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Add Docs glossary links (#16448)

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docs/en/datasets/classify/imagenette.md
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@ -23,7 +23,7 @@ The ImageNette dataset is split into two subsets:
## Applications
The ImageNette dataset is widely used for training and evaluating deep learning models in image classification tasks, such as Convolutional Neural Networks (CNNs), and various other machine learning algorithms. The dataset's straightforward format and well-chosen classes make it a handy resource for both beginner and experienced practitioners in the field of machine learning and computer vision.
The ImageNette 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), and various other machine learning algorithms. The dataset's straightforward format and well-chosen classes make it a handy resource for both beginner and experienced practitioners in the field of machine learning and computer vision.
## Usage
@ -52,7 +52,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
## Sample Images and Annotations
The ImageNette dataset contains colored images of various objects and scenes, providing a diverse dataset for image classification tasks. Here are some examples of images from the dataset:
The ImageNette dataset contains colored images of various objects and scenes, providing a diverse dataset for [image classification](https://www.ultralytics.com/glossary/image-classification) tasks. Here are some examples of images from the dataset:
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/imagenette-sample-image.avif)
@ -116,11 +116,11 @@ If you use the ImageNette dataset in your research or development work, please a
### What is the ImageNette dataset?
The [ImageNette dataset](https://github.com/fastai/imagenette) is a simplified subset of the larger [ImageNet dataset](https://www.image-net.org/), featuring only 10 easily distinguishable classes such as tench, English springer, and French horn. It was created to offer a more manageable dataset for efficient training and evaluation of image classification models. This dataset is particularly useful for quick software development and educational purposes in machine learning and computer vision.
The [ImageNette dataset](https://github.com/fastai/imagenette) is a simplified subset of the larger [ImageNet dataset](https://www.image-net.org/), featuring only 10 easily distinguishable classes such as tench, English springer, and French horn. It was created to offer a more manageable dataset for efficient training and evaluation of image classification models. This dataset is particularly useful for quick software development and educational purposes in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision.
### How can I use the ImageNette dataset for training a YOLO model?
To train a YOLO model on the ImageNette dataset for 100 epochs, you can use the following commands. Make sure to have the Ultralytics YOLO environment set up.
To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following commands. Make sure to have the Ultralytics YOLO environment set up.
!!! example "Train Example"
@ -186,8 +186,8 @@ For more information, refer to [Training with ImageNette160 and ImageNette320](#
The ImageNette dataset is extensively used in:
- **Educational Settings**: To educate beginners in machine learning and computer vision.
- **Educational Settings**: To educate beginners in machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
- **Software Development**: For rapid prototyping and development of image classification models.
- **Deep Learning Research**: To evaluate and benchmark the performance of various deep learning models, especially Convolutional Neural Networks (CNNs).
- **Deep Learning Research**: To evaluate and benchmark the performance of various deep learning models, especially Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs).
Explore the [Applications](#applications) section for detailed use cases.