Merge branch 'main' into main

.github/workflows/cla.yml

@@ -26,7 +26,7 @@ jobs:
     steps:
       - name: CLA Assistant
         if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target'
-        uses: contributor-assistant/[email protected].1
+        uses: contributor-assistant/[email protected].2
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           # Must be repository secret PAT

.github/workflows/docs.yml

@@ -48,7 +48,7 @@ jobs:
         continue-on-error: true
         run: ruff check --fix --unsafe-fixes --select D --ignore=D100,D104,D203,D205,D212,D213,D401,D406,D407,D413 .
       - name: Update Docs Reference Section and Push Changes
-        if: github.event_name == 'pull_request_target'
+        continue-on-error: true
         run: |
           python docs/build_reference.py
           git pull origin ${{ github.head_ref || github.ref }}
@@ -68,7 +68,7 @@ jobs:
           python docs/build_docs.py
       - name: Commit and Push Docs changes
         continue-on-error: true
-        if: always() && github.event_name == 'pull_request_target'
+        if: always()
         run: |
           git pull origin ${{ github.head_ref || github.ref }}
           git add --update  # only add updated files

docs/en/datasets/classify/caltech101.md

@@ -22,7 +22,7 @@ Unlike many other datasets, the Caltech-101 dataset is not formally split into t
 
 ## Applications
 
-The Caltech-101 dataset is extensively used for training and evaluating deep learning models in object recognition tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. Its wide variety of categories and high-quality images make it an excellent dataset for research and development in the field of machine learning and computer vision.
+The Caltech-101 dataset is extensively used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object recognition tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. Its wide variety of categories and high-quality images make it an excellent dataset for research and development in the field of machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
 
 ## Usage
 
@@ -84,11 +84,11 @@ We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creat
 
 ### What is the Caltech-101 dataset used for in machine learning?
 
-The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is widely used in machine learning for object recognition tasks. It contains around 9,000 images across 101 categories, providing a challenging benchmark for evaluating object recognition algorithms. Researchers leverage it to train and test models, especially Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), in computer vision.
+The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is widely used in machine learning for object recognition tasks. It contains around 9,000 images across 101 categories, providing a challenging benchmark for evaluating object recognition algorithms. Researchers leverage it to train and test models, especially Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs) and [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), in computer vision.
 
 ### How can I train an Ultralytics YOLO model on the Caltech-101 dataset?
 
-To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the provided code snippets. For example, to train for 100 epochs:
+To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the provided code snippets. For example, to train for 100 [epochs](https://www.ultralytics.com/glossary/epoch):
 
 !!! example "Train Example"
 
@@ -122,7 +122,7 @@ The Caltech-101 dataset includes:
 - Variable number of images per category, typically between 40 and 800.
 - Variable image sizes, with most being medium resolution.
 
-These features make it an excellent choice for training and evaluating object recognition models in machine learning and computer vision.
+These features make it an excellent choice for training and evaluating object recognition models in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision.
 
 ### Why should I cite the Caltech-101 dataset in my research?
 

docs/en/datasets/classify/caltech256.md

@@ -16,7 +16,7 @@ The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is an ex
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Train Image Classification Model using Caltech-256 Dataset with Ultralytics HUB
+  <strong>Watch:</strong> How to Train [Image Classification](https://www.ultralytics.com/glossary/image-classification) Model using Caltech-256 Dataset with Ultralytics HUB
 </p>
 
 ## Key Features
@@ -33,7 +33,7 @@ Like Caltech-101, the Caltech-256 dataset does not have a formal split between t
 
 ## Applications
 
-The Caltech-256 dataset is extensively used for training and evaluating deep learning models in object recognition tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. Its diverse set of categories and high-quality images make it an invaluable dataset for research and development in the field of machine learning and computer vision.
+The Caltech-256 dataset is extensively used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object recognition tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. Its diverse set of categories and high-quality images make it an invaluable dataset for research and development in the field of machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
 
 ## Usage
 
@@ -84,7 +84,7 @@ If you use the Caltech-256 dataset in your research or development work, please
         }
         ```
 
-We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the
+We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision research community. For more information about the
 
 Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
 
@@ -96,7 +96,7 @@ The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is a lar
 
 ### How can I train a YOLO model on the Caltech-256 dataset using Python or CLI?
 
-To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the following code snippets. Refer to the model [Training](../../modes/train.md) page for additional options.
+To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following code snippets. Refer to the model [Training](../../modes/train.md) page for additional options.
 
 !!! example "Train Example"
 
@@ -123,10 +123,10 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
 
 The Caltech-256 dataset is widely used for various object recognition tasks such as:
 
-- Training Convolutional Neural Networks (CNNs)
-- Evaluating the performance of Support Vector Machines (SVMs)
+- Training Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs)
+- Evaluating the performance of [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs)
 - Benchmarking new deep learning algorithms
-- Developing object detection models using frameworks like Ultralytics YOLO
+- Developing [object detection](https://www.ultralytics.com/glossary/object-detection) models using frameworks like Ultralytics YOLO
 
 Its diversity and comprehensive annotations make it ideal for research and development in machine learning and computer vision.
 
