From 7bce947435f98e846a2ddda2a9cb28f0b78b2011 Mon Sep 17 00:00:00 2001
From: Bubbles The Dev <152947339+KernFerm@users.noreply.github.com>
Date: Wed, 2 Oct 2024 03:59:04 -0400
Subject: [PATCH] Update README.MD

Signed-off-by: Bubbles The Dev <152947339+KernFerm@users.noreply.github.com>
---
 README.MD | 31 +++++++++++++++++++++++--------
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/README.MD b/README.MD
index f1322df..be11671 100644
--- a/README.MD
+++ b/README.MD
@@ -34,27 +34,42 @@ pip3 install torch==2.4.1+cu118 torchvision==0.19.1+cu118 torchaudio==2.4.1+cu11
 - **commands-to-export.txt:** A file containing useful commands for exporting your YOLO model.
 - **export.py:** The Python script responsible for handling the export process.
 
-## Exporting Your YOLO Model
+## Exporting YOLO Models (NVIDIA and AMD GPUs)
 
 ### Export to TensorRT Engine (For NVIDIA GPUs)
-- To export your YOLO model to a TensorRT engine, use the following command:
+
+To export your YOLO model to a TensorRT engine (for NVIDIA GPUs only), use the following command:
+
 ```
-python .\export.py --weights ./<your_model_path>.pt --include engine --half --imgsz 320 320 --device 0
+python .\export.py --weights ./"your_model_path.pt" --include engine --half --imgsz 320 320 --device 0
 ```
-- Replace `<your_model_path>` with the path to your YOLO `.pt` file.
-- The `--half` flag enables half-precision inference.
+- Replace `"your_model_path"` with the path to your YOLO `.pt` file.
+- The `--half` flag enables half-precision inference for faster performance and lower memory usage.
 - `--imgsz 320 320` sets the image size to 320x320 pixels for export.
 - `--device 0` specifies the GPU device ID (use `--device cpu` for CPU-based inference).
+- **Note**: TensorRT is only compatible with **NVIDIA GPUs**.
 
 ### Export to ONNX
-- To export your YOLO model to ONNX format, use the following command:
+
+To export your YOLO model to ONNX format, use the following command:
+
 ```
-python .\export.py --weights ./<your_model_path>.pt --include onnx --half --imgsz 320 320
+python .\export.py --weights ./"your_model_path.pt" --include onnx --half --imgsz 320 320 --device 0
 ```
-- As above, replace `<your_model_path>` with your YOLO `.pt` model.
+- Replace `"your_model_path"` with your YOLO `.pt` model.
 - The `--half` flag enables half-precision inference (if supported).
 - `--imgsz 320 320` sets the image size to 320x320 pixels.
 
+### Export for AMD GPU
+
+To export your YOLO model for an AMD GPU, use the following command:
+
+```
+python .\export.py --weights .\your_model_path.pt --include onnx --imgsz 320 320
+```
+- Replace `"your_model_path"` with the path to your YOLO `.pt` file.
+- This command will export the model in the ONNX format for AMD GPU inference.
+
 ## Troubleshooting
 
 - If you encounter issues during export, ensure that your `CUDA`, `cuDNN`, and `TensorRT` versions are compatible with the version of `PyTorch` you are using.