Reformat Markdown code blocks (#12795)

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

docs/en/datasets/detect/african-wildlife.md

@@ -42,10 +42,10 @@ To train a YOLOv8n model on the African wildlife dataset for 100 epochs with an
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='african-wildlife.yaml', epochs=100, imgsz=640)
+        results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
@@ -63,7 +63,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 epochs with an
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('path/to/best.pt')  # load a brain-tumor fine-tuned model
+        model = YOLO("path/to/best.pt")  # load a brain-tumor fine-tuned model
 
         # Inference using the model
         results = model.predict("https://ultralytics.com/assets/african-wildlife-sample.jpg")

docs/en/datasets/detect/argoverse.md

@@ -53,10 +53,10 @@ To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image s
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='Argoverse.yaml', epochs=100, imgsz=640)
+        results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/brain-tumor.md

@@ -52,10 +52,10 @@ To train a YOLOv8n model on the brain tumor dataset for 100 epochs with an image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='brain-tumor.yaml', epochs=100, imgsz=640)
+        results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
@@ -73,7 +73,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 epochs with an image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('path/to/best.pt')  # load a brain-tumor fine-tuned model
+        model = YOLO("path/to/best.pt")  # load a brain-tumor fine-tuned model
 
         # Inference using the model
         results = model.predict("https://ultralytics.com/assets/brain-tumor-sample.jpg")

docs/en/datasets/detect/coco.md

@@ -70,10 +70,10 @@ To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size o
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/coco8.md

@@ -45,10 +45,10 @@ To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/globalwheat2020.md

@@ -48,10 +48,10 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='GlobalWheat2020.yaml', epochs=100, imgsz=640)
+        results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/index.md

@@ -56,10 +56,10 @@ Here's how you can use these formats to train your model:
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
         ```
     === "CLI"
 
@@ -103,7 +103,7 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
         ```python
         from ultralytics.data.converter import convert_coco
 
-        convert_coco(labels_dir='path/to/coco/annotations/')
+        convert_coco(labels_dir="path/to/coco/annotations/")
         ```
 
 This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.

docs/en/datasets/detect/lvis.md

@@ -66,10 +66,10 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='lvis.yaml', epochs=100, imgsz=640)
+        results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/objects365.md

@@ -48,10 +48,10 @@ To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='Objects365.yaml', epochs=100, imgsz=640)
+        results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/open-images-v7.md

@@ -88,10 +88,10 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an im
         from ultralytics import YOLO
 
         # Load a COCO-pretrained YOLOv8n model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Train the model on the Open Images V7 dataset
-        results = model.train(data='open-images-v7.yaml', epochs=100, imgsz=640)
+        results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/roboflow-100.md

@@ -46,39 +46,40 @@ Dataset benchmarking evaluates machine learning model performance on specific da
     === "Python"
 
         ```python
-        from pathlib import Path
-        import shutil
         import os
+        import shutil
+        from pathlib import Path
+
         from ultralytics.utils.benchmarks import RF100Benchmark
-        
+
         # Initialize RF100Benchmark and set API key
         benchmark = RF100Benchmark()
         benchmark.set_key(api_key="YOUR_ROBOFLOW_API_KEY")
-        
+
         # Parse dataset and define file paths
         names, cfg_yamls = benchmark.parse_dataset()
         val_log_file = Path("ultralytics-benchmarks") / "validation.txt"
         eval_log_file = Path("ultralytics-benchmarks") / "evaluation.txt"
-        
+
         # Run benchmarks on each dataset in RF100
         for ind, path in enumerate(cfg_yamls):
             path = Path(path)
             if path.exists():
                 # Fix YAML file and run training
                 benchmark.fix_yaml(str(path))
-                os.system(f'yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16')
-                
+                os.system(f"yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16")
+
                 # Run validation and evaluate
-                os.system(f'yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1')
+                os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")
                 benchmark.evaluate(str(path), str(val_log_file), str(eval_log_file), ind)
-                
+
                 # Remove the 'runs' directory
                 runs_dir = Path.cwd() / "runs"
                 shutil.rmtree(runs_dir)
             else:
                 print("YAML file path does not exist")
                 continue
-        
+
         print("RF100 Benchmarking completed!")
         ```
 

docs/en/datasets/detect/sku-110k.md

@@ -50,10 +50,10 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image si
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='SKU-110K.yaml', epochs=100, imgsz=640)
+        results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/visdrone.md

@@ -46,10 +46,10 @@ To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image si
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='VisDrone.yaml', epochs=100, imgsz=640)
+        results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/voc.md

@@ -49,10 +49,10 @@ To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='VOC.yaml', epochs=100, imgsz=640)
+        results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/xview.md

@@ -52,10 +52,10 @@ To train a model on the xView dataset for 100 epochs with an image size of 640,
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='xView.yaml', epochs=100, imgsz=640)
+        results = model.train(data="xView.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"