重构：将 Baseline.py 迁移至 baseline/ 目录，新增多模型对比脚本

.gitignore

@@ -12,6 +12,10 @@
 !/Finetune.py
 !/Curve.py
 !/Evaluate.py
+!/baseline/
+!/baseline/__init__.py
+!/baseline/VGG_KNN.py
+!/baseline/compare_models.py
 !/training_log.csv
 !/confusion_matrix.png
 !/roc_curve.png

AGENTS.md

@@ -2,23 +2,25 @@
 
 ## 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.
+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 11 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 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 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 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 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
+python Curve.py                         # plots loss/f1/acc/lr curves from training_log.csv
+python baseline/VGG_KNN.py             # VGG16 feature extraction + KNN baseline
+python baseline/compare_models.py      # compares multiple models (ROC + accuracy bar chart)
 ```
 
 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`.
+No `requirements.txt` — install manually: `torch`, `torchvision`, `tqdm`, `matplotlib`, `pandas`, `Pillow`, `torchsummary`. `Evaluate.py` and `baseline/*.py` additionally need `scikit-learn`.
 
 ## Data setup
 
@@ -26,7 +28,7 @@ Data expected **outside repo** at `../trash_division_data/` (sibling dir). `Merg
 
 ## .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`.
+`.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 includes: `Dataloader.py`, `LICENSE`, `Merge_classes.py`, `Model.py`, `README.md`, `THIRD_PARTY_LICENSES.md`, `Train.py`, `.gitattributes`, `.gitignore`, plus `Finetune.py`, `Curve.py`, `Evaluate.py`, `AGENTS.md`, 4× output PNG, `training_log.csv`, and `baseline/`.
 
 `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).
 
@@ -60,6 +62,16 @@ Data expected **outside repo** at `../trash_division_data/` (sibling dir). `Merg
 - Saves `confusion_matrix.png`, `roc_curve.png`, `pr_curve.png`
 - Requires `scikit-learn`
 
+### baseline/ (VGG_KNN.py + compare_models.py)
+
+- `baseline/VGG_KNN.py` can run standalone (`python baseline/VGG_KNN.py`) or be imported from `compare_models.py`
+- Uses `sys.path.insert` at top so it can import root-level modules (`Model`, `Dataloader`) from subdirectory
+- `compare_models.py` has a `MODELS` registry list — add new models by writing a `get_xxx_preds(train_loader, val_loader, device)` function and adding one line to the list; no plot code changes needed
+- VGG16 feature dimension: 25088 (512 channels × 7×7 avgpool)
+- KNN uses `predict_proba` (neighbor voting proportions) for ROC curves — coarse-grained but valid AUC
+- Output: `baseline/roc_comparison.png`, `baseline/accuracy_bar.png`, `baseline/vgg_knn_confusion_matrix.png`
+- Compare scripts output images to `baseline/` dir (not repo root)
+
 ## Model architecture reference
 
 `Model.py` attribute names (for freezing / layer access):

Baseline.py

@@ -1,129 +0,0 @@
-"""
-Baseline.py
-VGG16 预训练模型特征提取 + KNN 四分类基线
-author: yukun-hh
-date: 2026-5-14
-"""
-import os
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib
-
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader
-from torchvision import models, transforms
-from tqdm import tqdm
-
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.metrics import (
-    accuracy_score, f1_score,
-    confusion_matrix, ConfusionMatrixDisplay,
-    classification_report,
-)
-
-from Dataloader import RobustImageFolder
-
-matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
-matplotlib.rcParams['axes.unicode_minus'] = False
-
-# ============================================================
-# ★★★ 可配置参数 ★★★
-# ============================================================
-DATA_ROOT   = '../trash_division_data/ultimate_4_class/'
-BATCH_SIZE  = 32
-IMAGE_SIZE  = 256
-NUM_WORKERS = 4
