diff --git a/README.md b/README.md
index 600bece..49a100e 100644
--- a/README.md
+++ b/README.md
@@ -131,7 +131,9 @@ If you don't use Anaconda, type `python -V` in terminal to make sure python>=3.9
    *For faster inference, you can run on the GPU. Install pyTorch with CUDA and cuDNN support, and ONNX Runtime with GPU support (not available on MacOS).*\
    Be aware that GPU support takes an additional 6 GB on disk. The full installation is then 10.75 GB instead of 4.75 GB.
    
-   Go to the [ONNXruntime requirement page](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), check the latest CUDA and cuDNN requirements. Then go to the [pyTorch website]( https://pytorch.org/get-started/locally) and install the latest version that satisfies these requirements (beware that torch 2.4 ships with cuDNN 9, while torch 2.3 installs cuDNN 8). For example:
+   Run `nvidia-smi` in a terminal. If this results in an error, your GPU is probably not compatible with CUDA. If not, note the "CUDA version": it is the latest version your driver is compatible with (more information [on this post](https://stackoverflow.com/questions/60987997/why-torch-cuda-is-available-returns-false-even-after-installing-pytorch-with)).
+
+   Then go to the [ONNXruntime requirement page](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), note the latest compatible CUDA and cuDNN requirements. Finally, go to the [pyTorch website]( https://pytorch.org/get-started/locally) and install the latest version that satisfies these requirements (beware that torch 2.4 ships with cuDNN 9, while torch 2.3 installs cuDNN 8). You may need to opt for a previous version, downloadable [there](https://pytorch.org/get-started/previous-versions/). For example:
    ``` cmd
     pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
    ```
@@ -140,16 +142,21 @@ If you don't use Anaconda, type `python -V` in terminal to make sure python>=3.9
    pip install onnxruntime-gpu
    ```
 
+   Check that everything went well within Python with these commands:
+   ``` python
+   import torch; import onnxruntime as ort
+   print(torch.cuda.is_available(), ort.get_available_providers())
+   # Should print "True ['CUDAExecutionProvider', ...]"
+   ```  
+
+  <!-- print(f'torch version: {torch.__version__}, cuda version: {torch.version.cuda}, cudnn version: {torch.backends.cudnn.version()}, onnxruntime version: {ort.__version__}') -->
+
 > **Note on storage use:**\
      A full installation takes up to 11 GB of storage spate. However, GPU support is not mandatory and takes about 6 GB. Moreover, [marker augmentation](#marker-augmentation) requires Tensorflow and does not necessarily yield better results. You can save an additional 1.3 GB by uninstalling it: `pip uninstall tensorflow`.\
      A minimal installation with carefully chosen pose models and without GPU support, Tensorflow, PyQt5 **would take less than 3 GB**.
     <img src="Content/Storage.png" width="760">
 
 
-  <!-- import torch; print(torch.cuda.is_available()) 
-      import onnxruntime as ort; ort.get_available_providers()
-      print(f'torch version: {torch.__version__}, cuda version: {torch.version.cuda}, cudnn version: {torch.backends.cudnn.version()}, onnxruntime version: {ort.__version__}') -->
-
 </br>
 
 ## Demonstration Part-1: Build 3D TRC file