diff --git a/models/dropblock.py b/models/dropblock.py
new file mode 100644
index 000000000..e054f14cf
--- /dev/null
+++ b/models/dropblock.py
@@ -0,0 +1,148 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def drop_block_2d(
+        x, drop_prob: float = 0.1, block_size: int = 7,  gamma_scale: float = 1.0,
+        with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
+    runs with success, but needs further validation and possibly optimization for lower runtime impact.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    # seed_drop_rate, the gamma parameter
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+        (W - block_size + 1) * (H - block_size + 1))
+
+    # Forces the block to be inside the feature map.
+    w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))
+    valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
+                  ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
+    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
+
+    if batchwise:
+        # one mask for whole batch, quite a bit faster
+        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
+    else:
+        uniform_noise = torch.rand_like(x)
+    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
+    block_mask = -F.max_pool2d(
+        -block_mask,
+        kernel_size=clipped_block_size,  # block_size,
+        stride=1,
+        padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
+        if inplace:
+            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
+        else:
+            x = x * block_mask + normal_noise * (1 - block_mask)
+    else:
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+def drop_block_fast_2d(
+        x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7,
+        gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
+    block mask at edges.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+            (W - block_size + 1) * (H - block_size + 1))
+
+    if batchwise:
+        # one mask for whole batch, quite a bit faster
+        block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma
+    else:
+        # mask per batch element
+        block_mask = torch.rand_like(x) < gamma
+    block_mask = F.max_pool2d(
+        block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
+        if inplace:
+            x.mul_(1. - block_mask).add_(normal_noise * block_mask)
+        else:
+            x = x * (1. - block_mask) + normal_noise * block_mask
+    else:
+        block_mask = 1 - block_mask
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(dtype=x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+class DropBlock2d(nn.Module):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    """
+    def __init__(self,
+                 drop_prob=0.1,
+                 block_size=7,
+                 gamma_scale=1.0,
+                 with_noise=False,
+                 inplace=False,
+                 batchwise=False,
+                 fast=True):
+        super(DropBlock2d, self).__init__()
+        self.drop_prob = drop_prob
+        self.gamma_scale = gamma_scale
+        self.block_size = block_size
+        self.with_noise = with_noise
+        self.inplace = inplace
+        self.batchwise = batchwise
+        self.fast = fast  # FIXME finish comparisons of fast vs not
+
+    def forward(self, x):
+        if not self.training or not self.drop_prob:
+            return x
+        if self.fast:
+            return drop_block_fast_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
+        else:
+            return drop_block_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
\ No newline at end of file
diff --git a/models/efficientnetB0_Regularized.py b/models/efficientnetB0_Regularized.py
new file mode 100644
index 000000000..844cfa56b
--- /dev/null
+++ b/models/efficientnetB0_Regularized.py
@@ -0,0 +1,181 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dropblock import DropBlock2d
+
+
+def swish(x):
+    return x * x.sigmoid()
+
+
+def drop_connect(x, drop_ratio):
+    keep_ratio = 1.0 - drop_ratio
+    mask = torch.empty([x.shape[0], 1, 1, 1], dtype=x.dtype, device=x.device)
+    mask.bernoulli_(keep_ratio)
+    x.div_(keep_ratio)
+    x.mul_(mask)
+    return x
+
+
+class SE(nn.Module):
+    '''Squeeze-and-Excitation block with Swish.'''
