diff --git a/configs/common/models/mask_rcnn_fpn.py b/configs/common/models/mask_rcnn_fpn.py
index 5e5c501cd1..9502503990 100644
--- a/configs/common/models/mask_rcnn_fpn.py
+++ b/configs/common/models/mask_rcnn_fpn.py
@@ -75,6 +75,8 @@
             test_score_thresh=0.05,
             box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)),
             num_classes="${..num_classes}",
+            test_topk_per_image = 2000,
+            use_focal_ce = False  
         ),
         mask_in_features=["p2", "p3", "p4", "p5"],
         mask_pooler=L(ROIPooler)(
diff --git a/detectron2/modeling/roi_heads/fast_rcnn.py b/detectron2/modeling/roi_heads/fast_rcnn.py
index 039e2490fa..96a6eee9e8 100644
--- a/detectron2/modeling/roi_heads/fast_rcnn.py
+++ b/detectron2/modeling/roi_heads/fast_rcnn.py
@@ -11,6 +11,7 @@
 from detectron2.modeling.box_regression import Box2BoxTransform, _dense_box_regression_loss
 from detectron2.structures import Boxes, Instances
 from detectron2.utils.events import get_event_storage
+from fvcore.nn import sigmoid_focal_loss
 
 __all__ = ["fast_rcnn_inference", "FastRCNNOutputLayers"]
 
@@ -195,6 +196,7 @@ def __init__(
         loss_weight: Union[float, Dict[str, float]] = 1.0,
         use_fed_loss: bool = False,
         use_sigmoid_ce: bool = False,
+        use_focal_ce: bool = False,
         get_fed_loss_cls_weights: Optional[Callable] = None,
         fed_loss_num_classes: int = 50,
     ):
@@ -221,6 +223,8 @@ def __init__(
                 classes to calculate the loss
             use_sigmoid_ce (bool): whether to calculate the loss using weighted average of binary
                 cross entropy with logits. This could be used together with federated loss
+            use_focal_ce (bool): whether or not to calculate the loss using focal_loss as detailed in RetinaNet,
+                https://arxiv.org/pdf/1708.02002v2
             get_fed_loss_cls_weights (Callable): a callable which takes dataset name and frequency
                 weight power, and returns the probabilities to sample negative classes for
                 federated loss. The implementation can be found in
@@ -254,6 +258,7 @@ def __init__(
         self.loss_weight = loss_weight
         self.use_fed_loss = use_fed_loss
         self.use_sigmoid_ce = use_sigmoid_ce
+        self.use_focal_ce = use_focal_ce
         self.fed_loss_num_classes = fed_loss_num_classes
 
         if self.use_fed_loss:
@@ -280,6 +285,7 @@ def from_config(cls, cfg, input_shape):
             "loss_weight"               : {"loss_box_reg": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT},  # noqa
             "use_fed_loss"              : cfg.MODEL.ROI_BOX_HEAD.USE_FED_LOSS,
             "use_sigmoid_ce"            : cfg.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE,
+            "use_focal_ce"              : cfg.MODEL.ROI_BOX_HEAD.USE_FOCAL_CE,
             "get_fed_loss_cls_weights"  : lambda: get_fed_loss_cls_weights(dataset_names=cfg.DATASETS.TRAIN, freq_weight_power=cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT_POWER),  # noqa
             "fed_loss_num_classes"      : cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CLASSES,
             # fmt: on
@@ -340,6 +346,17 @@ def losses(self, predictions, proposals):
 
         if self.use_sigmoid_ce:
             loss_cls = self.sigmoid_cross_entropy_loss(scores, gt_classes)
+
+        if self.use_focal_ce:
+            N = scores.shape[0]
+            K = scores.shape[1] - 1
+
+            target = scores.new_zeros(N, K + 1)
+            target[range(len(gt_classes)), gt_classes] = 1
+            target = target[:, :K]
+
+            loss_cls = sigmoid_focal_loss(scores[:, :-1], target, reduction="mean")
+
         else:
             loss_cls = cross_entropy(scores, gt_classes, reduction="mean")