Update training_log.csv

不小心上传空白csv，现已恢复

.gitignore

@@ -0,0 +1,29 @@
+*
+!/.gitattributes
+!/Dataloader.py
+!/LICENSE
+!/Merge_classes.py
+!/Model.py
+!/README.md
+!/requirements.txt
+!/THIRD_PARTY_LICENSES.md
+!/Train.py
+!/Baseline.py
+!/Finetune.py
+!/Curve.py
+!/Evaluate.py
+!/baseline/
+!/baseline/__init__.py
+!/baseline/VGG_KNN.py
+!/baseline/compare_models.py
+!/baseline/ResNet34_Pretrained_10pct.py
+!/baseline/HOG_Baseline.py
+!/baseline/roc_comparison.png
+!/baseline/pr_comparison.png
+!/baseline/accuracy_bar.png
+!/training_log.csv
+!/confusion_matrix.png
+!/roc_curve.png
+!/pr_curve.png
+!/training_curves.png
+!.gitignore

Curve.py

@@ -0,0 +1,50 @@
+"""
+plot_training_curves.py
+从 training_log.csv 读取日志，绘制 Loss / F1 / Accuracy / LR 曲线
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib
+
+matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
+matplotlib.rcParams['axes.unicode_minus'] = False
+
+# ============ 读取数据 ============
+df = pd.read_csv('training_log.csv')
+best_rows = df[df['best'] == 'best']
+
+fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+
+# ---- 1. Loss ----
+ax = axes[0, 0]
+ax.plot(df['epoch'], df['train_loss'], label='Train Loss', color='#1f77b4', lw=1.5)
+ax.plot(df['epoch'], df['val_loss'],   label='Val Loss',   color='#ff7f0e', lw=1.5)
+ax.set_xlabel('Epoch'); ax.set_ylabel('Loss'); ax.set_title('Loss vs Epoch')
+ax.legend(); ax.grid(True, alpha=0.3)
+
+# ---- 2. F1 Score ----
+ax = axes[0, 1]
+ax.plot(df['epoch'], df['train_f1'], label='Train F1', color='#1f77b4', lw=1.5)
+ax.plot(df['epoch'], df['val_f1'],   label='Val F1',   color='#ff7f0e', lw=1.5)
+ax.set_xlabel('Epoch'); ax.set_ylabel('F1 Score'); ax.set_title('F1 Score vs Epoch')
+ax.legend(); ax.grid(True, alpha=0.3)
+
+# ---- 3. Accuracy ----
+ax = axes[1, 0]
+ax.plot(df['epoch'], df['train_acc'], label='Train Acc', color='#1f77b4', lw=1.5)
+ax.plot(df['epoch'], df['val_acc'],   label='Val Acc',   color='#ff7f0e', lw=1.5)
+ax.set_xlabel('Epoch'); ax.set_ylabel('Accuracy (%)'); ax.set_title('Accuracy vs Epoch')
+ax.legend(); ax.grid(True, alpha=0.3)
+
+# ---- 4. Learning Rate ----
+ax = axes[1, 1]
+ax.plot(df['epoch'], df['lr'], color='#2ca02c', lw=1.5)
+ax.set_xlabel('Epoch'); ax.set_ylabel('Learning Rate'); ax.set_title('Learning Rate vs Epoch')
+ax.ticklabel_format(style='scientific', axis='y', scilimits=(0, 0))
+ax.grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.savefig('training_curves.png', dpi=150, bbox_inches='tight')
+plt.show()
+print("训练曲线已保存: training_curves.png")

