diff --git a/README.md b/README.md
index 29ef0aa4..bab4ed4e 100644
--- a/README.md
+++ b/README.md
@@ -87,7 +87,7 @@ If you are using a GPU, make sure its drivers and the cuda libraries are correct
 
 1. Install a [miniforge](https://github.com/conda-forge/miniforge) distribution of Python. Note you might need to use an anaconda prompt if you did not add anaconda to the path.
 2. Open an anaconda prompt / command prompt which has `conda` for **python 3** in the path
-3. Create a new environment with `conda create --name cellpose python=3.9`. We recommend python 3.10, but python 3.9 and 3.11 will also work.
+3. Create a new environment with `conda create --name cellpose python=3.10`. We recommend python 3.10, but python 3.9 and 3.11 will also work.
 4. To activate this new environment, run `conda activate cellpose`
 5. (option 1) To install cellpose with the GUI, run `python -m pip install cellpose[gui]`.  If you're on a zsh server, you may need to use ' ': `python -m pip install 'cellpose[gui]'`.
 6. (option 2) To install cellpose without the GUI, run `python -m pip install cellpose`. 
@@ -240,4 +240,4 @@ Check out [Omnipose](https://github.com/kevinjohncutler/omnipose), an extension
 
 Pytorch is now the default deep neural network software for cellpose. Mxnet will still be supported. To install mxnet (CPU), run `pip install mxnet-mkl`. To use mxnet in a notebook, declare `torch=False` when creating a model, e.g. `model = models.Cellpose(torch=False)`. To use mxnet on the command line, add the flag `--mxnet`, e.g. `python -m cellpose --dir ~/images/ --mxnet`. The pytorch implementation is 20% faster than the mxnet implementation when running on the GPU and 20% slower when running on the CPU. 
 
-Dynamics are computed using bilinear interpolation by default instead of nearest neighbor interpolation. Set `interp=False` in `model.eval` to turn off. The bilinear interpolation will be slightly slower on the CPU, but it is faster than nearest neighbor if using torch and the GPU is enabled.
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+Dynamics are computed using bilinear interpolation by default instead of nearest neighbor interpolation. Set `interp=False` in `model.eval` to turn off. The bilinear interpolation will be slightly slower on the CPU, but it is faster than nearest neighbor if using torch and the GPU is enabled.