-K           = 5
-# ============================================================
-
-CLASS_NAMES = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
-
-
-def load_vgg16_extractor(device):
-    try:
-        model = models.vgg16(weights='IMAGENET1K_V1')
-    except TypeError:
-        model = models.vgg16(pretrained=True)
-    model.classifier = nn.Identity()
-    model = model.to(device).eval()
-    for param in model.parameters():
-        param.requires_grad = False
-    return model
-
-
-def extract_features(model, loader, device):
-    model.eval()
-    all_features = []
-    all_labels = []
-    with torch.no_grad():
-        for images, labels in tqdm(loader, desc='Extracting features'):
-            images = images.to(device)
-            feats = model(images)
-            all_features.append(feats.cpu().numpy())
-            all_labels.append(labels.numpy())
-    return np.concatenate(all_features), np.concatenate(all_labels)
-
-
-if __name__ == '__main__':
-    val_transform = transforms.Compose([
-        transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                             std=[0.229, 0.224, 0.225]),
-    ])
-
-    train_dataset = RobustImageFolder(
-        root=os.path.join(DATA_ROOT, 'train'),
-        transform=val_transform,
-    )
-    val_dataset = RobustImageFolder(
-        root=os.path.join(DATA_ROOT, 'val'),
-        transform=val_transform,
-    )
-    print(f"训练集: {len(train_dataset)}  验证集: {len(val_dataset)}")
-
-    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE,
-                              shuffle=False, num_workers=NUM_WORKERS,
-                              pin_memory=True, drop_last=False)
-    val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE,
-                            shuffle=False, num_workers=NUM_WORKERS,
-                            pin_memory=True, drop_last=False)
-
-    device = torch.device('cuda' if torch.cuda.is_available()
-                          else 'xpu' if hasattr(torch, 'xpu') and torch.xpu.is_available()
-                          else 'cpu')
-    print(f"Device: {device}")
-
-    extractor = load_vgg16_extractor(device)
-    print("VGG16 特征提取器加载完成 (classifier 已移除)")
-
-    print("提取训练集特征 ...")
-    train_feats, train_labels = extract_features(extractor, train_loader, device)
-    print(f"训练特征: {train_feats.shape}")
-
-    print("提取验证集特征 ...")
-    val_feats, val_labels = extract_features(extractor, val_loader, device)
-    print(f"验证特征: {val_feats.shape}")
-
-    knn = KNeighborsClassifier(n_neighbors=K, n_jobs=-1)
-    knn.fit(train_feats, train_labels)
-    print(f"KNN (K={K}) 训练完成")
-
-    val_preds = knn.predict(val_feats)
-
-    acc = accuracy_score(val_labels, val_preds)
-    macro_f1 = f1_score(val_labels, val_preds, average='macro')
-    print(f"\n验证集 Accuracy: {acc:.4f}")
-    print(f"验证集 Macro-F1: {macro_f1:.4f}")
-    print(f"\n分类报告:\n{classification_report(val_labels, val_preds, target_names=CLASS_NAMES)}")
-
-    cm = confusion_matrix(val_labels, val_preds)
-    fig, ax = plt.subplots(figsize=(8, 7))
-    ConfusionMatrixDisplay(cm, display_labels=CLASS_NAMES).plot(
-        ax=ax, cmap='Blues', values_format='d', xticks_rotation=30)
-    ax.set_title(f'Baseline Confusion Matrix (VGG16 + KNN, K={K})', fontsize=14)
-    plt.tight_layout()
-    plt.savefig('baseline_confusion_matrix.png', dpi=150, bbox_inches='tight')
-    plt.show()
-    print("混淆矩阵已保存: baseline_confusion_matrix.png")

README.md

@@ -123,7 +123,9 @@
 
 | 文件 | 功能 |
 |---|---|
-| `Baseline.py` | 基线模型，VGG16 预训练特征提取 + KNN 四分类 |
+| `Baseline.py` → `baseline/` | 基线模型目录，VGG16+KNN 及多模型对比 |
+| `baseline/VGG_KNN.py` | VGG16 预训练特征提取 + KNN 四分类 |
+| `baseline/compare_models.py` | 多模型 ROC 曲线与准确率柱状图对比 |
 | `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 |
@@ -141,7 +143,9 @@
