What are the common datasets used for training Generative AI models

Generative AI models require high-quality datasets to learn and generate realistic outputs. Various datasets are commonly used across different domains, including images, text, and audio. Below are some of the most popular datasets used for training generative models:

1. MNIST

The MNIST dataset consists of 70,000 images of handwritten digits (0-9) and is widely used for training and evaluating generative models, especially in image generation tasks.

Example: Loading MNIST Dataset


import torchvision.datasets as datasets

# Load the MNIST dataset
mnist_dataset = datasets.MNIST(root='./data', train=True, download=True)
print(f'Total MNIST samples: {len(mnist_dataset)}')

2. CIFAR-10

The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes (e.g., airplane, car, bird). It is commonly used for training generative models that focus on more complex image generation tasks.

Example: Loading CIFAR-10 Dataset


# Load the CIFAR-10 dataset
cifar10_dataset = datasets.CIFAR10(root='./data', train=True, download=True)
print(f'Total CIFAR-10 samples: {len(cifar10_dataset)}')

3. CelebA

The CelebA dataset contains over 200,000 celebrity images with various attributes. It is widely used for training generative models in tasks such as face generation and image manipulation.

Example: Loading CelebA Dataset


# Load the CelebA dataset (requires specific setup)
from torchvision.datasets import CelebA

celeba_dataset = CelebA(root='./data', split='train', download=True)
print(f'Total CelebA samples: {len(celeba_dataset)}')

4. LJSpeech

The LJSpeech dataset is a collection of 13,100 short audio clips of a single speaker reading passages from books. It is commonly used for training text-to-speech (TTS) models and other audio generation tasks.

Example: Loading LJSpeech Dataset


import os

# Define the path to the LJSpeech dataset
ljspeech_path = './data/LJSpeech-1.1/'

# List the audio files in the dataset
audio_files = [f for f in os.listdir(ljspeech_path) if f.endswith('.wav')]
print(f'Total audio files in LJSpeech: {len(audio_files)}')

5. Text Datasets (e.g., WikiText, Common Crawl)

Text datasets like WikiText and Common Crawl are used for training language models and text generation tasks. These datasets contain large amounts of text data from various sources, making them suitable for training generative models in NLP.

Example: Loading WikiText Dataset


from torchtext.datasets import WikiText2

# Load the WikiText2 dataset
wikitext_dataset = WikiText2(root='./data', split='train')
print(f'Total WikiText samples: {len(list(wikitext_dataset))}')

6. ImageNet

ImageNet is a large-scale dataset containing over 14 million images across thousands of categories. It is widely used for training various computer vision models, including generative models for image synthesis.

Example: Loading ImageNet Dataset


# ImageNet requires specific setup and is not directly available via torchvision
# However, you can use the following code to load it if you have the dataset locally
from torchvision.datasets import ImageNet

imagenet_dataset = ImageNet(root='./data', split='train', download=True)
print(f'Total ImageNet samples: {len(imagenet_dataset)}')

Conclusion

Choosing the right dataset is crucial for training effective generative AI models. The datasets mentioned above are commonly used across various domains and can serve as a foundation for building and evaluating generative models. Depending on the specific application, researchers and developers can select the most appropriate dataset to achieve their goals.