VGG19 bn model ,great accuracy gap between training phase and test phase

[cvjena]

VGG19 bn model ,great accuracy gap between training phase and test phase

[zbxzc35]

here is my test code

``# -- coding: UTF-8 --
from numpy import *
import operator
import os
from os import listdir
import sys
sys.path.append("/home/hj/caffe/python")
sys.path.append("/home/hj/caffe/python/caffe")
import caffe
import cv2
import numpy as np

val_file = '../densenet121_gap/test.txt'

data_root = ''

base_size = 234 # short size
crop_size = 224

def center_crop(img): # single crop
short_edge = min(img.shape[:2])
if short_edge < crop_size:
return
yy = int((img.shape[0] - crop_size) / 2)
xx = int((img.shape[1] - crop_size) / 2)
return img[yy: yy + crop_size, xx: xx + crop_size]

def test(my_project_root, deploy_proto):
caffe_model = my_project_root + 'vgg19_cvgj_iter_750.caffemodel'
net = caffe.Net(deploy_proto, caffe_model, caffe.TEST)
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
#transformer.set_mean('data', np.load(root_str+'imagenet_mean.npy').mean(1).mean(1))
transformer.set_raw_scale('data', 255.0)
transformer.set_channel_swap('data', (2,1,0))
eval_images = []
ground_truth = []
f = open(val_file, 'r')
for i in f:
eval_images.append(i[:i.rindex(' ')])
ground_truth.append(int(i[i.rindex(' ')+1:]))
f.close()
ture_num=0.0
false_num=0.0
for idx,filename in enumerate(eval_images):
print filename
#im = caffe.io.load_image(filename,color=True)
_img = cv2.imread(data_root + eval_images[idx])
#print data_root + eval_images[i + skip_num]
#_img = cv2.resize(_img, (int(_img.shape[1] * base_size / min(_img.shape[:2])),
# int(_img.shape[0] * base_size / min(_img.shape[:2]))))
im = cv2.resize(_img, (base_size,base_size))
im=center_crop(im)
net.blobs['data'].data[...] = transformer.preprocess('data',im)
out = net.forward()
prob = net.blobs['prob'].data[0].flatten()
print "prob:",prob
order = prob.argsort()[-1]
if order==ground_truth[idx]:
ture_num+=1
else:
false_num+=1
print("true: %15s predict: %10s "%(ground_truth[idx], order))

    print("accuracy:%.4f"%(ture_num/(ture_num+false_num)))

if name == 'main':
caffe.set_device(0)
caffe.set_mode_gpu()
my_project_root = '/home/hj/bingzao-detect/normal_abnormal_classify/vgg_bn_224/'
deploy_proto = my_project_root + "deploy.prototxt"
test(my_project_root, deploy_proto)

[MarcelSimon]

I didn't check in detail, but can't spot a problem right now. One thing I'm wondering: vgg19_cvgj_iter_750 means the model of iteration 750. Is that correct?

Another thing you can try is comparing the images loaded by the ImageDataLayer to the ones loaded with your script.