The MatSim Dataset and benchmark Lastest version Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures. MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning). Based on the paper: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering Benchmark_MATSIM.zip: contain the benchmark made of real-world images as described in the paper MatSim_object_train_split_1,2,3.zip: Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper. MatSim_Vessels_Train_1,2,3.zip : Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.*Note: these are subsets of the dataset; the full dataset can be found at:https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX orhttps://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF Code: Up to date code for generating the dataset, reading and evaluation and trained nets can be found in this URL:https://github.com/sagieppel/MatSim-Dataset-Generator-Scripts-And-Neural-net Dataset Generation Scripts.zip: Contain the Blender (3.1) Python scripts used for generating the dataset, this code might be odl up to date code can be found here Net_Code_And_Trained_Model.zip: Contain a reference neural net code, including loaders, trained models, and evaluators scripts that can be used to read and train with the synthetic dataset or test the model with the benchmark. Note code in the ZIP file is not up to date and contains some bugs For the Latest version of this code see this URL Further documentation can be found inside the zip files or in the paper.