Evaluate.py

@@ -0,0 +1,147 @@
+"""
+evaluate_and_plot.py
+加载模型，在验证集上推理，绘制混淆矩阵 / ROC / PR 曲线
+"""
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+import torch
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from sklearn.metrics import (
+    confusion_matrix, ConfusionMatrixDisplay,
+    roc_curve, auc,
+    precision_recall_curve, average_precision_score,
+)
+
+from Model import Net
+from Dataloader import RobustImageFolder
+
+matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
+matplotlib.rcParams['axes.unicode_minus'] = False
+
+# ============================================================
+# ★★★ 需要你修改的参数 ★★★
+# ============================================================
+MODEL_PATH  = 'best_model.pth'                                    # 模型权重路径
+DATA_ROOT   = '../trash_division_data/ultimate_4_class/'          # 数据集根目录
+BATCH_SIZE  = 32
+IMAGE_SIZE  = 256
+NUM_WORKERS = 4
+# ============================================================
+
+# ---------- 1. 加载验证集 ----------
+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]),
+])
+
+val_dataset = RobustImageFolder(
+    root=os.path.join(DATA_ROOT, 'val'),
+    transform=val_transform,
+)
+val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE,
+                        shuffle=False, num_workers=NUM_WORKERS,
+                        pin_memory=True, drop_last=False)
+
+class_names = val_dataset.classes
+num_classes = len(class_names)
+print(f"类别: {class_names}")
+
+# ---------- 2. 加载模型 ----------
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = Net(num_classes=num_classes)
+state_dict = torch.load(MODEL_PATH, map_location=device)
+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()
+print("模型加载完成")
+
+# ---------- 3. 推理 ----------
+all_labels = []
+all_probs  = []
+
+with torch.no_grad():
+    for images, labels in val_loader:
+        images = images.to(device)
+        probs = torch.softmax(model(images), dim=1)
+        all_labels.append(labels.numpy())
+        all_probs.append(probs.cpu().numpy())
+
+all_labels = np.concatenate(all_labels)
+all_probs  = np.concatenate(all_probs)
+all_preds  = np.argmax(all_probs, axis=1)
+print(f"推理完成, 共 {len(all_labels)} 样本")
+
+# ============================================================
+# ① 混淆矩阵
+# ============================================================
+cm = confusion_matrix(all_labels, all_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('Confusion Matrix', fontsize=14)
+plt.tight_layout()
+plt.savefig('confusion_matrix.png', dpi=150, bbox_inches='tight')
+plt.show()
+print("混淆矩阵已保存: confusion_matrix.png")
+
+# ============================================================
+# ② ROC 曲线 (One-vs-Rest + Macro-average)
+# ============================================================
+one_hot = np.eye(num_classes)[all_labels]
+colors  = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+
+fig, ax = plt.subplots(figsize=(8, 7))
+fpr_d, tpr_d, auc_d = {}, {}, {}
+
+for i in range(num_classes):
+    fpr_d[i], tpr_d[i], _ = roc_curve(one_hot[:, i], all_probs[:, i])
+    auc_d[i] = auc(fpr_d[i], tpr_d[i])
+    ax.plot(fpr_d[i], tpr_d[i], color=colors[i], lw=2,
+            label=f'{class_names[i]} (AUC={auc_d[i]:.4f})')
+
+# Macro-average
+all_fpr  = np.unique(np.concatenate([fpr_d[i] for i in range(num_classes)]))
+mean_tpr = sum(np.interp(all_fpr, fpr_d[i], tpr_d[i]) for i in range(num_classes)) / num_classes
+macro_auc = auc(all_fpr, mean_tpr)
+ax.plot(all_fpr, mean_tpr, 'navy', lw=2, ls='--',
+        label=f'Macro-avg (AUC={macro_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', fontsize=14)
+ax.legend(loc='lower right'); ax.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig('roc_curve.png', dpi=150, bbox_inches='tight')
+plt.show()
+print("ROC 曲线已保存: roc_curve.png")
+
+# ============================================================
+# ③ Precision-Recall 曲线
+# ============================================================
+fig, ax = plt.subplots(figsize=(8, 7))
+
+for i in range(num_classes):
+    prec, rec, _ = precision_recall_curve(one_hot[:, i], all_probs[:, i])
+    ap = average_precision_score(one_hot[:, i], all_probs[:, i])
+    ax.plot(rec, prec, color=colors[i], lw=2,
+            label=f'{class_names[i]} (AP={ap:.4f})')
+
+ax.set_xlim(0, 1); ax.set_ylim(0, 1.05)
+ax.set_xlabel('Recall'); ax.set_ylabel('Precision')
+ax.set_title('Precision-Recall Curve', fontsize=14)
+ax.legend(loc='best'); ax.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig('pr_curve.png', dpi=150, bbox_inches='tight')
+plt.show()
+print("PR 曲线已保存: pr_curve.png")

Model.py

@@ -1,6 +1,5 @@
 """
-模型定义文件 - 使用瓶颈结构 (Bottleneck) 的深度残差网络
-目标：约50层，参数量约80M
+模型定义文件 - ResNet-34
 author : yukun-hh
 date : 2026-4-10
 """
@@ -10,27 +9,19 @@ from torch.nn import functional as F
 from torchsummary import summary
 
 
-class Bottleneck(nn.Module):
+class BasicBlock(nn.Module):
     """
-    瓶颈残差块：1x1(降维) -> 3x3 -> 1x1(升维)
+    ResNet-34 基础残差块：3x3 -> 3x3
     若需要下采样或通道变化，则在跳跃连接中使用 1x1 卷积
     """
-    expansion = 4  # 输出通道是中间通道的4倍
+    expansion = 1
 
-    def __init__(self, in_channels, mid_channels, stride=1, downsample=None):
-        """
-        :param in_channels: 输入通道数
-        :param mid_channels: 中间层通道数（1x1降维后的通道数）
-        :param stride: 步长，用于下采样
-        :param downsample: 下采样模块（当stride≠1或通道变化时使用）
-        """
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
         super().__init__()
-        self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(mid_channels)
-        self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(mid_channels)
-        self.conv3 = nn.Conv2d(mid_channels, mid_channels * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(mid_channels * self.expansion)
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channels)
         self.relu = nn.ReLU(inplace=True)
         self.downsample = downsample
 
