Merge branch 'docusaurus-version' of https://github.com/meislisha/wik…

…i-documents into docusaurus-version

docs/Edge/Edge_Series/Edge_Box_RPi_200/Edgebox-rpi-200-n3uron.md

@@ -132,6 +132,7 @@ By creating and using [Templates](https://docs.n3uron.com/docs/platform-template
 Just drag and drop the desired object into the templates section and start building your template using [custom properties](https://docs.n3uron.com/docs/platform-templates-custom-properties), [inheritance](https://docs.n3uron.com/docs/platform-templates-inheritance) and [more](https://docs.n3uron.com/docs/platform-templates-nesting).
 
 <div align="center"><img src="https://files.seeedstudio.com/wiki/Edge_Box/n3uron/gif3.gif" alt="pir" width="600" height="auto" /></div>
+
 ### Configure MQTT Client
 
 **Step 1**: Go to Config→Modules, click on the menu and then create a **New Module** named MqttClient.

docs/Edge/reComputer/Application/YOLOv8-DeepStream-TRT-Jetson.md

@@ -11,7 +11,7 @@ last_update:
   author: Lakshantha
 ---
 
-# Deploy YOLOv8 on NVIDIA Jetson using TensorRT and DeepStream SDK
+# Deploy YOLOv8 on NVIDIA Jetson using TensorRT and DeepStream SDK Support
 
 This guide explains how to deploy a trained AI model into NVIDIA Jetson Platform and perform inference using TensorRT and DeepStream SDK. Here we use TensorRT to maximize the inference performance on the Jetson platform.
 

docs/Edge/reComputer/Application/YOLOv8-TRT-Jetson.md

@@ -32,7 +32,7 @@ Different computer vision tasks will be introduced here such as:
 - [reComputer Jetson](https://www.seeedstudio.com/reComputer-J4012-p-5586.html) or any other NVIDIA Jetson device running JetPack 5.1.1 or higher
 
 :::note
-This wiki has been tested and verified on a [reComputer J4012](https://www.seeedstudio.com/reComputer-J4012-p-5586.html) powered by NVIDIA Jetson orin NX 16GB module 
+This wiki has been tested and verified on a [reComputer J4012](https://www.seeedstudio.com/reComputer-J4012-p-5586.html) and reComputer Industrial J4012[https://www.seeedstudio.com/reComputer-Industrial-J4012-p-5684.html] powered by NVIDIA Jetson orin NX 16GB module 
 :::
 
 ## Flash JetPack to Jetson
@@ -241,7 +241,7 @@ If you face any errors when executing the above commands, try adding "device=0"
 " style={{width:1000, height:'auto'}}/></div>
 
 :::note
-The above is run on a reComputer J4012 and uses YOLOv8s model trained with 640x640 input and uses TensorRT FP16 precision.
+The above is run on a reComputer J4012/ reComputer Industrial J4012 and uses YOLOv8s model trained with 640x640 input and uses TensorRT FP16 precision.
 :::
 
 </TabItem>
@@ -341,7 +341,7 @@ If you face any errors when executing the above commands, try adding "device=0"
 " style={{width:1000, height:'auto'}}/></div>
 
 :::note
-The above is run on a reComputer J4012 and uses YOLOv8s-cls model trained with 224x224 input and uses TensorRT FP16 precision. Also, make sure to pass the argument **imgsz=224** inside the inference command with TensorRT exports because the inference engine accepts 640 image size by default when using TensorRT models.
+The above is run on a reComputer J4012/ reComputer Industrial J4012 and uses YOLOv8s-cls model trained with 224x224 input and uses TensorRT FP16 precision. Also, make sure to pass the argument **imgsz=224** inside the inference command with TensorRT exports because the inference engine accepts 640 image size by default when using TensorRT models.
 :::
 
 </TabItem>
@@ -440,7 +440,7 @@ If you face any errors when executing the above commands, try adding "device=0"
 " style={{width:1000, height:'auto'}}/></div>
 
 :::note
-The above is run on a reComputer J4012 and uses YOLOv8s-seg model trained with 640x640 input and uses TensorRT FP16 precision.
+The above is run on a reComputer J4012/ reComputer Industrial J4012 and uses YOLOv8s-seg model trained with 640x640 input and uses TensorRT FP16 precision.
 :::
 
 </TabItem>
@@ -933,7 +933,7 @@ yolo detect predict model=<your_model.pt> source='0' show=True
 
 ### Preparation
 
-We have done performance benchmarks for all computer vision tasks supported by YOLOv8 running on reComputer J4012 powered by NVIDIA Jetson Orin NX 16GB module. 
+We have done performance benchmarks for all computer vision tasks supported by YOLOv8 running on reComputer J4012/ reComputer Industrial J4012 powered by NVIDIA Jetson Orin NX 16GB module. 
 
 Included in the samples directory is a command-line wrapper tool called [trtexec](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#trtexec). trtexec is a tool to use TensorRT without having to develop your own application. The trtexec tool has three main purposes:
 
@@ -981,7 +981,7 @@ However, if you want **INT8** precision which offers better performance than **F
 
 ### Results
 
-Below we summarize the results that we get from all the four computer vision tasks running on reComputer J4012.
+Below we summarize the results that we get from all the four computer vision tasks running on reComputer J4012/ reComputer Industrial J4012.
 
 <div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/YOLOV8-TRT/45.png
 " style={{width:1000, height:'auto'}}/></div>