Caltech-101 Dataset

The Caltech-101 dataset is a widely used dataset for object recognition tasks, containing around 9,000 images from 101 object categories. The categories were chosen to reflect a variety of real-world objects, and the images themselves were carefully selected and annotated to provide a challenging benchmark for object recognition algorithms.

Key Features

• The Caltech-101 dataset comprises around 9,000 color images divided into 101 categories.
• The categories encompass a wide variety of objects, including animals, vehicles, household items, and people.
• The number of images per category varies, with about 40 to 800 images in each category.
• Images are of variable sizes, with most images being medium resolution.
• Caltech-101 is widely used for training and testing in the field of machine learning, particularly for object recognition tasks.

Dataset Structure

Unlike many other datasets, the Caltech-101 dataset is not formally split into training and testing sets. Users typically create their own splits based on their specific needs. However, a common practice is to use a random subset of images for training (e.g., 30 images per category) and the remaining images for testing.

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.

Usage

To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="caltech101", epochs=100, imgsz=416)

# Start training from a pretrained *.pt model
yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416

Sample Images and Annotations

The Caltech-101 dataset contains high-quality color images of various objects, providing a well-structured dataset for object recognition tasks. Here are some examples of images from the dataset:

The example showcases the variety and complexity of the objects in the Caltech-101 dataset, emphasizing the significance of a diverse dataset for training robust object recognition models.

Citations and Acknowledgments

If you use the Caltech-101 dataset in your research or development work, please cite the following paper:

@article{fei2007learning,
  title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
  author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
  journal={Computer vision and Image understanding},
  volume={106},
  number={1},
  pages={59--70},
  year={2007},
  publisher={Elsevier}
}

We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the Caltech-101 dataset website.

FAQ

What is the Caltech-101 dataset used for in machine learning?

The Caltech-101 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.

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:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="caltech101", epochs=100, imgsz=416)

# Start training from a pretrained *.pt model
yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416

For more detailed arguments and options, refer to the model Training page.

What are the key features of the Caltech-101 dataset?

The Caltech-101 dataset includes:

• Around 9,000 color images across 101 categories.
• Categories covering a diverse range of objects, including animals, vehicles, and household items.
• 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.

Why should I cite the Caltech-101 dataset in my research?

Citing the Caltech-101 dataset in your research acknowledges the creators' contributions and provides a reference for others who might use the dataset. The recommended citation is:

@article{fei2007learning,
  title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
  author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
  journal={Computer vision and Image understanding},
  volume={106},
  number={1},
  pages={59--70},
  year={2007},
  publisher={Elsevier}
}

Citing helps in maintaining the integrity of academic work and assists peers in locating the original resource.

Can I use Ultralytics HUB for training models on the Caltech-101 dataset?

Yes, you can use Ultralytics HUB for training models on the Caltech-101 dataset. Ultralytics HUB provides an intuitive platform for managing datasets, training models, and deploying them without extensive coding. For a detailed guide, refer to the how to train your custom models with Ultralytics HUB blog post.

📅 Created 1 year ago ✏️ Updated 2 months ago

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