chore: 初始化 uv 项目配置与忽略规则

- pyproject.toml 采用 uv 应用模式（package = false），直接从仓库根运行入口脚本
- 依赖：torch / torchvision / numpy / pillow / pyyaml / swanlab
- dev 组含 pytest 与 ruff，export 组按需安装 onnx / onnxscript
- 忽略数据集本体、runs/ 训练产物、权重与 .DS_Store

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

pyproject.toml

@@ -0,0 +1,48 @@
+[project]
+name = "modeltraineval"
+version = "0.1.0"
+description = "薄膜缺陷分类定位模型的训练与评估（纯卷积，面向实时设备部署）"
+readme = "README.md"
+authors = [
+    { name = "Shaun Zhou", email = "xiangyuzhou823@gmail.com" }
+]
+requires-python = ">=3.12"
+dependencies = [
+    "torch>=2.4",
+    "torchvision>=0.19",
+    "numpy>=1.26",
+    "pillow>=10.3",
+    "pyyaml>=6.0",
+    "swanlab>=0.10.0",
+]
+
+[dependency-groups]
+dev = [
+    "pytest>=8.0",
+    "ruff>=0.6",
+]
+# ONNX 导出（部署用），按需安装：uv sync --group export
+export = [
+    "onnx>=1.16",
+    "onnxscript>=0.2",
+]
+
+[tool.uv]
+# 本仓库是应用而非库：不打包安装，直接从仓库根目录运行 train.py / eval.py。
+package = false
+
+# 训练机（Linux + Nvidia GPU）请用 CUDA 版 torch，例如：
+#   uv pip install --index-url https://download.pytorch.org/whl/cu124 torch torchvision
+# 默认 PyPI 源在 Linux 上装的是 CUDA 构建，在 macOS 上只有 CPU 构建（仅可用于评估/导出）。
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["."]
+addopts = "-q"
+
+[tool.ruff]
+line-length = 110
+target-version = "py312"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "UP", "B"]