Showing 3 open source projects for "win-builds"

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    Build AI Apps with Gemini 3 on Vertex AI

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    Deploy Apps in Seconds with Cloud Run

    Host and run your applications without the need to manage infrastructure. Scales up from and down to zero automatically.

    Cloud Run is the fastest way to deploy containerized apps. Push your code in Go, Python, Node.js, Java, or any language and Cloud Run builds and deploys it automatically. Get fast autoscaling, pay only when your code runs, and skip the infrastructure headaches. Two million requests free per month. And new customers get $300 in free credit.
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  • 1
    Penzai

    Penzai

    A JAX research toolkit to build, edit, & visualize neural networks

    Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained...
    Downloads: 0 This Week
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  • 2
    AdaNet

    AdaNet

    Fast and flexible AutoML with learning guarantees

    AdaNet is a TensorFlow framework for fast and flexible AutoML with learning guarantees. AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture but also for learning to the ensemble to obtain even better models. At each iteration, it measures the ensemble loss for each candidate, and selects the best one to move onto the next iteration. ...
    Downloads: 0 This Week
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  • 3
    SFD

    SFD

    S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017

    ...Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with modifications tailored for face detection tasks. It includes training scripts, evaluation code, and pre-trained models that achieve strong results on popular benchmarks such as AFW, PASCAL Face, FDDB, and WIDER FACE. The framework is optimized for speed and accuracy, making it suitable for both academic research and practical applications in computer vision.
    Downloads: 1 This Week
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