 ```
 trash-division/
 ├── AGENTS.md               # AI 助手指南
-├── Baseline.py              # 基线模型脚本
+├── baseline/                # 基线模型目录
+│   ├── VGG_KNN.py           # VGG16 + KNN 分类脚本
+│   └── compare_models.py    # 多模型对比脚本
 ├── 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

baseline/VGG_KNN.py

@@ -0,0 +1,145 @@
+"""
+baseline/VGG_KNN.py
+VGG16 预训练模型特征提取 + KNN 四分类基线
+可独立运行，也可被 compare_models.py 导入复用
+author: yukun-hh
+date: 2026-5-14
+"""
+import sys, os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torchvision import models, transforms
+from tqdm import tqdm
+
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import (
+    accuracy_score, f1_score,
+    confusion_matrix, ConfusionMatrixDisplay,
+    classification_report,
+)
+
+from Dataloader import RobustImageFolder
+
+matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
+matplotlib.rcParams['axes.unicode_minus'] = False
+
+
+CLASS_NAMES = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
+
+
+def load_vgg16_extractor(device):
+    try:
+        model = models.vgg16(weights='IMAGENET1K_V1')
+    except TypeError:
+        model = models.vgg16(pretrained=True)
+    model.classifier = nn.Identity()
+    model = model.to(device).eval()
+    for param in model.parameters():
+        param.requires_grad = False
+    return model
+
+
+def extract_features(model, loader, device):
+    model.eval()
+    all_features = []
+    all_labels = []
+    with torch.no_grad():
+        for images, labels in tqdm(loader, desc='Extracting features'):
+            images = images.to(device)
+            feats = model(images)
+            all_features.append(feats.cpu().numpy())
+            all_labels.append(labels.numpy())
+    return np.concatenate(all_features), np.concatenate(all_labels)
+
+
+class VGGKNNBaseline:
+    def __init__(self, k=5, device='cpu',
+                 data_root='../trash_division_data/ultimate_4_class/',
+                 image_size=256, batch_size=32, num_workers=4):
+        self.k = k
+        self.device = device
+        self.data_root = data_root
+        self.image_size = image_size
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.extractor = load_vgg16_extractor(device)
+        self.knn = KNeighborsClassifier(n_neighbors=k, n_jobs=-1)
+
+    def _get_loader(self, split):
+        transform = transforms.Compose([
+            transforms.Resize((self.image_size, self.image_size)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                 std=[0.229, 0.224, 0.225]),
+        ])
+        dataset = RobustImageFolder(
+            root=os.path.join(self.data_root, split),
+            transform=transform,
+        )
+        print(f"  {split}: {len(dataset)} 张")
+        return DataLoader(dataset, batch_size=self.batch_size,
+                          shuffle=False, num_workers=self.num_workers,
+                          pin_memory=True, drop_last=False)
+
+    def fit(self, train_loader=None):
+        if train_loader is None:
+            train_loader = self._get_loader('train')
+        print("  提取训练集特征 ...")
+        train_feats, train_labels = extract_features(self.extractor, train_loader, self.device)
+        self.knn.fit(train_feats, train_labels)
+
+    def predict(self, val_loader=None):
+        if val_loader is None:
+            val_loader = self._get_loader('val')
+        print("  提取验证集特征 ...")
+        val_feats, val_labels = extract_features(self.extractor, val_loader, self.device)
+        preds = self.knn.predict(val_feats)
+        probs = self.knn.predict_proba(val_feats)
+        return val_labels, preds, probs
+
+
+if __name__ == '__main__':
+    DATA_ROOT   = '../trash_division_data/ultimate_4_class/'
+    BATCH_SIZE  = 32
+    IMAGE_SIZE  = 256
+    NUM_WORKERS = 4
+    K           = 5
+
