Darknet 评估训练好的网络的性能

前言：训练一个网络，需要评价这个网络，并根据评价的结果想一下为什么是这样，怎样去优化这个网络，这样才是一个闭环。

如何评价训练好的网络

首先网络有一个参数是loss值，这反应了你训练好的网络得到的结果和真实值之间的差距。查看loss曲线随着迭代次数的增多，如何变化，有助于查看训练是否过拟合，是否学习率太小。

一. 生成loss变化曲线

1, 训练时保存log文件

nohup ./darknet detector train khadas_ai/khadas_ai.data khadas_ai/yolov3-khadas_ai.cfg_train darknet53.conv.74 -dont_show > train.log 2>&1 &

2, 使用extract_log.py脚本转化所需格式log

import inspect
import os
import random
import sys
def extract_log(log_file,new_log_file,key_word):
    with open(log_file, 'r') as f:
      with open(new_log_file, 'w') as train_log:
  #f = open(log_file)
    #train_log = open(new_log_file, 'w')
        for line in f:
          if 'Syncing' in line:
            continue
          if 'nan' in line:
            continue
          if 'Region 82 Avg' in line:
            continue
          if 'Region 94 Avg' in line:
            continue
          if 'Region 106 Avg' in line:
            continue
          if 'total_bbox' in line:
            continue  
          if 'Loaded' in line:
            continue              
          if key_word in line:
            train_log.write(line)
    f.close()
    train_log.close()

def extract_log2(log_file,new_log_file,key_word):
    with open(log_file, 'r') as f:
      with open(new_log_file, 'w') as train_log:
  #f = open(log_file)
    #train_log = open(new_log_file, 'w')
        for line in f:
          if 'Syncing' in line:
            continue
          if 'nan' in line:
            continue
          if 'Region 94 Avg' in line:
            continue
          if 'Region 106 Avg' in line:
            continue
          if 'to