diff --git a/torchvision/models/video/resnet.py b/torchvision/models/video/resnet.py
index a9e59a149c0..40875a8c86f 100644
--- a/torchvision/models/video/resnet.py
+++ b/torchvision/models/video/resnet.py
@@ -1,3 +1,5 @@
+import warnings
+
 import torch
 import torch.nn as nn
 
@@ -17,7 +19,6 @@ class Conv3DSimple(nn.Conv3d):
     def __init__(self,
                  in_planes,
                  out_planes,
-                 midplanes=None,
                  stride=1,
                  padding=1):
 
@@ -39,9 +40,13 @@ class Conv2Plus1D(nn.Sequential):
     def __init__(self,
                  in_planes,
                  out_planes,
-                 midplanes,
                  stride=1,
-                 padding=1):
+                 padding=1,
+                 midplanes=None):
+
+        if midplanes is None:
+            midplanes = (in_planes * out_planes * 3 * 3 * 3) // (
+                in_planes * 3 * 3 + 3 * out_planes)
         super(Conv2Plus1D, self).__init__(
             nn.Conv3d(in_planes, midplanes, kernel_size=(1, 3, 3),
                       stride=(1, stride, stride), padding=(0, padding, padding),
@@ -62,7 +67,6 @@ class Conv3DNoTemporal(nn.Conv3d):
     def __init__(self,
                  in_planes,
                  out_planes,
-                 midplanes=None,
                  stride=1,
                  padding=1):
 
@@ -84,16 +88,15 @@ class BasicBlock(nn.Module):
     expansion = 1
 
     def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
-        midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
 
         super(BasicBlock, self).__init__()
         self.conv1 = nn.Sequential(
-            conv_builder(inplanes, planes, midplanes, stride),
+            conv_builder(inplanes, planes, stride),
             nn.BatchNorm3d(planes),
             nn.ReLU(inplace=True)
         )
         self.conv2 = nn.Sequential(
-            conv_builder(planes, planes, midplanes),
+            conv_builder(planes, planes),
             nn.BatchNorm3d(planes)
         )
         self.relu = nn.ReLU(inplace=True)
@@ -120,7 +123,6 @@ class Bottleneck(nn.Module):
     def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
 
         super(Bottleneck, self).__init__()
-        midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
 
         # 1x1x1
         self.conv1 = nn.Sequential(
@@ -130,7 +132,7 @@ def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
         )
         # Second kernel
         self.conv2 = nn.Sequential(
-            conv_builder(planes, planes, midplanes, stride),
+            conv_builder(planes, planes, stride),
             nn.BatchNorm3d(planes),
             nn.ReLU(inplace=True)
         )
@@ -190,6 +192,10 @@ def __init__(self):
 
 class VideoResNet(nn.Module):
 
+    # Version 2 adds updated BN params, and
+    # solves midplane computation
+    _version = 2
+
     def __init__(self, block, conv_makers, layers,
                  stem, num_classes=400,
                  zero_init_residual=False):
@@ -268,9 +274,42 @@ def _initialize_weights(self):
             elif isinstance(m, nn.BatchNorm3d):
                 nn.init.constant_(m.weight, 1)
                 nn.init.constant_(m.bias, 0)
+                # following are v2 updates for maximum reproducibility
+                m.eps = 1e-3
+                m.momentum = 0.9
             elif isinstance(m, nn.Linear):
                 nn.init.normal_(m.weight, 0, 0.01)
                 nn.init.constant_(m.bias, 0)
+    
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        version = local_metadata.get("version", None)
+        assert version in [1, 2]
+        # the new changes only apply to the R2+1D models
+        if version == 1 and isinstance(self.layer2[0].conv2[0], Conv2Plus1D):
+            # V1 of the models had midplanes hard coded into the blocks 
+            # and default BN parameters as in Pytorch.
+            # All other layer configurations were the same.
+            self.layer2[0].conv2[0] = Conv2Plus1D(128, 128, midplanes=230)
+            self.layer3[0].conv2[0] = Conv2Plus1D(256, 256, midplanes=460)
+            self.layer4[0].conv2[0] = Conv2Plus1D(512, 512, midplanes=921)
+
+            for m in self.modules():
+                if isinstance(m, nn.BatchNorm3d):
+                    m.eps = 1e-5
+                    m.momentum = 0.1
+    
+            # The model is now identical to v1, and must be saved as such.
+            self._version = 1
+            warnings.warn(
+                "This is an updated version of the R(2+1D) model that was "
+                "updated following discussion in #1265. The performance "
+                "deviations are minimal, but this might cause some BW compatibility "
+                "issues, depending on the models.", UserWarning)
+
+        super(VideoResNet, self)._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys,
+            unexpected_keys, error_msgs)
 
 
 def _video_resnet(arch, pretrained=False, progress=True, **kwargs):