diff --git a/yoeo/models.py b/yoeo/models.py
index 1b69cd8..cd964c0 100644
--- a/yoeo/models.py
+++ b/yoeo/models.py
@@ -60,8 +60,40 @@ def create_modules(module_defs):
                                    nn.BatchNorm2d(filters, momentum=0.1, eps=1e-5))
             if module_def["activation"] == "leaky":
                 modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1))
-            if module_def["activation"] == "mish":
+            elif module_def["activation"] == "mish":
                 modules.add_module(f"mish_{module_i}", Mish())
+            elif module_def["activation"] == "linear":
+                pass
+            else:
+                raise ValueError(f"Unknown activation: {module_def['activation']}")
+
+        elif module_def["type"] == "deconvolutional":
+            bn = int(module_def["batch_normalize"])
+            filters = int(module_def["filters"])
+            kernel_size = int(module_def["size"])
+            pad = int(module_def["pad"])
+            modules.add_module(
+                f"deconv_{module_i}",
+                nn.ConvTranspose2d(
+                    in_channels=output_filters[-1],
+                    out_channels=filters,
+                    kernel_size=kernel_size,
+                    stride=int(module_def["stride"]),
+                    padding=pad,
+                    bias=not bn,
+                ),
+            )
+            if bn:
+                modules.add_module(f"batch_norm_{module_i}",
+                                   nn.BatchNorm2d(filters, momentum=0.1, eps=1e-5))
+            if module_def["activation"] == "leaky":
+                modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1))
+            elif module_def["activation"] == "mish":
+                modules.add_module(f"mish_{module_i}", Mish())
+            elif module_def["activation"] == "linear":
+                pass
+            else:
+                raise ValueError(f"Unknown activation: {module_def['activation']}")
 
         elif module_def["type"] == "maxpool":
             kernel_size = int(module_def["size"])
@@ -197,10 +229,9 @@ def __init__(self, config_path):
 
     def forward(self, x, bb_targets=None, mask_targets=None):
         img_size = x.size(2)
-        loss = 0
         layer_outputs, yolo_outputs, segmentation_outputs = [], [], []
         for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
-            if module_def["type"] in ["convolutional", "upsample", "maxpool"]:
+            if module_def["type"] in ["convolutional", "deconvolutional", "upsample", "maxpool"]:
                 x = module(x)
             elif module_def["type"] == "route":
                 combined_outputs = torch.cat([layer_outputs[int(layer_i)] for layer_i in module_def["layers"].split(",")], 1)