diff --git a/src/cryo_sbi/inference/models/embedding_nets.py b/src/cryo_sbi/inference/models/embedding_nets.py
index daa99ed..e502ff2 100644
--- a/src/cryo_sbi/inference/models/embedding_nets.py
+++ b/src/cryo_sbi/inference/models/embedding_nets.py
@@ -77,22 +77,27 @@ def forward(self, x):
 
 
 @add_embedding("RESNET18_FFT")
-class ResNet18_Encoder(nn.Module):
+class ResNet18_FFT(nn.Module):
     def __init__(self, output_dimension: int):
-        super(ResNet18_Encoder, self).__init__()
-        print("Using FFT ResNet18")
-        self.resnet = models.resnet18()
-        self.resnet.conv1 = nn.Conv2d(
-            2, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
-        )
-        self.resnet.fc = nn.Linear(
-            in_features=512, out_features=output_dimension, bias=True
+        super(ResNet18_FFT, self).__init__()
+        print("Using FFT1")
+
+        self.drgn_encoder = nn.Sequential(
+            nn.Linear(12892, 512),
+            nn.GELU(),
+            nn.Linear(512, 256),
+            nn.GELU(),
+            nn.Linear(256, output_dimension),
+            nn.GELU(),
         )
+        self.mask = Mask(128, 64, inside=True).mask.flatten()
 
     def forward(self, x):
-        x = torch.fft.fftshift(torch.fft.fft2(x, dim=(-2, -1)))
-        x = torch.stack([x.real, x.imag], dim=1)
-        x = self.resnet(x)
+        if x.dim == 2:
+            x = x.unsqueeze(0)
+        x = torch.fft.fftshift(torch.fft.fft2(x, dim=(-2, -1))).real
+        x = x.flatten(start_dim=1)[:, self.mask]
+        x = self.drgn_encoder(x)
         return x