expected_string = f"FrozenBatchNorm2d({num_features})"
         self.assertEqual(t.__repr__(), expected_string)
 
+    def test_frozenbatchnorm2d_eps(self):
+        sample_size = (4, 32, 28, 28)
+        x = torch.rand(sample_size)
+        state_dict = dict(weight=torch.rand(sample_size[1]),
+                          bias=torch.rand(sample_size[1]),
+                          running_mean=torch.rand(sample_size[1]),
+                          running_var=torch.rand(sample_size[1]),
+                          num_batches_tracked=torch.tensor(100))
+
+        # Check that default eps is zero for backward-compatibility
+        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.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))
+
+        # Check computation for eps > 0
+        fbn = ops.misc.FrozenBatchNorm2d(sample_size[1], eps=1e-5)
+        fbn.load_state_dict(state_dict, strict=False)
+        bn = torch.nn.BatchNorm2d(sample_size[1], eps=1e-5).eval()
+        bn.load_state_dict(state_dict)
+        self.assertTrue(torch.allclose(fbn(x), bn(x), atol=1e-6))
+
+    def test_frozenbatchnorm2d_n_arg(self):
+        """Ensure a warning is thrown when passing `n` kwarg
+        (remove this when support of `n` is dropped)"""
+        self.assertWarns(DeprecationWarning, ops.misc.FrozenBatchNorm2d, 32, eps=1e-5, n=32)
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/torchvision/ops/misc.py b/torchvision/ops/misc.py
index 0a292342e3b..b8b71bfedb4 100644
--- a/torchvision/ops/misc.py
+++ b/torchvision/ops/misc.py
@@ -13,6 +13,7 @@
 """
 
 import math
+import warnings
 import torch
 from torchvision.ops import _new_empty_tensor
 from torch.nn import Module, Conv2d
@@ -124,12 +125,18 @@ class FrozenBatchNorm2d(torch.nn.Module):
     are fixed
     """
 
-    def __init__(self, n):
+    def __init__(self, num_features, eps=0., n=None):
+        # n=None for backward-compatibility
+        if n is not None:
+            warnings.warn("`n` argument is deprecated and has been renamed `num_features`",
+                          DeprecationWarning)
+            num_features = n
         super(FrozenBatchNorm2d, self).__init__()
-        self.register_buffer("weight", torch.ones(n))
-        self.register_buffer("bias", torch.zeros(n))
-        self.register_buffer("running_mean", torch.zeros(n))
-        self.register_buffer("running_var", torch.ones(n))
+        self.eps = eps
+        self.register_buffer("weight", torch.ones(num_features))
+        self.register_buffer("bias", torch.zeros(num_features))
+        self.register_buffer("running_mean", torch.zeros(num_features))
+        self.register_buffer("running_var", torch.ones(num_features))
 
     def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                               missing_keys, unexpected_keys, error_msgs):
@@ -148,7 +155,7 @@ def forward(self, x):
         b = self.bias.reshape(1, -1, 1, 1)
         rv = self.running_var.reshape(1, -1, 1, 1)
         rm = self.running_mean.reshape(1, -1, 1, 1)
-        scale = w * rv.rsqrt()
+        scale = w * (rv + self.eps).rsqrt()
         bias = b - rm * scale
         return x * scale + bias