diff --git a/fluid/icnet/.run_ce.sh b/fluid/icnet/.run_ce.sh
index a46081c7978395697b843c5fef95e6091b47e4e5..643c1ed4cd1bd1012935e063cd8b3e3bbfd4f6d0 100755
--- a/fluid/icnet/.run_ce.sh
+++ b/fluid/icnet/.run_ce.sh
@@ -2,6 +2,7 @@
 
 # This file is only used for continuous evaluation.
 
+export ce_mode=1
 rm -rf *_factor.txt
-python train.py --use_gpu=True 1> log
+python train.py --use_gpu=True --random_mirror=False --random_scaling=False 1> log
 cat log | python _ce.py
diff --git a/fluid/icnet/_ce.py b/fluid/icnet/_ce.py
index 3844eefde620f9587d747594ad0d5351999859c8..af4a4f6b83fe62991571b4c193badb67301d235d 100644
--- a/fluid/icnet/_ce.py
+++ b/fluid/icnet/_ce.py
@@ -7,7 +7,7 @@ from kpi import CostKpi, DurationKpi, AccKpi
 
 # NOTE kpi.py should shared in models in some way!!!!
 
-train_cost_kpi = CostKpi('train_cost', 0.02, actived=True)
+train_cost_kpi = CostKpi('train_cost', 0.05, actived=True)
 train_duration_kpi = DurationKpi('train_duration', 0.06, actived=True)
 
 tracking_kpis = [
diff --git a/fluid/icnet/train.py b/fluid/icnet/train.py
index b38f08258b9b3e1bd28d808b2779416259f9d827..9b0f4fd4e727baa5986f6dd28dd0dd67b8f07628 100644
--- a/fluid/icnet/train.py
+++ b/fluid/icnet/train.py
@@ -4,6 +4,7 @@ import cityscape
 import argparse
 import functools
 import sys
+import os
 import time
 import paddle.fluid as fluid
 import numpy as np
@@ -11,9 +12,8 @@ from utils import add_arguments, print_arguments, get_feeder_data
 from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
 from paddle.fluid.initializer import init_on_cpu
 
-SEED = 90
-# random seed must set before configuring the network.
-fluid.default_startup_program().random_seed = SEED
+if 'ce_mode' in os.environ:
+    np.random.seed(10)
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
@@ -87,6 +87,10 @@ def train(args):
     if args.use_gpu:
         place = fluid.CUDAPlace(0)
     exe = fluid.Executor(place)
+
+    if 'ce_mode' in os.environ:
+        fluid.default_startup_program().random_seed = 90
+
     exe.run(fluid.default_startup_program())
 
     if args.init_model is not None: