diff --git a/python/paddle/fluid/contrib/slim/tests/test_post_training_quantization.py b/python/paddle/fluid/contrib/slim/tests/test_post_training_quantization.py
index 3c86a74612f0d89cfb8417f93515f03f797381ed..7cf5b96c01a93e4dd1e533309ac13a804ceaae1f 100644
--- a/python/paddle/fluid/contrib/slim/tests/test_post_training_quantization.py
+++ b/python/paddle/fluid/contrib/slim/tests/test_post_training_quantization.py
@@ -262,50 +262,6 @@ class TestPostTrainingQuantization(unittest.TestCase):
         ptq.save_quantized_model(self.int8_model)
 
 
-class TestPostTrainingForResnet50(TestPostTrainingQuantization):
-    def download_model(self):
-        # resnet50 fp32 data
-        data_urls = [
-            'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model.tar.gz'
-        ]
-        data_md5s = ['4a5194524823d9b76da6e738e1367881']
-        self.model_cache_folder = self.download_data(data_urls, data_md5s,
-                                                     "resnet50_fp32")
-        self.model = "ResNet-50"
-        self.algo = "KL"
-
-    def test_post_training_resnet50(self):
-        self.download_model()
-
-        print("Start FP32 inference for {0} on {1} images ...".format(
-            self.model, self.infer_iterations * self.batch_size))
-        (fp32_throughput, fp32_latency,
-         fp32_acc1) = self.run_program(self.model_cache_folder + "/model")
-
-        print("Start INT8 post training quantization for {0} on {1} images ...".
-              format(self.model, self.sample_iterations * self.batch_size))
-        self.generate_quantized_model(
-            self.model_cache_folder + "/model", algo=self.algo)
-
-        print("Start INT8 inference for {0} on {1} images ...".format(
-            self.model, self.infer_iterations * self.batch_size))
-        (int8_throughput, int8_latency,
-         int8_acc1) = self.run_program(self.int8_model)
-
-        print(
-            "FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
-            format(self.model, self.batch_size, fp32_throughput, fp32_latency,
-                   fp32_acc1))
-        print(
-            "INT8 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
-            format(self.model, self.batch_size, int8_throughput, int8_latency,
-                   int8_acc1))
-        sys.stdout.flush()
-
-        delta_value = fp32_acc1 - int8_acc1
-        self.assertLess(delta_value, 0.025)
-
-
 class TestPostTrainingForMobilenetv1(TestPostTrainingQuantization):
     def download_model(self):
         # mobilenetv1 fp32 data