Reformat Markdown code blocks (#12795)

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

docs/en/integrations/amazon-sagemaker.md

@@ -117,21 +117,22 @@ After creating the AWS CloudFormation Stack, the next step is to deploy YOLOv8.
 ```python
 import json
 
+
 def output_fn(prediction_output, content_type):
     """Formats model outputs as JSON string according to content_type, extracting attributes like boxes, masks, keypoints."""
     print("Executing output_fn from inference.py ...")
     infer = {}
     for result in prediction_output:
         if result.boxes is not None:
-            infer['boxes'] = result.boxes.numpy().data.tolist()
+            infer["boxes"] = result.boxes.numpy().data.tolist()
         if result.masks is not None:
-            infer['masks'] = result.masks.numpy().data.tolist()
+            infer["masks"] = result.masks.numpy().data.tolist()
         if result.keypoints is not None:
-            infer['keypoints'] = result.keypoints.numpy().data.tolist()
+            infer["keypoints"] = result.keypoints.numpy().data.tolist()
         if result.obb is not None:
-            infer['obb'] = result.obb.numpy().data.tolist()
+            infer["obb"] = result.obb.numpy().data.tolist()
         if result.probs is not None:
-            infer['probs'] = result.probs.numpy().data.tolist()
+            infer["probs"] = result.probs.numpy().data.tolist()
     return json.dumps(infer)
 ```
 

docs/en/integrations/clearml.md

@@ -67,17 +67,14 @@ Before diving into the usage instructions, be sure to check out the range of [YO
         from ultralytics import YOLO
 
         # Step 1: Creating a ClearML Task
-        task = Task.init(
-            project_name="my_project",
-            task_name="my_yolov8_task"
-        )
+        task = Task.init(project_name="my_project", task_name="my_yolov8_task")
 
         # Step 2: Selecting the YOLOv8 Model
         model_variant = "yolov8n"
         task.set_parameter("model_variant", model_variant)
 
         # Step 3: Loading the YOLOv8 Model
-        model = YOLO(f'{model_variant}.pt')
+        model = YOLO(f"{model_variant}.pt")
 
         # Step 4: Setting Up Training Arguments
         args = dict(data="coco8.yaml", epochs=16)

docs/en/integrations/comet.md

@@ -74,12 +74,12 @@ Before diving into the usage instructions, be sure to check out the range of [YO
 
         # train the model
         results = model.train(
-        data="coco8.yaml",
-        project="comet-example-yolov8-coco128",
-        batch=32,
-        save_period=1,
-        save_json=True,
-        epochs=3
+            data="coco8.yaml",
+            project="comet-example-yolov8-coco128",
+            batch=32,
+            save_period=1,
+            save_json=True,
+            epochs=3,
         )
         ```
 
@@ -144,7 +144,7 @@ Comet ML allows you to specify how often batches of image predictions are logged
 ```python
 import os
 
-os.environ['COMET_EVAL_BATCH_LOGGING_INTERVAL'] = "4"
+os.environ["COMET_EVAL_BATCH_LOGGING_INTERVAL"] = "4"
 ```
 
 ### Disabling Confusion Matrix Logging

docs/en/integrations/coreml.md

@@ -83,16 +83,16 @@ Before diving into the usage instructions, be sure to check out the range of [YO
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to CoreML format
-        model.export(format='coreml')  # creates 'yolov8n.mlpackage'
+        model.export(format="coreml")  # creates 'yolov8n.mlpackage'
 
         # Load the exported CoreML model
-        coreml_model = YOLO('yolov8n.mlpackage')
+        coreml_model = YOLO("yolov8n.mlpackage")
 
         # Run inference
-        results = coreml_model('https://ultralytics.com/images/bus.jpg')
+        results = coreml_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"

docs/en/integrations/dvc.md

@@ -149,7 +149,7 @@ If you are using a Jupyter Notebook and you want to display the generated DVC pl
 from IPython.display import HTML
 
