Showing 17 open source projects for "competition"

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  • 1
    DeepSeek V2

    DeepSeek V2

    Strong, Economical, and Efficient Mixture-of-Experts Language Model

    ...The V2 model is expected to support more advanced features like better context window handling, more efficient inference, better performance on challenging tasks, and stronger alignment with human feedback. Because DeepSeek is pushing open-weight competition, this V2 iteration is meant to solidify its position in benchmark rankings and in developer adoption. The code in the repository may include description files, support for tool use or plug-in architectures, and artifacts showing fine-tuning or prompt templates.
    Downloads: 21 This Week
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  • 2
    LiteMultiAgent

    LiteMultiAgent

    The Library for LLM-based multi-agent applications

    LiteMultiAgent is a lightweight and extensible multi-agent reinforcement learning (MARL) platform designed for rapid experimentation. It allows researchers to design and test coordination, competition, and collaboration scenarios in simulated environments.
    Downloads: 0 This Week
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  • 3
    DeepSeekMath-V2

    DeepSeekMath-V2

    Towards self-verifiable mathematical reasoning

    DeepSeekMath-V2 is a large-scale open-source AI model designed specifically for advanced mathematical reasoning, theorem proving, and rigorous proof verification. It’s built by DeepSeek as a successor to their earlier math-specialist models. Unlike general-purpose LLMs that might generate plausible-looking math but sometimes hallucinate or mishandle rigorous logic, Math-V2 is engineered to not only generate solutions but also self-verify them, meaning it examines the derivations, checks...
    Downloads: 2 This Week
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  • 4
    Learn Prompting

    Learn Prompting

    This website is a free, open-source guide on prompt engineering

    This website is a free, open-source guide on prompt engineering. Contributions are welcome! Harsh criticism is welcome too. We launched the first ever prompt hacking competition designed to enhance AI safety and education by challenging participants to outsmart large language models from May 5th to June 3rd! The competition featured 10 increasingly difficult levels of prompt hacking defenses and the chance to win over $35,000 in prizes. Coding is a great skill to learn alongside prompt engineering. We recommend learning Python, as it is a popular language for AI and machine learning. ...
    Downloads: 0 This Week
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  • 5
    ConvNeXt V2

    ConvNeXt V2

    Code release for ConvNeXt V2 model

    ...The V2 version introduces a fully convolutional masked autoencoder (FCMAE) framework where parts of the image are masked and the network reconstructs the missing content, marrying convolutional inductive bias with powerful pretraining. A key innovation is a new Global Response Normalization (GRN) layer added to the ConvNeXt backbone, which enhances feature competition across channels. The result is a convnet that competes strongly with transformer architectures on recognition benchmarks while being efficient and hardware-friendly. The repository provides official PyTorch implementations for multiple model sizes (Atto, Femto, Pico, up through Huge), conversion from JAX weights, code for pretraining/fine-tuning, and pretrained checkpoints. ...
    Downloads: 0 This Week
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  • 6
    CodeContests

    CodeContests

    Large dataset of coding contests designed for AI and ML model training

    ...This dataset played a central role in the development of AlphaCode, DeepMind’s model for solving programming problems at a human-competitive level, as published in Science. CodeContests aggregates problems and human-written solutions from multiple programming competition platforms, including AtCoder, Codeforces, CodeChef, Aizu, and HackerEarth. Each problem includes structured metadata, problem descriptions, paired input/output test cases, and multiple correct and incorrect solutions in various programming languages. The dataset is distributed in Riegeli format using Protocol Buffers, with separate training, validation, and test splits for reproducible machine learning experiments.
    Downloads: 1 This Week
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  • 7
    BCI

    BCI

    BCI: Breast Cancer Immunohistochemical Image Generation

    Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix. We have released the trained model on BCI and LLVIP datasets. We host a competition for breast cancer immunohistochemistry image generation on Grand Challenge. Project pix2pix provides a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene, these can be pairs {HE, IHC}. Then we can learn to translate A(HE images) to B(IHC images). ...
    Downloads: 0 This Week
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  • 8
    mAP

    mAP

    Evaluates the performance of your neural net for object recognition

    In practice, a higher mAP value indicates a better performance of your neural net, given your ground truth and set of classes. The performance of your neural net will be judged using the mAP criteria defined in the PASCAL VOC 2012 competition. We simply adapted the official Matlab code into Python (in our tests they both give the same results). First, your neural net detection-results are sorted by decreasing confidence and are assigned to ground-truth objects. We have "a match" when they share the same label and an IoU >= 0.5 (Intersection over Union greater than 50%). ...
    Downloads: 0 This Week
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  • 9
    EEG Seizure Prediction

    EEG Seizure Prediction

    Seizure prediction from EEG data using machine learning

    The Kaggle-EEG project is a machine learning solution developed for seizure prediction from EEG data, achieving 3rd place in the Kaggle/University of Melbourne Seizure Prediction competition. The repository processes EEG data to predict seizures by training machine learning models, specifically using SVM (Support Vector Machine) and RUS Boosted Tree ensemble models. The framework processes EEG data into features, trains models, and outputs predictions, handling temporal data to ensure accuracy.
    Downloads: 0 This Week
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  • 10
    JCAT is the platform for TAC Market Design Competition and an ideal tool for experimental mechanism design research. It allows multiple electronic markets to compete against each other, and trading agents, as traders, to move between them.
    Downloads: 0 This Week
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  • 11
    Interactive4J
    Project aim to provide simple easy APIs for Java developers to use interactive abilities in their Java Applications like speech recognition, handwriting recognition, use of web cam , sound record/play, decision trees , text to speech and many others.
    Downloads: 0 This Week
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  • 12
    RARS is the Robot Auto Racing Simulation, in which the drivers are robot programs. It is intended as a competition among programmers. It consists of a simulation of the physics of cars, a graphic display of the race, and a robot driver for each car.
    Downloads: 0 This Week
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  • 13
    Snackware is a project aimed to create a "competition of coders" by making different classes or functions to battle amongst themselves against the rules of a game. It's not a traditional game, anyway, since it work with no human intervention.
    Downloads: 0 This Week
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  • 14
    SimpleDomino is a Server for a Programming Competition from Schoolinux and Linux-dubai.com for School Students. It uses TCP/IP for Communications. (It's base on Linux but can port to Windows) You can start your own Competition or Help us to develop this P
    Downloads: 0 This Week
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  • 15
    MARDG is a project to design a robot team to compete in the BYU Robot Soccer competition of 2004.
    Downloads: 0 This Week
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  • 16
    The RoboCup competition is an international event in which teams of autonomous robots play soccer against one another. This project provides resources for the RoboCup F180, or small-sized league. It also provides a mechanism through which teams can c
    Downloads: 0 This Week
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  • 17
    Development of a snazzy Java program to simulate the growth and competition of entities in a limited environment.
    Downloads: 0 This Week
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