Docs Ruff codeblocks reformat and fix (ultralytics#12847)

Signed-off-by: Glenn Jocher <[email protected]>

docs/en/datasets/pose/tiger-pose.md

@@ -80,17 +80,17 @@ The example showcases the variety and complexity of the images in the Tiger-Pose
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('path/to/best.pt')  # load a tiger-pose trained model
+        model = YOLO("path/to/best.pt")  # load a tiger-pose trained model
 
         # Run inference
-        results = model.predict(source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True)
+        results = model.predict(source="https://youtu.be/MIBAT6BGE6U", show=True)
         ```
 
     === "CLI"
 
         ```bash
         # Run inference using a tiger-pose trained model
-        yolo task=pose mode=predict source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True model="path/to/best.pt"
+        yolo task=pose mode=predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"
         ```
 
 ## Citations and Acknowledgments

docs/en/guides/index.md

@@ -35,7 +35,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Conda Quickstart](conda-quickstart.md) 🚀 NEW: Step-by-step guide to setting up a [Conda](https://anaconda.org/conda-forge/ultralytics) environment for Ultralytics. Learn how to install and start using the Ultralytics package efficiently with Conda.
 - [Docker Quickstart](docker-quickstart.md) 🚀 NEW: Complete guide to setting up and using Ultralytics YOLO models with [Docker](https://hub.docker.com/r/ultralytics/ultralytics). Learn how to install Docker, manage GPU support, and run YOLO models in isolated containers for consistent development and deployment.
 - [Raspberry Pi](raspberry-pi.md) 🚀 NEW: Quickstart tutorial to run YOLO models to the latest Raspberry Pi hardware.
-- [Nvidia-Jetson](nvidia-jetson.md)🚀 NEW: Quickstart guide for deploying YOLO models on Nvidia Jetson devices.
+- [NVIDIA-Jetson](nvidia-jetson.md)🚀 NEW: Quickstart guide for deploying YOLO models on NVIDIA Jetson devices.
 - [Triton Inference Server Integration](triton-inference-server.md) 🚀 NEW: Dive into the integration of Ultralytics YOLOv8 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
 - [YOLO Thread-Safe Inference](yolo-thread-safe-inference.md) 🚀 NEW: Guidelines for performing inference with YOLO models in a thread-safe manner. Learn the importance of thread safety and best practices to prevent race conditions and ensure consistent predictions.
 - [Isolating Segmentation Objects](isolating-segmentation-objects.md) 🚀 NEW: Step-by-step recipe and explanation on how to extract and/or isolate objects from images using Ultralytics Segmentation.

docs/en/guides/isolating-segmentation-objects.md

@@ -63,13 +63,12 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
     # (2) Iterate detection results (helpful for multiple images)
     for r in res:
         img = np.copy(r.orig_img)
-        img_name = Path(r.path).stem # source image base-name
+        img_name = Path(r.path).stem  # source image base-name
 
         # Iterate each object contour (multiple detections)
-        for ci,c in enumerate(r):
+        for ci, c in enumerate(r):
             # (1) Get detection class name
             label = c.names[c.boxes.cls.tolist().pop()]
-
     ```
 
     1. To learn more about working with detection results, see [Boxes Section for Predict Mode](../modes/predict.md#boxes).
@@ -98,12 +97,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
 
     # Draw contour onto mask
-    _ = cv2.drawContours(b_mask,
-                        [contour],
-                        -1,
-                        (255, 255, 255),
-                        cv2.FILLED)
-
+    _ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)
     ```
 
     1. For more info on `c.masks.xy` see [Masks Section from Predict Mode](../modes/predict.md#masks).
@@ -280,16 +274,16 @@ import cv2
 import numpy as np
 from ultralytics import YOLO
 
-m = YOLO('yolov8n-seg.pt')#(4)!
-res = m.predict()#(3)!
+m = YOLO("yolov8n-seg.pt")  # (4)!
+res = m.predict()  # (3)!
 
-# iterate detection results (5)
+# Iterate detection results (5)
 for r in res:
     img = np.copy(r.orig_img)
     img_name = Path(r.path).stem
 
-    # iterate each object contour (6)
-    for ci,c in enumerate(r):
+    # Iterate each object contour (6)
+    for ci, c in enumerate(r):
         label = c.names[c.boxes.cls.tolist().pop()]
 
         b_mask = np.zeros(img.shape[:2], np.uint8)
@@ -312,7 +306,6 @@ for r in res:
         iso_crop = isolated[y1:y2, x1:x2]
 
         # TODO your actions go here (2)
-
 ```
 
 1. The line populating `contour` is combined into a single line here, where it was split to multiple above.

docs/en/guides/view-results-in-terminal.md

@@ -61,9 +61,9 @@ The VSCode compatible protocols for viewing images using the integrated terminal
 
     # Run inference on an image
     results = model.predict(source="ultralytics/assets/bus.jpg")
-    
+
     # Plot inference results
-    plot = results[0].plot() #(1)!
+    plot = results[0].plot()  # (1)!
     ```
 
     1. See [plot method parameters](../modes/predict.md#plot-method-parameters) to see possible arguments to use.
@@ -73,9 +73,9 @@ The VSCode compatible protocols for viewing images using the integrated terminal
     ```{ .py .annotate }
     # Results image as bytes
     im_bytes = cv.imencode(
-        ".png", #(1)!
+        ".png",  # (1)!
         plot,
-        )[1].tobytes() #(2)!
+    )[1].tobytes()  # (2)!
 
