Showing 2 open source projects for "decoder"

View related business solutions
  • $300 in Free Credit for Your Google Cloud Projects Icon
    $300 in Free Credit for Your Google Cloud Projects

    Build, test, and explore on Google Cloud with $300 in free credit. No hidden charges. No surprise bills.

    Launch your next project with $300 in free Google Cloud credit—no hidden charges. Test, build, and deploy without risk. Use your credit across the Google Cloud platform to find what works best for your needs. After your credits are used, continue building with free monthly usage products. Only pay when you're ready to scale. Sign up in minutes and start exploring.
    Start Free Trial
  • 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
    Pytorch-toolbelt

    Pytorch-toolbelt

    PyTorch extensions for fast R&D prototyping and Kaggle farming

    ...Extras for Catalyst library (Visualization of batch predictions, additional metrics). By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed 0) to coarse (low-resolution) feature maps. Access to all intermediate feature maps is beneficial if you want to apply deep supervision losses on them or encoder-decoder of object detection task.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    DETR

    DETR

    End-to-end object detection with transformers

    ...Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB
Gen AI apps are built with MongoDB Atlas
Atlas offers built-in vector search and global availability across 125+ regions. Start building AI apps faster, all in one place.
Try Free →