Add Ultralytics tasks and YOLO-NAS models (#2735)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/models/yolov8.md

@@ -9,6 +9,8 @@ description: Learn about YOLOv8's pre-trained weights supporting detection, inst
 
 YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces new features and optimizations that make it an ideal choice for various object detection tasks in a wide range of applications.
 
+![Ultralytics YOLOv8](https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png)
+
 ## Key Features
 
 - **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved feature extraction and object detection performance.
@@ -76,7 +78,6 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
         | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |
         | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |
 
-
 ## Usage
 
 You can use YOLOv8 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv8 models for inference:
@@ -94,7 +95,6 @@ results = model('image.jpg')
 results.print()
 ```
 
-
 ## Citation
 
 If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format: