Caltech 101
The Caltech 101 dataset is a collection of approximately 9,144 images divided into 101 object categories, plus an additional background category. Categories span a wide range of objects, including animals, household items, vehicles, and plants, with about 40 to 800 images per category. Each image is roughly 300 x 200 pixels in size. Developed by the California Institute of Technology, this dataset is primarily used for computer vision research in object recognition. The diversity of categories and the moderate size of the dataset make it suitable for testing and benchmarking image recognition algorithms, especially for those new to machine learning and computer vision.
Description:
- Content: Includes approximately 9,144 images.
- Categories: Features 101 object categories plus one background category.
- Images Per Category: Varies from about 40 to 800 images per category, with most categories having about 50 images.
- Purpose: Used primarily for computer vision tasks including object recognition and categorization.
- Characteristic: Known for its diversity in image representations and relatively small sample size per category, posing a challenge for deep learning models without overfitting.
Dataset for Image Classification
The field of computer vision has witnessed remarkable progress in recent years, largely driven by the availability of large-scale datasets for image classification tasks. These datasets play a pivotal role in training and evaluating machine learning models, enabling them to recognize and categorize visual content with increasing accuracy.
In this article, we will discuss some of the famous datasets used for image classification.