+
+    def __init__(self, in_channels, se_channels):
+        super(SE, self).__init__()
+        self.se1 = nn.Conv2d(in_channels, se_channels,
+                             kernel_size=1, bias=True)
+        self.drop1 = DropBlock2d(block_size=1)
+        self.se2 = nn.Conv2d(se_channels, in_channels,
+                             kernel_size=1, bias=True)
+        self.drop2 = DropBlock2d(block_size=1)
+
+    def forward(self, x):
+        out = F.adaptive_avg_pool2d(x, (1, 1))
+        out = swish(self.se1(out))
+        out = self.drop1(out)
+        out = self.se2(out).sigmoid()
+        out = self.drop2(out)
+        out = x * out
+        return out
+
+
+class Block(nn.Module):
+    '''expansion + depthwise + pointwise + squeeze-excitation'''
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 expand_ratio=1,
+                 se_ratio=0.,
+                 drop_rate=0.):
+        super(Block, self).__init__()
+        self.stride = stride
+        self.drop_rate = drop_rate
+        self.expand_ratio = expand_ratio
+
+        # Expansion
+        channels = expand_ratio * in_channels
+        self.conv1 = nn.Conv2d(in_channels,
+                               channels,
+                               kernel_size=1,
+                               stride=1,
+                               padding=0,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(channels)
+        self.drop1 = DropBlock2d(block_size=1)
+
+        # Depthwise conv
+        self.conv2 = nn.Conv2d(channels,
+                               channels,
+                               kernel_size=kernel_size,
+                               stride=stride,
+                               padding=(1 if kernel_size == 3 else 2),
+                               groups=channels,
+                               bias=False)
+        self.bn2 = nn.BatchNorm2d(channels)
+        self.drop2 = DropBlock2d(block_size=1)
+
+        # SE layers
+        se_channels = int(in_channels * se_ratio)
+        self.se = SE(channels, se_channels)
+
+        # Output
+        self.conv3 = nn.Conv2d(channels,
+                               out_channels,
+                               kernel_size=1,
+                               stride=1,
+                               padding=0,
+                               bias=False)
+        self.bn3 = nn.BatchNorm2d(out_channels)
+        self.drop3 = DropBlock2d(block_size=1)
+
+        # Skip connection if in and out shapes are the same (MV-V2 style)
+        self.has_skip = (stride == 1) and (in_channels == out_channels)
+
+    def forward(self, x):
+        out = x if self.expand_ratio == 1 else swish(self.bn1(self.conv1(x)))
+        out = self.drop1(out)
+        out = swish(self.bn2(self.conv2(out)))
+        out = self.drop2(out)
+        out = self.se(out)
+        out = self.bn3(self.conv3(out))
+        out = self.drop3(out)
+
+        if self.has_skip:
+            if self.training and self.drop_rate > 0:
+                out = drop_connect(out, self.drop_rate)
+            out = out + x
+        return out
+
+
+class EfficientNet(nn.Module):
+    def __init__(self, cfg, num_classes=10):
+        super(EfficientNet, self).__init__()
+        self.cfg = cfg
+        self.conv1 = nn.Conv2d(3,
+                               32,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.drop1 = DropBlock2d(block_size=1)
+        self.layers = self._make_layers(in_channels=32)
+        self.linear = nn.Linear(cfg['out_channels'][-1], num_classes)
+
+    def _make_layers(self, in_channels):
+        layers = []
+        cfg = [self.cfg[k] for k in ['expansion', 'out_channels', 'num_blocks', 'kernel_size',
+                                     'stride']]
+        b = 0
+        blocks = sum(self.cfg['num_blocks'])
+        for expansion, out_channels, num_blocks, kernel_size, stride in zip(*cfg):
+            strides = [stride] + [1] * (num_blocks - 1)
+            for stride in strides:
+                drop_rate = self.cfg['drop_connect_rate'] * b / blocks
+                layers.append(
+                    Block(in_channels,
+                          out_channels,
+                          kernel_size,
+                          stride,
+                          expansion,
+                          se_ratio=0.25,
+                          drop_rate=drop_rate))
+                in_channels = out_channels
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = swish(self.bn1(self.conv1(x)))
+        out = self.drop1(out)
+        out = self.layers(out)
+        out = F.adaptive_avg_pool2d(out, 1)
+        out = out.view(out.size(0), -1)
+        dropout_rate = self.cfg['dropout_rate']
+        if self.training and dropout_rate > 0:
+            out = F.dropout(out, p=dropout_rate)
+        out = self.linear(out)
+        return out
+
+
+def EfficientNetB0():
+    cfg = {
+        'num_blocks': [1, 2, 2, 3, 3, 4, 1],
+        'expansion': [1, 6, 6, 6, 6, 6, 6],
+        'out_channels': [16, 24, 40, 80, 112, 192, 320],
+        'kernel_size': [3, 3, 5, 3, 5, 5, 3],
+        'stride': [1, 2, 2, 2, 1, 2, 1],
+        'dropout_rate': 0.2,
+        'drop_connect_rate': 0.2,
+    }
+    return EfficientNet(cfg)
+
+
+def test():
+    net = EfficientNetB0()
+    x = torch.randn(2, 3, 32, 32)
+    y = net(x)
+    print(y.shape)
+
+