     # Image bytes as a file-like object
     mem_file = io.BytesIO(im_bytes)
@@ -110,9 +110,8 @@ The VSCode compatible protocols for viewing images using the integrated terminal
 import io
 
 import cv2 as cv
-
-from ultralytics import YOLO
 from sixel import SixelWriter
+from ultralytics import YOLO
 
 # Load a model
 model = YOLO("yolov8n.pt")
@@ -121,13 +120,13 @@ model = YOLO("yolov8n.pt")
 results = model.predict(source="ultralytics/assets/bus.jpg")
 
 # Plot inference results
-plot = results[0].plot() #(3)!
+plot = results[0].plot()  # (3)!
 
 # Results image as bytes
 im_bytes = cv.imencode(
-    ".png", #(1)!
+    ".png",  # (1)!
     plot,
-    )[1].tobytes() #(2)!
+)[1].tobytes()  # (2)!
 
 mem_file = io.BytesIO(im_bytes)
 w = SixelWriter()

docs/en/integrations/tensorboard.md

@@ -39,15 +39,15 @@ TensorBoard is conveniently pre-installed with YOLOv8, eliminating the need for
 
 For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
 
-## Configuring TensorBoard for Google Collab
+## Configuring TensorBoard for Google Colab
 
 When using Google Colab, it's important to set up TensorBoard before starting your training code:
 
-!!! Example "Configure TensorBoard for Google Collab"
+!!! Example "Configure TensorBoard for Google Colab"
 
     === "Python"
 
-        ```python
+        ```ipython
         %load_ext tensorboard
         %tensorboard --logdir path/to/runs
         ```

docs/en/integrations/tensorrt.md

@@ -153,15 +153,16 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
         model = YOLO("yolov8n.pt")
         model.export(
             format="engine",
-            dynamic=True, #(1)!
-            batch=8, #(2)!
-            workspace=4, #(3)!
+            dynamic=True,  # (1)!
+            batch=8,  # (2)!
+            workspace=4,  # (3)!
             int8=True,
-            data="coco.yaml", #(4)!
+            data="coco.yaml",  # (4)!
         )
 
         # Load the exported TensorRT INT8 model
         model = YOLO("yolov8n.engine", task="detect")
+
         # Run inference
         result = model.predict("https://ultralytics.com/images/bus.jpg")
         ```
@@ -385,36 +386,14 @@ Expand sections below for information on how these models were exported and test
     model = YOLO("yolov8n.pt")
 
     # TensorRT FP32
-    out = model.export(
-        format="engine",
-        imgsz:640,
-        dynamic:True,
-        verbose:False,
-        batch:8,
-        workspace:2
-    )
-
+    out = model.export(format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2)
+
     # TensorRT FP16
+    out = model.export(format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2, half=True)
+
+    # TensorRT INT8 with calibration `data` (i.e. COCO, ImageNet, or DOTAv1 for appropriate model task)
     out = model.export(
-        format="engine",
-        imgsz:640,
-        dynamic:True,
-        verbose:False,
-        batch:8,
-        workspace:2,
-        half=True
-    )
-
-    # TensorRT INT8
-    out = model.export(
-        format="engine",
-        imgsz:640,
-        dynamic:True,
-        verbose:False,
-        batch:8,
-        workspace:2,
-        int8=True,
-        data:"data.yaml"  # COCO, ImageNet, or DOTAv1 for appropriate model task
+        format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2, int8=True, data="coco8.yaml"
     )
     ```
 

docs/en/usage/python.md

@@ -87,23 +87,31 @@ Val mode is used for validating a YOLOv8 model after it has been trained. In thi
     === "Val after training"
 
         ```python
-          from ultralytics import YOLO
+        from ultralytics import YOLO
+
+        # Load a YOLOv8 model
+        model = YOLO("yolov8n.yaml")
 
-          model = YOLO('yolov8n.yaml')
-          model.train(data='coco8.yaml', epochs=5)
-          model.val()  # It'll automatically evaluate the data you trained.
+        # Train the model
+        model.train(data="coco8.yaml", epochs=5)
+
+        # Validate on training data
+        model.val()
         ```
 
-    === "Val independently"
+    === "Val on another dataset"
 
         ```python
-          from ultralytics import YOLO
+        from ultralytics import YOLO
 
-          model = YOLO("model.pt")
-          # It'll use the data YAML file in model.pt if you don't set data.
-          model.val()
-          # or you can set the data you want to val
-          model.val(data='coco8.yaml')
+        # Load a YOLOv8 model
+        model = YOLO("yolov8n.yaml")
+
+        # Train the model
+        model.train(data="coco8.yaml", epochs=5)
+
+        # Validate on separate data
+        model.val(data="path/to/separate/data.yaml")
         ```
 
 [Val Examples](../modes/val.md){ .md-button }
@@ -188,20 +196,20 @@ Export mode is used for exporting a YOLOv8 model to a format that can be used fo
 
         Export an official YOLOv8n model to ONNX with dynamic batch-size and image-size.
         ```python
-          from ultralytics import YOLO
+        from ultralytics import YOLO
 
-          model = YOLO('yolov8n.pt')
-          model.export(format='onnx', dynamic=True)
+        model = YOLO("yolov8n.pt")
+        model.export(format="onnx", dynamic=True)
         ```
 
     === "Export to TensorRT"
 
         Export an official YOLOv8n model to TensorRT on `device=0` for acceleration on CUDA devices.
         ```python
-          from ultralytics import YOLO
+        from ultralytics import YOLO
 
-          model = YOLO('yolov8n.pt')
-          model.export(format='onnx', device=0)
+        model = YOLO("yolov8n.pt")
+        model.export(format="onnx", device=0)
         ```
 
 [Export Examples](../modes/export.md){ .md-button }