diff --git a/yolo/utils/bounding_box_utils.py b/yolo/utils/bounding_box_utils.py
index ca94286..63896e3 100644
--- a/yolo/utils/bounding_box_utils.py
+++ b/yolo/utils/bounding_box_utils.py
@@ -2,7 +2,6 @@
 from typing import Dict, List, Optional, Tuple, Union
 
 import torch
-import torch.nn.functional as F
 from einops import rearrange
 from torch import Tensor, tensor
 from torchmetrics.detection import MeanAveragePrecision
@@ -235,7 +234,8 @@ def ensure_one_anchor(self, target_matrix: Tensor, topk_mask: tensor) -> Tensor:
             topk_mask [batch x targets x anchors]: A boolean mask indicating the updated top-k scores' positions.
         """
         values, indices = target_matrix.max(dim=-1)
-        best_anchor_mask = F.one_hot(indices, target_matrix.size(-1))
+        best_anchor_mask = torch.zeros_like(target_matrix, dtype=torch.bool)
+        best_anchor_mask.scatter_(-1, index=indices[..., None], src=~best_anchor_mask)
         matched_anchor_num = torch.sum(topk_mask, dim=-1)
         target_without_anchor = (matched_anchor_num == 0) & (values > 0)
         topk_mask = torch.where(target_without_anchor[..., None], best_anchor_mask, topk_mask)
@@ -256,10 +256,12 @@ def filter_duplicates(self, iou_mat: Tensor, topk_mask: Tensor):
         """
         duplicates = (topk_mask.sum(1, keepdim=True) > 1).repeat([1, topk_mask.size(1), 1])
         masked_iou_mat = topk_mask * iou_mat
-        max_idx = F.one_hot(masked_iou_mat.argmax(1), topk_mask.size(1)).permute(0, 2, 1)
-        topk_mask = torch.where(duplicates, max_idx, topk_mask)
-        unique_indices = topk_mask.argmax(dim=1)
-        return unique_indices[..., None], topk_mask.sum(1), topk_mask
+        best_indices = masked_iou_mat.argmax(1)[:, None, :]
+        best_target_mask = torch.zeros_like(duplicates, dtype=torch.bool)
+        best_target_mask.scatter_(1, index=best_indices, src=~best_target_mask)
+        topk_mask = torch.where(duplicates, best_target_mask, topk_mask)
+        unique_indices = topk_mask.to(torch.uint8).argmax(dim=1)
+        return unique_indices[..., None], topk_mask.any(dim=1), topk_mask
 
     def __call__(self, target: Tensor, predict: Tuple[Tensor]) -> Tuple[Tensor, Tensor]:
         """Matches each target to the most suitable anchor.
@@ -317,8 +319,9 @@ def __call__(self, target: Tensor, predict: Tuple[Tensor]) -> Tuple[Tensor, Tens
         unique_indices, valid_mask, topk_mask = self.filter_duplicates(iou_mat, topk_mask)
 
         align_bbox = torch.gather(target_bbox, 1, unique_indices.repeat(1, 1, 4))
-        align_cls = torch.gather(target_cls, 1, unique_indices).squeeze(-1)
-        align_cls = F.one_hot(align_cls, self.class_num)
+        align_cls_indices = torch.gather(target_cls, 1, unique_indices)
+        align_cls = torch.zeros_like(align_cls_indices, dtype=torch.bool).repeat(1, 1, self.class_num)
+        align_cls.scatter_(-1, index=align_cls_indices, src=~align_cls)
 
         # normalize class ditribution
         iou_mat *= topk_mask
@@ -329,7 +332,7 @@ def __call__(self, target: Tensor, predict: Tuple[Tensor]) -> Tuple[Tensor, Tens
         normalize_term = normalize_term.permute(0, 2, 1).gather(2, unique_indices)
         align_cls = align_cls * normalize_term * valid_mask[:, :, None]
         anchor_matched_targets = torch.cat([align_cls, align_bbox], dim=-1)
-        return anchor_matched_targets, valid_mask.bool()
+        return anchor_matched_targets, valid_mask
 
 
 class Vec2Box: