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Train, evaluate, and run machine learning models.

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7,564 projects

mimicry

kwotsin

609stars
mimicry preview

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

RSPrompter

KyanChen

675stars

This is the pytorch implement of our paper "RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model"

Machine LearningPythonApache-2.0GitHub

ScreenAI

kyegomez

387stars
ScreenAI preview

Implementation of the ScreenAI model from the paper: "A Vision-Language Model for UI and Infographics Understanding"

VisionMamba

kyegomez

496stars
VisionMamba preview

Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images

OpenDPD

lab-emi

182stars
OpenDPD preview

OpenDPD is an end-to-end learning framework built in PyTorch for power amplifier (PA) modeling and digital pre-distortion (DPD). You are cordially invited to contribute to this project by providing your own backbone neural networks, pretrained models or measured PA datasets.

Machine LearningPythonApache-2.0GitHub

OpenHI

lachlanchen

108stars
OpenHI preview

Self‑calibrated neuromorphic hyperspectral imaging pipeline for event cameras with diffractive illumination. Includes end‑to‑end tools for RAW segmentation, multi‑window time‑warping compensation, spectral visualization, and hardware control for synchronized event/frame capture and scanning.

  • End-to-end RAW-to-spectrum event processing workflow.
  • Auto/manual scan period detection and forward/backward segmentation.

PyOmniTS

Ladbaby

109stars
PyOmniTS preview

🔬 A Researcher&Agent-Friendly Framework for Time Series Analysis. Train Any Model on Any Dataset!

  • Extensibility: Adapt your model/dataset once, train almost any combination of "model" $\times$ "dataset" $\times$ "loss function".
  • Compatibility: Accept models with any number/type of arguments in forward; Accept datasets with any number/type of return values in __getitem__; Accept tailored loss calculation for specific models.