diff --git a/ppdet/data/data_feed.py b/ppdet/data/data_feed.py
index 568f00de9867d6220dbce3d459cd1be66c651e22..4f67bed1a61f4ab7597bac608b542e0347fad3c9 100644
--- a/ppdet/data/data_feed.py
+++ b/ppdet/data/data_feed.py
@@ -30,8 +30,8 @@ from ppdet.data.transform.operators import (
     Permute)
 
 from ppdet.data.transform.arrange_sample import (
-    ArrangeRCNN, ArrangeTestRCNN, ArrangeSSD, ArrangeEvalSSD, ArrangeTestSSD, 
-    ArrangeYOLO, ArrangeEvalYOLO, ArrangeTestYOLO)
+    ArrangeRCNN, ArrangeEvalRCNN, ArrangeTestRCNN, ArrangeSSD, ArrangeEvalSSD,
+    ArrangeTestSSD, ArrangeYOLO, ArrangeEvalYOLO, ArrangeTestYOLO)
 
 __all__ = [
     'PadBatch', 'MultiScale', 'RandomShape', 'DataSet', 'CocoDataSet',
@@ -476,7 +476,8 @@ class FasterRCNNEvalFeed(DataFeed):
     def __init__(self,
                  dataset=CocoDataSet(COCO_VAL_ANNOTATION,
                                      COCO_VAL_IMAGE_DIR).__dict__,
-                 fields=['image', 'im_info', 'im_id', 'im_shape'],
+                 fields=['image', 'im_info', 'im_id', 'im_shape', 'gt_box',
+                         'gt_label', 'is_difficult'],
                  image_shape=[3, 800, 1333],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
@@ -494,7 +495,7 @@ class FasterRCNNEvalFeed(DataFeed):
                  drop_last=False,
                  num_workers=2,
                  use_padded_im_info=True):
-        sample_transforms.append(ArrangeTestRCNN())
+        sample_transforms.append(ArrangeEvalRCNN())
         super(FasterRCNNEvalFeed, self).__init__(
             dataset,
             fields,
diff --git a/ppdet/data/transform/arrange_sample.py b/ppdet/data/transform/arrange_sample.py
index 13f70bfa30dc8710ff0277004a67c3c75fdad65e..697995cd72d37a7dee29f6c18ca527ffa6ad5077 100644
--- a/ppdet/data/transform/arrange_sample.py
+++ b/ppdet/data/transform/arrange_sample.py
@@ -90,6 +90,47 @@ class ArrangeRCNN(BaseOperator):
         return outs
 
 
+@register_op
+class ArrangeEvalRCNN(BaseOperator):
+    """
+    Transform dict to the tuple format needed for evaluation.
+    """
+
+    def __init__(self):
+        super(ArrangeEvalRCNN, self).__init__()
+
+    def __call__(self, sample, context=None):
+        """
+        Args:
+            sample: a dict which contains image
+                    info and annotation info.
+            context: a dict which contains additional info.
+        Returns:
+            sample: a tuple containing the following items:
+                    (image, im_info, im_id, im_shape, gt_bbox,
+                    gt_class, difficult)
+        """
+        im = sample['image']
+        keys = list(sample.keys())
+        if 'im_info' in keys:
+            im_info = sample['im_info']
+        else:
+            raise KeyError("The dataset doesn't have 'im_info' key.")
+        im_id = sample['im_id']
+        h = sample['h']
+        w = sample['w']
+        # For rcnn models in eval and infer stage, original image size
+        # is needed to clip the bounding boxes. And box clip op in
+        # bbox prediction needs im_info as input in format of [N, 3],
+        # so im_shape is appended by 1 to match dimension.
+        im_shape = np.array((h, w, 1), dtype=np.float32)
+        gt_bbox = sample['gt_bbox']
+        gt_class = sample['gt_class']
+        difficult = sample['difficult']
+        outs = (im, im_info, im_id, im_shape, gt_bbox, gt_class, difficult)
+        return outs
+
+
 @register_op
 class ArrangeTestRCNN(BaseOperator):
     """
@@ -152,6 +193,7 @@ class ArrangeSSD(BaseOperator):
         outs = (im, gt_bbox, gt_class)
         return outs
 
+
 @register_op
 class ArrangeEvalSSD(BaseOperator):
     """
@@ -184,6 +226,7 @@ class ArrangeEvalSSD(BaseOperator):
 
         return outs
 
+
 @register_op
 class ArrangeTestSSD(BaseOperator):
     """