@@ -43,10 +34,6 @@ class Bottleneck(nn.Module):
 
         out = self.conv2(out)
         out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
 
         if self.downsample is not None:
             identity = self.downsample(x)
@@ -57,68 +44,49 @@ class Bottleneck(nn.Module):
 
 
 class Net(nn.Module):
-    """
-    基于 Bottleneck 的 ResNet 风格模型
-    各阶段配置仿照 ResNet-50，适当调整宽度以达到约80M参数
-    """
 
-    def __init__(self, num_classes=4):
+    def __init__(self, num_classes=4, dropout=0.5):
         super().__init__()
 
-        # 第一阶段：7x7卷积 + 最大池化
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
         self.bn1 = nn.BatchNorm2d(64)
         self.relu = nn.ReLU(inplace=True)
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 
-        # 残差阶段定义
-        # 每个阶段的参数：[块数, 中间通道数, 步长]
-        # 为了达到80M参数，我们略微加宽网络（相比标准ResNet-50）
         layers_config = [
-            (3, 64, 1),    # stage2: 3个瓶颈块，输出通道 64*4=256
-            (4, 128, 2),   # stage3: 4个瓶颈块，输出通道 128*4=512
-            (14, 256, 2),  # stage4: 14个瓶颈块，输出通道 256*4=1024（加深至此阶段）
-            (3, 512, 2)    # stage5: 3个瓶颈块，输出通道 512*4=2048
+            (3, 64, 1),    # layer1
+            (4, 128, 2),   # layer2
+            (6, 256, 2),   # layer3
+            (3, 512, 2),   # layer4
         ]
 
         self.in_channels = 64
-        self.stage2 = self._make_layer(layers_config[0])
-        self.stage3 = self._make_layer(layers_config[1])
-        self.stage4 = self._make_layer(layers_config[2])
-        self.stage5 = self._make_layer(layers_config[3])
+        self.layer1 = self._make_layer(layers_config[0])
+        self.layer2 = self._make_layer(layers_config[1])
+        self.layer3 = self._make_layer(layers_config[2])
+        self.layer4 = self._make_layer(layers_config[3])
 
-        # 全局池化与分类层
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        self.fc = nn.Linear(2048, num_classes)
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(512, num_classes)
 
     def _make_layer(self, config):
-        """
-        构建一个残差阶段
-        :param config: (块数, 中间通道数, 第一阶段步长)
-        :return: nn.Sequential
-        """
-        num_blocks, mid_channels, stride = config
+        num_blocks, out_channels, stride = config
         downsample = None
         layers = []
 
-        # 第一个块可能需要下采样和通道匹配
-        if stride != 1 or self.in_channels != mid_channels * Bottleneck.expansion:
+        if stride != 1 or self.in_channels != out_channels:
             downsample = nn.Sequential(
-                nn.Conv2d(self.in_channels, mid_channels * Bottleneck.expansion,
+                nn.Conv2d(self.in_channels, out_channels,
                           kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(mid_channels * Bottleneck.expansion),
+                nn.BatchNorm2d(out_channels),
             )
 
-        layers.append(
-            Bottleneck(self.in_channels, mid_channels, stride, downsample)
-        )
-        self.in_channels = mid_channels * Bottleneck.expansion
+        layers.append(BasicBlock(self.in_channels, out_channels, stride, downsample))
+        self.in_channels = out_channels
 
-        # 后续块
         for _ in range(1, num_blocks):
-            layers.append(
-                Bottleneck(self.in_channels, mid_channels)
-            )
+            layers.append(BasicBlock(self.in_channels, out_channels))
 
         return nn.Sequential(*layers)
 
@@ -128,13 +96,14 @@ class Net(nn.Module):
         x = self.relu(x)
         x = self.maxpool(x)
 
-        x = self.stage2(x)
-        x = self.stage3(x)
-        x = self.stage4(x)
-        x = self.stage5(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
 
         x = self.avgpool(x)
         x = torch.flatten(x, 1)
+        x = self.dropout(x)
         x = self.fc(x)
         return x
 

README.md

@@ -4,7 +4,7 @@
 
 > 同济大学 Python 人工智能程序设计课程小组作业
 
-基于自定义 ResNet 风格 Bottleneck 架构的 CNN 模型（约 80M 参数），将生活垃圾分为厨余垃圾、可回收物、其他垃圾、有害垃圾四个类别，输入为 256×256 RGB 图像。
+基于 ResNet-34 架构的 CNN 模型（约 21M 参数），将生活垃圾分为厨余垃圾、可回收物、其他垃圾、有害垃圾四个类别，输入为 256×256 RGB 图像。
 
 ---
 
@@ -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)
 
 ---
@@ -25,7 +26,7 @@
 ## 项目特点
 
 - **四类垃圾分类**：厨余垃圾（1）、可回收物（2）、其他垃圾（3）、有害垃圾（4）
-- **自定义 ResNet Bottleneck 架构**：约 80M 参数，50 层深度残差网络
+- **ResNet-34 架构**：约 21M 参数，34 层深度残差网络，含 Dropout 正则化
 - **数据增强**：训练时使用随机裁剪、水平翻转、旋转、色彩抖动
 - **Macro-F1 评估**：采用宏平均 F1 分数作为主要评估指标，兼顾各类别表现
 - **类别加权损失**：自动计算类别权重，缓解类别不平衡问题
@@ -35,28 +36,27 @@
 
 ## 模型架构
 
-模型基于残差网络（ResNet）的 Bottleneck 构建块设计。
+模型基于标准 ResNet-34 架构，使用 BasicBlock 构建。
 
-### Bottleneck 块
+### BasicBlock 块
 
-每个 Bottleneck 块包含三个卷积层：
+每个 BasicBlock 包含两个 3x3 卷积层 + 跳跃连接：
 
 | 层 | 卷积 | 作用 |
 |---|---|---|
-| 1x1 Conv | 降维 | 减少通道数，降低计算量 |
-| 3x3 Conv | 特征提取 | 核心卷积操作 |
-| 1x1 Conv | 升维 (x4) | 恢复通道数至输入的 4 倍 |
+| 3x3 Conv | 特征提取 | 第一层卷积 |
+| 3x3 Conv | 特征提取 | 第二层卷积 |
 
 ### 网络结构
 
 | 阶段 | 块数 | 输出通道数 | 说明 |
 |---|---|---|---|
 | 初始层 | - | 64 | 7x7 Conv, stride=2 + MaxPool |
-| Stage 1 | 3 | 256 | 第一个残差阶段 |
-| Stage 2 | 4 | 512 | - |
-| Stage 3 | 14 | 1024 | 最深阶段（比 ResNet-50 加深） |
-| Stage 4 | 3 | 2048 | 最终残差阶段 |
-| 分类头 | - | 4 | 全局平均池化 + 全连接层 |
+| Layer1 | 3 | 64 | 第一个残差阶段 |
+| Layer2 | 4 | 128 | - |
+| Layer3 | 6 | 256 | - |
+| Layer4 | 3 | 512 | 最终残差阶段 |
+| 分类头 | - | 4 | 全局平均池化 + Dropout + 全连接层 |
 
 ## 数据集
 
@@ -75,16 +75,14 @@
 
 ## 环境要求
 
-本项目无 `requirements.txt`，需手动安装以下依赖：
+```bash
+pip install -r requirements.txt
+```
 
-- Python 3.8+
-- PyTorch（推荐 1.10+）
-- torchvision
-- tqdm
-- matplotlib
-- pandas
-- Pillow
-- torchsummary
+> **注意**：`requirements.txt` 不锁定 PyTorch 的 CUDA / XPU 版本，请根据硬件自行安装对应版本，例如：
+> - NVIDIA GPU：`pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121`
+> - Intel GPU (XPU)：`pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121` 安装
+> - CPU：`pip install torch torchvision` 即可
 
 ## 快速开始
 
@@ -100,6 +98,20 @@
    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
+   python baseline/compare_models.py   # 多模型基线对比（ROC/PR/准确率）
+   ```
+
 > **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`）
@@ -110,27 +122,49 @@
 | 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，Bottleneck 残差块 + Net 主模型 |
+| `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） |
-| `AGENTS.md` | AI 助手指南（开发辅助） |
+| `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 |
+| `baseline/VGG_KNN.py` | VGG16 预训练特征提取 + KNN 四分类基线 |
+| `baseline/ResNet34_Pretrained_10pct.py` | ResNet-34 ImageNet 预训练 + 10% 数据微调 |
+| `baseline/HOG_Baseline.py` | HOG + 颜色直方图 + LogisticRegression（纯传统 CV） |
+| `baseline/compare_models.py` | 多模型对比（ROC / PR 曲线 + 准确率柱状图） |
+| `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） |
 | `THIRD_PARTY_LICENSES.md` | 第三方数据集许可证声明 |
 
 ## 目录结构
 
 ```
 trash-division/
-├── AGENTS.md               # AI 助手指南
-├── best_model.pth           # 最佳模型权重
+├── baseline/                # 基线模型目录
+│   ├── VGG_KNN.py           # VGG16 + KNN 分类脚本
+│   ├── ResNet34_Pretrained_10pct.py  # ResNet-34 ImageNet 预训练 + 10% 微调
+│   ├── HOG_Baseline.py      # HOG + LogisticRegression 纯传统基线
+│   ├── compare_models.py    # 多模型对比脚本
+│   ├── roc_comparison.png   # 多模型 ROC 对比（compare_models.py 输出）
+│   ├── pr_comparison.png    # 多模型 PR 对比（compare_models.py 输出）
+│   └── accuracy_bar.png     # 多模型准确率对比（compare_models.py 输出）
+├── 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
@@ -150,6 +184,64 @@ 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：
+
+```bash
+python Evaluate.py
+```
+
+脚本顶部的 `MODEL_PATH`、`DATA_ROOT`、`BATCH_SIZE`、`NUM_WORKERS` 可按需修改。
+
+**混淆矩阵**
+
+![confusion_matrix](confusion_matrix.png)
+
+**ROC 曲线**
+
+![roc_curve](roc_curve.png)
+
+**PR 曲线**
+
+![pr_curve](pr_curve.png)
+
+### Curve.py — 训练曲线
+
+从 `training_log.csv` 读取训练日志，绘制四张子图：
+
+```bash
+python Curve.py
+```
+
+![training_curves](training_curves.png)
+
+### 基线模型对比
+
+`compare_models.py` 对所有模型在验证集上统一评估，生成三张对比图表：
+
+```bash
+python baseline/compare_models.py
+```
+
+对比阵容：ResNet-34、ResNet-34 (10% Fine-tune)、VGG16 + KNN、HOG + LogisticRegression。
+
+**ROC 曲线对比**
+
+![roc_comparison](baseline/roc_comparison.png)
+
+**PR 曲线对比**
+
+![pr_comparison](baseline/pr_comparison.png)
+
+**准确率柱状图**
+
+![accuracy_bar](baseline/accuracy_bar.png)
+
 ## 许可证
 
 本项目主代码采用 [MIT 许可证](LICENSE)。

baseline/HOG_Baseline.py

@@ -0,0 +1,145 @@
+"""
+baseline/HOG_Baseline.py
+HOG + 颜色直方图特征提取 + LogisticRegression 四分类
+纯传统 CV/ML 基线，零神经网络依赖
+可独立运行，也可被 compare_models.py 导入
+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
+from PIL import Image
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+import matplotlib
+
+from skimage.feature import hog
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import (
+    accuracy_score, f1_score,
+    confusion_matrix, ConfusionMatrixDisplay,
+    classification_report,
+)
+
+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/'
+IMAGE_SIZE  = 128
+HOG_ORIENTATIONS = 9
+HOG_PIXELS_PER_CELL = (8, 8)
+HOG_CELLS_PER_BLOCK = (2, 2)
+COLOR_BINS  = 32
+# ============================================================
+
+CLASS_NAMES = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
+NUM_CLASSES = 4
+
+
+def extract_hog_color(image):
+    img = image.convert('RGB').resize((IMAGE_SIZE, IMAGE_SIZE))
+    arr = np.array(img, dtype=np.float64) / 255.0
+
+    hog_feat = hog(arr, orientations=HOG_ORIENTATIONS,
+                   pixels_per_cell=HOG_PIXELS_PER_CELL,
+                   cells_per_block=HOG_CELLS_PER_BLOCK,
+                   channel_axis=2, feature_vector=True)
+
+    color_feat = []
+    for c in range(3):
+        hist, _ = np.histogram(arr[:, :, c], bins=COLOR_BINS, range=(0, 1))
+        color_feat.append(hist)
+    color_feat = np.concatenate(color_feat)
+
+    return np.concatenate([hog_feat, color_feat])
+
+
+class HOGLRBaseline:
+    def __init__(self, data_root=DATA_ROOT, image_size=IMAGE_SIZE):
+        self.data_root = data_root
+        self.image_size = image_size
+        self.clf = LogisticRegression(
+            C=1.0, max_iter=1000, solver='lbfgs', n_jobs=-1,
+        )
+
+    def _load_data(self, split):
+        dir_path = os.path.join(self.data_root, split)
+        features, labels = [], []
+        for class_id in range(1, NUM_CLASSES + 1):
+            class_dir = os.path.join(dir_path, str(class_id))
+            if not os.path.isdir(class_dir):
+                continue
+            files = sorted(os.listdir(class_dir))
+            for fname in tqdm(files, desc=f'{split}/class_{class_id}'):
+                fpath = os.path.join(class_dir, fname)
+                try:
+                    with Image.open(fpath) as img:
+                        feat = extract_hog_color(img)
+                    features.append(feat)
+                    labels.append(class_id - 1)
+                except Exception:
+                    pass
+        print(f"  {split}: {len(features)} 张")
+        return np.array(features, dtype=np.float32), np.array(labels)
+
+    def fit(self, train_dir=None):
+        if train_dir is None:
+            train_dir = 'train'
+        print("  提取训练集 HOG 特征 ...")
+        X, y = self._load_data(train_dir)
+        self.clf.fit(X, y)
+
+    def predict(self, val_dir=None):
+        if val_dir is None:
+            val_dir = 'val'
+        print("  提取验证集 HOG 特征 ...")
+        X, y = self._load_data(val_dir)
+        preds = self.clf.predict(X)
+        probs = self.clf.predict_proba(X)
+        return y, preds, probs
+
+
+# ============================================================
+# compare_models.py 导入接口
+# ============================================================
+
+def get_hog_lr_preds(train_loader, val_loader, device):
+    baseline = HOGLRBaseline()
+    baseline.fit('train')
+    return baseline.predict('val')
+
+
+# ============================================================
+# 独立运行入口
+# ============================================================
+
+if __name__ == '__main__':
+    out_dir = os.path.dirname(os.path.abspath(__file__))
+
+    print("HOG + LogisticRegression 基线")
+    baseline = HOGLRBaseline()
+
+    baseline.fit('train')
+    y_true, y_preds, y_probs = baseline.predict('val')
+
+    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('HOG + LogisticRegression 混淆矩阵', fontsize=14)
+    plt.tight_layout()
+    cm_path = os.path.join(out_dir, 'hog_lr_confusion_matrix.png')
+    plt.savefig(cm_path, dpi=150, bbox_inches='tight')
+    plt.show()
+    print(f"混淆矩阵已保存: {cm_path}")

