🚀Top Tutorials for Deploying Custom YOLOv8🔥 on Android⚡️
部署自訂義 YOLOv8🔥 到 Android⚡️ 端的🚀最佳教程
Medium link 👉 https://lnkd.in/gEXbQyDk
Linkedin link 👉 https://www.linkedin.com/in/gary-tsai-taiwan/
In this tutorial, I’ll show you how to deploy YOLOv8 using custom datasets on an Android device.
Want to learn how to deploy YOLOv8 with your own data set on an Android device? I’ll walk you through it in this tutorial.
If you’re interested in deploying YOLOv8 with custom data sets on an Android device, you’re in luck! This tutorial will show you how.
🔥Step 1— Training YOLOv8 with a Custom Dataset
⭐Clone the Git Repository and Install YOLOv8
⭐Performing Inference using a Pre-trained Weights
⭐Data Preparation and Format Conversion
⭐Running the Training Process
⭐Converting the Weights to ONNX Format
⭐Converting the Weights to NCNN Format
🔥Step 2— Building and running on Android Studio
⭐Download ncnn-android-yolov8
⭐Download ncnn
⭐Download opencv-mobile
⭐Opening ncnn-android-yolov8 with Android Studio
⭐Placing NCNN Format Weights in Folder
⭐Modifying yolo.cpp
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🚀About Author
Gary Tsai, I have over 2 years of experience in developing AI solutions and integrating AI technologies into foundry operations. My areas of expertise include cross-departmental coordination, independent project design, establishing project development and maintenance processes, and introducing deep learning techniques for foundry wafer image classification and object detection. Throughout my career, I have collaborated with various departments within the foundry, such as Layout, Etch, and Model departments, to successfully complete multiple AI projects, including circuit inductance component object detection, GaAs wafer defect image classification, etc.
My contributions to these projects have enabled the foundry to streamline operations, improve product quality, and reduce costs through the use of AI technologies.
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