用Python绘制yolo训练结果比较图-论文需要

代码内容来自于网络用博客记录

利用训练生成的result.csv中数据，形成多模型的比较图。

代码中演示的是map50、map50-95、losss的比较图

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

if __name__ == '__main__':
   # 列出待获取数据内容的文件位置
   # v5、v8都是csv格式的，v7是txt格式的
   result_dict = {
    
    
       'YOLOv5n-SPPF': r'/Users/Desktop/results/YOLOv5n-SPPF.csv',
       'YOLOv5s-SPPF': r'/Users/Desktop/results/YOLOv5s-SPPF.csv',
       'YOLOv8s-SPPF': r'/Users/Desktop/results/YOLOv8s-SPPF.csv',
       'YOLOv8s-simSPPF': r'/Users/Desktop/results/YOLOv8s-simSPPF.csv',
       'YOLOv8s-RELU': r'/Users/Desktop/results/YOLOv8s-RELU.csv',
       'YOLOv8s-ASPP': r'/Users/Desktop/results/YOLOv8s-ASPP.csv',
   }

   # 绘制map50
   for modelname in result_dict:
       res_path = result_dict[modelname]
       ext = res_path.split('.')[-1]
       if ext == 'csv':
           data = pd.read_csv(res_path, usecols=[6]).values.ravel()    # 6是指map50的下标（每行从0开始向右数）
       else:   # 文件后缀是txt
           with open(res_path, 'r') as f:
               datalist = f.readlines()
               data = []
               for d in datalist:
                   data.append(float(d.strip().split()[10]))   # 10是指map50的下标（每行从0开始向右数）
               data = np.array(data)
       x = range(len(data))
       plt.plot(x, data, label=modelname, linewidth='1')   # 线条粗细设为1

   # 添加x轴和y轴标签
   plt.xlabel('Epochs')
   plt.ylabel('[email protected]')
   # 添加图例
   plt.legend()
   # 添加网格
   plt.grid()
   # 显示图像
   plt.savefig("mAP50.png", dpi=600)   # dpi可设为300/600/900，表示存为更高清的矢量图
   plt.show()


   # 绘制map50-95
   for modelname in result_dict:
       res_path = result_dict[modelname]
       ext = res_path.split('.')[-1]
       if ext == 'csv':
           data = pd.read_csv(res_path, usecols=[7]).values.ravel()    # 7是指map50-95的下标（每行从0开始向右数）
       else:
           with open(res_path, 'r') as f:
               datalist = f.readlines()
               data = []
               for d in datalist:
                   data.append(float(d.strip().split()[11]))   # 11是指map50-95的下标（每行从0开始向右数）
               data = np.array(data)
       x = range(len(data))
       plt.plot(x, data, label=modelname, linewidth='1')

   # 添加x轴和y轴标签
   plt.xlabel('Epochs')
   plt.ylabel('[email protected]:0.95')
   plt.legend()
   plt.grid()
   # 显示图像
   plt.savefig("mAP50-95.png", dpi=600)
   plt.show()

   # 绘制训练的总loss
   for modelname in result_dict:
       res_path = result_dict[modelname]
       ext = res_path.split('.')[-1]
       if ext == 'csv':
           box_loss = pd.read_csv(res_path, usecols=[1]).values.ravel()
           obj_loss = pd.read_csv(res_path, usecols=[2]).values.ravel()
           cls_loss = pd.read_csv(res_path, usecols=[3]).values.ravel()
           data = np.round(box_loss + obj_loss + cls_loss, 5)    # 3个loss相加并且保留小数点后5位（与v7一致）

       else:
           with open(res_path, 'r') as f:
               datalist = f.readlines()
               data = []
               for d in datalist:
                   data.append(float(d.strip().split()[5]))
               data = np.array(data)
       x = range(len(data))
       plt.plot(x, data, label=modelname, linewidth='1')

   # 添加x轴和y轴标签
   plt.xlabel('Epochs')
   plt.ylabel('Loss')
   plt.legend()
   plt.grid()
   # 显示图像
   plt.savefig("loss.png", dpi=600)
   plt.show()