YOLO Usage

Install & Train Model

• YOLO training uses Python — download Anaconda
• Download https://www.anaconda.com/download
• Tsinghua mirror: https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2024.06-1-Windows-x86_64.exe
• More Anaconda info: https://blog.csdn.net/ddafei/article/details/140326536

Install Software

• After installing Anaconda, create a virtual environment
• Click Create, name it yolotest (or other name), Python 3.8.19, Create
  
• Click green triangle on yolotest → Open Terminal
  
• Terminal prompt should show (yolotest)
  

Configure Environment

• Install Python packages in terminal:

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 pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple/
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 pip config set install.trusted-host pypi.tuna.tsinghua.edu.cn
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 # yolo 0.3.1
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 # Latest: pip install yolo
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 pip install yolo==0.3.1
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 # ultralytics 8.2.79
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 # Latest: pip install ultralytics
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 pip install ultralytics==8.2.79
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 pip install ncnn==1.0.20240410
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 pip install labelimg
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 # Finally: pip list

• yolo, ultralytics installed
  
• Run yolo in terminal to verify
  
• Run labelimg to open annotation tool
  

Create Training Directory

• Create folder e.g. yolotrain on E: drive
• Under yolotrain: labels/, images/; under images/: test, train, val; under labels/: train, val
• Structure:
  
• Put same training images in images/train, images/val, and images/test (copy files)
• labels/train and labels/val hold labelimg annotation files

Annotate Data

• Put images in images/train, copy to images/val and images/test

• Run labelimg from configured terminal

• Open Dir → images/train (e.g. E:/yolotest/images/train)

• Change Save Dir → labels/train (e.g. E:/yolotest/labels/train)

• Set format to YOLO via Save format button
  

• Annotate

• Example: Like and Comment buttons on a short-video app

• Right-click → Create RectBox or click left Create RectBox

  

• Draw box on Like area, class name aixin, OK — used in API calls later

  

• Draw box on Comment, class pinglun, OK

  

• Save, Next Image for next image

• labels/train gets classes.txt and per-image .txt files

  

• Copy labels/train to labels/val for validation

Train Model

• In yolotrain, create aixin.yaml:

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 path: E:/yolotrain
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 train: images/train
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 val: images/val
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 test: images/test
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 nc: 2
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 names: ["aixin","pinglun"]

• Parameters:

• path: training root (adjust drive/path)

• train, val, test: image folders relative to path

• nc: number of classes (2 here)

• names: class names from labelimg — order matters

• Train in cmd: e:/ then cd yolotrain

• Training command (pick one):

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 yolo detect train data=e:/yolotrain/aixin.yaml model=e:/yolotrain/yolov8s.pt imgsz=640

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 yolo detect train data=e:/yolotrain/aixin.yaml model=e:/yolotrain/yolov8s.pt epochs=100 imgsz=640

• Downloads yolov8s.pt — if fail, get from demo zip or https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt

  

• Training in progress

  

• Done — Results saved path varies; example runs/detect/train under E:/yolotrain

  

• Find best.pt and annotated result images in output folder

Validate Model

• Validate on images:

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 yolo detect val data=e:/yolotrain/aixin.yaml model=e:/yolotrain/runs/detect/train/weights/best.pt

• Check Results saved path for annotated images
  

Export ONNX Model

• Export trained .pt to ONNX for EC API

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 yolo export model=e:/yolotrain/runs/detect/train/weights/best.pt format=onnx

• export success shows path — copy .onnx to phone /sdcard/ (or other path)
• Initialize with yolov8Api.newYolov8Onxx; other API calls are the same

Export NCNN Model

• Export to ncnn for EC

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 yolo export model=e:/yolotrain/runs/detect/train/weights/best.pt format=ncnn

• Downloads pnnx — use demo pnnx-20240819-windows.zip in e:/yolotrain/ if download fails
• Match filename in error screenshot or download manually
  
• Output has model.ncnn.param and model.ncnn.bin — copy both to phone sdcard
  
• Initialize with yolov8Api.newYolov8 for ncnn

API Usage

• Put trained model on phone /sdcard/ (or other path)
• See YOLOv8 API Module

GPU

• GPU training: https://blog.csdn.net/shangyanaf/article/details/139029717

CONDA Virtual Environment Error

• Cannot select Python version when creating env — often network; switch to Tsinghua mirror
• 1. Click channels
     
• 2. Remove default channels
     
• 3. Add Tsinghua channels (Enter after each)

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 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/

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 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/

• 4. Click Update channels
  

Arial.ttf Download Error

• Download Arial.ttf
• Place in C:\Users\Administrator\AppData\Roaming\Ultralytics as Arial.ttf (Administrator = your Windows username)