Add docstrings and improve comments (#11229)

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

tests/test_python.py

@@ -345,12 +345,12 @@ def test_labels_and_crops():
         labels = save_path / f"labels/{im_name}.txt"
         assert labels.exists()
         # Check detections match label count
-        assert len(r.boxes.data) == len([l for l in labels.read_text().splitlines() if l])
+        assert len(r.boxes.data) == len([line for line in labels.read_text().splitlines() if line])
         # Check crops path and files
-        crop_dirs = [p for p in (save_path / "crops").iterdir()]
+        crop_dirs = list((save_path / "crops").iterdir())
         crop_files = [f for p in crop_dirs for f in p.glob("*")]
         # Crop directories match detections
-        assert all([r.names.get(c) in {d.name for d in crop_dirs} for c in cls_idxs])
+        assert all(r.names.get(c) in {d.name for d in crop_dirs} for c in cls_idxs)
         # Same number of crops as detections
         assert len([f for f in crop_files if im_name in f.name]) == len(r.boxes.data)
 
@@ -643,8 +643,8 @@ def test_yolo_world():
     model(ASSETS / "bus.jpg", conf=0.01)
 
     model = YOLO("yolov8s-worldv2.pt")  # no YOLOv8n-world model yet
-    # Training from pretrain, evaluation process is included at the final stage of training.
-    # Use dota8.yaml which has less categories to reduce the inference time of CLIP model
+    # Training from a pretrained model. Eval is included at the final stage of training.
+    # Use dota8.yaml which has fewer categories to reduce the inference time of CLIP model
     model.train(
         data="dota8.yaml",
         epochs=1,