142 lines
7.9 KiB
Markdown
142 lines
7.9 KiB
Markdown
---
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comments: true
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description: Explore the CIFAR-100 dataset, consisting of 60,000 32x32 color images across 100 classes. Ideal for machine learning and computer vision tasks.
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keywords: CIFAR-100, dataset, machine learning, computer vision, image classification, deep learning, YOLO, training, testing, Alex Krizhevsky
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---
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# CIFAR-100 Dataset
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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.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/6bZeCs0xwO4"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> How to Train Ultralytics YOLO11 on CIFAR-100 | Step-by-Step Image Classification Tutorial 🚀
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</p>
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## Key Features
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- The CIFAR-100 dataset consists of 60,000 images, divided into 100 classes.
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- Each class contains 600 images, split into 500 for training and 100 for testing.
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- The images are colored and of size 32x32 pixels.
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- The 100 different classes are grouped into 20 coarse categories for higher level classification.
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- CIFAR-100 is commonly used for training and testing in the field of machine learning and computer vision.
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## Dataset Structure
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The CIFAR-100 dataset is split into two subsets:
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1. **Training Set**: This subset contains 50,000 images used for training machine learning models.
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2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
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## Applications
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The CIFAR-100 dataset is extensively used for training and evaluating deep learning models in [image classification](https://www.ultralytics.com/glossary/image-classification) tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (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](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision.
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## Usage
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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.
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!!! example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="cifar100", epochs=100, imgsz=32)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
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```
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## Sample Images and Annotations
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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:
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The example showcases the variety and complexity of the objects in the CIFAR-100 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
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## Citations and Acknowledgments
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If you use the CIFAR-100 dataset in your research or development work, please cite the following paper:
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!!! quote ""
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=== "BibTeX"
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```bibtex
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@TECHREPORT{Krizhevsky09learningmultiple,
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author={Alex Krizhevsky},
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title={Learning multiple layers of features from tiny images},
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institution={},
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year={2009}
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}
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```
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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).
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## FAQ
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### What is the CIFAR-100 dataset and why is it significant?
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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](https://www.ultralytics.com/glossary/deep-learning-dl) models, like Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs), using frameworks such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/).
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### How do I train a YOLO model on the CIFAR-100 dataset?
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You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI commands. Here's how:
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!!! example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="cifar100", epochs=100, imgsz=32)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
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```
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For a comprehensive list of available arguments, please refer to the model [Training](../../modes/train.md) page.
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### What are the primary applications of the CIFAR-100 dataset?
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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, particularly for [object recognition](https://docs.ultralytics.com/tasks/classify/) and classification tasks.
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### How is the CIFAR-100 dataset structured?
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The CIFAR-100 dataset is split into two main subsets:
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1. **Training Set**: Contains 50,000 images used for training machine learning models.
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2. **Testing Set**: Consists of 10,000 images used for testing and benchmarking the trained models.
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Each of the 100 classes contains 600 images, with 500 images for training and 100 for testing, making it uniquely suited for rigorous academic and industrial research.
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### Where can I find sample images and annotations from the CIFAR-100 dataset?
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The CIFAR-100 dataset includes a variety of color images of various objects, making it a structured dataset for image classification tasks. You can refer to the documentation page to see [sample images and annotations](#sample-images-and-annotations). These examples highlight the dataset's diversity and complexity, important for training robust image classification models. For more datasets suitable for classification tasks, check out [Ultralytics' classification datasets overview](https://docs.ultralytics.com/datasets/classify/).
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