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A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.

• Broad algorithm coverage: linear models, tree-based methods, ensembles, SVMs, neighbors, clustering, decomposition, and preprocessing.
• Strong linear algebra traits with optional ndarray integration for users who prefer array-first workflows.

3D visual probe for 3D monitoring and Digital Twinning

基于Pytorch和torchtext的自然语言处理深度学习框架。

Official implementation for "Stable Flow: Vital Layers for Training-Free Image Editing" [CVPR 2025]

Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks

• 🧠 Versatile GNN Applications: Supports easy customization in using GNNs in supervised and unsupervised ML applications like node classification and link prediction.
• 🚀 Designed for Scalability: The architecture is built with horizontal scaling in mind, ensuring cost-effective performance throughout the process of data preprocessing and transformation, model training, and inference.

an implementation of softmax splatting for differentiable forward warping using PyTorch

AI Auto Annotation & YOLO Training Pipeline, End-to-end object detection auto-labeling and YOLO training platform. VLM-powered annotation with NVIDIA LocateAnything-3B, manual refinement, one-click YOLO training, video keyframe extraction, and model validation. Supports image and video.

• 🤖 VLM auto-labeling: Open-vocabulary object detection with LocateAnything-3B
• 🎯 SAM2 / SAM3 segmentation: Bbox → pixel-precise mask with SAM 2.1 or SAM3 text-driven detection+segmentation in one pass, BBox/Mask toggle on canvas

A Pytorch Implementation of "Neural Speech Synthesis with Transformer Network"