diff --git a/dygraph/ppdet/engine/trainer.py b/dygraph/ppdet/engine/trainer.py
index 25a5babc45eb9adb599ccf6506fcc0310f6f8bd9..8377a25d171d714ea21c3a8d96b815e665b3dd49 100644
--- a/dygraph/ppdet/engine/trainer.py
+++ b/dygraph/ppdet/engine/trainer.py
@@ -116,9 +116,13 @@ class Trainer(object):
         if self.cfg.metric == 'COCO':
             # TODO: bias should be unified
             bias = self.cfg['bias'] if 'bias' in self.cfg else 0
+            save_prediction_only = self.cfg['save_prediction_only'] \
+                if 'save_prediction_only' in self.cfg else False
             self._metrics = [
                 COCOMetric(
-                    anno_file=self.dataset.get_anno(), bias=bias)
+                    anno_file=self.dataset.get_anno(),
+                    bias=bias,
+                    save_prediction_only=save_prediction_only)
             ]
         elif self.cfg.metric == 'VOC':
             self._metrics = [
diff --git a/dygraph/ppdet/metrics/metrics.py b/dygraph/ppdet/metrics/metrics.py
index bed5ae18a38d50c049359d8f5c71f5243e7b8428..9f801a135dc7dc2c7ca56b99c0099a4d1371c26a 100644
--- a/dygraph/ppdet/metrics/metrics.py
+++ b/dygraph/ppdet/metrics/metrics.py
@@ -65,6 +65,7 @@ class COCOMetric(Metric):
         self.clsid2catid, self.catid2name = get_categories('COCO', anno_file)
         # TODO: bias should be unified
         self.bias = kwargs.get('bias', 0)
+        self.save_prediction_only = kwargs.get('save_prediction_only', False)
         self.reset()
 
     def reset(self):
@@ -97,30 +98,42 @@ class COCOMetric(Metric):
                 json.dump(self.results['bbox'], f)
                 logger.info('The bbox result is saved to bbox.json.')
 
-            bbox_stats = cocoapi_eval(
-                'bbox.json', 'bbox', anno_file=self.anno_file)
-            self.eval_results['bbox'] = bbox_stats
-            sys.stdout.flush()
+            if self.save_prediction_only:
+                logger.info('The bbox result is saved to bbox.json and do not '
+                            'evaluate the mAP.')
+            else:
+                bbox_stats = cocoapi_eval(
+                    'bbox.json', 'bbox', anno_file=self.anno_file)
+                self.eval_results['bbox'] = bbox_stats
+                sys.stdout.flush()
 
         if len(self.results['mask']) > 0:
             with open("mask.json", 'w') as f:
                 json.dump(self.results['mask'], f)
                 logger.info('The mask result is saved to mask.json.')
 
-            seg_stats = cocoapi_eval(
-                'mask.json', 'segm', anno_file=self.anno_file)
-            self.eval_results['mask'] = seg_stats
-            sys.stdout.flush()
+            if self.save_prediction_only:
+                logger.info('The mask result is saved to mask.json and do not '
+                            'evaluate the mAP.')
+            else:
+                seg_stats = cocoapi_eval(
+                    'mask.json', 'segm', anno_file=self.anno_file)
+                self.eval_results['mask'] = seg_stats
+                sys.stdout.flush()
 
         if len(self.results['segm']) > 0:
             with open("segm.json", 'w') as f:
                 json.dump(self.results['segm'], f)
                 logger.info('The segm result is saved to segm.json.')
 
-            seg_stats = cocoapi_eval(
-                'segm.json', 'segm', anno_file=self.anno_file)
-            self.eval_results['mask'] = seg_stats
-            sys.stdout.flush()
+            if self.save_prediction_only:
+                logger.info('The segm result is saved to segm.json and do not '
+                            'evaluate the mAP.')
+            else:
+                seg_stats = cocoapi_eval(
+                    'segm.json', 'segm', anno_file=self.anno_file)
+                self.eval_results['mask'] = seg_stats
+                sys.stdout.flush()
 
     def log(self):
         pass
diff --git a/dygraph/tools/eval.py b/dygraph/tools/eval.py
index 690cc55017b5c7cbdad9c25fb33a68fa498d4bde..2536f5d09b4d48799cf90d2ff4389b79a99397e4 100755
--- a/dygraph/tools/eval.py
+++ b/dygraph/tools/eval.py
@@ -64,6 +64,12 @@ def parse_args():
         action="store_true",
         help="whether add bias or not while getting w and h")
 
+    parser.add_argument(
+        '--save_prediction_only',
+        action='store_true',
+        default=False,
+        help='Whether to save the evaluation results only')
+
     args = parser.parse_args()
     return args
 
@@ -88,6 +94,7 @@ def main():
     cfg = load_config(FLAGS.config)
     # TODO: bias should be unified
     cfg['bias'] = 1 if FLAGS.bias else 0
+    cfg['save_prediction_only'] = FLAGS.save_prediction_only
     merge_config(FLAGS.opt)
     if FLAGS.slim_config:
         slim_cfg = load_config(FLAGS.slim_config)
diff --git a/dygraph/tools/train.py b/dygraph/tools/train.py
index b621991a69f771ae5815ab4270110fcbc07a06ff..59351028f3b85ef83d30d4969e0eaa26619761d5 100755
--- a/dygraph/tools/train.py
+++ b/dygraph/tools/train.py
@@ -65,6 +65,11 @@ def parse_args():
         default=False,
         help="If set True, enable continuous evaluation job."
         "This flag is only used for internal test.")
+    parser.add_argument(
+        '--save_prediction_only',
+        action='store_true',
+        default=False,
+        help='Whether to save the evaluation results only')
     args = parser.parse_args()
     return args
 
@@ -91,6 +96,7 @@ def main():
     FLAGS = parse_args()
 
     cfg = load_config(FLAGS.config)
+    cfg['save_prediction_only'] = FLAGS.save_prediction_only
     merge_config(FLAGS.opt)
     if FLAGS.slim_config:
         slim_cfg = load_config(FLAGS.slim_config)