diff --git a/torchvision/prototype/models/__init__.py b/torchvision/prototype/models/__init__.py
index a187af7f090..54b76496b08 100644
--- a/torchvision/prototype/models/__init__.py
+++ b/torchvision/prototype/models/__init__.py
@@ -4,6 +4,7 @@
 from .vgg import *
 from .efficientnet import *
 from .mobilenetv3 import *
+from .mobilenetv2 import *
 from .mnasnet import *
 from . import detection
 from . import quantization
diff --git a/torchvision/prototype/models/mobilenetv2.py b/torchvision/prototype/models/mobilenetv2.py
new file mode 100644
index 00000000000..9b06c824c0d
--- /dev/null
+++ b/torchvision/prototype/models/mobilenetv2.py
@@ -0,0 +1,46 @@
+import warnings
+from functools import partial
+from typing import Any, Optional
+
+from torchvision.transforms.functional import InterpolationMode
+
+from ...models.mobilenetv2 import MobileNetV2
+from ..transforms.presets import ImageNetEval
+from ._api import Weights, WeightEntry
+from ._meta import _IMAGENET_CATEGORIES
+
+
+__all__ = ["MobileNetV2", "MobileNetV2Weights", "mobilenet_v2"]
+
+
+_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+
+
+class MobileNetV2Weights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
+            "acc@1": 71.878,
+            "acc@5": 90.286,
+        },
+    )
+
+
+def mobilenet_v2(weights: Optional[MobileNetV2Weights] = None, progress: bool = True, **kwargs: Any) -> MobileNetV2:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = MobileNetV2Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = MobileNetV2Weights.verify(weights)
+
+    if weights is not None:
+        kwargs["num_classes"] = len(weights.meta["categories"])
+
+    model = MobileNetV2(**kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model