Reformat Docs and YAMLs (#12806)

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

docs/en/integrations/tensorrt.md

@@ -111,7 +111,7 @@ For more details about the export process, visit the [Ultralytics documentation
 
 ### Exporting TensorRT with INT8 Quantization
 
-Exporting Ultralytics YOLO models using TensorRT with INT8 precision executes post-training quantization (PTQ). TensorRT uses calibration for PTQ, which measures the distribution of activations within each activation tensor as the YOLO model processes inference on representative input data, and then uses that distribution to estimate scale values for each tensor. Each activation tensor that is a candidate for quantization has an associated scale that is deduced by a calibration process. 
+Exporting Ultralytics YOLO models using TensorRT with INT8 precision executes post-training quantization (PTQ). TensorRT uses calibration for PTQ, which measures the distribution of activations within each activation tensor as the YOLO model processes inference on representative input data, and then uses that distribution to estimate scale values for each tensor. Each activation tensor that is a candidate for quantization has an associated scale that is deduced by a calibration process.
 
 When processing implicitly quantized networks TensorRT uses INT8 opportunistically to optimize layer execution time. If a layer runs faster in INT8 and has assigned quantization scales on its data inputs and outputs, then a kernel with INT8 precision is assigned to that layer, otherwise TensorRT selects a precision of either FP32 or FP16 for the kernel based on whichever results in faster execution time for that layer.
 
@@ -123,20 +123,20 @@ When processing implicitly quantized networks TensorRT uses INT8 opportunistical
 
 The arguments provided when using [export](../modes/export.md) for an Ultralytics YOLO model will **greatly** influence the performance of the exported model. They will also need to be selected based on the device resources available, however the default arguments _should_ work for most [Ampere (or newer) NVIDIA discrete GPUs](https://developer.nvidia.com/blog/nvidia-ampere-architecture-in-depth/). The calibration algorithm used is `"ENTROPY_CALIBRATION_2"` and you can read more details about the options available [in the TensorRT Developer Guide](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#enable_int8_c). Ultralytics tests found that `"ENTROPY_CALIBRATION_2"` was the best choice and exports are fixed to using this algorithm.
 
-  - `workspace` : Controls the size (in GiB) of the device memory allocation while converting the model weights.
-    
+- `workspace` : Controls the size (in GiB) of the device memory allocation while converting the model weights.
+
     - Aim to use the <u>minimum</u> `workspace` value required as this prevents testing algorithms that require more `workspace` from being considered by the TensorRT builder. Setting a higher value for `workspace` may take **considerably longer** to calibrate and export.
 
     - Default is `workspace=4` (GiB), this value may need to be increased if calibration crashes (exits without warning).
-    
+
     - TensorRT will report `UNSUPPORTED_STATE` during export if the value for `workspace` is larger than the memory available to the device, which means the value for `workspace` should be lowered.
-    
+
     - If `workspace` is set to max value and calibration fails/crashes, consider reducing the values for `imgsz` and `batch` to reduce memory requirements.
 
     - <u><b>Remember</b> calibration for INT8 is specific to each device</u>, borrowing a "high-end" GPU for calibration, might result in poor performance when inference is run on another device.
 
-  - `batch` : The maximum batch-size that will be used for inference. During inference smaller batches can be used, but inference will not accept batches any larger than what is specified.
-    
+- `batch` : The maximum batch-size that will be used for inference. During inference smaller batches can be used, but inference will not accept batches any larger than what is specified.
+
     !!! note
 
         During calibration, twice the `batch` size provided will be used. Using small batches can lead to inaccurate scaling during calibration. This is because the process adjusts based on the data it sees. Small batches might not capture the full range of values, leading to issues with the final calibration, so the `batch` size is doubled automatically. If no batch size is specified `batch=1`, calibration will be run at `batch=1 * 2` to reduce calibration scaling errors.
@@ -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.