Docs partial mdformat improvements (#7378)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/en/usage/cfg.md

@@ -51,10 +51,10 @@ Default `ARG` values are defined on this page from the `cfg/defaults.yaml` [file
 
 YOLO models can be used for a variety of tasks, including detection, segmentation, classification and pose. These tasks differ in the type of output they produce and the specific problem they are designed to solve.
 
-**Detect**: For identifying and localizing objects or regions of interest in an image or video.
-**Segment**: For dividing an image or video into regions or pixels that correspond to different objects or classes.
-**Classify**: For predicting the class label of an input image.
-**Pose**: For identifying objects and estimating their keypoints in an image or video.
+- **Detect**: For identifying and localizing objects or regions of interest in an image or video.
+- **Segment**: For dividing an image or video into regions or pixels that correspond to different objects or classes.
+- **Classify**: For predicting the class label of an input image.
+- **Pose**: For identifying objects and estimating their keypoints in an image or video.
 
 | Key    | Value      | Description                                     |
 |--------|------------|-------------------------------------------------|
@@ -66,12 +66,12 @@ YOLO models can be used for a variety of tasks, including detection, segmentatio
 
 YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes include:
 
-**Train**: For training a YOLOv8 model on a custom dataset.
-**Val**: For validating a YOLOv8 model after it has been trained.
-**Predict**: For making predictions using a trained YOLOv8 model on new images or videos.
-**Export**: For exporting a YOLOv8 model to a format that can be used for deployment.
-**Track**: For tracking objects in real-time using a YOLOv8 model.
-**Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy.
+- **Train**: For training a YOLOv8 model on a custom dataset.
+- **Val**: For validating a YOLOv8 model after it has been trained.
+- **Predict**: For making predictions using a trained YOLOv8 model on new images or videos.
+- **Export**: For exporting a YOLOv8 model to a format that can be used for deployment.
+- **Track**: For tracking objects in real-time using a YOLOv8 model.
+- **Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy.
 
 | Key    | Value     | Description                                                   |
 |--------|-----------|---------------------------------------------------------------|

docs/en/usage/engine.md

@@ -21,8 +21,8 @@ Both the Ultralytics YOLO command-line and Python interfaces are simply a high-l
 
 BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overriding the required functions or operations as long the as correct formats are followed. For example, you can support your own custom model and dataloader by just overriding these functions:
 
-* `get_model(cfg, weights)` - The function that builds the model to be trained
-* `get_dataloader()` - The function that builds the dataloader More details and source code can be found in [`BaseTrainer` Reference](../reference/engine/trainer.md)
+- `get_model(cfg, weights)` - The function that builds the model to be trained
+- `get_dataloader()` - The function that builds the dataloader More details and source code can be found in [`BaseTrainer` Reference](../reference/engine/trainer.md)
 
 ## DetectionTrainer
 
@@ -55,9 +55,8 @@ trainer.train()
 
 You now realize that you need to customize the trainer further to:
 
-* Customize the `loss function`.
-* Add `callback` that uploads model to your Google Drive after every 10 `epochs`
-  Here's how you can do it:
+- Customize the `loss function`.
+- Add `callback` that uploads model to your Google Drive after every 10 `epochs` Here's how you can do it:
 
 ```python
 from ultralytics.models.yolo.detect import DetectionTrainer
@@ -89,5 +88,4 @@ To know more about Callback triggering events and entry point, checkout our [Cal
 
 ## Other engine components
 
-There are other components that can be customized similarly like `Validators` and `Predictors`
-See Reference section for more information on these.
+There are other components that can be customized similarly like `Validators` and `Predictors`. See Reference section for more information on these.

docs/en/usage/python.md

@@ -224,8 +224,7 @@ Track mode is used for tracking objects in real-time using a YOLOv8 model. In th
 
 ## [Benchmark](../modes/benchmark.md)
 
-Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation)
-or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.
+Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation) or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.
 
 !!! Example "Benchmark"