再voc.data中有valid的文件路径
./darknet detector valid cfg/voc-mask.data cfg/yolov4-mask-test.cfg backup/yolov4-mask_final.weights会在results中生成 .txt文件
python reval_voc.py --voc_dir VOCdevkit --year 2007 --image_set test --classes data/voc-mask.names testmask会生成pkl文件
import pickle as cPickle import matplotlib.pyplot as plt fr1= open('/home/xxp/darknet/testmask/v3mask_pr.pkl', 'rb') # 这里open中第一个参数需要修改成自己生产的pkl文件 inf1 = cPickle.load(fr1) #使用 load() 方法将该文件中的数据反序列化后输出： fr1.close() x1= inf1['rec'] y1= inf1['prec'] fr2= open('/home/xxp/darknet/testmask/v4mask_pr.pkl', 'rb') # 这里open中第一个参数需要修改成自己生产的pkl文件 inf2 = cPickle.load(fr2) fr2.close() x2= inf2['rec'] y2= inf2['prec'] fr3= open('/home/xxp/darknet/testmask/v3-3mask_pr.pkl', 'rb') # 这里open中第一个参数需要修改成自己生产的pkl文件 inf3 = cPickle.load(fr3) fr3.close() x3= inf3['rec'] y3= inf3['prec'] plt.figure() plt.xlabel('recall') plt.ylabel('precision') plt.title('PR cruve') plt.plot(x1, y1, color="red") plt.plot(x2, y2, color="blue") plt.plot(x3, y3, color="green") plt.show() print('AP：', inf['ap'])
./darknet detector recall ./cfg/voc-mask.data ./cfg/yolov3-tiny-mask5-test.cfg ./backup/yolov3-tiny-mask5_best.weights
./darknet detector map ./cfg/voc-mask.data ./cfg/yolov3-tiny-mask5-test.cfg ./backup/yolov3-tiny-mask5_best.weights
