The LAION-400M dataset is completely openly, freely accessible.

Check https://laion.ai/laion-400-open-dataset/ for the full description of this dataset.

All images and texts in the LAION-400M dataset have been filtered with OpenAI‘s CLIP by calculating the cosine similarity between the text and image embeddings and dropping those with a similarity below 0.3. The threshold of 0.3 had been determined through human evaluations and seemed to be a good heuristic for estimating semantic image-text-content matching.

The image-text-pairs have been extracted from the Common Crawl web data dump and are from random web pages crawled between 2014 and 2021.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('laion400m', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

LAION-400M The world’s largest openly available image-text-pair dataset with 400 million samples. # Concept and Content The LAION-400M dataset is completely openly, freely accessible. All images and texts in the LAION-400M dataset have been filtered with OpenAI‘s CLIP by calculating the cosine similarity between the text and image embeddings and dropping those with a similarity below 0.3 The threshold of 0.3 had been determined through human evaluations and seems to be a good heuristic for estimating semantic image-text-content matching. The image-text-pairs have been extracted from the Common Crawl web data dump and are from random web pages crawled between 2014 and 2021. # Download Information You can find The CLIP image embeddings (NumPy files) The parquet files KNN index of image embeddings # LAION-400M Dataset Statistics The LAION-400M and future even bigger ones are in fact datasets of datasets. For instance, it can be filtered out by image sizes into smaller datasets like th

jp1924/Laion400m-2 dataset hosted on Hugging Face and contributed by the HF Datasets community

LAION-Aesthetic is a large-scale dataset for training next generation image-text models.

Text-to-image Latent Diffusion model, CLIP model, Blended Diffusion model, GLIDE model, GLIDE-filtered model

The dataset used for pre-training the MS-CLIP model, which consists of 20 million image-text pairs filtered from Laion-400M.

MIRAGE Pretraining/Finetuning Dataset Card

  Dataset details

Dataset type: This dataset is designed to train the visual-RAG model, MIRAGE-8.3B. It contains files to do (multi-stage) pre-training as well as fine-tuning.

Data Preparation:

Stage1 Pretraining: Q-Former and visual alignment layer (low-quality data)

Source: LAION-400M, CC12M, and MSCOCO from here Put all these .tar files under /datasets directory. stage1_pretraining.txt provides an example dataset.

Stage2… See the full description on the dataset page: https://huggingface.co/datasets/tsunghanwu/MIRAGE-training-set.