Add new Colab Notebooks badges to Docs (#18575)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>

docs/en/datasets/detect/brain-tumor.md

@@ -6,6 +6,8 @@ keywords: brain tumor dataset, MRI scans, CT scans, brain tumor detection, medic
 
 # Brain Tumor Dataset
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-brain-tumor-detection-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Brain Tumor Dataset In Colab"></a>
+
 A brain tumor detection dataset consists of medical images from MRI or CT scans, containing information about brain tumor presence, location, and characteristics. This dataset is essential for training [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) algorithms to automate brain tumor identification, aiding in early diagnosis and treatment planning.
 
 <p align="center">

docs/en/datasets/detect/medical-pills.md

@@ -6,6 +6,8 @@ keywords: medical-pills dataset, pill detection, pharmaceutical imaging, AI in h
 
 # Medical Pills Dataset
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Medical Pills Dataset In Colab"></a>
+
 The medical-pills detection dataset is a proof-of-concept (POC) dataset, carefully curated to demonstrate the potential of AI in pharmaceutical applications. It contains labeled images specifically designed to train [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) [models](https://docs.ultralytics.com/models/) for identifying medical-pills. This dataset serves as a foundational resource for automating essential [tasks](https://docs.ultralytics.com/tasks/) such as quality control, packaging automation, and efficient sorting in pharmaceutical workflows. By integrating this dataset into projects, researchers and developers can explore innovative [solutions](https://docs.ultralytics.com/solutions/) that enhance [accuracy](https://www.ultralytics.com/glossary/accuracy), streamline operations, and ultimately contribute to improved healthcare outcomes.
 
 ## Dataset Structure

docs/en/datasets/segment/carparts-seg.md

@@ -6,6 +6,8 @@ keywords: Carparts Segmentation Dataset, Roboflow, computer vision, automotive A
 
 # Roboflow Universe Carparts Segmentation Dataset
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-carparts-segmentation-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Carparts Segmentation Dataset In Colab"></a>
+
 The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) is a curated collection of images and videos designed for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications, specifically focusing on segmentation tasks related to car parts. This dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable annotated examples for training and testing segmentation models.
 
 Whether you're working on automotive research, developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing accuracy and efficiency in your projects.

docs/en/datasets/segment/package-seg.md

@@ -6,6 +6,8 @@ keywords: Roboflow, Package Segmentation Dataset, computer vision, package ident
 
 # Roboflow Universe Package Segmentation Dataset
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-package-segmentation-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Package Segmentation Dataset In Colab"></a>
+
 The [Roboflow](https://roboflow.com/?ref=ultralytics) [Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images specifically tailored for tasks related to package segmentation in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv). This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling.
 
 Containing a diverse set of images showcasing various packages in different contexts and environments, the dataset serves as a valuable resource for training and evaluating segmentation models. Whether you are engaged in logistics, warehouse automation, or any application requiring precise package analysis, the Package Segmentation Dataset provides a targeted and comprehensive set of images to enhance the performance of your computer vision algorithms.

docs/en/guides/heatmaps.md

@@ -8,6 +8,8 @@ keywords: Ultralytics, YOLO11, heatmaps, data visualization, data analysis, comp
 
 ## Introduction to Heatmaps
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-generate-heatmaps-using-ultralytics-yolo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Heatmaps In Colab"></a>
+
 A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.
 
 <p align="center">

docs/en/guides/object-counting.md

@@ -8,6 +8,8 @@ keywords: object counting, YOLO11, Ultralytics, real-time object detection, AI,
 
 ## What is Object Counting?
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-count-the-objects-using-ultralytics-yolo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Object Counting In Colab"></a>
+
 Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLO11 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
 
 <p align="center">

docs/en/guides/sahi-tiled-inference.md

@@ -6,6 +6,8 @@ keywords: YOLO11, SAHI, Sliced Inference, Object Detection, Ultralytics, High-re
 
 # Ultralytics Docs: Using YOLO11 with SAHI for Sliced Inference
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-use-ultralytics-yolo-with-sahi.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open SAHI for Sliced Inference In Colab"></a>
+
 Welcome to the Ultralytics documentation on how to use YOLO11 with [SAHI](https://github.com/obss/sahi) (Slicing Aided Hyper Inference). This comprehensive guide aims to furnish you with all the essential knowledge you'll need to implement SAHI alongside YOLO11. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLO11 for enhanced [object detection](https://www.ultralytics.com/glossary/object-detection) performance.
 
 <p align="center">
@@ -117,7 +119,7 @@ from sahi.predict import get_prediction
 result = get_prediction("demo_data/small-vehicles1.jpeg", detection_model)
 
 # With a numpy image
-result = get_prediction(read_image("demo_data/small-vehicles1.jpeg"), detection_model)
+result_with_np_image = get_prediction(read_image("demo_data/small-vehicles1.jpeg"), detection_model)
 ```
 
 ### Visualize Results

docs/en/guides/trackzone.md

@@ -6,6 +6,8 @@ keywords: TrackZone, object tracking, YOLO11, Ultralytics, real-time object dete
 
 # TrackZone using Ultralytics YOLO11
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-track-the-objects-in-zone-using-ultralytics-yolo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open TrackZone In Colab"></a>
+
 ## What is TrackZone?
 
 TrackZone specializes in monitoring objects within designated areas of a frame instead of the whole frame. Built on [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/), it integrates object detection and tracking specifically within zones for videos and live camera feeds. YOLO11's advanced algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) technologies make it a perfect choice for real-time use cases, offering precise and efficient object tracking in applications like crowd monitoring and surveillance.

docs/en/guides/workouts-monitoring.md

@@ -6,6 +6,8 @@ keywords: workouts monitoring, Ultralytics YOLO11, pose estimation, fitness trac
 
 # Workouts Monitoring using Ultralytics YOLO11
 
+<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-monitor-workouts-using-ultralytics-yolo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Workouts Monitoring In Colab"></a>
+
 Monitoring workouts through pose estimation with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) enhances exercise assessment by accurately tracking key body landmarks and joints in real-time. This technology provides instant feedback on exercise form, tracks workout routines, and measures performance metrics, optimizing training sessions for users and trainers alike.
 
 <p align="center">