Common datasets used for deep learning cover diverse applications, including image recognition, natural language processing, and speech recognition. One of the most widely used image datasets is the ImageNet dataset, containing over 14 million images categorized into over 20,000 classes. It serves as a benchmark for training convolutional neural networks (CNNs) in tasks like object detection and image classification. For natural language processing, the GLUE benchmark is popular, which consists of several datasets for various language understanding tasks, helping to evaluate and fine-tune models effectively.
In addition to these, the CIFAR-10 and CIFAR-100 datasets are often used for evaluating image classification algorithms. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes, making it a good option for testing simpler models after they have been trained on more complex datasets like ImageNet. For more challenging scenarios, the CIFAR-100 dataset expands upon CIFAR-10, offering 100 classes with 600 images each, thus providing a richer set for training and understanding model capabilities.
For tasks related to speech and audio processing, the LibriSpeech dataset has gained popularity. It contains thousands of hours of spoken English, making it useful for training automatic speech recognition systems. Similarly, the Common Voice dataset, created by Mozilla, allows developers to train speech models with diverse languages and accents. These datasets provide solid foundations for various deep learning tasks, making them essential for developers looking to build effective models in their projects.