+    device = torch.device('cuda' if torch.cuda.is_available()
+                          else 'xpu' if hasattr(torch, 'xpu') and torch.xpu.is_available()
+                          else 'cpu')
+    print(f"Device: {device}")
+
+    baseline = VGGKNNBaseline(k=K, device=device,
+                               data_root=DATA_ROOT, image_size=IMAGE_SIZE,
+                               batch_size=BATCH_SIZE, num_workers=NUM_WORKERS)
+
+    train_loader = baseline._get_loader('train')
+    val_loader = baseline._get_loader('val')
+
+    baseline.fit(train_loader)
+    y_true, y_preds, y_probs = baseline.predict(val_loader)
+
+    acc = accuracy_score(y_true, y_preds)
+    macro_f1 = f1_score(y_true, y_preds, average='macro')
+    print(f"\n验证集 Accuracy: {acc:.4f}")
+    print(f"验证集 Macro-F1: {macro_f1:.4f}")
+    print(f"\n分类报告:\n{classification_report(y_true, y_preds, target_names=CLASS_NAMES)}")
+
+    cm = confusion_matrix(y_true, y_preds)
+    fig, ax = plt.subplots(figsize=(8, 7))
+    ConfusionMatrixDisplay(cm, display_labels=CLASS_NAMES).plot(
+        ax=ax, cmap='Blues', values_format='d', xticks_rotation=30)
+    ax.set_title(f'Baseline Confusion Matrix (VGG16 + KNN, K={K})', fontsize=14)
+    plt.tight_layout()
+    out_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'vgg_knn_confusion_matrix.png')
+    plt.savefig(out_path, dpi=150, bbox_inches='tight')
+    plt.show()
+    print(f"混淆矩阵已保存: {out_path}")

baseline/__init__.py

@@ -0,0 +1 @@
+# baseline package

baseline/compare_models.py

@@ -0,0 +1,180 @@
+"""
+baseline/compare_models.py
+多模型对比：ROC 曲线 + 准确率柱状图
+添加新模型只需在 MODELS 列表加一行，无需修改绘图代码
+author: yukun-hh
+date: 2026-5-14
+"""
+import sys, os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+import torch
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from tqdm import tqdm
+
+from sklearn.metrics import roc_curve, auc, accuracy_score
+
+from Model import Net
+from Dataloader import RobustImageFolder
+from baseline.VGG_KNN import VGGKNNBaseline
+
+matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
+matplotlib.rcParams['axes.unicode_minus'] = False
+
+# ============================================================
+# ★★★ 可配置参数 ★★★
+# ============================================================
+DATA_ROOT   = '../trash_division_data/ultimate_4_class/'
+BATCH_SIZE  = 32
+IMAGE_SIZE  = 256
+NUM_WORKERS = 4
+K_KNN       = 5
+# ============================================================
+
+CLASS_NAMES = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
+NUM_CLASSES = 4
+
+# ============================================================
+# 预测函数 — 每个函数签名: (train_loader, val_loader, device) -> (y_true, y_preds, y_probs)
+# ============================================================
+
+def get_resnet34_preds(train_loader, val_loader, device):
+    model = Net(num_classes=NUM_CLASSES)
+    state_dict = torch.load('best_model.pth', map_location='cpu')
+    if 'model_state_dict' in state_dict:
+        state_dict = state_dict['model_state_dict']
+    elif 'model' in state_dict:
+        state_dict = state_dict['model']
+    model.load_state_dict(state_dict)
+    model = model.to(device).eval()
+
+    y_true, y_preds, y_probs = [], [], []
+    with torch.no_grad():
+        for images, labels in tqdm(val_loader, desc='ResNet-34'):
+            images, labels = images.to(device), labels
+            logits = model(images)
+            probs = torch.softmax(logits, dim=1)
+            preds = probs.argmax(dim=1)
+            y_true.append(labels.numpy())
+            y_preds.append(preds.cpu().numpy())
+            y_probs.append(probs.cpu().numpy())
+    return np.concatenate(y_true), np.concatenate(y_preds), np.concatenate(y_probs)
+
+
+def get_vgg_knn_preds(train_loader, val_loader, device):
+    baseline = VGGKNNBaseline(k=K_KNN, device=device)
