Reformat Docs and YAMLs (#12806)

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

docs/en/guides/docker-quickstart.md

@@ -93,12 +93,16 @@ sudo docker pull $t
 ## Running Ultralytics in Docker Container
 
 Here's how to execute the Ultralytics Docker container:
+
 ### Using only the CPU
+
 ```bash
 # Run with all GPUs
 sudo docker run -it --ipc=host $t
 ```
+
 ### Using GPUs
+
 ```bash
 # Run with all GPUs
 sudo docker run -it --ipc=host --gpus all $t
@@ -109,7 +113,6 @@ sudo docker run -it --ipc=host --gpus '"device=2,3"' $t
 
 The `-it` flag assigns a pseudo-TTY and keeps stdin open, allowing you to interact with the container. The `--ipc=host` flag enables sharing of host's IPC namespace, essential for sharing memory between processes. The `--gpus` flag allows the container to access the host's GPUs.
 
-
 ## Running Ultralytics in Docker Container
 
 Here's how to execute the Ultralytics Docker container:

docs/en/guides/nvidia-jetson.md

@@ -23,7 +23,7 @@ NVIDIA Jetson is a series of embedded computing boards designed to bring acceler
 [Jetson Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/) is the latest iteration of the NVIDIA Jetson family based on NVIDIA Ampere architecture which brings drastically improved AI performance when compared to the previous generations. Below table compared few of the Jetson devices in the ecosystem.
 
 |                   | Jetson AGX Orin 64GB                                             | Jetson Orin NX 16GB                                             | Jetson Orin Nano 8GB                                          | Jetson AGX Xavier                                           | Jetson Xavier NX                                             | Jetson Nano                                 |
-| ----------------- | ---------------------------------------------------------------- | --------------------------------------------------------------- | ------------------------------------------------------------- | ----------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------- |
+|-------------------|------------------------------------------------------------------|-----------------------------------------------------------------|---------------------------------------------------------------|-------------------------------------------------------------|--------------------------------------------------------------|---------------------------------------------|
 | AI Performance    | 275 TOPS                                                         | 100 TOPS                                                        | 40 TOPs                                                       | 32 TOPS                                                     | 21 TOPS                                                      | 472 GFLOPS                                  |
 | GPU               | 2048-core NVIDIA Ampere architecture GPU with 64 Tensor Cores    | 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores   | 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores | 512-core NVIDIA Volta architecture GPU with 64 Tensor Cores | 384-core NVIDIA Volta™ architecture GPU with 48 Tensor Cores | 128-core NVIDIA Maxwell™ architecture GPU   |
 | GPU Max Frequency | 1.3 GHz                                                          | 918 MHz                                                         | 625 MHz                                                       | 1377 MHz                                                    | 1100 MHz                                                     | 921MHz                                      |

docs/en/guides/vision-eye.md

@@ -167,7 +167,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
 ### `visioneye` Arguments
 
 | Name        | Type    | Default          | Description                    |
-|---------------|---------|------------------|--------------------------------------------------|
+|-------------|---------|------------------|--------------------------------|
 | `color`     | `tuple` | `(235, 219, 11)` | Line and object centroid color |
 | `pin_color` | `tuple` | `(255, 0, 255)`  | VisionEye pinpoint color       |
 

docs/en/hub/inference-api.md

@@ -70,7 +70,7 @@ curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
 See the table below for a full list of available inference arguments.
 
 | Argument     | Default | Type    | Description                            |
-| ------------ | ------- | ------- | -------------------------------------- |
+|--------------|---------|---------|----------------------------------------|
 | `image`      |         | `image` | image file                             |
 | `url`        |         | `str`   | URL of the image if not passing a file |
 | `size`       | `640`   | `int`   | valid range `32` - `1280` pixels       |
@@ -91,10 +91,10 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         from ultralytics import YOLO
 
         # Load model
-        model = YOLO('yolov8n-cls.pt')
+        model = YOLO("yolov8n-cls.pt")
 
         # Run inference
-        results = model('image.jpg')
+        results = model("image.jpg")
 
         # Print image.jpg results in JSON format
         print(results[0].tojson())
@@ -159,10 +159,10 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         from ultralytics import YOLO
 
         # Load model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Run inference
-        results = model('image.jpg')
+        results = model("image.jpg")
 
         # Print image.jpg results in JSON format
         print(results[0].tojson())
@@ -231,10 +231,10 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         from ultralytics import YOLO
 
         # Load model
-        model = YOLO('yolov8n-obb.pt')
+        model = YOLO("yolov8n-obb.pt")
 
         # Run inference
-        results = model('image.jpg')
+        results = model("image.jpg")
 
         # Print image.jpg results in JSON format
         print(results[0].tojson())
@@ -305,10 +305,10 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         from ultralytics import YOLO
 
         # Load model
-        model = YOLO('yolov8n-seg.pt')
+        model = YOLO("yolov8n-seg.pt")
 
         # Run inference
-        results = model('image.jpg')
+        results = model("image.jpg")
 
         # Print image.jpg results in JSON format
         print(results[0].tojson())
@@ -374,10 +374,10 @@ The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JS
         from ultralytics import YOLO
 
         # Load model
-        model = YOLO('yolov8n-pose.pt')
+        model = YOLO("yolov8n-pose.pt")
 
         # Run inference
-        results = model('image.jpg')
+        results = model("image.jpg")
 
         # Print image.jpg results in JSON format
         print(results[0].tojson())

docs/en/integrations/tensorrt.md

@@ -182,7 +182,6 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
         yolo predict model=yolov8n.engine source='https://ultralytics.com/images/bus.jpg'
         ```
 
-
 ???+ warning "Calibration Cache"
 
     TensorRT will generate a calibration `.cache` which can be re-used to speed up export of future model weights using the same data, but this may result in poor calibration when the data is vastly different or if the `batch` value is changed drastically. In these circumstances, the existing `.cache` should be renamed and moved to a different directory or deleted entirely.
@@ -467,7 +466,6 @@ Having successfully exported your Ultralytics YOLOv8 models to TensorRT format,
 
 - **[GitHub Repository for NVIDIA TensorRT:](https://github.com/NVIDIA/TensorRT)**: This is the official GitHub repository that contains the source code and documentation for NVIDIA TensorRT.
 
-
 ## Summary
 
 In this guide, we focused on converting Ultralytics YOLOv8 models to NVIDIA's TensorRT model format. This conversion step is crucial for improving the efficiency and speed of YOLOv8 models, making them more effective and suitable for diverse deployment environments.

docs/en/modes/predict.md

@@ -404,7 +404,7 @@ YOLOv8 supports various image and video formats, as specified in [ultralytics/da
 The below table contains valid Ultralytics image formats.
 
 | Image Suffixes | Example Predict Command          | Reference                                                                  |
-|----------------|----------------------------------|-------------------------------------------------------------------------------|
+|----------------|----------------------------------|----------------------------------------------------------------------------|
 | `.bmp`         | `yolo predict source=image.bmp`  | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format) |
 | `.dng`         | `yolo predict source=image.dng`  | [Adobe DNG](https://helpx.adobe.com/camera-raw/digital-negative.html)      |
 | `.jpeg`        | `yolo predict source=image.jpeg` | [JPEG](https://en.wikipedia.org/wiki/JPEG)                                 |

ultralytics/cfg/models/v9/yolov9c-seg.yaml

@@ -19,11 +19,11 @@ backbone:
   - [-1, 1, SPPELAN, [512, 256]] # 9
 
 head:
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
   - [[-1, 6], 1, Concat, [1]] # cat backbone P4
   - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 12
 
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
   - [[-1, 4], 1, Concat, [1]] # cat backbone P3
   - [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]] # 15 (P3/8-small)
 

ultralytics/cfg/models/v9/yolov9c.yaml

@@ -19,11 +19,11 @@ backbone:
   - [-1, 1, SPPELAN, [512, 256]] # 9
 
 head:
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
   - [[-1, 6], 1, Concat, [1]] # cat backbone P4
   - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 12
 
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
   - [[-1, 4], 1, Concat, [1]] # cat backbone P3
   - [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]] # 15 (P3/8-small)
 

ultralytics/cfg/models/v9/yolov9e-seg.yaml

@@ -42,11 +42,11 @@ backbone:
 
 # gelan head
 head:
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
   - [[-1, 25], 1, Concat, [1]] # cat backbone P4
   - [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 32
 
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
   - [[-1, 22], 1, Concat, [1]] # cat backbone P3
   - [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]] # 35 (P3/8-small)
 

ultralytics/cfg/models/v9/yolov9e.yaml

@@ -42,11 +42,11 @@ backbone:
 
 # gelan head
 head:
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
   - [[-1, 25], 1, Concat, [1]] # cat backbone P4
   - [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 32
 
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
   - [[-1, 22], 1, Concat, [1]] # cat backbone P3
   - [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]] # 35 (P3/8-small)