Update yolov5-bifpn.yaml
diff --git a/models/hub/yolov5-bifpn.yaml b/models/hub/yolov5-bifpn.yaml
index f1dd7c601b9..69f7b5938c5 100644
--- a/models/hub/yolov5-bifpn.yaml
+++ b/models/hub/yolov5-bifpn.yaml
@@ -3,38 +3,44 @@ nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
 anchors:
-  - [ 10,13, 16,30, 33,23 ]  # P3/8
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
 
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, Bottleneck, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, BottleneckCSP, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, BottleneckCSP, [ 512 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
-    [ -1, 6, BottleneckCSP, [ 1024 ] ],  # 9
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]]
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
+   [-1, 3, C3, [1024, False]],  # 9
   ]
 
 # YOLOv5 BiFPN head
 head:
-  [ [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 10 (P5/32-large)
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 13
 
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 20, 6 ], 1, Concat, [ 1 ] ],  # cat P4
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 14 (P4/16-medium)
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
 
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 3, BottleneckCSP, [ 256, False ] ],  # 18 (P3/8-small)
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14, 6], 1, Concat, [1]],  # cat P4
+   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
 
-    [ [ 18, 14, 10 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
   ]



## 🛠️ PR Summary

<sub>Made with ❤️ by [Ultralytics Actions](https://github.com/ultralytics/actions)<sub>

### 🌟 Summary
Enhanced YOLOv5 architecture with BiFPN and C3 module integration for improved object detection.

### 📊 Key Changes
- **Anchor Format**: Whitespaces have been removed for consistency in anchor definitions.
- **Backbone Updates**: Replaced `Bottleneck` and `BottleneckCSP` with `C3` modules, which may provide improved feature extraction.
- **Head Changes**: Simplified and modified the BiFPN head structure, introducing `C3` modules and additional concatenation steps to fuse features from different scales more effectively.

### 🎯 Purpose & Impact
- **Performance**: The introduction of `C3` modules is aimed at enhancing learning representations, which can lead to better detection performance.
- **Feature Integration**: BiFPN alterations are designed to strengthen feature pyramid networking, producing a more robust multi-scale feature integration, potentially leading to improved detection accuracy.
- **User Experience**: This update should increase the model's object detection capabilities and potentially improve inference times, benefiting developers and end-users who require high-performance object detection. 🏎️🔍