Showing 4 open source projects for "matlab code generator"

View related business solutions
  • Deploy Apps in Seconds with Cloud Run Icon
    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.
    Try Cloud Run Free
  • 99.99% Uptime for MySQL and PostgreSQL on Google Cloud Icon
    99.99% Uptime for MySQL and PostgreSQL on Google Cloud

    Enterprise Plus edition delivers sub-second maintenance downtime and 2x read/write performance. Built for critical apps.

    Cloud SQL Enterprise Plus gives you a 99.99% availability SLA with near-zero downtime maintenance—typically under 10 seconds. Get 2x better read/write performance, intelligent data caching, and 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server with built-in vector search for gen AI apps. New customers get $300 in free credit.
    Try Cloud SQL Free
  • 1
    Lightweight' GAN

    Lightweight' GAN

    Implementation of 'lightweight' GAN, proposed in ICLR 2021

    Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before...
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    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%). This "match" is considered a true positive if that ground-truth object has not been already used (to avoid multiple detections of the same object).
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3

    LWPR

    Locally Weighted Projection Regression (LWPR)

    Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 4
    pyIRDG

    pyIRDG

    IMDb Relational Dataset Generator

    pyIRDG is a program written in Python to generate relational datasets in Prolog format. It uses data from the Internet Movie Database in combination with IMDbPY as backend. A graphical user interface written in pyQt allows the user to link multiple entities together as model for the generation process. The big four entities are Title, Person, Company and Character. Many attributes can be chosen for adding to the output .pl file. Three types of constraints on attributes are available to limit...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Build AI Apps with Gemini 3 on Vertex AI Icon
    Build AI Apps with Gemini 3 on Vertex AI

    Access Google’s most capable multimodal models. Train, test, and deploy AI with 200+ foundation models on one platform.

    Vertex AI gives developers access to Gemini 3—Google’s most advanced reasoning and coding model—plus 200+ foundation models including Claude, Llama, and Gemma. Build generative AI apps with Vertex AI Studio, customize with fine-tuning, and deploy to production with enterprise-grade MLOps. New customers get $300 in free credits.
    Try Vertex AI Free
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB