diff --git a/src/models/__init__.py b/src/models/__init__.py
index 658b9ab..d56e2ab 100644
--- a/src/models/__init__.py
+++ b/src/models/__init__.py
@@ -8,6 +8,7 @@
     resnet50,
     resnet50_fc512,
 )
+from .tvmodels import mobilenet_v3_small, vgg16
 
 
 __model_factory = {
@@ -18,6 +19,8 @@
     "resnet34_fc512": resnet34_fc512,
     "resnet50": resnet50,
     "resnet50_fc512": resnet50_fc512,
+    "mobilenet_v3_small": mobilenet_v3_small,
+    "vgg16": vgg16,
 }
 
 
diff --git a/src/models/tvmodels.py b/src/models/tvmodels.py
new file mode 100644
index 0000000..795fc23
--- /dev/null
+++ b/src/models/tvmodels.py
@@ -0,0 +1,62 @@
+# Copyright (c) EEEM071, University of Surrey
+
+import torch.nn as nn
+import torchvision.models as tvmodels
+
+
+__all__ = ["mobilenet_v3_small", "vgg16"]
+
+
+class TorchVisionModel(nn.Module):
+    def __init__(self, name, num_classes, loss, pretrained, **kwargs):
+        super().__init__()
+
+        self.loss = loss
+        self.backbone = tvmodels.__dict__[name](pretrained=pretrained)
+        self.feature_dim = self.backbone.classifier[0].in_features
+
+        # overwrite the classifier used for ImageNet pretrianing
+        # nn.Identity() will do nothing, it's just a place-holder
+        self.backbone.classifier = nn.Identity()
+        self.classifier = nn.Linear(self.feature_dim, num_classes)
+
+    def forward(self, x):
+        v = self.backbone(x)
+
+        if not self.training:
+            return v
+
+        y = self.classifier(v)
+
+        if self.loss == {"xent"}:
+            return y
+        elif self.loss == {"xent", "htri"}:
+            return y, v
+        else:
+            raise KeyError(f"Unsupported loss: {self.loss}")
+
+
+def vgg16(num_classes, loss={"xent"}, pretrained=True, **kwargs):
+    model = TorchVisionModel(
+        "vgg16",
+        num_classes=num_classes,
+        loss=loss,
+        pretrained=pretrained,
+        **kwargs,
+    )
+    return model
+
+
+def mobilenet_v3_small(num_classes, loss={"xent"}, pretrained=True, **kwargs):
+    model = TorchVisionModel(
+        "mobilenet_v3_small",
+        num_classes=num_classes,
+        loss=loss,
+        pretrained=pretrained,
+        **kwargs,
+    )
+    return model
+
+
+# Define any models supported by torchvision bellow
+# https://pytorch.org/vision/0.11/models.html
diff --git a/test.sh b/test.sh
index 3dd87c6..593adc9 100644
--- a/test.sh
+++ b/test.sh
@@ -3,9 +3,9 @@
 python main.py \
 -s veri \
 -t veri \
--a resnet18 \
+-a mobilenet_v3_small \
 --height 224 \
 --width 224 \
 --test-batch-size 100 \
 --evaluate \
---save-dir logs/eval-resnet18-veri
+--save-dir logs/eval-mobilenet_v3_small-veri
diff --git a/train.sh b/train.sh
index 8338f04..a7a32d8 100644
--- a/train.sh
+++ b/train.sh
@@ -3,13 +3,13 @@
 python main.py \
 -s veri \
 -t veri \
--a resnet18 \
+-a mobilenet_v3_small \
 --height 224 \
 --width 224 \
 --optim amsgrad \
 --lr 0.0003 \
---max-epoch 60 \
---stepsize 20 40 \
---train-batch-size 64 \
+--max-epoch 30 \
+--stepsize 10 20 \
+--train-batch-size 256 \
 --test-batch-size 100 \
---save-dir logs/resnet18-veri
+--save-dir logs/mobilenet_v3_small-veri