tools
Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
PyTorch extensions for fast R&D prototyping and Kaggle farming
A treasure chest for visual classification and recognition powered by PaddlePaddle
An easy and fast way to create a Python GUI 🐍
🐍+🔥This project is aimed to help Pytorch machine learning developers to quickly build a Flask web app in a Docker container ready to be deployed.
Toolbox of models, callbacks, and datasets for AI/ML researchers.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
alfred-py: A deep learning utility library for **human**, more detail about the usage of lib to: https://zhuanlan.zhihu.com/p/341446046
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MulimgViewer is a multi-image viewer that can open multiple images in one interface, which is convenient for image comparison and image stitching.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Neural Network Blocks - Collect all kinds of fancy model blocks for you to build more powerful neural network model.
遥感变化检测标注工具『Remote sensing change detection annotation tool』
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
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Master programming by recreating your favorite technologies from scratch.
ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。
A playbook for systematically maximizing the performance of deep learning models.



