Popular GitHub Repositories for Pre Train Recommendation

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.

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)

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

PaddleFormers is an easy-to-use library of pre-trained large language model zoo based on PaddlePaddle.

Awesome pre-trained models toolkit based on PaddlePaddle. (400+ models including Image, Text, Audio, Video and Cross-Modal with Easy Inference & Serving)

The official GitHub page for the survey paper "A Survey of Large Language Models".

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.

Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）

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

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

OpenVINO? is an open source toolkit for optimizing and deploying AI inference

A collection of pre-trained, state-of-the-art models in the ONNX format

Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .