学术论文图表规范：分辨率要求、配色方案与Caption写作标准

实际分辨率(dpi) = 图片像素宽度 / 论文中显示宽度(英寸)

示例：图片1800px宽，论文中显示为3.5英寸(单栏宽)
实际分辨率 = 1800 / 3.5 = 514 dpi（合格）

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
# ...绑图...

# 矢量PDF（推荐）
plt.savefig('fig.pdf', bbox_inches='tight')

# 高分辨率PNG
plt.savefig('fig.png', dpi=600, bbox_inches='tight')

% 矢量格式
print('-dpdf', 'fig.pdf')

% 高分辨率PNG
print('-dpng', '-r600', 'fig.png')

# Python中使用
import matplotlib.pyplot as plt
plt.style.use('seaborn-v0_8-colorblind')

# 或手动指定ColorBrewer Set2
colors = ['#66c2a5','#fc8d62','#8da0cb','#e78ac3','#a6d854']

Figure [编号]. [简要标题/描述。] [可选：补充说明/条件/细节。]

Figure 3. Comparison of classification accuracy across five
datasets. Our method (blue) consistently outperforms all
baselines. Error bars indicate standard deviation over 5 runs.

Figure 3. Results.
（太简短，无任何信息量）

Figure 3. This figure shows the comparison results of our
proposed method with other baseline methods including
ResNet-50, VGG-16, and DenseNet on CIFAR-10, CIFAR-100,
ImageNet, CelebA, and LSUN datasets with respect to top-1
classification accuracy metric averaged over 5 independent
runs with different random seeds...
（太冗长，应该在正文中描述）

Table [编号]. [描述表格内容。] [可选：数据条件/单位说明。]
Bold indicates the best result.

Table 2. Performance comparison on CIFAR-100 test set.
All models are trained for 200 epochs with the same
hyperparameters. Bold denotes the best, underline the
second best.

% Figure
\begin{figure}[t]
  \centering
  \includegraphics[width=\columnwidth]{fig.pdf}
  \caption{Description of the figure.}  % 在图片之后
  \label{fig:xxx}
\end{figure}

% Table
\begin{table}[t]
  \centering
  \caption{Description of the table.}   % 在表格之前
  \label{tab:xxx}
  \begin{tabular}{...}
    ...
  \end{tabular}
\end{table}

\usepackage{booktabs}  % 提供专业表格线命令

\begin{table}[t]
  \centering
  \caption{Comparison results on benchmark datasets.}
  \label{tab:results}
  \begin{tabular}{lccc}
    \toprule
    Method & CIFAR-10 & CIFAR-100 & ImageNet \\
    \midrule
    ResNet-50 & 93.2 & 76.4 & 76.1 \\
    DenseNet & 94.1 & 77.8 & 77.3 \\
    \textbf{Ours} & \textbf{95.8} & \textbf{80.2} & \textbf{79.6} \\
    \bottomrule
  \end{tabular}
\end{table}

\usepackage{subcaption}  % 推荐（或subfig宏包）

\begin{figure}[t]
  \centering
  \begin{subfigure}[b]{0.48\columnwidth}
    \includegraphics[width=\textwidth]{fig_a.pdf}
    \caption{Training loss}
    \label{fig:loss}
  \end{subfigure}
  \hfill
  \begin{subfigure}[b]{0.48\columnwidth}
    \includegraphics[width=\textwidth]{fig_b.pdf}
    \caption{Validation accuracy}
    \label{fig:acc}
  \end{subfigure}
  \caption{Training dynamics. (a) Loss decreases steadily.
  (b) Accuracy converges after epoch 100.}
  \label{fig:training}
\end{figure}