Popular GitHub Repositories for Folder Structure Visualizer

For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 80+ languages.

Visualizer for neural network, deep learning and machine learning models

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.

notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under the /Resources folder.

PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ???

Visual analysis and diagnostic tools to facilitate machine learning model selection.

Photogrammetric Computer Vision Framework

.NET, Angular, Clean Architecture, Clean Code, SOLID Principles, KISS Principle, DRY Principle, Fail Fast Principle, Common Closure Principle, Common Reuse Principle, Acyclic Dependencies Principle, Mediator Pattern, Result Pattern, Folder-by-Feature Structure, Separation of Concerns

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

MemU is an open-source memory framework for AI companions—high accuracy, fast retrieval, low cost. It acts as an intelligent "memory folder" that adapts to different scenarios, from different companions senarios.

EdgeConnect: Structure Guided Image Inpainting using Edge Prediction, ICCV 2019 https://arxiv.org/abs/1901.00212

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020)

An IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.

Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, trace, persist, and execute on your own infrastructure.

Interact, analyze and structure massive text, image, embedding, audio and video datasets

[GenAI Application Development Framework] ? Build GenAI application quick and easy ? Easy to interact with GenAI agent in code using structure data and chained-calls syntax ? Use Agently Workflow to manage complex GenAI working logic ? Switch to any model without rewrite application code