+    baseline.fit(train_loader)
+    return baseline.predict(val_loader)
+
+
+# ============================================================
+# ★ 模型注册表 — 添加新模型只需在这里加一行 ★
+# ============================================================
+
+MODELS = [
+    ('ResNet-34',           get_resnet34_preds),
+    ('VGG16 + KNN  (K=5)',  get_vgg_knn_preds),
+    # 未来轻松扩展示例：
+    # ('ResNet-18 (pretrained)', get_resnet18_preds),
+    # ('ResNet-50 (pretrained)', get_resnet50_preds),
+    # ('ResNet-34 (finetuned)',  get_finetuned_preds),
+]
+
+# ============================================================
+# 调色板 (扩展时无需修改)
+# ============================================================
+COLORS = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
+          '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
+
+
+def compute_macro_roc(y_true, y_probs):
+    one_hot = np.eye(NUM_CLASSES)[y_true]
+    fpr_dict, tpr_dict = {}, {}
+    for c in range(NUM_CLASSES):
+        fpr_dict[c], tpr_dict[c], _ = roc_curve(one_hot[:, c], y_probs[:, c])
+    all_fpr = np.unique(np.concatenate([fpr_dict[c] for c in range(NUM_CLASSES)]))
+    mean_tpr = np.zeros_like(all_fpr)
+    for c in range(NUM_CLASSES):
+        mean_tpr += np.interp(all_fpr, fpr_dict[c], tpr_dict[c])
+    mean_tpr /= NUM_CLASSES
+    macro_auc = auc(all_fpr, mean_tpr)
+    return all_fpr, mean_tpr, macro_auc
+
+
+if __name__ == '__main__':
+    out_dir = os.path.dirname(os.path.abspath(__file__))
+
+    device = torch.device('cuda' if torch.cuda.is_available()
+                          else 'xpu' if hasattr(torch, 'xpu') and torch.xpu.is_available()
+                          else 'cpu')
+    print(f"Device: {device}")
+
+    val_transform = transforms.Compose([
+        transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225]),
+    ])
+
+    train_dataset = RobustImageFolder(root=os.path.join(DATA_ROOT, 'train'),
+                                      transform=val_transform)
+    val_dataset = RobustImageFolder(root=os.path.join(DATA_ROOT, 'val'),
+                                    transform=val_transform)
+    print(f"训练集: {len(train_dataset)}  验证集: {len(val_dataset)}")
+
+    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=False,
+                              num_workers=NUM_WORKERS, pin_memory=True, drop_last=False)
+    val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False,
+                            num_workers=NUM_WORKERS, pin_memory=True, drop_last=False)
+
+    # ———— 评估所有模型 ————
+    results = {}
+    for name, func in MODELS:
+        print(f"\n{'='*50}")
+        print(f"评估: {name}")
+        y_true, y_preds, y_probs = func(train_loader, val_loader, device)
+        acc = accuracy_score(y_true, y_preds)
+        fpr, tpr, roc_auc = compute_macro_roc(y_true, y_probs)
+        results[name] = {'y_true': y_true, 'y_preds': y_preds, 'y_probs': y_probs,
+                         'acc': acc, 'fpr': fpr, 'tpr': tpr, 'auc': roc_auc}
+        print(f"  Accuracy: {acc:.4f}  |  Macro-AUC: {roc_auc:.4f}")
+
+    # ———— ROC 对比图 ————
+    fig, ax = plt.subplots(figsize=(8, 7))
+    for i, (name, r) in enumerate(results.items()):
+        color = COLORS[i % len(COLORS)]
+        ax.plot(r['fpr'], r['tpr'], color=color, lw=2,
+                label=f"{name} (AUC={r['auc']:.4f})")
+    ax.plot([0, 1], [0, 1], 'k--', lw=1, alpha=0.5)
+    ax.set_xlim(0, 1); ax.set_ylim(0, 1.05)
+    ax.set_xlabel('False Positive Rate'); ax.set_ylabel('True Positive Rate')
+    ax.set_title('ROC Curve Comparison (Macro-Average)', fontsize=14)
+    ax.legend(loc='lower right'); ax.grid(True, alpha=0.3)
+    plt.tight_layout()
+    roc_path = os.path.join(out_dir, 'roc_comparison.png')
+    plt.savefig(roc_path, dpi=150, bbox_inches='tight')
+    plt.show()
+    print(f"\nROC 对比图已保存: {roc_path}")
