94 lines
2.3 KiB
Batchfile
94 lines
2.3 KiB
Batchfile
@echo off
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chcp 65001 >nul
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cls
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echo ============================================================
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echo 训练鞋子检测模型 (YOLOv8)
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echo ============================================================
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echo.
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:: 设置数据集路径
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set DATASET=datasets/safety-shoes-detection/data.yaml
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:: 检查数据集是否存在
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if not exist %DATASET% (
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echo [错误] 找不到数据集配置文件: %DATASET%
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echo.
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echo 请先下载数据集:
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echo 1. 访问 https://universe.roboflow.com/nedrick-chandra-gpg1l/safety-shoes-detection-5qgkg
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echo 2. 点击 Download -^> YOLOv8 格式
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echo 3. 解压到 datasets/safety-shoes-detection/
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echo 4. 运行 python 01_download_dataset.py --no-modify
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pause
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exit /b 1
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)
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echo [信息] 数据集: %DATASET%
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echo.
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:: 选择模型
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echo 选择模型:
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echo 1. YOLOv8n (轻量级, 速度快, 推荐)
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echo 2. YOLOv8s (精度更高, 稍慢)
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echo 3. YOLOv8m (高精度, 较慢)
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echo.
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set /p MODEL_CHOICE="输入选择 (1-3, 默认 1): "
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if "%MODEL_CHOICE%"=="" set MODEL_CHOICE=1
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if "%MODEL_CHOICE%"=="1" (
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set MODEL=yolov8n.pt
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set DESC=YOLOv8n (轻量级)
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)
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if "%MODEL_CHOICE%"=="2" (
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set MODEL=yolov8s.pt
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set DESC=YOLOv8s (标准)
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)
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if "%MODEL_CHOICE%"=="3" (
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set MODEL=yolov8m.pt
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set DESC=YOLOv8m (高精度)
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)
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echo.
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echo [信息] 使用模型: %DESC%
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echo.
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:: 设置训练参数
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set EPOCHS=150
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set IMGSZ=640
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set BATCH=16
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echo 训练参数:
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echo - Epochs: %EPOCHS%
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echo - Image Size: %IMGSZ%
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echo - Batch Size: %BATCH%
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echo - Device: GPU (cuda:0)
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echo.
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echo ============================================================
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echo 开始训练
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echo ============================================================
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echo.
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:: 开始训练
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yolo detect train data=%DATASET% model=%MODEL% epochs=%EPOCHS% imgsz=%IMGSZ% batch=%BATCH% device=0
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if %ERRORLEVEL% neq 0 (
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echo.
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echo [错误] 训练失败！
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pause
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exit /b 1
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)
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echo.
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echo ============================================================
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echo 训练完成！
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echo ============================================================
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echo.
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echo 模型保存在: runs/detect/train/weights/
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echo - best.pt (最佳模型)
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echo - last.pt (最后模型)
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echo.
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echo 下一步: 运行 03_export_onnx.bat 导出 ONNX 格式
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echo.
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pause
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