ultralytics 8.2.29 new fractional AutoBatch feature (#13446)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/cfg/models/README.md

@@ -1,6 +1,6 @@
 ## Models
 
-Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.
+Welcome to the [Ultralytics](https://ultralytics.com) Models directory! Here you will find a wide variety of pre-configured model configuration files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.
 
 These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms, from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this directory provides a great starting point for your custom model development needs.
 
@@ -8,26 +8,34 @@ To get started, simply browse through the models in this directory and find one
 
 ### Usage
 
-Model `*.yaml` files may be used directly in the Command Line Interface (CLI) with a `yolo` command:
+Model `*.yaml` files may be used directly in the [Command Line Interface (CLI)](https://docs.ultralytics.com/usage/cli) with a `yolo` command:
 
 ```bash
+# Train a YOLOv8n model using the coco8 dataset for 100 epochs
 yolo task=detect mode=train model=yolov8n.yaml data=coco8.yaml epochs=100
 ```
 
-They may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
+They may also be used directly in a Python environment, and accept the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
 
 ```python
 from ultralytics import YOLO
 
-model = YOLO("model.yaml")  # build a YOLOv8n model from scratch
-# YOLO("model.pt")  use pre-trained model if available
-model.info()  # display model information
-model.train(data="coco8.yaml", epochs=100)  # train the model
+# Initialize a YOLOv8n model from a YAML configuration file
+model = YOLO("model.yaml")
+
+# If a pre-trained model is available, use it instead
+# model = YOLO("model.pt")
+
+# Display model information
+model.info()
+
+# Train the model using the COCO8 dataset for 100 epochs
+model.train(data="coco8.yaml", epochs=100)
 ```
 
 ## Pre-trained Model Architectures
 
-Ultralytics supports many model architectures. Visit https://docs.ultralytics.com/models to view detailed information and usage. Any of these models can be used by loading their configs or pretrained checkpoints if available.
+Ultralytics supports many model architectures. Visit [Ultralytics Models](https://docs.ultralytics.com/models) to view detailed information and usage. Any of these models can be used by loading their configurations or pretrained checkpoints if available.
 
 ## Contribute New Models