baseline/ResNet34_Pretrained_10pct.py

@@ -0,0 +1,278 @@
+"""
+baseline/ResNet34_Pretrained_10pct.py
+ResNet-34 ImageNet 预训练权重 + 10% 训练集微调
+可独立运行训练，也可被 compare_models.py 导入
+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 random
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, Subset
+from torchvision import models, transforms
+from tqdm import tqdm
+import csv
+import matplotlib.pyplot as plt
+import matplotlib
+
+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
+EPOCHS      = 30
+LR          = 0.001
+TRAIN_PCT   = 0.1
+SEED        = 42
+DROPOUT     = 0.3
+MODEL_SAVE_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'resnet34_10pct.pth')
+LOG_PATH        = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'resnet34_10pct_log.csv')
+# ============================================================
+
+NUM_CLASSES = 4
+CLASS_NAMES = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
+
+
+class PretrainedResNet34(nn.Module):
+    def __init__(self, num_classes=NUM_CLASSES, dropout=DROPOUT):
+        super().__init__()
+        self.backbone = models.resnet34(weights='IMAGENET1K_V1')
+        in_features = self.backbone.fc.in_features
+        self.backbone.fc = nn.Identity()
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(in_features, num_classes)
+
+    def forward(self, x):
+        x = self.backbone(x)
+        x = self.dropout(x)
+        x = self.fc(x)
+        return x
+
+    def freeze_early_layers(self):
+        for param in self.backbone.conv1.parameters():
+            param.requires_grad = False
+        for param in self.backbone.bn1.parameters():
+            param.requires_grad = False
+        for param in self.backbone.layer1.parameters():
+            param.requires_grad = False
+        for param in self.backbone.layer2.parameters():
+            param.requires_grad = False
+
+    def print_trainable_info(self):
+        frozen = sum(p.numel() for p in self.parameters() if not p.requires_grad)
+        trainable = sum(p.numel() for p in self.parameters() if p.requires_grad)
+        total = frozen + trainable
+        print(f"  冻结参数: {frozen:,}  可训练参数: {trainable:,}  ({100.*trainable/total:.1f}%)")
+
+
+def compute_macro_f1(predicted, targets, num_classes=NUM_CLASSES):
+    tp = torch.zeros(num_classes, device=predicted.device)
+    fp = torch.zeros(num_classes, device=predicted.device)
+    fn = torch.zeros(num_classes, device=predicted.device)
+    for c in range(num_classes):
+        tp[c] = ((predicted == c) & (targets == c)).sum()
+        fp[c] = ((predicted == c) & (targets != c)).sum()
+        fn[c] = ((predicted != c) & (targets == c)).sum()
+    precision = tp / (tp + fp + 1e-8)
+    recall = tp / (tp + fn + 1e-8)
+    f1 = 2 * precision * recall / (precision + recall + 1e-8)
+    return f1.mean().item()
+
+
+def train_one_epoch(model, loader, criterion, optimizer, device, epoch):
+    model.train()
+    running_loss, correct, total = 0.0, 0, 0
+    all_preds, all_labels = [], []
+    pbar = tqdm(loader, desc=f'Epoch {epoch+1} [Train]')
+    for images, labels in pbar:
+        images, labels = images.to(device), labels.to(device)
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        running_loss += loss.item() * images.size(0)
+        _, predicted = outputs.max(1)
+        total += labels.size(0)
+        correct += predicted.eq(labels).sum().item()
+        all_preds.append(predicted)
+        all_labels.append(labels)
+        batch_f1 = compute_macro_f1(predicted, labels)
+        pbar.set_postfix({'loss': loss.item(), 'F1': f'{batch_f1:.4f}',
+                          'Acc': f'{100.*correct/total:.2f}%'})
+    epoch_loss = running_loss / total
+    epoch_f1 = compute_macro_f1(torch.cat(all_preds), torch.cat(all_labels))
+    epoch_acc = 100. * correct / total
+    return epoch_loss, epoch_f1, epoch_acc
+
+
+@torch.no_grad()
+def validate(model, loader, criterion, device):
+    model.eval()
+    running_loss, correct, total = 0.0, 0, 0
+    all_preds, all_labels = [], []
+    for images, labels in tqdm(loader, desc='[Validate]'):
+        images, labels = images.to(device), labels.to(device)
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        running_loss += loss.item() * images.size(0)
+        _, predicted = outputs.max(1)
+        total += labels.size(0)
+        correct += predicted.eq(labels).sum().item()
+        all_preds.append(predicted)
+        all_labels.append(labels)
+    epoch_loss = running_loss / total
+    epoch_f1 = compute_macro_f1(torch.cat(all_preds), torch.cat(all_labels))
+    epoch_acc = 100. * correct / total
+    return epoch_loss, epoch_f1, epoch_acc
