更新 AGENTS.md、README.md，添加可视化脚本与输出文件到版本控制

.gitignore

@@ -0,0 +1,19 @@
+*
+!/.gitattributes
+!/Dataloader.py
+!/LICENSE
+!/Merge_classes.py
+!/Model.py
+!/README.md
+!/THIRD_PARTY_LICENSES.md
+!/Train.py
+!/AGENTS.md
+!/Finetune.py
+!/Curve.py
+!/Evaluate.py
+!/training_log.csv
+!/confusion_matrix.png
+!/roc_curve.png
+!/pr_curve.png
+!/training_curves.png
+!.gitignore

AGENTS.md

@@ -0,0 +1,72 @@
+# AGENTS.md
+
+## Project
+
+CNN-based garbage classification (4 classes: 厨余垃圾/可回收物/其他垃圾/有害垃圾). ResNet-34 architecture, ~21M params, 256×256 RGB input, ~900 lines across 8 Python files. No package structure.
+
+## Pipeline (order matters)
+
+```bash
+python Merge_classes.py   # merges 265 → 4 classes, creates ../trash_division_data/ultimate_4_class/
+python Train.py           # trains the model, saves best_model.pth + training_log.csv
+python Finetune.py        # optional: freezes early layers, saves finetuned_model.pth + finetune_log.csv
+python Evaluate.py        # plots confusion matrix / ROC / PR curves from best_model.pth
+python Curve.py           # plots loss/f1/acc/lr curves from training_log.csv
+```
+
+Also usable standalone: `python Model.py` prints `torchsummary` parameter summary.
+
+## Dependencies
+
+No `requirements.txt` — install manually: `torch`, `torchvision`, `tqdm`, `matplotlib`, `pandas`, `Pillow`, `torchsummary`. `Evaluate.py` additionally needs `scikit-learn`.
+
+## Data setup
+
+Data expected **outside repo** at `../trash_division_data/` (sibling dir). `Merge_classes.py` reads `val/classname.txt` there; `Train.py` and `Finetune.py` expect `ultimate_4_class/{train,val}/` with class-numbered subdirs (`1/` to `4/`). All paths relative to repo root.
+
+## .gitignore — whitelist pattern
+
+`.gitignore` uses `*` (ignore everything) then un-ignores specific files with `!` patterns. **Any new file you add to the repo must be explicitly whitelisted** or it will be invisible to git. The current whitelist: `Dataloader.py`, `LICENSE`, `Merge_classes.py`, `Model.py`, `README.md`, `THIRD_PARTY_LICENSES.md`, `Train.py`, `.gitattributes`, `.gitignore`.
+
+`best_model.pth` and `finetuned_model.pth` are **untracked** (~125 MB each) — back them up manually if needed. `Finetune.py`, `Curve.py`, `Evaluate.py`, `AGENTS.md`, `training_log*.csv`, and `finetune_log.csv` are also untracked (not in whitelist).
+
+## Gotchas
+
+- **Windows: set `num_workers=0`** in `create_dataloaders()` call sites (`Train.py:191`, `Finetune.py:196`, `Dataloader.py:229`)
+- Device selection priority: `cuda > xpu > cpu` (`xpu` = Intel GPU)
+- Training auto-resumes from `best_model.pth` if present in repo root; fine-tuning auto-loads it too
+- `Dataloader.py` uses `RobustImageFolder` — scans all images, skips corrupted ones (tqdm progress), slow on first load
+- Image normalization: hardcoded ImageNet stats (`mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]`)
+- `create_dataloaders()` has a `val_split` parameter that's **never used** — the code always expects a pre-split `val/` folder
+
+### Finetune-specific
+
+- **BUG**: `freeze_base_layers()` references `model.stage2` and `model.stage3` but the model uses `layer2`/`layer3`. This crashes at runtime — fix to `model.layer2`/`model.layer3` (or delete the function, since it would freeze `layer2`+`layer3` while docstring says only conv1+stage2).
+- `freeze_base_layers()` actually freezes **conv1, bn1, layer2, AND layer3** (despite docstring saying only conv1 + stage2). Only layer1, layer4, and fc are trainable.
+- Class weights use `power=1.5` (vs `power=1.0` in Train) — amplifies minority-class weighting
+- Defaults: `lr=0.0001`, `epochs=30` (vs `lr=0.001`, `epochs=20` in Train)
+- Writes `finetune_log.csv` (Train writes `training_log.csv`)
+- Loads `best_model.pth` then saves `finetuned_model.pth`
+
+### Curve.py
+
+- Hardcoded to read `training_log.csv` only — won't work for `finetune_log.csv`
+- Requires `pandas`, saves `training_curves.png`
+
+### Evaluate.py
+
+- Hardcoded constants at top of `__main__` block: `MODEL_PATH`, `DATA_ROOT`, `BATCH_SIZE`, `NUM_WORKERS`
+- Loads model from `best_model.pth` by default; handles both bare state_dict and `model_state_dict`/`model` key wrappers
+- Saves `confusion_matrix.png`, `roc_curve.png`, `pr_curve.png`
+- Requires `scikit-learn`
+
+## Model architecture reference
+
+`Model.py` attribute names (for freezing / layer access):
+- `conv1`, `bn1`, `relu`, `maxpool`
+- `layer1`, `layer2`, `layer3`, `layer4`
+- `avgpool`, `dropout` (nn.Dropout), `fc` (nn.Linear(512, 4))
+
+## Testing
+
+No test suite.

