Add https://www.reddit.com/r/Ultralytics/ badge (#13284)

* Refactor code for speed and clarity

* Update README.zh-CN.md

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

utils/loss.py

@@ -14,7 +14,8 @@ def smooth_BCE(eps=0.1):
 
 
 class BCEBlurWithLogitsLoss(nn.Module):
-    # BCEwithLogitLoss() with reduced missing label effects.
+    """Modified BCEWithLogitsLoss to reduce missing label effects in YOLOv5 training with optional alpha smoothing."""
+
     def __init__(self, alpha=0.05):
         """Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing
         parameter.
@@ -37,7 +38,8 @@ class BCEBlurWithLogitsLoss(nn.Module):
 
 
 class FocalLoss(nn.Module):
-    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+    """Applies focal loss to address class imbalance by modifying BCEWithLogitsLoss with gamma and alpha parameters."""
+
     def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
         """Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to
         'none'.
@@ -71,7 +73,8 @@ class FocalLoss(nn.Module):
 
 
 class QFocalLoss(nn.Module):
-    # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+    """Implements Quality Focal Loss to address class imbalance by modulating loss based on prediction confidence."""
+
     def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
         """Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'."""
         super().__init__()
@@ -101,6 +104,8 @@ class QFocalLoss(nn.Module):
 
 
 class ComputeLoss:
+    """Computes the total loss for YOLOv5 model predictions, including classification, box, and objectness losses."""
+
     sort_obj_iou = False
 
     # Compute losses