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Merge pull request Seeed-Studio#2190 from KasunThushara/docusaurus-version
Update: recomputer r1000 collection page vision sub topic
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‎docs/Edge/Raspberry_Pi_Devices/Edge_Controller/reComputer_R1000/recomputer_r1000_intro.md‎

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@@ -430,74 +430,52 @@ The reComputer R1000 edge IoT controller, powered by Raspberry Pi CM4, features
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<th class="table-trnobg"><font size={"4"}>YOLOv8 Object Detection on reComputer R1000 with Hailo-8L</font></th>
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<th class="table-trnobg"><font size={"4"}>YOLOv8 Pose Estimation on reComputer R1000 with Hailo-8L</font></th>
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<th class="table-trnobg"><font size={"4"}>Benchmark on RPi5 and CM4 running yolov8s with rpi ai kit</font></th>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/YOLOV8/object_detection_with_AIkit.gif" style={{width:300, height:'auto'}}/></div></td>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/YOLOV8/YOLOv8-pose-estimation-with-AIkit.gif" style={{width:300, height:'auto'}}/></div></td>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/YOLOV8/YOLOv8-pose-estimation-with-AIkit.gif" style={{width:300, height:'auto'}}/></div></td>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/YOLOV8/object-detection-benchmark.png" style={{width:300, height:'auto'}}/></div></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> This wiki demonstrates object detection using YOLOv8 on reComputer R1000 with and without Raspberry-pi-AI-kit acceleration. The Raspberry Pi AI Kit enhances the performance of the Raspberry Pi and unlock its potential in artificial intelligence and machine learning applications</font></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> This wiki demonstrates pose estimation using YOLOv8 on reComputer R1000 with and without Raspberry-pi-AI-kit acceleration. The Raspberry Pi AI Kit enhances the performance of the Raspberry Pi and unlock its potential in artificial intelligence and machine learning applications</font></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> This wiki showcases benchmarking of YOLOv8s for pose estimation and object detection on Raspberry Pi 5 and Raspberry Pi Compute Module 4. All tests utilize the same model (YOLOv8s), quantized to int8, with an input size of 640x640 resolution, batch size set to 1, and input from the same video at 240 FPS.</font></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/yolov8_object_detection_on_recomputer_r1000_with_hailo_8l/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/yolov8_pose_estimation_on_recomputer_r1000_with_hailo_8l/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/yolov8_pose_estimation_on_recomputer_r1000_with_hailo_8l/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/benchmark_on_rpi5_and_cm4_running_yolov8s_with_rpi_ai_kit/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<th class="table-trnobg"><font size={"4"}>Benchmark on RPi5 and CM4 running yolov8s with rpi ai kit</font></th>
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<th class="table-trnobg"><font size={"4"}>Install M.2 Coral to Raspberry Pi 5</font></th>
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<th class="table-trnobg"><font size={"4"}>Pose-Based Light Control with Node-Red and Raspberry Pi with AI kit</font></th>
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<th class="table-trnobg"><font size={"4"}>Clip Application on reComputer</font></th>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/YOLOV8/object-detection-benchmark.png" style={{width:300, height:'auto'}}/></div></td>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/YOLOV8/pycoral_install.gif" style={{width:300, height:'auto'}}/></div></td>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/pose_control_light/pose_control.jpeg" style={{width:300, height:'auto'}}/></div></td>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/AI-Box/CLIP.PNG" style={{width:300, height:'auto'}}/></div></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> This wiki showcases benchmarking of YOLOv8s for pose estimation and object detection on Raspberry Pi 5 and Raspberry Pi Compute Module 4. All tests utilize the same model (YOLOv8s), quantized to int8, with an input size of 640x640 resolution, batch size set to 1, and input from the same video at 240 FPS.</font></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> This wiki we will show you how to install Coral M.2 Accelerator to Raspberry Pi 5 and finally we will test Coral M.2 Accelerator.</font></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> This wiki will guide you on how to run YOLOv8 using an AI kit, use YOLOv8 to monitor your posture, and ultimately control your lights based on your posture.</font></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}>This tutorial guides you through installing CLIP on a reComputer. CLIP enables zero-shot image recognition by matching images with text without traditional labels.</font></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/benchmark_on_rpi5_and_cm4_running_yolov8s_with_rpi_ai_kit/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/install_m2_coral_to_rpi5/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/pose_based_light_control_with_nodered_and_rpi_with_aikit/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/clip_application_on_rpi5_with_ai_kit/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<th class="table-trnobg"><font size={"4"}>Tutorial of AI Kit with Raspberry Pi 5 about YOLOv8n object detection</font></th>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/YOLOV8/pycoral_install.gif" style={{width:300, height:'auto'}}/></div></td>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/pose_control_light/pose_control.jpeg" style={{width:300, height:'auto'}}/></div></td>
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<td class="table-trnobg"><div style={{textAlign:'center'}}><img src="https://files.seeedstudio.com/wiki/reComputer-R1000/hailo-tutorial/model_compile.png" style={{width:300, height:'auto'}}/></div></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> This wiki article will guide you through the process of compiling a model and running it on the Google Coral TPU, enabling you to leverage its capabilities for high-performance machine learning applications.</font></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> his wiki will guide you on how to run YOLOv8 using an AI kit, use YOLOv8 to monitor your posture, and ultimately control your lights based on your posture. </font></td>
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<td className="table-trnobg" style={{ textAlign: 'justify' }}><font size={"2"}> This wiki will guide you on how to use YOLOv8n for object detection with AI Kit on Raspberry Pi 5, from training to deployment.</font></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/convert_model_to_edge_tpu_tflite_format_for_google_coral/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/pose_based_light_control_with_nodered_and_rpi_with_aikit/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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<td class="table-trnobg"><div class="get_one_now_container" style={{textAlign: 'center'}}><a class="get_one_now_item" href="https://wiki.seeedstudio.com/tutorial_of_ai_kit_with_raspberrypi5_about_yolov8n_object_detection/"><strong><span><font color={'FFFFFF'} size={"4"}>📚 Learn More</font></span></strong></a></div></td>
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