diff --git a/mmdeploy/backend/coreml/ops.py b/mmdeploy/backend/coreml/ops.py
index 2e72be3123..0af1aa42ee 100644
--- a/mmdeploy/backend/coreml/ops.py
+++ b/mmdeploy/backend/coreml/ops.py
@@ -22,3 +22,66 @@ def coreml_nms(context, node):
         max_boxes=max_boxes)
 
     context.add(tuple(results), torch_name=node.outputs[0])
+
+
+@register_torch_op
+def log2(context, node):
+    """bind log2."""
+    import numpy as np
+    inputs = _get_inputs(context, node)
+    x = inputs[0]
+    log_x = mb.log(x=x)
+    context.add(mb.mul(x=log_x, y=1 / np.log(2.0)), node.name)
+
+
+@register_torch_op
+def roi_align(context, node):
+    """roi align."""
+    inputs = _get_inputs(context, node)
+
+    x = context[node.inputs[0]]
+    input_shape = x.shape  # (B, C, h_in, w_in)
+    if len(input_shape) != 4:
+        raise ValueError(
+            '"CropResize" op: expected input rank 4, got {}'.format(x.rank))
+
+    const_box_info = True
+    if context[node.inputs[1]].val is None or context[
+            node.inputs[2]].val is None:
+        const_box_info = False
+
+    extrapolation_value = context[node.inputs[2]].val
+    # CoreML index information along with boxes
+    if const_box_info:
+        boxes = context[node.inputs[1]].val
+        # CoreML expects boxes/ROI in
+        # [N, 1, 5, 1, 1] format
+        boxes = boxes.reshape(boxes.shape[0], 1, boxes.shape[1], 1, 1)
+    else:
+        boxes = inputs[1]
+        boxes = mb.reshape(
+            x=boxes, shape=[boxes.shape[0], 1, boxes.shape[1], 1, 1])
+    # Get Height and Width of crop
+    h_out = inputs[3]
+    w_out = inputs[4]
+
+    # Torch input format: [B, C, h_in, w_in]
+    # CoreML input format: [B, C, h_in, w_in]
+
+    # Crop Resize
+    x = mb.crop_resize(
+        x=x,
+        roi=boxes,
+        target_height=h_out.val,
+        target_width=w_out.val,
+        normalized_coordinates=False,
+        spatial_scale=extrapolation_value,
+        box_coordinate_mode='CORNERS_WIDTH_FIRST',
+        sampling_mode='OFFSET_CORNERS',
+    )
+
+    # CoreML output format: [N, 1, C, h_out, w_out]
+    # Torch output format: [N, C, h_out, w_out]
+    x = mb.squeeze(x=x, axes=[1])
+
+    context.add(x, torch_name=node.outputs[0])
diff --git a/mmdeploy/codebase/mmdet/models/dense_heads/rpn_head.py b/mmdeploy/codebase/mmdet/models/dense_heads/rpn_head.py
index 52558dbfaa..2652d889b4 100644
--- a/mmdeploy/codebase/mmdet/models/dense_heads/rpn_head.py
+++ b/mmdeploy/codebase/mmdet/models/dense_heads/rpn_head.py
@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
-from mmdeploy.codebase.mmdet import (get_post_processing_params,
+from mmdeploy.codebase.mmdet import (gather_topk, get_post_processing_params,
                                      multiclass_nms,
                                      pad_with_value_if_necessary)
 from mmdeploy.core import FUNCTION_REWRITER
@@ -104,11 +104,17 @@ def rpn_head__get_bboxes(ctx,
 
         if pre_topk > 0:
             _, topk_inds = scores.squeeze(2).topk(pre_topk)
-            batch_inds = torch.arange(batch_size, device=device).unsqueeze(-1)
-            prior_inds = topk_inds.new_zeros((1, 1))
-            anchors = anchors[prior_inds, topk_inds, :]
-            bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-            scores = scores[batch_inds, topk_inds, :]
+            bbox_pred, scores = gather_topk(
+                bbox_pred,
+                scores,
+                inds=topk_inds,
+                batch_size=batch_size,
+                is_batched=True)
+            anchors = gather_topk(
+                anchors,
+                inds=topk_inds,
+                batch_size=batch_size,
+                is_batched=False)
         mlvl_valid_bboxes.append(bbox_pred)
         mlvl_scores.append(scores)
         mlvl_valid_anchors.append(anchors)
diff --git a/mmdeploy/codebase/mmdet/models/roi_heads/single_level_roi_extractor.py b/mmdeploy/codebase/mmdet/models/roi_heads/single_level_roi_extractor.py
index 4bbc055b42..ee76072211 100644
--- a/mmdeploy/codebase/mmdet/models/roi_heads/single_level_roi_extractor.py
+++ b/mmdeploy/codebase/mmdet/models/roi_heads/single_level_roi_extractor.py
@@ -316,3 +316,39 @@ def single_roi_extractor__forward__openvino(ctx,
     args = (output_size, featmap_strides, sample_num, rois, *feats)
     result = SingleRoIExtractorOpenVINO.apply(*args)
     return result
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    func_name='mmdet.models.roi_heads.SingleRoIExtractor.forward',
+    backend=Backend.COREML.value)
+@mark('roi_extractor', inputs=['feats', 'rois'], outputs=['bbox_feats'])
+def single_roi_extractor__forward__coreml(ctx,
+                                          self,
+                                          feats,
+                                          rois,
+                                          roi_scale_factor=None):
+    """Rewrite `forward` of SingleRoIExtractor for coreml."""
+    out_size = self.roi_layers[0].output_size
+    num_levels = len(feats)
+    roi_feats = feats[0].new_zeros(rois.shape[0], self.out_channels, *out_size)
+    if num_levels == 1:
+        assert len(rois) > 0, 'The number of rois should be positive'
+        self.roi_layers[0].use_torchvision = True
+        return self.roi_layers[0](feats[0], rois)
+
+    target_lvls = self.map_roi_levels(rois, num_levels)
+
+    if roi_scale_factor is not None:
+        rois = self.roi_rescale(rois, roi_scale_factor)
+
+    for i in range(num_levels):
+        mask = target_lvls == i
+        # inds = mask.nonzero(as_tuple=False).squeeze(1)
+        rois_t = rois * mask.unsqueeze(-1)
+        # use the roi align in torhcvision
+        self.roi_layers[i].use_torchvision = True
+        roi_feats_t = self.roi_layers[i](feats[i], rois_t)
+        roi_feats = roi_feats + roi_feats_t * (rois_t[:, -1] > 0).reshape(
+            -1, 1, 1, 1)
+    # slice to recover original size
+    return roi_feats