+
+    # ———— 准确率柱状图 ————
+    names = list(results.keys())
+    accs  = [results[n]['acc'] for n in names]
+    fig, ax = plt.subplots(figsize=(8, 5))
+    bar_colors = [COLORS[i % len(COLORS)] for i in range(len(names))]
+    bars = ax.bar(names, accs, color=bar_colors, edgecolor='white', linewidth=1.2)
+    for bar, acc in zip(bars, accs):
+        ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.005,
+                f'{acc:.4f}', ha='center', va='bottom', fontsize=12, fontweight='bold')
+    ax.set_ylim(0, max(accs) * 1.15)
+    ax.set_ylabel('Accuracy'); ax.set_title('Accuracy Comparison', fontsize=14)
+    ax.grid(True, alpha=0.3, axis='y')
+    plt.tight_layout()
+    bar_path = os.path.join(out_dir, 'accuracy_bar.png')
+    plt.savefig(bar_path, dpi=150, bbox_inches='tight')
+    plt.show()
+    print(f"准确率柱状图已保存: {bar_path}")

training_log.csv

@@ -1,81 +1 @@
 epoch,train_loss,train_f1,train_acc,val_loss,val_f1,val_acc,lr,best
-1,1.0409312975676923,0.4329540729522705,48.04254100337675,1.1043149583345566,0.4398210048675537,48.66548042704626,0.004998072590601808,best
-2,0.9862563783744079,0.4695238769054413,52.5943680656054,0.9867177669753319,0.5062971115112305,58.397207774431976,0.00499229333433282,best
-3,0.9462850892451784,0.49421826004981995,55.40279787747226,1.0144445673589866,0.4907984733581543,53.15494114426499,0.004982671142387316,
-4,0.910117163585685,0.514958381652832,57.832097202122526,0.8787865286946395,0.5453917980194092,62.544483985765126,0.004969220851487844,best
-5,0.8786031692946986,0.5320333242416382,59.74282440906898,1.0686318878927787,0.4803737998008728,52.73063235696688,0.004951963201008076,
-6,0.8518873820889128,0.5481140613555908,61.51938615533044,0.7650798693964196,0.6073676347732544,68.98439638653161,0.004930924800994191,best
-7,0.8256270786701512,0.5604796409606934,62.90249638205499,0.8796401012116773,0.5789190530776978,62.63345195729537,0.004906138091134118,
-8,0.8003699506646013,0.5742803812026978,64.3014351181862,0.9246643470014378,0.5521833896636963,60.88831097727895,0.004877641290737884,
-9,0.780536473588097,0.5827116966247559,65.25642185238785,0.8404132533719564,0.5876226425170898,65.89789214344374,0.00484547833980621,
-10,0.7604798049209087,0.595557451248169,66.54079232995659,0.9228097118810533,0.564703643321991,60.77881193539557,0.004809698831278217,
-11,0.7410275131047088,0.6043155789375305,67.35784491075735,0.7576604621266131,0.6295210123062134,69.83985765124555,0.0047703579345627035,best
-12,0.7195374228732343,0.6127941608428955,68.07766521948867,0.9624476881507583,0.5630610585212708,61.59321105940323,0.00472751631047092,
-13,0.6997139808122973,0.6210756301879883,68.85175470332851,0.7615812296349155,0.6177672147750854,69.12127018888584,0.004681240017681994,
-14,0.6824904908837182,0.630592942237854,69.65448625180898,0.6715762626299165,0.6534035205841064,73.48754448398577,0.004631600410885231,best
-15,0.6653590450468583,0.6379610300064087,70.39088880849012,0.694461440047988,0.6517682075500488,73.0906104571585,0.004578674030756364,
-16,0.6514209577758935,0.6478185653686523,71.27879281234925,0.7036816360785346,0.6470745801925659,71.76977826444019,0.004522542485937369,
-17,0.6330186040776395,0.6530008316040039,71.7935962373372,0.7222367905930823,0.6418735980987549,71.07856556255133,0.004463292327201863,
-18,0.6166394593717968,0.6634106040000916,72.6038651712494,0.6067476719332303,0.6886636018753052,77.49110320284697,0.004401014914000078,best
-19,0.5973944908975692,0.6721534729003906,73.47367945007235,0.6952472055509845,0.6622275114059448,71.79715302491103,0.004335806273589214,
-20,0.5820678306183721,0.6758297681808472,73.73145803183792,0.7708474785342401,0.6217234134674072,68.74486723241172,0.004267766952966369,
