ROS Quickstart Guide (#13553)

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

docs/en/yolov5/tutorials/architecture_description.md

@@ -60,7 +60,7 @@ class SPPF(nn.Module):
         self.maxpool = nn.MaxPool2d(5, 1, padding=2)
 
     def forward(self, x):
-        """Applies sequential max pooling and concatenates results with input tensor; expects input tensor x of any shape."""
+        """Applies sequential max pooling and concatenates results with input tensor."""
         o1 = self.maxpool(x)
         o2 = self.maxpool(o1)
         o3 = self.maxpool(o2)

docs/en/yolov5/tutorials/hyperparameter_evolution.md

@@ -65,7 +65,7 @@ Fitness is the value we seek to maximize. In YOLOv5 we define a default fitness
 
 ```python
 def fitness(x):
-    """Evaluates model fitness by summing weighted metrics [P, R, mAP@0.5, mAP@0.5:0.95], x is a numpy array of shape (n, 4)."""
+    """Return model fitness as the sum of weighted metrics [P, R, mAP@0.5, mAP@0.5:0.95]."""
     w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
     return (x[:, :4] * w).sum(1)
 ```