Showing 5 open source projects for "high"

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  • 1
    Neovim 99

    Neovim 99

    Neovim AI agent done right

    Neovim 99 is an experimental GitHub repository created by well-known developer and educator ThePrimeagen that explores what he describes as the “ideal AI workflow” for developers who want a streamlined, high-quality integration of AI tooling into real coding environments — particularly focused on tools like Neovim and agent-centric workflows. Rather than a polished end-product, this repo serves as a playground for testing, iterating, and documenting workflows that integrate AI agents directly into everyday coding tools, emphasizing rapid feedback loops, automation, and minimal friction. ...
    Downloads: 0 This Week
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  • 2
    Databend

    Databend

    Cloud-native open source data warehouse for analytics and AI queries

    Databend is an open source cloud-native data warehouse designed for large-scale analytics and modern data workloads. Built in Rust, the system focuses on high performance, scalability, and efficient data processing for analytical queries. It is designed with a separation of compute and storage, allowing compute nodes to scale independently while storing data in object storage systems. This architecture enables cost-efficient storage and elastic scaling for workloads that involve large datasets and complex queries. ...
    Downloads: 0 This Week
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  • 3
    KoboldAI

    KoboldAI

    Your gateway to GPT writing

    ...The way you play and how good the AI will be depends on the model or service you decide to use. No matter if you want to use the free, fast power of Google Colab, your own high end graphics card, an online service you have an API key for (Like OpenAI or Inferkit) or if you rather just run it slower on your CPU you will be able to find a way to use KoboldAI that works for you.
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    Downloads: 127 This Week
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  • 4
    MultiPathNet

    MultiPathNet

    A Torch implementation of the object detection network

    ...It extends the Fast R-CNN framework by introducing multiple network “paths” to enhance feature extraction and object recognition robustness. The MultiPath architecture incorporates skip connections and multi-scale processing to capture both fine-grained details and high-level context within a single detection pipeline. This results in improved detection accuracy across various object sizes and categories compared to standard single-path architectures. The repository supports training, evaluation, and visualization for object detection tasks on popular datasets such as PASCAL VOC and MS COCO. It provides pre-trained models for VGG, AlexNet, and ResNet backbones, along with integration for SharpMask and DeepMask proposal generators.
    Downloads: 0 This Week
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  • Stop Cyber Threats with VM-Series Next-Gen Firewall on Azure Icon
    Stop Cyber Threats with VM-Series Next-Gen Firewall on Azure

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  • 5
    DeepMask

    DeepMask

    Torch implementation of DeepMask and SharpMask

    DeepMask is an early, influential approach to class-agnostic object segmentation that learns to propose pixel-accurate masks directly from images. Instead of first generating boxes and then refining them, the network predicts a foreground mask and an “objectness” score for a given image patch, yielding high-quality segment proposals suitable for downstream detection or instance segmentation. The model is trained end-to-end to align mask shape with object extent, which markedly improves recall at a manageable number of proposals. In practice, DeepMask is run on an image pyramid with a sliding window, followed by non-maximum suppression to produce a compact set of candidates. ...
    Downloads: 0 This Week
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