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docs/en/datasets/classify/cifar100.md
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@ -6,7 +6,7 @@ keywords: CIFAR-100, dataset, machine learning, computer vision, image classific
# CIFAR-100 Dataset
The [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a significant extension of the CIFAR-10 dataset, composed of 60,000 32x32 color images in 100 different classes. It was developed by researchers at the CIFAR institute, offering a more challenging dataset for more complex machine learning and computer vision tasks.
The [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a significant extension of the CIFAR-10 dataset, composed of 60,000 32x32 color images in 100 different classes. It was developed by researchers at the CIFAR institute, offering a more challenging dataset for more complex machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
## Key Features
@ -25,11 +25,11 @@ The CIFAR-100 dataset is split into two subsets:
## Applications
The CIFAR-100 dataset is extensively 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 more challenging and comprehensive dataset for research and development in the field of machine learning and computer vision.
The CIFAR-100 dataset is extensively used for training and evaluating deep learning 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 more challenging and comprehensive dataset for research and development in the field of machine learning and computer vision.
## Usage
To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -54,7 +54,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size
## Sample Images and Annotations
The CIFAR-100 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
The CIFAR-100 dataset contains color images of various objects, providing a well-structured 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/cifar100-sample-image.avif)
@ -77,13 +77,13 @@ If you use the CIFAR-100 dataset in your research or development work, please ci
}
```
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 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 CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
## FAQ
### What is the CIFAR-100 dataset and why is it significant?
The [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) is a large collection of 60,000 32x32 color images classified into 100 classes. Developed by the Canadian Institute For Advanced Research (CIFAR), it provides a challenging dataset ideal for complex machine learning and computer vision tasks. Its significance lies in the diversity of classes and the small size of the images, making it a valuable resource for training and testing deep learning models, like Convolutional Neural Networks (CNNs), using frameworks such as Ultralytics YOLO.
The [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) is a large collection of 60,000 32x32 color images classified into 100 classes. Developed by the Canadian Institute For Advanced Research (CIFAR), it provides a challenging dataset ideal for complex machine learning and computer vision tasks. Its significance lies in the diversity of classes and the small size of the images, making it a valuable resource for training and testing deep learning models, like Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs), using frameworks such as Ultralytics YOLO.
### How do I train a YOLO model on the CIFAR-100 dataset?
@ -114,7 +114,7 @@ For a comprehensive list of available arguments, please refer to the model [Trai
### What are the primary applications of the CIFAR-100 dataset?
The CIFAR-100 dataset is extensively used in training and evaluating deep learning models for image classification. Its diverse set of 100 classes, grouped into 20 coarse categories, provides a challenging environment for testing algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning approaches. This dataset is a key resource in research and development within machine learning and computer vision fields.
The CIFAR-100 dataset is extensively used in training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models for image classification. Its diverse set of 100 classes, grouped into 20 coarse categories, provides a challenging environment for testing algorithms such as Convolutional Neural Networks (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning approaches. This dataset is a key resource in research and development within machine learning and computer vision fields.
### How is the CIFAR-100 dataset structured?