Image search with 🤗 datasets

Announcing New Dataset Search Features

Open Preference Dataset for Text-to-Image Generation by the 🤗 Community

The Power and Pitfalls of Vector-Based Image Search

A hands-on guide to setting up image similarity search in Milvus, and why visual replication isn't always enough. The post The Power and Pitfalls of Vector-Based Image Search appeared first on Towards Data Science .

DODA: A Database of Datasets for Aesthetics Research

arXiv:2608. 00089v1 Announce Type: cross Abstract: With rapid growth in the fields of empirical and computational aesthetics we have seen a vast increase in large image datasets annotated for aesthetics.

Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All

Enterprise Document Intelligence [Vol. 1 #5sexies] - image_df tells you where every picture is.

Semantic search for 100M+ galaxy images using AI-generated captions

arXiv:2512. 11982v2 Announce Type: replace-cross Abstract: Finding scientifically interesting phenomena through slow manual labeling campaigns severely limits our ability to explore the billions of galaxy images produced by telescopes.

Fine-Tune ViT for Image Classification with 🤗 Transformers

Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

arXiv:2605. 01965v2 Announce Type: replace Abstract: A classical vector retrieval problem typically considers a \emph{single} query embedding vector as input and retrieves the most similar embedding vectors from a vector database.

DuckDB: analyze 50,000+ datasets stored on the Hugging Face Hub

Croissant: a metadata format for ML-ready datasets

Posted by Omar Benjelloun, Software Engineer, Google Research, and Peter Mattson, Software Engineer, Google Core ML and President, MLCommons Association Machine learning (ML) practitioners looking to reuse existing datasets to train an ML model often spend a lot of time understanding the data, making sense of its organization, or figuring out what subset to use as features. So much time, in fact, that progress in the field of ML is hampered by a fundamental obstacle: the wide variety of data representations.

Image Similarity with Hugging Face Datasets and Transformers

Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval