153 lines
7.7 KiB
Markdown
153 lines
7.7 KiB
Markdown
---
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comments: true
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description: Explore the widely-used Caltech-101 dataset with 9,000 images across 101 categories. Ideal for object recognition tasks in machine learning and computer vision.
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keywords: Caltech-101, dataset, object recognition, machine learning, computer vision, YOLO, deep learning, research, AI
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---
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# Caltech-101 Dataset
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The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) 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.
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## Key Features
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- The Caltech-101 dataset comprises around 9,000 color images divided into 101 categories.
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- The categories encompass a wide variety of objects, including animals, vehicles, household items, and people.
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- The number of images per category varies, with about 40 to 800 images in each category.
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- Images are of variable sizes, with most images being medium resolution.
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- Caltech-101 is widely used for training and testing in the field of machine learning, particularly for object recognition tasks.
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## Dataset Structure
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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.
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## Applications
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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).
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## Usage
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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](../../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="caltech101", epochs=100, imgsz=416)
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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=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
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```
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## Sample Images and Annotations
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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:
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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.
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## Citations and Acknowledgments
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If you use the Caltech-101 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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@article{fei2007learning,
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title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
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author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
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journal={Computer vision and Image understanding},
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volume={106},
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number={1},
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pages={59--70},
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year={2007},
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publisher={Elsevier}
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}
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```
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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](https://data.caltech.edu/records/mzrjq-6wc02).
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## FAQ
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### What is the Caltech-101 dataset used for in machine learning?
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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.
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### How can I train an Ultralytics YOLO model on the Caltech-101 dataset?
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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):
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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="caltech101", epochs=100, imgsz=416)
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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=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
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```
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For more detailed arguments and options, refer to the model [Training](../../modes/train.md) page.
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### What are the key features of the Caltech-101 dataset?
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The Caltech-101 dataset includes:
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- Around 9,000 color images across 101 categories.
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- Categories covering a diverse range of objects, including animals, vehicles, and household items.
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- Variable number of images per category, typically between 40 and 800.
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- Variable image sizes, with most being medium resolution.
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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.
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### Why should I cite the Caltech-101 dataset in my research?
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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:
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!!! quote ""
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=== "BibTeX"
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```bibtex
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@article{fei2007learning,
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title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
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author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
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journal={Computer vision and Image understanding},
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volume={106},
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number={1},
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pages={59--70},
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year={2007},
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publisher={Elsevier}
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}
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```
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Citing helps in maintaining the integrity of academic work and assists peers in locating the original resource.
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### Can I use Ultralytics HUB for training models on the Caltech-101 dataset?
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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](https://www.ultralytics.com/blog/how-to-train-your-custom-models-with-ultralytics-hub) blog post.
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