一、单独一个文件可视化（源码对应utils文件夹下的plots.py文件的plot_results类）

from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
def plot_results(file='runs/train/exp9/results.csv', dir=''):
    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
    save_dir = Path(file).parent if file else Path(dir)
    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
    ax = ax.ravel()
    files = list(save_dir.glob(file))
    assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
    for fi, f in enumerate(files):
        try:
            data = pd.read_csv(f)
            s = [x.strip() for x in data.columns]
            x = data.values[:, 0]
            for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
                y = data.values[:, j]
                # y[y == 0] = np.nan  # don't show zero values
                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
                ax[i].set_title(s[j], fontsize=12)
                # if j in [8, 9, 10]:  # share train and val loss y axes
                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
        except Exception as e:
            print(f'Warning: Plotting error for {f}: {e}')
    ax[1].legend()
    fig.savefig(save_dir / 'results.png', dpi=200)  #修改保存路径
    plt.close()
 
 
if __name__ == '__main__':
    plot_results(file='results.csv')   #该python文件位于根目录下（此文件和传入文件在同一目录下），注意修改传入文件路径

单独把代码拿出来建立py文件，注意上传文件路径以及文件保存路径。

效果图展示：（results.png文件）

二、两个results.csv文件对比（经常用于原模型与改进模型训练效果对比）：
这里用到了两个csv文件（results.csv（改进模型训练80轮）和results100.csv（原模型训练100轮））

from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
def plot_results(file='runs/train/exp9/results.csv', file2='runs/train/exp9/results100.csv' , dir=''):
    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
    save_dir = Path(file).parent if file else Path(dir)
    save_dir2 = Path(file2).parent if file2 else Path(dir)
    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
    ax = ax.ravel()
    files = list(save_dir.glob(file))
    assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
    files2 = list(save_dir2.glob(file2))
    assert len(files2), f'No results.csv files found in {save_dir2.resolve()}, nothing to plot.'
    for fi, f in enumerate(files):
        try:
            data = pd.read_csv(f)
            s = [x.strip() for x in data.columns]
            x = data.values[:, 0]
            for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
                y = data.values[:, j]
                # y[y == 0] = np.nan  # don't show zero values
                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
                ax[i].set_title(s[j], fontsize=12)
                # if j in [8, 9, 10]:  # share train and val loss y axes
                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
        except Exception as e:
            print(f'Warning: Plotting error for {f}: {e}')
    for fi, f in enumerate(files2):
        try:
            data = pd.read_csv(f)
            s = [x.strip() for x in data.columns]
            x = data.values[:, 0]
            for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
                y = data.values[:, j]
                # y[y == 0] = np.nan  # don't show zero values
                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
                ax[i].set_title(s[j], fontsize=12)
                # if j in [8, 9, 10]:  # share train and val loss y axes
                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
        except Exception as e:
            print(f'Warning: Plotting error for {f}: {e}')
    ax[1].legend()
    fig.savefig(save_dir / 'results_vs.png', dpi=200)  #修改保存路径
    plt.close()
 
 
if __name__ == '__main__':
    plot_results(file='results.csv',file2='results100.csv')   #该python文件位于根目录下（此文件和传入文件在同一目录下），注意修改传入文件路径

效果图展示：（results_vs.png文件）

搬运自YOLO训练results.csv文件可视化（原模型与改进模型对比可视化）