@@ -141,6 +141,6 @@ Ultralytics YOLO models offer several advantages for training on the Caltech-256
 - **High Accuracy**: YOLO models are known for their state-of-the-art performance in object detection tasks.
 - **Speed**: They provide real-time inference capabilities, making them suitable for applications requiring quick predictions.
 - **Ease of Use**: With Ultralytics HUB, users can train, validate, and deploy models without extensive coding.
-- **Pretrained Models**: Starting from pretrained models, like `yolov8n-cls.pt`, can significantly reduce training time and improve model accuracy.
+- **Pretrained Models**: Starting from pretrained models, like `yolov8n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 For more details, explore our [comprehensive training guide](../../modes/train.md).

docs/en/datasets/classify/cifar10.md

@@ -6,7 +6,7 @@ keywords: CIFAR-10, dataset, machine learning, computer vision, image classifica
 
 # CIFAR-10 Dataset
 
-The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a collection of images used widely for machine learning and computer vision algorithms. It was developed by researchers at the CIFAR institute and consists of 60,000 32x32 color images in 10 different classes.
+The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a collection of images used widely for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision algorithms. It was developed by researchers at the CIFAR institute and consists of 60,000 32x32 color images in 10 different classes.
 
 <p align="center">
   <br>
@@ -16,7 +16,7 @@ The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Train an Image Classification Model with CIFAR-10 Dataset using Ultralytics YOLOv8
+  <strong>Watch:</strong> How to Train an [Image Classification](https://www.ultralytics.com/glossary/image-classification) Model with CIFAR-10 Dataset using Ultralytics YOLOv8
 </p>
 
 ## Key Features
@@ -36,7 +36,7 @@ The CIFAR-10 dataset is split into two subsets:
 
 ## Applications
 
-The CIFAR-10 dataset is widely used for training and evaluating deep learning models in image classification tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. The diversity of the dataset in terms of classes and the presence of color images make it a well-rounded dataset for research and development in the field of machine learning and computer vision.
+The CIFAR-10 dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. The diversity of the dataset in terms of classes and the presence of color images make it a well-rounded dataset for research and development in the field of machine learning and computer vision.
 
 ## Usage
 
@@ -88,13 +88,13 @@ If you use the CIFAR-10 dataset in your research or development work, please cit
         }
         ```
 
-We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
+We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
 
 ## FAQ
 
 ### How can I train a YOLO model on the CIFAR-10 dataset?
 
-To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow the examples provided for both Python and CLI. Here is a basic example to train your model for 100 epochs with an image size of 32x32 pixels:
+To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow the examples provided for both Python and CLI. Here is a basic example to train your model for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 32x32 pixels:
 
 !!! example
 
@@ -138,7 +138,7 @@ This diverse dataset is essential for training image classification models in fi
 
 ### Why use the CIFAR-10 dataset for image classification tasks?
 
-The CIFAR-10 dataset is an excellent benchmark for image classification due to its diversity and structure. It contains a balanced mix of 60,000 labeled images across 10 different categories, which helps in training robust and generalized models. It is widely used for evaluating deep learning models, including Convolutional Neural Networks (CNNs) and other machine learning algorithms. The dataset is relatively small, making it suitable for quick experimentation and algorithm development. Explore its numerous applications in the [applications](#applications) section.
+The CIFAR-10 dataset is an excellent benchmark for image classification due to its diversity and structure. It contains a balanced mix of 60,000 labeled images across 10 different categories, which helps in training robust and generalized models. It is widely used for evaluating deep learning models, including Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs) and other machine learning algorithms. The dataset is relatively small, making it suitable for quick experimentation and algorithm development. Explore its numerous applications in the [applications](#applications) section.
 
 ### How is the CIFAR-10 dataset structured?
 
@@ -170,4 +170,4 @@ Acknowledging the dataset's creators helps support continued research and develo
 
 ### What are some practical examples of using the CIFAR-10 dataset?
 
-The CIFAR-10 dataset is often used for training image classification models, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). These models can be employed in various computer vision tasks including object detection, image recognition, and automated tagging. To see some practical examples, check the code snippets in the [usage](#usage) section.
+The CIFAR-10 dataset is often used for training image classification models, such as Convolutional Neural Networks (CNNs) and [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs). These models can be employed in various computer vision tasks including [object detection](https://www.ultralytics.com/glossary/object-detection), [image recognition](https://www.ultralytics.com/glossary/image-recognition), and automated tagging. To see some practical examples, check the code snippets in the [usage](#usage) section.