OpenCV ile görüntü ve video işleme, makine öğrenmesi ve proje uygulamaları için Türkçe kapsamlı bir rehber. 🐙 Adım adım kod örnekleriyle öğrenin ve projeler geliştirin.

Enhanced Qdrant MCP Server is a production-ready fork of the original that transforms the basic MCP server into an enterprise-grade solution with GPU acceleration, multi-vector support, and automated deployment infrastructure.

AI platform that lets you predict trends, identify outliers in your userbase and predict individual user activity. Trained with a custom model that was developed by VolksHub.

A simple side project that uses cagliostrolab/animagine-xl-3.1 and igdb to create images of anime characters with elements of specific games. Can be used as a profile picture generator.

FracNeuPK: Fractional Neural Pharmacokinetics Simulator

A comprehensive pipeline for processing learning videos, generating subtitles, summarizing content, and creating structured documents.

Docker implementation of the Marker pdf to markdown

Docker implementation of the Surya OCR

Python module and scripts to analyse CRISPRs and off-targets from a genome

This repository presents a refined video summarization and sentiment analysis pipeline built entirely on video inputs using official Hugging Face models, including BLIP, SmolVLM (2.2B & 500M), XCLIP, and VideoMAE.

Intention is a configuration driven Youtube\Twitch downloader, sound file diarizer, transcriber and automatic host match and labeler. It also contains auto-topic classifiers and bias detection.

Docker implementation of the paper "Watermark Anything with Localized Messages"

"A neural network to rule them all, a neural network to find them, a neural network to bring them all and verify if is you !!" (Face recognition tool)

Docker implementation of the VideoSeal

Docker implementation of the AudioSeal

Docker implementation of the Tabled OCR

Docker implementation of the Texify OCR

Real-Time ASR with CNN-BiLSTM: End-to-End Live Streaming Using PyTorch Lightning⚡

Docker implementation of the EasyOCR

This Flask application utilizes the Pegasus Transformer model by Google for conditional text summarization. It allows users to input large text and receive a concise summary, making it ideal for processing articles, reports, or any long-form text data.