diff --git a/rust/tvm/README.md b/rust/tvm/README.md
index b1bb4687679e..3455975ad81d 100644
--- a/rust/tvm/README.md
+++ b/rust/tvm/README.md
@@ -26,7 +26,7 @@ You can find the API Documentation [here](https://tvm.apache.org/docs/api/rust/t
 
 The goal of this crate is to provide bindings to both the TVM compiler and runtime
 APIs. First train your **Deep Learning** model using any major framework such as
-[PyTorch](https://pytorch.org/), [Apache MXNet](https://mxnet.apache.org/) or [TensorFlow](https://www.tensorflow.org/).
+[PyTorch](https://pytorch.org/) or [TensorFlow](https://www.tensorflow.org/).
 Then use **TVM** to build and deploy optimized model artifacts on a supported devices such as CPU, GPU, OpenCL and specialized accelerators.
 
 The Rust bindings are composed of a few crates:
diff --git a/rust/tvm/examples/resnet/README.md b/rust/tvm/examples/resnet/README.md
index d6e32f7fa768..ad76ac0048a0 100644
--- a/rust/tvm/examples/resnet/README.md
+++ b/rust/tvm/examples/resnet/README.md
@@ -21,7 +21,7 @@ This end-to-end example shows how to:
 * build `Resnet 18` with `tvm` from Python
 * use the provided Rust frontend API to test for an input image
 
-To run the example with pretrained resnet weights, first `tvm`  and `mxnet` must be installed for the python build. To install mxnet for cpu, run `pip install mxnet`
+To run the example with pretrained resnet weights, first `tvm`  and `torchvision` must be installed for the python build. To install torchvision for cpu, run `pip install torch torchvision`
 and to install `tvm` with `llvm` follow the [TVM installation guide](https://tvm.apache.org/docs/install/index.html).
 
 * **Build the example**: `cargo build
diff --git a/rust/tvm/examples/resnet/build.rs b/rust/tvm/examples/resnet/build.rs
index 45e4d6d658d5..9e3a76433ffc 100644
--- a/rust/tvm/examples/resnet/build.rs
+++ b/rust/tvm/examples/resnet/build.rs
@@ -21,10 +21,6 @@ use anyhow::{Context, Result};
 use std::{io::Write, path::Path, process::Command};
 
 fn main() -> Result<()> {
-    // Currently disabled, as it depends on the no-longer-supported
-    // mxnet repo to download resnet.
-
-    /*
     let out_dir = std::env::var("CARGO_MANIFEST_DIR")?;
     let python_script = concat!(env!("CARGO_MANIFEST_DIR"), "/src/build_resnet.py");
     let synset_txt = concat!(env!("CARGO_MANIFEST_DIR"), "/synset.txt");
@@ -57,7 +53,5 @@ fn main() -> Result<()> {
     );
     println!("cargo:rustc-link-search=native={}", out_dir);
 
-    */
-
     Ok(())
 }
diff --git a/rust/tvm/examples/resnet/src/build_resnet.py b/rust/tvm/examples/resnet/src/build_resnet.py
index df02dd78f57c..4e8ae01c413b 100644
--- a/rust/tvm/examples/resnet/src/build_resnet.py
+++ b/rust/tvm/examples/resnet/src/build_resnet.py
@@ -17,22 +17,18 @@
 # under the License.
 
 import argparse
-import csv
 import logging
-from os import path as osp
-import sys
 import shutil
+from os import path as osp
 
 import numpy as np
-
+import torch
+import torchvision
 import tvm
-from tvm import te
-from tvm import relay, runtime
-from tvm.relay import testing
-from tvm.contrib import graph_executor, cc
 from PIL import Image
+from tvm import relay, runtime
+from tvm.contrib import cc, graph_executor
 from tvm.contrib.download import download_testdata
-from mxnet.gluon.model_zoo.vision import get_model
 
 logging.basicConfig(
     level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
@@ -64,11 +60,16 @@
 
 def build(target_dir):
     """Compiles resnet18 with TVM"""
-    # Download the pretrained model in MxNet's format.
-    block = get_model("resnet18_v1", pretrained=True)
+    # Download the pretrained model from Torchvision.
+    weights = torchvision.models.ResNet18_Weights.IMAGENET1K_V1
+    torch_model = torchvision.models.resnet18(weights=weights).eval()
+
+    input_shape = [1, 3, 224, 224]
+    input_data = torch.randn(input_shape)
+    scripted_model = torch.jit.trace(torch_model, input_data)
+    input_infos = [("data", input_data.shape)]
+    mod, params = relay.frontend.from_pytorch(scripted_model, input_infos)
 
-    shape_dict = {"data": (1, 3, 224, 224)}
-    mod, params = relay.frontend.from_mxnet(block, shape_dict)
     # Add softmax to do classification in last layer.
     func = mod["main"]
     func = relay.Function(
@@ -93,7 +94,6 @@ def build(target_dir):
 
 def download_img_labels():
     """Download an image and imagenet1k class labels for test"""
-    from mxnet.gluon.utils import download
 
     synset_url = "".join(
         [
diff --git a/rust/tvm/examples/resnet/src/main.rs b/rust/tvm/examples/resnet/src/main.rs
index 0ea8c4cf8bb5..c22d55f2e4da 100644
--- a/rust/tvm/examples/resnet/src/main.rs
+++ b/rust/tvm/examples/resnet/src/main.rs
@@ -31,10 +31,6 @@ use tvm_rt::graph_rt::GraphRt;
 use tvm_rt::*;
 
 fn main() -> anyhow::Result<()> {
-    // Currently disabled, as it depends on the no-longer-supported
-    // mxnet repo to download resnet.
-
-    /*
     let dev = Device::cpu(0);
     println!("{}", concat!(env!("CARGO_MANIFEST_DIR"), "/cat.png"));
 
@@ -138,7 +134,6 @@ fn main() -> anyhow::Result<()> {
         "input image belongs to the class `{}` with probability {}",
         label, max_prob
     );
-    */
 
     Ok(())
 }