Showing 896 open source projects for "anpr using python"

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  • 1
    PyPlayground is an environment for developing algorithms involving movement in a space of up to three dimensions using Python.
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  • 2
    Using this plugin-based framework, you can instantly start working on the *brain* of your bot (irc bot, chatterbot, robot, ...). With support for db, irc, logging and programming-language independent plugins, users can easily enhance the functionality.
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  • 3
    Recognition of playing poker cards using a webcam in linux And Neural networks
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  • 4
    This project simulates a multi-agent system (swarm) behavior both graphically and not. The purpose of this project is to research the properties suggested in "stability analysis of swarms" V.Gazi & K.M.Passino. Using the vpython library for 3D modeling
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  • 5
    Comprehensive framework for delivering personalized travel services using agent infrastructure.
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  • 6
    This project gives you a simple 2D world for playing with Artificial Intelligent actors. It contains of a world with objects. And a simple game, pyRugby, demonstrating the use of this world. All built in Python, using pygame for visualization.
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  • 7
    General purpose agents using reinforcement learning. Combines radial basis functions, temporal difference learning, planning, uncertainty estimations, and curiosity. Intended to be an out-of-the-box solution for roboticists and game developers.
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  • 8
    GoMad is a speech recognition system that allows you to control windows-based applications using your voice as input, instead of your mouse and keyboard.
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  • 9
    The Discrete Event Calculus Reasoner is an open source program for performing automated commonsense reasoning using the discrete event calculus, a comprehensive and highly usable formalism for reasoning about action, change, space, and mental states.
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  • 10
    Software to fit whole-sentence language models using the principle of maximum entropy. For developers of speech recognizers, text prediction interfaces, OCR, machine translation software.
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  • 11
    The tre project is simply the author\'s attempt at mimicking artificial intelligence, using a home-made HTTP daemon and simple text recognition.
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  • 12
    Deduce is an artificial intelligence program which accepts natural language sentences as input, then allows the user to ask questions against that input. Deduce attempts to answer questions using logical deduction techniques.
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  • 13
    The Genetic Architecture Framework is intended to explore and experiment with artificial life techniques using a genetic base for the physical and neural networks for the brain, in a game based simulated world.
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  • 14
    The goal of this project is to learn about and develop an AI. Current path is using a bot on AIM.
    Downloads: 1 This Week
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  • 15
    pySPACE

    pySPACE

    Signal Processing and Classification Environment in Python using YAML

    pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due...
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  • 16
    JFIPA is intended to be a scalable, easy-to-deploy router and parser of messages between agents using the FIPA Agent Communication Language represented as XML
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  • 17
    Jacomma is a framework for developing network-aware reactive information agents, using the ICM agent communication infrastructure. It offers transparent ICM bindings for java, and includes a lightweight agent execution environment.
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  • 18
    Nemotron 3 Nano

    Nemotron 3 Nano

    LL model providing reasoning and conversational capabilities

    NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 is a mid-sized open large language model created by NVIDIA to provide strong reasoning and conversational capabilities while maintaining efficient deployment requirements. The model contains roughly 30 billion parameters and is designed to balance performance and computational efficiency, making it suitable for developers building AI applications that cannot run extremely large models. It is trained from scratch and built using a hybrid architecture that...
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  • 19
    ExoPlanet

    ExoPlanet

    GUI based toolkit for running common Machine Learning algorithms.

    ExoPlanet provides a graphical interface for the construction, evaluation and application of a Machine Learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve,...
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  • 20
    Mellum-4b-base

    Mellum-4b-base

    JetBrains’ 4B parameter code model for completions

    ...With a context window of 8,192 tokens, it excels at code completion, fill-in-the-middle tasks, and intelligent code suggestions for professional developer tools and IDEs. The model is efficient for both cloud inference with vLLM and local deployment using llama.cpp or Ollama, thanks to its bf16 precision and AMP training. While the base model is not fine-tuned for downstream tasks, it is designed to be easily adapted through supervised fine-tuning (SFT) or reinforcement learning (RL). Benchmarks on RepoBench, SAFIM, and HumanEval demonstrate its competitive performance, with specialized fine-tuned versions for Python already showing strong improvements.
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  • 21
    DeepSeek-V3.2-Speciale

    DeepSeek-V3.2-Speciale

    High-compute ultra-reasoning model surpassing model surpassing GPT-5

    DeepSeek-V3.2-Speciale is the high-compute, ultra-reasoning variant of DeepSeek-V3.2, designed specifically to push the boundaries of mathematical, logical, and algorithmic intelligence. It builds on the DeepSeek Sparse Attention (DSA) framework, delivering dramatically improved long-context efficiency while preserving full model quality. Unlike the standard version, Speciale is tuned exclusively for deep reasoning and therefore does not support tool-calling, focusing its full capacity on...
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