-21,0.5650806297110982,0.6851130723953247,74.5741377231066,0.7461620579141478,0.6384793519973755,71.35231316725978,0.004197001863832355,
-22,0.5500074683958588,0.6915749311447144,75.099493487699,0.6420613189380593,0.672810435295105,74.9452504790583,0.00412362012082546,
-23,0.5367840825001858,0.6979560852050781,75.66102870236372,0.6252713002082977,0.6949211359024048,75.32849712565014,0.0040477348732745845,best
-24,0.5234906795055925,0.7052106857299805,76.26025084418717,0.7471277477021352,0.6447888016700745,69.70982753900904,0.003969463130731182,
-25,0.5044557179049829,0.7132176160812378,76.91148094548963,0.6325626891507145,0.6857903003692627,75.52012044894607,0.0038889255825490052,
-26,0.4938347232885195,0.7174828052520752,77.23784973468403,0.5635758755127437,0.70375657081604,78.50396934026827,0.003806246411789872,best
-27,0.4793313278116239,0.7242900133132935,77.87475880366618,0.5505193975847648,0.7201660871505737,78.89405967697783,0.003721553103742388,best
-28,0.46573570758837524,0.7336312532424927,78.60437771345876,0.640248272807638,0.6859503984451294,75.13003011223651,0.003634976249348867,
-29,0.44927708754289913,0.737967312335968,78.9352689339122,0.6151526539644867,0.7065733075141907,74.69887763482069,0.00354664934384357,
-30,0.4373503708129221,0.7443608045578003,79.40409430776653,0.5578661908719627,0.7272701263427734,78.20969066520668,0.0034567085809127244,best
-31,0.42717206794400175,0.7488712072372437,79.7779486251809,0.58909761693554,0.7034546136856079,76.88201478237066,0.003365292642693732,
-32,0.4100124511779706,0.7580570578575134,80.60630728412929,0.6458172935624336,0.6865078210830688,75.32849712565014,0.0032725424859373683,
-33,0.3993677339991451,0.763430118560791,80.9876989869754,0.47995706558097007,0.754202127456665,81.65891048453327,0.003178601124662685,best
-34,0.3858378949555808,0.7697042226791382,81.54772672455378,0.6427663844838523,0.6931804418563843,74.28141253764029,0.0030836134096397633,
-35,0.37397055771404913,0.7764154672622681,81.9992161119151,0.6085299244000101,0.7046636343002319,77.08048179578428,0.0029877258050403205,
-36,0.3597575335889638,0.7818952798843384,82.53587795465509,0.5254679805051781,0.7415529489517212,78.89405967697783,0.002891086162600577,
-37,0.3487578573732404,0.7871347665786743,82.8999336710082,0.5125140355052995,0.748577356338501,80.1875171092253,0.002793843493644594,
-38,0.3325814358527052,0.7965956330299377,83.59035817655571,0.5408413317834649,0.7290798425674438,79.87270736381056,0.002696147739319612,
-39,0.3261546248608721,0.7988470792770386,83.75316570188133,0.5301555857539602,0.7376729249954224,80.06433068710649,0.002598149539397671,
-40,0.30964642827472305,0.8070269823074341,84.52725518572117,0.5468305750249544,0.7365171313285828,78.73665480427046,0.0024999999999999996,
-41,0.3009217412674191,0.8119726777076721,84.96140858658949,0.46898612490165015,0.7599539756774902,82.18587462359704,0.002401850460602329,best
-42,0.28925693789887874,0.8200639486312866,85.6458031837916,0.5167866427621677,0.7465909123420715,80.83766767040788,0.0023038522606803878,
-43,0.2707157838268379,0.8313596248626709,86.46360950313556,0.5156203284349763,0.7596548199653625,80.6049822064057,0.0022061565063554063,
-44,0.2580273799019566,0.836384654045105,86.8412325132658,0.5318487190707494,0.746901273727417,80.27648508075555,0.0021089138373994237,
-45,0.2504911308580703,0.8410984873771667,87.30779667149059,0.49164087495639364,0.763725221157074,81.82315904735833,0.00201227419495968,best
-46,0.24104372076995695,0.8451772928237915,87.64094910757356,0.5290114263981752,0.7580969333648682,80.94032302217356,0.0019163865903602372,
-47,0.22337641519549614,0.8570870161056519,88.56201760733236,0.43634469677838694,0.7913081049919128,85.10128661374213,0.0018213988753373142,best
-48,0.2128122905210861,0.8645581603050232,89.1152616980222,0.43183545479456625,0.7972898483276367,85.49137695045168,0.001727457514062632,best