 # Display the DVC plots as HTML
-HTML(filename='./dvc_plots/index.html')
+HTML(filename="./dvc_plots/index.html")
 ```
 
 This code will render the HTML file containing the DVC plots directly in your Jupyter Notebook, providing an easy and convenient way to analyze the visualized experiment data.

docs/en/integrations/edge-tpu.md

@@ -73,16 +73,16 @@ Before diving into the usage instructions, it's important to note that while all
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to TFLite Edge TPU format
-        model.export(format='edgetpu')  # creates 'yolov8n_full_integer_quant_edgetpu.tflite’
+        model.export(format="edgetpu")  # creates 'yolov8n_full_integer_quant_edgetpu.tflite’
 
         # Load the exported TFLite Edge TPU model
-        edgetpu_model = YOLO('yolov8n_full_integer_quant_edgetpu.tflite')
+        edgetpu_model = YOLO("yolov8n_full_integer_quant_edgetpu.tflite")
 
         # Run inference
-        results = edgetpu_model('https://ultralytics.com/images/bus.jpg')
+        results = edgetpu_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"

docs/en/integrations/gradio.md

@@ -44,9 +44,8 @@ pip install gradio
 This section provides the Python code used to create the Gradio interface with the Ultralytics YOLOv8 model. Supports classification tasks, detection tasks, segmentation tasks, and key point tasks.
 
 ```python
-import PIL.Image as Image
 import gradio as gr
-
+import PIL.Image as Image
 from ultralytics import ASSETS, YOLO
 
