diff --git a/pcdet/models/dense_heads/anchor_head_single.py b/pcdet/models/dense_heads/anchor_head_single.py
new file mode 100644
index 0000000..83c62cc
--- /dev/null
+++ b/pcdet/models/dense_heads/anchor_head_single.py
@@ -0,0 +1,75 @@
+import numpy as np
+import torch.nn as nn
+
+from .anchor_head_template import AnchorHeadTemplate
+
+
+class AnchorHeadSingle(AnchorHeadTemplate):
+    def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range,
+                 predict_boxes_when_training=True, **kwargs):
+        super().__init__(
+            model_cfg=model_cfg, num_class=num_class, class_names=class_names, grid_size=grid_size, point_cloud_range=point_cloud_range,
+            predict_boxes_when_training=predict_boxes_when_training
+        )
+
+        self.num_anchors_per_location = sum(self.num_anchors_per_location)
+
+        self.conv_cls = nn.Conv2d(
+            input_channels, self.num_anchors_per_location * self.num_class,
+            kernel_size=1
+        )
+        self.conv_box = nn.Conv2d(
+            input_channels, self.num_anchors_per_location * self.box_coder.code_size,
+            kernel_size=1
+        )
+
+        if self.model_cfg.get('USE_DIRECTION_CLASSIFIER', None) is not None:
+            self.conv_dir_cls = nn.Conv2d(
+                input_channels,
+                self.num_anchors_per_location * self.model_cfg.NUM_DIR_BINS,
+                kernel_size=1
+            )
+        else:
+            self.conv_dir_cls = None
+        self.init_weights()
+
+    def init_weights(self):
+        pi = 0.01
+        nn.init.constant_(self.conv_cls.bias, -np.log((1 - pi) / pi))
+        nn.init.normal_(self.conv_box.weight, mean=0, std=0.001)
+
+    def forward(self, data_dict):
+        spatial_features_2d = data_dict['spatial_features_2d']
+
+        cls_preds = self.conv_cls(spatial_features_2d)
+        box_preds = self.conv_box(spatial_features_2d)
+
+        cls_preds = cls_preds.permute(0, 2, 3, 1).contiguous()  # [N, H, W, C]
+        box_preds = box_preds.permute(0, 2, 3, 1).contiguous()  # [N, H, W, C]
+
+        self.forward_ret_dict['cls_preds'] = cls_preds
+        self.forward_ret_dict['box_preds'] = box_preds
+
+        if self.conv_dir_cls is not None:
+            dir_cls_preds = self.conv_dir_cls(spatial_features_2d)
+            dir_cls_preds = dir_cls_preds.permute(0, 2, 3, 1).contiguous()
+            self.forward_ret_dict['dir_cls_preds'] = dir_cls_preds
+        else:
+            dir_cls_preds = None
+
+        if self.training:
+            targets_dict = self.assign_targets(
+                gt_boxes=data_dict['gt_boxes']
+            )
+            self.forward_ret_dict.update(targets_dict)
+
+        if not self.training or self.predict_boxes_when_training:
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=data_dict['batch_size'],
+                cls_preds=cls_preds, box_preds=box_preds, dir_cls_preds=dir_cls_preds
+            )
+            data_dict['batch_cls_preds'] = batch_cls_preds
+            data_dict['batch_box_preds'] = batch_box_preds
+            data_dict['cls_preds_normalized'] = False
+
+        return data_dict