+
+
+def train_model(model, train_loader, val_loader, device, epochs=EPOCHS, lr=LR):
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()),
+                          lr=lr, momentum=0.9, weight_decay=1e-4)
+    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
+
+    history = {'train_loss': [], 'train_f1': [], 'train_acc': [],
+               'val_loss': [], 'val_f1': [], 'val_acc': []}
+    best_val_f1 = 0.0
+
+    log_file = open(LOG_PATH, 'w', newline='')
+    log_writer = csv.writer(log_file)
+    log_writer.writerow(['epoch', 'train_loss', 'train_f1', 'train_acc',
+                         'val_loss', 'val_f1', 'val_acc', 'lr', 'best'])
+
+    for epoch in range(epochs):
+        print(f'\n{"="*50}')
+        print(f'Epoch {epoch+1}/{epochs}')
+
+        train_loss, train_f1, train_acc = train_one_epoch(
+            model, train_loader, criterion, optimizer, device, epoch)
+        val_loss, val_f1, val_acc = validate(model, val_loader, criterion, device)
+        scheduler.step()
+
+        history['train_loss'].append(train_loss)
+        history['train_f1'].append(train_f1)
+        history['train_acc'].append(train_acc)
+        history['val_loss'].append(val_loss)
+        history['val_f1'].append(val_f1)
+        history['val_acc'].append(val_acc)
+
+        print(f'Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.2f}% | Train Macro-F1: {train_f1:.4f}')
+        print(f'Val   Loss: {val_loss:.4f} | Val   Acc: {val_acc:.2f}% | Val   Macro-F1: {val_f1:.4f}')
+        print(f'Learning Rate: {optimizer.param_groups[0]["lr"]:.6f}')
+
+        best_mark = ''
+        if val_f1 > best_val_f1:
+            best_val_f1 = val_f1
+            torch.save(model.state_dict(), MODEL_SAVE_PATH)
+            best_mark = 'best'
+            print(f'✓ 保存最佳模型 (Macro-F1: {val_f1:.4f})')
+
+        lr_val = optimizer.param_groups[0]['lr']
+        log_writer.writerow([epoch+1, train_loss, train_f1, train_acc,
+                             val_loss, val_f1, val_acc, lr_val, best_mark])
+        log_file.flush()
+
+    log_file.close()
+    print(f'\n训练完成！最佳验证 Macro-F1: {best_val_f1:.4f}')
+    return history
+
+
+# ============================================================
+# compare_models.py 导入接口
+# ============================================================
+
+def get_resnet34_10pct_preds(train_loader, val_loader, device):
+    model = PretrainedResNet34(num_classes=NUM_CLASSES)
+    model.load_state_dict(torch.load(MODEL_SAVE_PATH, map_location='cpu'))
+    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 (10%)'):
+            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)
+
+
+# ============================================================
+# 独立训练入口
+# ============================================================
+
+if __name__ == '__main__':
+    random.seed(SEED)
+    np.random.seed(SEED)
+    torch.manual_seed(SEED)
+
+    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_transform = transforms.Compose([
+        transforms.RandomResizedCrop(IMAGE_SIZE, scale=(0.8, 1.0)),
+        transforms.RandomHorizontalFlip(p=0.5),
+        transforms.RandomRotation(degrees=15),
+        transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225]),
+    ])
+
+    full_train_dataset = RobustImageFolder(
+        root=os.path.join(DATA_ROOT, 'train'),
+        transform=train_transform,
+    )
+    val_dataset = RobustImageFolder(
+        root=os.path.join(DATA_ROOT, 'val'),
+        transform=val_transform,
+    )
+
+    n_train = len(full_train_dataset)
+    n_subset = max(1, int(n_train * TRAIN_PCT))
+    indices = random.sample(range(n_train), n_subset)
+    train_dataset = Subset(full_train_dataset, indices)
+    print(f"训练集: {len(train_dataset)} / {n_train} ({TRAIN_PCT*100:.0f}%)")
+    print(f"验证集: {len(val_dataset)}")
+
+    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE,
+                              shuffle=True, num_workers=NUM_WORKERS,
+                              pin_memory=True, drop_last=True)
+    val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE,
+                            shuffle=False, num_workers=NUM_WORKERS,
+                            pin_memory=True, drop_last=False)
+
+    model = PretrainedResNet34(num_classes=NUM_CLASSES, dropout=DROPOUT)
+    model.freeze_early_layers()
+    model.print_trainable_info()
+    model = model.to(device)
+
+    history = train_model(model, train_loader, val_loader, device, epochs=EPOCHS, lr=LR)
+
+    model.load_state_dict(torch.load(MODEL_SAVE_PATH, map_location='cpu'))
+    print(f"模型已保存: {MODEL_SAVE_PATH}")
+    print(f"训练日志已保存: {LOG_PATH}")