 model = YOLO("yolov8n.pt")
@@ -75,7 +74,7 @@ iface = gr.Interface(
     inputs=[
         gr.Image(type="pil", label="Upload Image"),
         gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
-        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold")
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
     ],
     outputs=gr.Image(type="pil", label="Result"),
     title="Ultralytics Gradio",
@@ -83,10 +82,10 @@ iface = gr.Interface(
     examples=[
         [ASSETS / "bus.jpg", 0.25, 0.45],
         [ASSETS / "zidane.jpg", 0.25, 0.45],
-    ]
+    ],
 )
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     iface.launch()
 ```
 

docs/en/integrations/mlflow.md

@@ -42,7 +42,7 @@ Make sure that MLflow logging is enabled in Ultralytics settings. Usually, this
         from ultralytics import settings
 
         # Update a setting
-        settings.update({'mlflow': True})
+        settings.update({"mlflow": True})
 
         # Reset settings to default values
         settings.reset()

docs/en/integrations/ncnn.md

@@ -73,18 +73,18 @@ Before diving into the usage instructions, it's important to note that while all
 
           ```python
           from ultralytics import YOLO
-          
+
           # Load the YOLOv8 model
-          model = YOLO('yolov8n.pt')
-          
+          model = YOLO("yolov8n.pt")
+
           # Export the model to NCNN format
-          model.export(format='ncnn') # creates '/yolov8n_ncnn_model'
-          
+          model.export(format="ncnn")  # creates '/yolov8n_ncnn_model'
+
           # Load the exported NCNN model
-          ncnn_model = YOLO('./yolov8n_ncnn_model')
-          
+          ncnn_model = YOLO("./yolov8n_ncnn_model")
+
           # Run inference
-          results = ncnn_model('https://ultralytics.com/images/bus.jpg')
+          results = ncnn_model("https://ultralytics.com/images/bus.jpg")
           ```
 
     === "CLI"

docs/en/integrations/neural-magic.md

@@ -109,10 +109,7 @@ With your YOLOv8 model in ONNX format, you can deploy and run inferences using D
         model_path = "path/to/yolov8n.onnx"
 
         # Set up the DeepSparse Pipeline
-        yolo_pipeline = Pipeline.create(
-            task="yolov8",
-            model_path=model_path
-        )
+        yolo_pipeline = Pipeline.create(task="yolov8", model_path=model_path)
 
         # Run the model on your images
         images = ["path/to/image.jpg"]

docs/en/integrations/onnx.md

@@ -91,16 +91,16 @@ Before diving into the usage instructions, be sure to check out the range of [YO
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to ONNX format
-        model.export(format='onnx')  # creates 'yolov8n.onnx'
+        model.export(format="onnx")  # creates 'yolov8n.onnx'
 
         # Load the exported ONNX model
-        onnx_model = YOLO('yolov8n.onnx')
+        onnx_model = YOLO("yolov8n.onnx")
 
         # Run inference
-        results = onnx_model('https://ultralytics.com/images/bus.jpg')
+        results = onnx_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"

docs/en/integrations/openvino.md

@@ -35,16 +35,16 @@ Export a YOLOv8n model to OpenVINO format and run inference with the exported mo
         from ultralytics import YOLO
 
         # Load a YOLOv8n PyTorch model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model
-        model.export(format='openvino')  # creates 'yolov8n_openvino_model/'
+        model.export(format="openvino")  # creates 'yolov8n_openvino_model/'
 
         # Load the exported OpenVINO model
-        ov_model = YOLO('yolov8n_openvino_model/')
+        ov_model = YOLO("yolov8n_openvino_model/")
 
         # Run inference
-        results = ov_model('https://ultralytics.com/images/bus.jpg')
+        results = ov_model("https://ultralytics.com/images/bus.jpg")
         ```
     === "CLI"
 
@@ -259,10 +259,10 @@ To reproduce the Ultralytics benchmarks above on all export [formats](../modes/e
         from ultralytics import YOLO
 
         # Load a YOLOv8n PyTorch model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
-        results= model.benchmarks(data='coco8.yaml')
+        results = model.benchmarks(data="coco8.yaml")
         ```
     === "CLI"
 

docs/en/integrations/paddlepaddle.md

@@ -77,16 +77,16 @@ Before diving into the usage instructions, it's important to note that while all
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to PaddlePaddle format
-        model.export(format='paddle')  # creates '/yolov8n_paddle_model'
+        model.export(format="paddle")  # creates '/yolov8n_paddle_model'
 
         # Load the exported PaddlePaddle model
-        paddle_model = YOLO('./yolov8n_paddle_model')
+        paddle_model = YOLO("./yolov8n_paddle_model")
 
         # Run inference
-        results = paddle_model('https://ultralytics.com/images/bus.jpg')
+        results = paddle_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"

docs/en/integrations/ray-tune.md

@@ -50,10 +50,10 @@ To install the required packages, run:
         from ultralytics import YOLO
 
         # Load a YOLOv8n model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Start tuning hyperparameters for YOLOv8n training on the COCO8 dataset
-        result_grid = model.tune(data='coco8.yaml', use_ray=True)
+        result_grid = model.tune(data="coco8.yaml", use_ray=True)
         ```
 
 ## `tune()` Method Parameters
@@ -112,10 +112,12 @@ In this example, we demonstrate how to use a custom search space for hyperparame
     model = YOLO("yolov8n.pt")
 
     # Run Ray Tune on the model
-    result_grid = model.tune(data="coco8.yaml",
-                             space={"lr0": tune.uniform(1e-5, 1e-1)},
-                             epochs=50,
-                             use_ray=True)
+    result_grid = model.tune(
+        data="coco8.yaml",
+        space={"lr0": tune.uniform(1e-5, 1e-1)},
+        epochs=50,
+        use_ray=True,
+    )
     ```
 
 In the code snippet above, we create a YOLO model with the "yolov8n.pt" pretrained weights. Then, we call the `tune()` method, specifying the dataset configuration with "coco8.yaml". We provide a custom search space for the initial learning rate `lr0` using a dictionary with the key "lr0" and the value `tune.uniform(1e-5, 1e-1)`. Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as `epochs=50`.
@@ -164,10 +166,14 @@ You can plot the history of reported metrics for each trial to see how the metri
 import matplotlib.pyplot as plt
 
 for result in result_grid:
-    plt.plot(result.metrics_dataframe["training_iteration"], result.metrics_dataframe["mean_accuracy"], label=f"Trial {i}")
+    plt.plot(
+        result.metrics_dataframe["training_iteration"],
+        result.metrics_dataframe["mean_accuracy"],
+        label=f"Trial {i}",
+    )
 
-plt.xlabel('Training Iterations')
-plt.ylabel('Mean Accuracy')
+plt.xlabel("Training Iterations")
+plt.ylabel("Mean Accuracy")
 plt.legend()
 plt.show()
 ```

docs/en/integrations/tensorrt.md

@@ -85,16 +85,16 @@ Before diving into the usage instructions, be sure to check out the range of [YO
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to TensorRT format
-        model.export(format='engine')  # creates 'yolov8n.engine'
+        model.export(format="engine")  # creates 'yolov8n.engine'
 
         # Load the exported TensorRT model
-        tensorrt_model = YOLO('yolov8n.engine')
+        tensorrt_model = YOLO("yolov8n.engine")
 
         # Run inference
-        results = tensorrt_model('https://ultralytics.com/images/bus.jpg')
+        results = tensorrt_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"
@@ -434,7 +434,7 @@ Expand sections below for information on how these models were exported and test
         result = model.predict(
             [img] * 8,  # batch=8 of the same image
             verbose=False,
-            device="cuda"
+            device="cuda",
         )
     ```
 
