Models and datasets for publication: Korfhage, N., Mühling, M., Freisleben, B. (2021). ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_2 ElasticHash uses Elasticsearch for high-quality, efficient, and large-scale semantic image similarity search. It is based on a deep hashing model to learn hash codes for fine-grained image similarity search in natural images and a two-stage method for efficiently searching binary hash codes using Elasticsearch (ES). In the first stage, a coarse search based on short hash codes is performed using multi-index hashing and ES terms lookup of neighboring hash codes. In the second stage, the list of results is re-ranked by computing the Hamming distance on long hash codes. The files published here are needed to set up the system for large-scale image similarity search and to reproduce the experiments. More details can be found in the Git-Repository: https://github.com/umr-ds/ElasticHash