Showing 4 open source projects for "smart-exception-java-0.2.1.jar"

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  • 1
    OpenFold

    OpenFold

    Trainable, memory-efficient, and GPU-friendly PyTorch reproduction

    OpenFold carefully reproduces (almost) all of the features of the original open source inference code (v2.0.1). The sole exception is model ensembling, which fared poorly in DeepMind's own ablation testing and is being phased out in future DeepMind experiments. It is omitted here for the sake of reducing clutter. In cases where the Nature paper differs from the source, we always defer to the latter. OpenFold is trainable in full precision, half precision, or bfloat16 with or without DeepSpeed, and we've trained it from scratch, matching the performance of the original. ...
    Downloads: 2 This Week
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  • 2
    DeepCamera

    DeepCamera

    Open-Source AI Camera. Empower any camera/CCTV

    ...The source code is here It leverages Yolov7 as a person detector, FastReID for person feature extraction, Milvus the local vector database for self-supervised learning to identify unseen persons, Labelstudio to host images locally and for further usage such as label data and train your own classifier. It also integrates with Home-Assistant to empower smart homes with AI technology.
    Downloads: 3 This Week
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  • 3
    Openfire LLM Chatbot Plugin

    Openfire LLM Chatbot Plugin

    LLM Chatbot Assistant for Openfire server

    This plugin is a wrapper to hosted AI Inference server for LLM chat models. It uses the Botz API to create a chatbot in Openfire which will engage in XMPP chat and groupchat conversations.
    Downloads: 5 This Week
    Last Update:
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  • 4
    KotlinDL

    KotlinDL

    High-level Deep Learning Framework written in Kotlin

    KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning models in production environments.
    Downloads: 0 This Week
    Last Update:
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