Update YOLOv3 and YOLOv5 YAMLs (#7574)

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

docs/en/models/fast-sam.md

@@ -122,17 +122,20 @@ FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/Fa
 ### Installation
 
 1. Clone the FastSAM repository:
+
    ```shell
    git clone https://github.com/CASIA-IVA-Lab/FastSAM.git
    ```
 
 2. Create and activate a Conda environment with Python 3.9:
+
    ```shell
    conda create -n FastSAM python=3.9
    conda activate FastSAM
    ```
 
 3. Navigate to the cloned repository and install the required packages:
+
    ```shell
    cd FastSAM
    pip install -r requirements.txt
@@ -149,25 +152,28 @@ FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/Fa
 
 2. Use FastSAM for inference. Example commands:
 
-    - Segment everything in an image:
-      ```shell
-      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg
-      ```
+   - Segment everything in an image:
 
-    - Segment specific objects using text prompt:
-      ```shell
-      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog"
-      ```
+     ```shell
+     python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg
+     ```
 
-    - Segment objects within a bounding box (provide box coordinates in xywh format):
-      ```shell
-      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]"
-      ```
+   - Segment specific objects using text prompt:
 
-    - Segment objects near specific points:
-      ```shell
-      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --point_prompt "[[520,360],[620,300]]" --point_label "[1,0]"
-      ```
+     ```shell
+     python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog"
+     ```
+
+   - Segment objects within a bounding box (provide box coordinates in xywh format):
+
+     ```shell
+     python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]"
+     ```
+
+   - Segment objects near specific points:
+     ```shell
+     python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --point_prompt "[[520,360],[620,300]]" --point_label "[1,0]"
+     ```
 
 Additionally, you can try FastSAM through a [Colab demo](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing) or on the [HuggingFace web demo](https://huggingface.co/spaces/An-619/FastSAM) for a visual experience.
 

docs/en/models/rtdetr.md

@@ -89,4 +89,4 @@ If you use Baidu's RT-DETR in your research or development work, please cite the
 
 We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
 
-*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*
+_Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API_

docs/en/models/sam.md

@@ -222,4 +222,4 @@ If you find SAM useful in your research or development work, please consider cit
 
 We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community.
 
-*keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI.*
+_keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI._

docs/en/models/yolo-nas.md

@@ -117,4 +117,4 @@ If you employ YOLO-NAS in your research or development work, please cite SuperGr
 
 We express our gratitude to Deci AI's [SuperGradients](https://github.com/Deci-AI/super-gradients/) team for their efforts in creating and maintaining this valuable resource for the computer vision community. We believe YOLO-NAS, with its innovative architecture and superior object detection capabilities, will become a critical tool for developers and researchers alike.
 
-*Keywords: YOLO-NAS, Deci AI, object detection, deep learning, neural architecture search, Ultralytics Python API, YOLO model, SuperGradients, pre-trained models, quantization-friendly basic block, advanced training schemes, post-training quantization, AutoNAC optimization, COCO, Objects365, Roboflow 100*
+_Keywords: YOLO-NAS, Deci AI, object detection, deep learning, neural architecture search, Ultralytics Python API, YOLO model, SuperGradients, pre-trained models, quantization-friendly basic block, advanced training schemes, post-training quantization, AutoNAC optimization, COCO, Objects365, Roboflow 100_