TensorFlow Similarity is a python package focused on making similarity learning quick and easy.

Blazing fast framework for fine-tuning similarity learning models

A python project for checking plagiarism of documents based on cosine similarity

Real-Time Face Recognition use SCRFD, ArcFace, ByteTrack, Similarity Measure

Tika-Similarity uses the Tika-Python package (Python port of Apache Tika) to compute file similarity based on Metadata features.

Web Application for checking the similarity between query and document using the concept of Cosine Similarity.

[NeurIPS 2021] Official implementation of the paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Multi-threaded matrix multiplication and cosine similarity calculations for dense and sparse matrices. Appropriate for calculating the K most similar items for a large number of items by chunking the item matrix representation (embeddings) and using Numba to accelerate the calculations.

It is a content based recommender system that uses tf-idf and cosine similarity for N Most SImilar Items from a dataset

Fast similarity search using DuckDB

Fast Image Retrieval (FIRe) is an open source project to promote image retrieval research. It implements most of the major binary hashing methods to date, together with different popular backbone networks and public datasets.

Implementation of TextRank with the option of using pre-trained Word2Vec embeddings as the similarity metric

To detect any reasonable change in a live cctv to avoid large storage of data. Once, we notice a change, our goal would be track that object or person causing it. We would be using Computer vision concepts. Our major focus will be on Deep Learning and will try to add as many features in the process.

A starting take on a fast and fully local NLP file organizer that organizes files based on their content.

Calculation and visualization of molecular networks based on t-SNE algorithm

This is a course recommendation system that generates recommendation based on the study patterns and cognitive level of the students. The features are extracted using huge and raw log files. The web interface is built using the flask framework.

📖 Use Bi-normal Separation to find document vectors which is used to compute similarity for shorter sentences.

計算關鍵詞重要程度(TF-IDF實作)Calculate cosine-similarity between documents using TF-IDF

There are Python 2.7 codes and learning notes for Spark 2.1.1

Two-part information retrieval system: 1) Pre-process text files, generate TF-IDF matrix and inverted index. 2) Retrieve relevant documents ranked by cosine similarity for given queries.