Showing 7 open source projects for "duplicate code detect"

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  • 1
    Video-subtitle-extractor

    Video-subtitle-extractor

    A GUI tool for extracting hard-coded subtitle (hardsub) from videos

    Video hard subtitle extraction, generate srt file. There is no need to apply for a third-party API, and text recognition can be implemented locally. A deep learning-based video subtitle extraction framework, including subtitle region detection and subtitle content extraction. A GUI tool for extracting hard-coded subtitles (hardsub) from videos and generating srt files. Use local OCR recognition, no need to set up and call any API, and do not need to access online OCR services such as Baidu...
    Downloads: 62 This Week
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  • 2
    Face Alignment

    Face Alignment

    2D and 3D Face alignment library build using pytorch

    Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. More models will be added soon. By default, the package will use the SFD face detector. However, the users can alternatively use...
    Downloads: 0 This Week
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  • 3
    CleanRL

    CleanRL

    High-quality single file implementation of Deep Reinforcement Learning

    CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. CleanRL is not a modular library and therefore it is not meant to be imported. At the cost of duplicate code, we make all implementation details of a DRL algorithm variant easy to understand, so CleanRL comes with its own pros and cons. You should...
    Downloads: 0 This Week
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  • 4
    UpTrain

    UpTrain

    Your open-source LLM evaluation toolkit

    ... and optimal prompt selection. Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users. Unleash unparalleled power with a single line of code and tailor every detail as per as your use-case.
    Downloads: 0 This Week
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  • 5
    Swirl

    Swirl

    Swirl queries any number of data sources with APIs

    Swirl queries any number of data sources with APIs and uses spaCy and NLTK to re-rank the unified results without extracting and indexing anything! Includes zero-code configs for Apache Solr, ChatGPT, Elastic Search, OpenSearch, PostgreSQL, Google BigQuery, RequestsGet, Google PSE, NLResearch.com, Miro & more! SWIRL adapts and distributes queries to anything with a search API - search engines, databases, noSQL engines, cloud/SaaS services etc - and uses AI (Large Language Models) to re-rank...
    Downloads: 0 This Week
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  • 6
    Jittor

    Jittor

    Jittor is a high-performance deep learning framework

    Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement...
    Downloads: 0 This Week
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  • 7
    Reliable Metrics for Generative Models

    Reliable Metrics for Generative Models

    Code base for the precision, recall, density, and coverage metrics

    ... properties separately. In this paper, we show that even the latest version of the precision and recall (Kynkäänniemi et al., 2019) metrics are not reliable yet. For example, they fail to detect the match between two identical distributions, they are not robust against outliers, and the evaluation hyperparameters are selected arbitrarily. We propose density and coverage metrics that solve the above issues.
    Downloads: 0 This Week
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