diff --git a/docs/en/guides/deepstream-nvidia-jetson.md b/docs/en/guides/deepstream-nvidia-jetson.md
index c6b86d5906..3f7f27f297 100644
--- a/docs/en/guides/deepstream-nvidia-jetson.md
+++ b/docs/en/guides/deepstream-nvidia-jetson.md
@@ -66,17 +66,13 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
     git clone https://github.com/marcoslucianops/DeepStream-Yolo
     ```
 
-3.  Copy the `export_yoloV8.py` file from `DeepStream-Yolo/utils` directory to the `ultralytics` folder
+3.  Copy the `export_yolo11.py` file from `DeepStream-Yolo/utils` directory to the `ultralytics` folder
 
     ```bash
-    cp ~/DeepStream-Yolo/utils/export_yoloV8.py ~/ultralytics
+    cp ~/DeepStream-Yolo/utils/export_yolo11.py ~/ultralytics
     cd ultralytics
     ```
 
-    !!! note
-
-        `export_yoloV8.py` works for both YOLOv8 and YOLO11 models.
-
 4.  Download Ultralytics YOLO11 detection model (.pt) of your choice from [YOLO11 releases](https://github.com/ultralytics/assets/releases). Here we use [yolo11s.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt).
 
     ```bash
@@ -90,12 +86,12 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
 5.  Convert model to ONNX
 
     ```bash
-    python3 export_yoloV8.py -w yolo11s.pt
+    python3 export_yolo11.py -w yolo11s.pt
     ```
 
     !!! note "Pass the below arguments to the above command"
 
-        For DeepStream 6.0.1, use opset 12 or lower. The default opset is 16.
+        For DeepStream 5.1, remove the `--dynamic` arg and use `opset` 12 or lower. The default `opset` is 17.
 
         ```bash
         --opset 12
@@ -169,7 +165,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
     make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo
     ```
 
-9.  Edit the `config_infer_primary_yoloV8.txt` file according to your model (for YOLO11s with 80 classes)
+9.  Edit the `config_infer_primary_yolo11.txt` file according to your model (for YOLO11s with 80 classes)
 
     ```bash
     [property]
@@ -186,7 +182,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
     ...
     [primary-gie]
     ...
-    config-file=config_infer_primary_yoloV8.txt
+    config-file=config_infer_primary_yolo11.txt
     ```
 
 11. You can also change the video source in `deepstream_app_config` file. Here a default video file is loaded
@@ -212,7 +208,7 @@ deepstream-app -c deepstream_app_config.txt
 
 !!! tip
 
-    If you want to convert the model to FP16 precision, simply set `model-engine-file=model_b1_gpu0_fp16.engine` and `network-mode=2` inside `config_infer_primary_yoloV8.txt`
+    If you want to convert the model to FP16 precision, simply set `model-engine-file=model_b1_gpu0_fp16.engine` and `network-mode=2` inside `config_infer_primary_yolo11.txt`
 
 ## INT8 Calibration
 
@@ -271,7 +267,7 @@ If you want to use INT8 precision for inference, you need to follow the steps be
 
         Higher INT8_CALIB_BATCH_SIZE values will result in more accuracy and faster calibration speed. Set it according to you GPU memory.
 
-8.  Update the `config_infer_primary_yoloV8.txt` file
+8.  Update the `config_infer_primary_yolo11.txt` file
 
     From
 
@@ -417,12 +413,12 @@ Yes, the guide for deploying Ultralytics YOLO11 with the DeepStream SDK and Tens
 
 ### How can I convert a YOLO11 model to ONNX for DeepStream?
 
-To convert a YOLO11 model to ONNX format for deployment with DeepStream, use the `utils/export_yoloV8.py` script from the [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) repository.
+To convert a YOLO11 model to ONNX format for deployment with DeepStream, use the `utils/export_yolo11.py` script from the [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) repository.
 
 Here's an example command:
 
 ```bash
-python3 utils/export_yoloV8.py -w yolo11s.pt --opset 12 --simplify
+python3 utils/export_yolo11.py -w yolo11s.pt --opset 12 --simplify
 ```
 
 For more details on model conversion, check out our [model export section](../modes/export.md).