diff --git a/lfw_eval.py b/lfw_eval.py
index 16d70bc..def0b4b 100755
--- a/lfw_eval.py
+++ b/lfw_eval.py
@@ -85,36 +85,38 @@ def find_best_threshold(thresholds, predicts):
     landmark[l[0]] = [int(k) for k in l[1:]]
 
 with open('data/pairs.txt') as f:
-    pairs_lines = f.readlines()[1:]
-
-for i in range(6000):
-    p = pairs_lines[i].replace('\n','').split('\t')
-
-    if 3==len(p):
-        sameflag = 1
-        name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
-        name2 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[2]))
-    if 4==len(p):
-        sameflag = 0
-        name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
-        name2 = p[2]+'/'+p[2]+'_'+'{:04}.jpg'.format(int(p[3]))
-
-    img1 = alignment(cv2.imdecode(np.frombuffer(zfile.read(name1),np.uint8),1),landmark[name1])
-    img2 = alignment(cv2.imdecode(np.frombuffer(zfile.read(name2),np.uint8),1),landmark[name2])
-
-    imglist = [img1,cv2.flip(img1,1),img2,cv2.flip(img2,1)]
-    for i in range(len(imglist)):
-        imglist[i] = imglist[i].transpose(2, 0, 1).reshape((1,3,112,96))
-        imglist[i] = (imglist[i]-127.5)/128.0
-
-    img = np.vstack(imglist)
-    img = Variable(torch.from_numpy(img).float(),volatile=True).cuda()
-    output = net(img)
-    f = output.data
-    f1,f2 = f[0],f[2]
-    cosdistance = f1.dot(f2)/(f1.norm()*f2.norm()+1e-5)
-    predicts.append('{}\t{}\t{}\t{}\n'.format(name1,name2,cosdistance,sameflag))
-
+    _ = next(f, None)     # skip header
+
+    for line_no, line in enumerate(f, start=1):
+        p = line.replace('\n','').split('\t')
+
+        if 3==len(p):
+            sameflag = 1
+            name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
+            name2 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[2]))
+        if 4==len(p):
+            sameflag = 0
+            name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
+            name2 = p[2]+'/'+p[2]+'_'+'{:04}.jpg'.format(int(p[3]))
+
+        img1 = alignment(cv2.imdecode(np.frombuffer(zfile.read(name1),np.uint8),1),landmark[name1])
+        img2 = alignment(cv2.imdecode(np.frombuffer(zfile.read(name2),np.uint8),1),landmark[name2])
+
+        imglist = [img1,cv2.flip(img1,1),img2,cv2.flip(img2,1)]
+        for i, image in enumerate(imglist):
+            image = image.transpose(2, 0, 1).reshape((1,3,112,96))
+            imglist[i] = (image - 127.5) / 128.0
+
+        img = np.vstack(imglist)
+        img = Variable(torch.from_numpy(img).float(),volatile=True).cuda()
+        output = net(img)
+        f = output.data
+        f1,f2 = f[0],f[2]
+        cosdistance = f1.dot(f2)/(f1.norm()*f2.norm()+1e-5)
+        predicts.append('{}\t{}\t{}\t{}\n'.format(name1,name2,cosdistance,sameflag))
+
+        if line_no >= 6000:     # break as soon as 6000 lines have been processed.
+            break
 
 accuracy = []
 thd = []