update readme

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:

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