diff --git a/frontends/pytorch/examples/torchscript_resnet18_e2e.py b/frontends/pytorch/examples/torchscript_resnet18_e2e.py
new file mode 100644
index 000000000..2778a1d9b
--- /dev/null
+++ b/frontends/pytorch/examples/torchscript_resnet18_e2e.py
@@ -0,0 +1,106 @@
+# -*- Python -*-
+# This file is licensed under a pytorch-style license
+# See frontends/pytorch/LICENSE for license information.
+
+from PIL import Image
+import requests
+import torch
+import torchvision.models as models
+from torchvision import transforms
+import typing
+
+import torch_mlir
+
+import npcomp
+from npcomp.compiler.pytorch.backend import refjit, frontend_lowering, iree
+from npcomp.compiler.utils import logging
+
+logging.enable()
+
+mb = torch_mlir.ModuleBuilder()
+
+def load_and_preprocess_image(url: str):
+    img = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+    # preprocessing pipeline
+    preprocess = transforms.Compose(
+        [
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+    img_preprocessed = preprocess(img)
+    return torch.unsqueeze(img_preprocessed, 0)
+
+
+def load_labels():
+    classes_text = requests.get(
+        "https://raw.githubusercontent.com/cathyzhyi/ml-data/main/imagenet-classes.txt",
+        stream=True,
+    ).text
+    labels = [line.strip() for line in classes_text.splitlines()]
+    return labels
+
+
+def top3_possibilities(res):
+    _, indexes = torch.sort(res, descending=True)
+    percentage = torch.nn.functional.softmax(res, dim=1)[0] * 100
+    top3 = [(labels[idx], percentage[idx].item()) for idx in indexes[0][:3]]
+    return top3
+
+
+def predictions(torch_func, jit_func, img, labels):
+    golden_prediction = top3_possibilities(torch_func(img))
+    print("PyTorch prediction")
+    print(golden_prediction)
+    prediction = top3_possibilities(torch.from_numpy(jit_func(img)))
+    print("NPCOMP prediction")
+    print(prediction)
+
+
+class ResNet18Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.resnet = models.resnet18(pretrained=True)
+        self.train(False)
+
+    def forward(self, img):
+        return self.resnet.forward(img)
+
+
+class TestModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.s = ResNet18Module()
+
+    def forward(self, x):
+        return self.s.forward(x)
+
+
+test_module = TestModule()
+class_annotator = torch_mlir.ClassAnnotator()
+recursivescriptmodule = torch.jit.script(test_module)
+torch.jit.save(recursivescriptmodule, "/tmp/foo.pt")
+
+class_annotator.exportNone(recursivescriptmodule._c._type())
+class_annotator.exportPath(recursivescriptmodule._c._type(), ["forward"])
+class_annotator.annotateArgs(
+    recursivescriptmodule._c._type(),
+    ["forward"],
+    [None, ([-1, -1, -1, -1], torch.float32, True),],
+)
+# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
+mb.import_module(recursivescriptmodule._c, class_annotator)
+
+backend = refjit.RefjitNpcompBackend()
+compiled = backend.compile(frontend_lowering.lower_object_graph(mb.module))
+jit_module = backend.load(compiled)
+
+image_url = (
+    "https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg"
+)
+print("load image from " + image_url)
+img = load_and_preprocess_image(image_url)
+labels = load_labels()
+predictions(test_module.forward, jit_module.forward, img, labels)