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Machine Learning · GitHub

Train, evaluate, and run machine learning models.

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7,461 projects
595stars

An efficient modular implementation of Associating Objects with Transformers for Video Object Segmentation in PyTorch

Machine LearningPythonBSD-3-ClauseGitHub
475stars

Audio Development Tools (ADT) is a project for advancing sound, speech, and music technologies, featuring components for machine learning, sound synthesis, speech and music generation, signal processing, game audio, digital audio workstations (DAWs), and more.

ltu

YuanGongND

480stars
ltu preview

Code, Dataset, and Pretrained Models for Audio and Speech Large Language Model "Listen, Think, and Understand".

exposure

yuanming-hu

783stars
exposure preview

Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model.

637stars

Here is a pytorch implementation of deeplabv3+ supporting ResNet(79.155%) and Xception(79.945%). Multi-scale & flip test and COCO dataset interface has been finished.

daisyRec

yudi-mars

550stars

Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)

33stars
catseye preview

Neural network library written in C and Javascript

  • Just include catseye.h and write your model in c. There is nothing to install.
  • Small dependency & simple implementation

PoseFlow

YuliangXiu

442stars
PoseFlow preview

PoseFlow: Efficient Online Pose Tracking (BMVC'18)

  • Dec 2018: PoseFlow(General Version) released! Support ANY DATASET and pose tracking results visualization.
  • Oct 2018: Support generating correspondence files with ORB(OpenCV), 3X FASTER and no need to compile DeepMatching library.

pytorch_advanced

YutaroOgawa

871stars

書籍「つくりながら学ぶ! PyTorchによる発展ディープラーニング」の実装コードを配置したリポジトリです

Machine LearningJupyter NotebookMITGitHub

smolcluster

YuvrajSingh-mist

90stars
smolcluster preview

Educational distributed training and inference library for heterogeneous local hardware: Mac minis, Raspberry Pis and GPUs over plain Python sockets. FSDP, GRPO, parameter-server, expert and data parallelism.

  • Distributed Training Algorithms: Fully Sharded Data Parallel (ZeRO-optimized), Classic Data Parallelism (All-Reduce), Elastic Distributed Parallelism, Synchronous Parameter Server (SyncPS), Expert Parallelism (EP), and Model Parallelism
  • Heterogeneous Hardware: Mac minis, Raspberry Pis, MacBooks, and Windows machines
Machine LearningPythonApache-2.0GitHub