Showing 2 open source projects for "squeeze"

View related business solutions
  • Our Free Plans just got better! | Auth0 Icon
    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
    Try free now
  • Build Agents and Models on One Platform Icon
    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

    Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
    Try It Free
  • 1
    U-Net Fusion RFI

    U-Net Fusion RFI

    U-Net for RFI Detection based on @jakeret's implementation

    ...The primary improvements to this code base include a training and evaluation framework, along with a fusion based approach to detection, combining a number of models (currently hard coded to two trained models) along with Sum Threshold as an additional "expert." Additional work is being done to add custom layers to this model for further experimentation, including Squeeze/Excitation layers (unimplemented.) Sum Threshold (in fusion as an expert, and in testing as a comparison) requires the use of AOFlagger by Andre Offringa. You can find this code at https://gitlab.com/aroffringa/aoflagger. This project will use the aoflagger program within the code, so you may need to ensure that any environment variables are set for aoflagger before use. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2
    Tensorpack

    Tensorpack

    A Neural Net Training Interface on TensorFlow, with focus on speed

    ...On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. There are too many symbolic function wrappers already. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next