-49,0.2003101470318182,0.8717849254608154,89.69187168355042,0.4289672785945178,0.806715726852417,85.7993430057487,0.0016347073573062686,best
-50,0.1888495338707803,0.8796613216400146,90.34687047756874,0.4568697272132202,0.7956517338752747,84.64275937585546,0.0015432914190872762,
-51,0.1756466486088274,0.886497437953949,90.97096599131693,0.4541556305781541,0.7938134670257568,84.45113605255953,0.001453350656156431,
-52,0.16742963044469736,0.8907681703567505,91.3101483357453,0.4230425570913775,0.8147625923156738,86.100465370928,0.0013650237506511336,best
-53,0.15311133117022804,0.9007841944694519,92.11212614568258,0.4146759752586969,0.8208259344100952,86.83274021352314,0.0012784468962576128,best
-54,0.1423091164071722,0.9078108668327332,92.6714001447178,0.4709351422719488,0.8029968738555908,85.71721872433616,0.0011937535882101285,
-55,0.13160189816137902,0.9135022163391113,93.10932223830197,0.40829240685941226,0.8264325857162476,87.42814125376403,0.0011110744174509947,best
-56,0.12707359487800943,0.9174070358276367,93.4409671972986,0.42565728100299705,0.8230471611022949,87.65398302764851,0.0010305368692688178,
-57,0.11291898237482914,0.925153374671936,94.10953328509407,0.43774206922898184,0.8247347474098206,87.5376402956474,0.0009522651267254161,
-58,0.10370979833767516,0.9329074025154114,94.62584418716835,0.4068256767521223,0.8333848118782043,88.05091705447578,0.0008763798791745416,best
-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,
-61,0.07913849578165794,0.9495129585266113,95.9916184273999,0.4083157278823748,0.8420299291610718,89.00218998083767,0.0006641937264107861,best
-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,
-66,0.04614944799553407,0.9710246324539185,97.6912988422576,0.4035414461747053,0.8538572788238525,89.85080755543389,0.00036839958911476966,
-67,0.042909558690492254,0.9727644920349121,97.8428002894356,0.41578795896298,0.8530543446540833,89.91924445661101,0.0003187599823180077,
-68,0.03640206224887977,0.9769999980926514,98.15710926193921,0.42073161891477256,0.8551151156425476,89.94661921708185,0.0002724836895290806,best
-69,0.034432517173010414,0.9790080785751343,98.34328268210324,0.4133664407223553,0.8576940298080444,90.28880372296743,0.00022964206543729668,best
-70,0.030669637766023813,0.9817556142807007,98.5249336710082,0.418308269951463,0.8569411039352417,90.12455516014235,0.00019030116872178321,
-71,0.028112305183924133,0.9827903509140015,98.606337433671,0.4151474575991667,0.8596312999725342,90.37092800437996,0.00015452166019378966,best
-72,0.024704152367817256,0.9853801727294922,98.81135431741437,0.4153705465811558,0.8635820746421814,90.68573774979468,0.0001223587092621162,best
-73,0.024846541488804174,0.9855506420135498,98.8369814278823,0.4177400436290088,0.8632140159606934,90.59676977826444,9.38619088658821e-05,
-74,0.022639600746625622,0.9868491888046265,98.94702725518572,0.41732572613841307,0.8648342490196228,90.78154941144265,6.907519900580863e-05,best
-75,0.02120214593173326,0.9878177642822266,99.0231548480463,0.4163925825270714,0.866214394569397,90.89789214344374,4.803679899192394e-05,best
-76,0.019741657997631577,0.9883521795272827,99.06385672937772,0.42005763620917286,0.8647006750106812,90.82945524226663,3.077914851215586e-05,
-77,0.019116416042495393,0.9889511466026306,99.10003617945007,0.4159400745789841,0.8657370805740356,90.84998631261976,1.7328857612684272e-05,
-78,0.019259902796210714,0.9888157844543457,99.0962674867342,0.4192042892654481,0.8641382455825806,90.69942513003011,7.706665667180091e-06,
-79,0.01933925595445387,0.9887675046920776,99.0759165460685,0.4180937778044573,0.8662786483764648,90.84998631261976,1.9274093981927482e-06,best
-80,0.01922732148408437,0.9889604449272156,99.10078991799324,0.41794140280912484,0.864332914352417,90.82261155214891,0.0,