8938661850
Features: - Auto-labeling pipeline: CSV values -> PDF search -> YOLO annotations - Flexible date matching: year-month match, nearby date tolerance - PDF text extraction with PyMuPDF - OCR support for scanned documents (PaddleOCR) - YOLO training and inference pipeline - 7 field types: InvoiceNumber, InvoiceDate, InvoiceDueDate, OCR, Bankgiro, Plusgiro, Amount Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
68 lines
1.6 KiB
Bash
68 lines
1.6 KiB
Bash
#!/bin/bash
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# 训练运行脚本
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# 使用方法: bash scripts/run_train.sh
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set -e
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# 项目根目录
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PROJECT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
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cd "$PROJECT_DIR"
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# 激活虚拟环境
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if [ -f "venv/bin/activate" ]; then
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source venv/bin/activate
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else
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echo "错误: 虚拟环境不存在，请先运行 setup_wsl.sh"
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exit 1
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fi
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# 默认参数
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DATA_YAML="${DATA_YAML:-data/dataset/dataset.yaml}"
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MODEL="${MODEL:-yolov8s.pt}"
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EPOCHS="${EPOCHS:-100}"
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BATCH_SIZE="${BATCH_SIZE:-16}"
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IMG_SIZE="${IMG_SIZE:-1280}"
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DEVICE="${DEVICE:-0}"
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# 检查数据集是否存在
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if [ ! -f "$DATA_YAML" ]; then
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echo "错误: 数据集配置文件不存在: $DATA_YAML"
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echo "请先运行自动标注: bash scripts/run_autolabel.sh"
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exit 1
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fi
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# 显示配置
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echo "=========================================="
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echo "训练配置"
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echo "=========================================="
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echo "数据集: $DATA_YAML"
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echo "基础模型: $MODEL"
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echo "Epochs: $EPOCHS"
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echo "Batch Size: $BATCH_SIZE"
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echo "图像尺寸: $IMG_SIZE"
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echo "设备: $DEVICE"
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echo "=========================================="
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echo ""
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# 检查 GPU
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if command -v nvidia-smi &> /dev/null; then
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echo "GPU 状态:"
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nvidia-smi --query-gpu=name,memory.used,memory.total --format=csv,noheader
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echo ""
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else
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echo "警告: 未检测到 GPU，将使用 CPU 训练 (较慢)"
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DEVICE="cpu"
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fi
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# 运行训练
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python -m src.cli.train \
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--data "$DATA_YAML" \
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--model "$MODEL" \
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--epochs "$EPOCHS" \
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--batch "$BATCH_SIZE" \
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--imgsz "$IMG_SIZE" \
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--device "$DEVICE"
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echo ""
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echo "训练完成! 模型保存在: runs/train/invoice_fields/weights/"
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