README.md

@@ -18,6 +18,7 @@
 - [文件说明](#文件说明)
 - [目录结构](#目录结构)
 - [训练细节](#训练细节)
+- [评估与可视化](#评估与可视化)
 - [许可证](#许可证)
 
 ---
@@ -84,6 +85,7 @@
 - pandas
 - Pillow
 - torchsummary
+- scikit-learn（仅 `Evaluate.py` 需要）
 
 ## 快速开始
 
@@ -99,6 +101,19 @@
    python Train.py
    ```
 
+3. **微调模型**（可选，冻结浅层、微调深层）：
+
+   ```bash
+   python Finetune.py
+   ```
+
+4. **评估与可视化**：
+
+   ```bash
+   python Evaluate.py     # 混淆矩阵、ROC 曲线、PR 曲线
+   python Curve.py         # 训练过程的 loss/f1/acc/lr 曲线
+   ```
+
 > **注意**：
 > - 数据目录默认为 `../trash_division_data/ultimate_4_class/`，需先运行合并脚本
 > - Windows 系统需将 `num_workers` 设为 `0`（参见 `Dataloader.py` 和 `Train.py`）
@@ -109,10 +124,14 @@
 | 文件 | 功能 |
 |---|---|
 | `Train.py` | 训练主脚本，包含训练循环、验证、评估 |
+| `Finetune.py` | 微调脚本，冻结浅层后微调深层网络 |
 | `Dataloader.py` | 数据加载模块，包含 RobustImageFolder 和 DataLoader 创建 |
 | `Model.py` | 模型定义，ResNet-34（BasicBlock）+ Dropout |
 | `Merge_classes.py` | 数据集预处理，265 类合并为 4 类 |
-| `best_model.pth` | 训练好的最佳模型权重（约 125 MB） |
+| `Evaluate.py` | 模型评估，绘制混淆矩阵、ROC 曲线、PR 曲线 |
+| `Curve.py` | 训练曲线绘制，从 CSV 读取并绘制 loss/f1/acc/lr 曲线 |
+| `training_log.csv` | 训练日志，记录每轮 epoch 的 loss、f1、acc、lr |
+| `best_model.pth` | 训练好的最佳模型权重（约 125 MB，不纳入版本控制） |
 | `AGENTS.md` | AI 助手指南（开发辅助） |
 | `THIRD_PARTY_LICENSES.md` | 第三方数据集许可证声明 |
 