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 +1,249 @@
+"""
+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, re
+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,
+    precision_recall_curve, average_precision_score,
+)
+
+from Model import Net
+from Dataloader import RobustImageFolder
+from baseline.VGG_KNN import VGGKNNBaseline
+from baseline.ResNet34_Pretrained_10pct import get_resnet34_10pct_preds
+from baseline.HOG_Baseline import get_hog_lr_preds
+
+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),
+    ('ResNet-34 (10% Fine-tune)',  get_resnet34_10pct_preds),
+    ('VGG16 + KNN  (K=5)',        get_vgg_knn_preds),
+    ('HOG + LogisticRegression',   get_hog_lr_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
+
+
+def compute_macro_pr(y_true, y_probs):
+    one_hot = np.eye(NUM_CLASSES)[y_true]
+    prec_dict, rec_dict = {}, {}
+    for c in range(NUM_CLASSES):
+        prec_dict[c], rec_dict[c], _ = precision_recall_curve(one_hot[:, c], y_probs[:, c])
+    all_rec = np.linspace(0, 1, 200)
+    mean_prec = np.zeros_like(all_rec)
+    for c in range(NUM_CLASSES):
+        mean_prec += np.interp(all_rec, rec_dict[c][::-1], prec_dict[c][::-1])
+    mean_prec /= NUM_CLASSES
+    macro_ap = average_precision_score(one_hot, y_probs, average='macro')
+    return all_rec, mean_prec, macro_ap
+
+
+def sanitize_filename(name):
+    return re.sub(r'[^\w\-_]', '_', name).strip('_')
+
+
+def preds_csv_path(out_dir, model_name):
+    safe = sanitize_filename(model_name)
+    return os.path.join(out_dir, f'{safe}_preds.csv')
+
+
+def save_preds_csv(path, y_true, y_preds, y_probs):
+    header = 'y_true,y_pred,' + ','.join(f'prob_{c}' for c in range(NUM_CLASSES))
+    data = np.column_stack([y_true.astype(float), y_preds.astype(float), y_probs])
+    np.savetxt(path, data, delimiter=',', header=header, comments='', fmt='%.6f')
+
+
+def load_preds_csv(path):
+    data = np.loadtxt(path, delimiter=',', skiprows=1)
+    y_true = data[:, 0].astype(int)
+    y_preds = data[:, 1].astype(int)
+    y_probs = data[:, 2:2 + NUM_CLASSES]
+    return y_true, y_preds, y_probs
+
+
+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（有缓存则跳过）————
+    results = {}
+    for name, func in MODELS:
+        print(f"\n{'='*50}")
+        csv_path = preds_csv_path(out_dir, name)
+        if os.path.exists(csv_path):
+            print(f"加载缓存: {os.path.basename(csv_path)}")
+            y_true, y_preds, y_probs = load_preds_csv(csv_path)
+        else:
+            print(f"评估: {name}")
+            y_true, y_preds, y_probs = func(train_loader, val_loader, device)
+            save_preds_csv(csv_path, y_true, y_preds, y_probs)
+            print(f"  预测数据已保存: {os.path.basename(csv_path)}")
+        acc = accuracy_score(y_true, y_preds)
+        fpr, tpr, roc_auc = compute_macro_roc(y_true, y_probs)
+        rec, prec, macro_ap = compute_macro_pr(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,
+                         'rec': rec, 'prec': prec, 'ap': macro_ap}
+        print(f"  Accuracy: {acc:.4f}  |  Macro-AUC: {roc_auc:.4f}  |  Macro-AP: {macro_ap:.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}")
+
+    # ———— PR 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['rec'], r['prec'], color=color, lw=2,
+                label=f"{name} (AP={r['ap']:.4f})")
+    ax.set_xlim(0, 1); ax.set_ylim(0, 1.05)
+    ax.set_xlabel('Recall'); ax.set_ylabel('Precision')
+    ax.set_title('PR Curve Comparison (Macro-Average)', fontsize=14)
+    ax.legend(loc='lower left'); ax.grid(True, alpha=0.3)
+    plt.tight_layout()
+    pr_path = os.path.join(out_dir, 'pr_comparison.png')
+    plt.savefig(pr_path, dpi=150, bbox_inches='tight')
+    plt.show()
+    print(f"PR 对比图已保存: {pr_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(min(accs) - 0.03, max(accs) * 1.08)
+    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}")

requirements.txt

@@ -0,0 +1,10 @@
+torch>=1.10
+torchvision>=0.11
+tqdm
+matplotlib
+pandas
+Pillow
+scikit-learn
+scikit-image
+numpy
+torchsummary

training_log.csv

@@ -0,0 +1,81 @@
+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,