diff --git a/utils/layers.py b/utils/layers.py
index 81d3408c..35c13c9f 100644
--- a/utils/layers.py
+++ b/utils/layers.py
@@ -98,14 +98,29 @@ class MixConv2d(nn.Module):  # MixConv: Mixed Depthwise Convolutional Kernels ht
 # Activation functions below -------------------------------------------------------------------------------------------
 class SwishImplementation(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, i):
-        ctx.save_for_backward(i)
-        return i * torch.sigmoid(i)
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return x * torch.sigmoid(x)
 
     @staticmethod
     def backward(ctx, grad_output):
-        sigmoid_i = torch.sigmoid(ctx.saved_variables[0])
-        return grad_output * (sigmoid_i * (1 + ctx.saved_variables[0] * (1 - sigmoid_i)))
+        x = ctx.saved_tensors[0]
+        sx = torch.sigmoid(x)  # sigmoid(ctx)
+        return grad_output * (sx * (1 + x * (1 - sx)))
+
+
+class MishImplementation(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        x = ctx.saved_tensors[0]
+        sx = torch.sigmoid(x)
+        fx = F.softplus(x).tanh()
+        return grad_output * (fx + x * sx * (1 - fx * fx))
 
 
 class MemoryEfficientSwish(nn.Module):
@@ -113,9 +128,14 @@ class MemoryEfficientSwish(nn.Module):
         return SwishImplementation.apply(x)
 
 
+class MemoryEfficientMish(nn.Module):
+    def forward(self, x):
+        return MishImplementation.apply(x)
+
+
 class Swish(nn.Module):
     def forward(self, x):
-        return x.mul(torch.sigmoid(x))
+        return x * torch.sigmoid(x)
 
 
 class HardSwish(nn.Module):  # https://arxiv.org/pdf/1905.02244.pdf
@@ -125,4 +145,4 @@ class HardSwish(nn.Module):  # https://arxiv.org/pdf/1905.02244.pdf
 
 class Mish(nn.Module):  # https://github.com/digantamisra98/Mish
     def forward(self, x):
-        return x.mul(F.softplus(x).tanh())
+        return x * F.softplus(x).tanh()