@@ -121,15 +140,23 @@
 ```
 trash-division/
 ├── AGENTS.md               # AI 助手指南
-├── best_model.pth           # 最佳模型权重
+├── best_model.pth           # 最佳模型权重（不纳入版本控制）
+├── Curve.py                 # 训练曲线绘制脚本
 ├── Dataloader.py            # 数据加载模块
+├── Evaluate.py              # 模型评估可视化脚本
+├── Finetune.py              # 微调脚本
 ├── .gitattributes           # Git 属性配置
 ├── LICENSE                  # MIT 许可证
 ├── Merge_classes.py         # 数据集预处理脚本
 ├── Model.py                 # 模型定义
 ├── README.md                # 项目说明（本文件）
 ├── THIRD_PARTY_LICENSES.md  # 第三方许可证声明
-└── Train.py                 # 训练主脚本
+├── Train.py                 # 训练主脚本
+├── training_log.csv         # 训练日志
+├── confusion_matrix.png     # 混淆矩阵（Evaluate.py 输出）
+├── roc_curve.png            # ROC 曲线（Evaluate.py 输出）
+├── pr_curve.png             # PR 曲线（Evaluate.py 输出）
+└── training_curves.png      # 训练曲线（Curve.py 输出）
 ```
 
 ## 训练细节
@@ -149,6 +176,43 @@ trash-division/
 
 训练时数据增强管线：RandomResizedCrop(256, scale=(0.8, 1.0)) + RandomHorizontalFlip(p=0.5) + RandomRotation(+-15 deg) + ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2)
 
+## 评估与可视化
+
+训练完成后，`training_log.csv` 会记录每个 epoch 的训练/验证指标。以下两个脚本用于可视化分析：
+
+### Evaluate.py — 模型评估
+
+在验证集上推理，生成三张评估图表：
+
+| 图表 | 说明 |
+|---|---|
+| `confusion_matrix.png` | 混淆矩阵，展示各类别的预测分布 |
+| `roc_curve.png` | ROC 曲线（One-vs-Rest），含各类别 AUC 和 Macro-avg AUC |
+| `pr_curve.png` | Precision-Recall 曲线，含各类别 Average Precision |
+
+```bash
+python Evaluate.py
+```
+
+脚本顶部的 `MODEL_PATH`、`DATA_ROOT`、`BATCH_SIZE`、`NUM_WORKERS` 可按需修改。
+
+### Curve.py — 训练曲线
+
+从 `training_log.csv` 读取训练日志，绘制四张子图：
+
+| 子图 | 说明 |
+|---|---|
+| Loss vs Epoch | 训练/验证损失曲线 |
+| F1 Score vs Epoch | 训练/验证宏平均 F1 曲线 |
+| Accuracy vs Epoch | 训练/验证准确率曲线 |
+| Learning Rate vs Epoch | 余弦退火学习率变化曲线 |
+
+```bash
+python Curve.py
+```
+
+输出文件为 `training_curves.png`。
+
 ## 许可证
 
 本项目主代码采用 [MIT 许可证](LICENSE)。

training_log.csv

@@ -0,0 +1,81 @@
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+59,0.0946220946482491,0.9380815029144287,95.09768451519537,0.41103389083751746,0.8357677459716797,88.4889132220093,0.0008029981361676465,best
+60,0.08804238645213414,0.9423004388809204,95.45194163048721,0.4207381762007377,0.8308929204940796,88.17410347659458,0.0007322330470336316,
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+62,0.06981624146470565,0.9559226036071777,96.49662325132658,0.41332166066585485,0.843511700630188,88.98850260060225,0.0005989850859999229,best
+63,0.06394793773276639,0.9602090120315552,96.79811866859623,0.41052334102789995,0.8489691019058228,89.35121817684096,0.0005367076727981376,best
+64,0.057007493751794744,0.9636315107345581,97.11016642547034,0.3970402057488879,0.8546013832092285,90.04927456884752,0.00047745751406263185,best
+65,0.05285091761448427,0.967146635055542,97.40035576459238,0.4247235585867853,0.8433754444122314,89.34437448672324,0.0004213259692436376,
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+69,0.034432517173010414,0.9790080785751343,98.34328268210324,0.4133664407223553,0.8576940298080444,90.28880372296743,0.00022964206543729668,best
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+80,0.01922732148408437,0.9889604449272156,99.10078991799324,0.41794140280912484,0.864332914352417,90.82261155214891,0.0,