保存日志命令:
/darknet detector train my/mot/person.data my/mot/densenet201_yolo.cfg backup/mot/densenet201_yolo_final.weights >> log/mot-ramdon.log
|
python可视化代码:
#plot.py
import argparse
import sys
import matplotlib.pyplot as plt
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument(
"-file",
help = "path to log file"
)
args = parser.parse_args()
f = open(args.file)
lines = [line.rstrip("\n") for line in f.readlines()]
numbers = {'1','2','3','4','5','6','7','8','9'}
iters = []
loss = []
fig,ax = plt.subplots()
prev_line = ""
for line in lines:
args = line.split(' ')
if args[0][-1:]==':' and args[0][0] in numbers :
iters.append(int(args[0][:-1]))
loss.append(float(args[2]))
ax.plot(iters,loss)
plt.xlabel('iters')
plt.ylabel('loss')
plt.grid()
ticks = range(0,250,10)
#ax.set_yticks(ticks)
plt.show()
if __name__ == "__main__":
main(sys.argv)
效果图:
源地址:https://ricky.moe/2017/11/04/yolo-training-visualization/