Popular GitHub Repositories for Lightly Train

1 min voice data can also be used to train a good TTS model! (few shot voice cloning)

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Easily train a good VC model with voice data <= 10 mins!

Code and documentation to train Stanford's Alpaca models, and generate the data.

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Example ? Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using ? Amazon SageMaker.

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

A server-client framework to train any AI agent with rollouts and feedbacks

? 「大模型」1小时从0训练26M参数的视觉多模态VLM！? Train a 26M-parameter VLM from scratch in just 1 hours!

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.

Lightweight library to build and train neural networks in Theano

创建、动画制作和安排你的AI角色

a delightful machine learning tool that allows you to train, test, and use models without writing code

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

A modular high-level library to train embodied AI agents across a variety of tasks and environments.

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

Images to inference with no labeling (use foundation models to train supervised models).

The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol.

Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.