@@ -451,7 +451,7 @@ Expand sections below for information on how these models were exported and test
         batch=1,
         imgsz=640,
         verbose=False,
-        device="cuda"
+        device="cuda",
     )
     ```
 

docs/en/integrations/tf-graphdef.md

@@ -81,16 +81,16 @@ Before diving into the usage instructions, it's important to note that while all
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to TF GraphDef format
-        model.export(format='pb')  # creates 'yolov8n.pb'
+        model.export(format="pb")  # creates 'yolov8n.pb'
 
         # Load the exported TF GraphDef model
-        tf_graphdef_model = YOLO('yolov8n.pb')
+        tf_graphdef_model = YOLO("yolov8n.pb")
 
         # Run inference
-        results = tf_graphdef_model('https://ultralytics.com/images/bus.jpg')
+        results = tf_graphdef_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"

docs/en/integrations/tf-savedmodel.md

@@ -75,16 +75,16 @@ Before diving into the usage instructions, it's important to note that while all
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to TF SavedModel format
-        model.export(format='saved_model')  # creates '/yolov8n_saved_model'
+        model.export(format="saved_model")  # creates '/yolov8n_saved_model'
 
         # Load the exported TF SavedModel model
-        tf_savedmodel_model = YOLO('./yolov8n_saved_model')
+        tf_savedmodel_model = YOLO("./yolov8n_saved_model")
 
         # Run inference
-        results = tf_savedmodel_model('https://ultralytics.com/images/bus.jpg')
+        results = tf_savedmodel_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"

docs/en/integrations/tfjs.md

@@ -73,16 +73,16 @@ Before diving into the usage instructions, it's important to note that while all
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to TF.js format
-        model.export(format='tfjs')  # creates '/yolov8n_web_model'
+        model.export(format="tfjs")  # creates '/yolov8n_web_model'
 
         # Load the exported TF.js model
-        tfjs_model = YOLO('./yolov8n_web_model')
+        tfjs_model = YOLO("./yolov8n_web_model")
 
         # Run inference
-        results = tfjs_model('https://ultralytics.com/images/bus.jpg')
+        results = tfjs_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"

docs/en/integrations/tflite.md

@@ -77,18 +77,18 @@ Before diving into the usage instructions, it's important to note that while all
 
           ```python
           from ultralytics import YOLO
-          
+
           # Load the YOLOv8 model
-          model = YOLO('yolov8n.pt')
-          
+          model = YOLO("yolov8n.pt")
+
           # Export the model to TFLite format
-          model.export(format='tflite') # creates 'yolov8n_float32.tflite'
-          
+          model.export(format="tflite")  # creates 'yolov8n_float32.tflite'
+
           # Load the exported TFLite model
-          tflite_model = YOLO('yolov8n_float32.tflite')
-          
+          tflite_model = YOLO("yolov8n_float32.tflite")
+
           # Run inference
-          results = tflite_model('https://ultralytics.com/images/bus.jpg')
+          results = tflite_model("https://ultralytics.com/images/bus.jpg")
           ```
 
     === "CLI"

docs/en/integrations/torchscript.md

@@ -83,16 +83,16 @@ Before diving into the usage instructions, it's important to note that while all
         from ultralytics import YOLO
 
         # Load the YOLOv8 model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Export the model to TorchScript format
-        model.export(format='torchscript')  # creates 'yolov8n.torchscript'
+        model.export(format="torchscript")  # creates 'yolov8n.torchscript'
 
         # Load the exported TorchScript model
-        torchscript_model = YOLO('yolov8n.torchscript')
+        torchscript_model = YOLO("yolov8n.torchscript")
 
         # Run inference
-        results = torchscript_model('https://ultralytics.com/images/bus.jpg')
+        results = torchscript_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"

docs/en/integrations/weights-biases.md

@@ -63,9 +63,9 @@ Before diving into the usage instructions for YOLOv8 model training with Weights
 
     === "Python"
        ```python
+       import wandb
        from ultralytics import YOLO
        from wandb.integration.ultralytics import add_wandb_callback
-       import wandb
 
        # Step 1: Initialize a Weights & Biases run
        wandb.init(project="ultralytics", job_type="training")