The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface. The fastest local inference engine in the world.
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Readest is a modern, feature-rich ebook reader designed for avid readers offering seamless cross-platform access, powerful tools, and an intuitive interface to elevate your reading experience.
- ebook
- ebook-reader
- epub
- foliate
pix2code: Generating Code from a Graphical User Interface Screenshot
- deep-learning
- datasets
- deep-neural-networks
- front-end-development
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
- distance
- algorithm
- python
- textdistance
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
Python library and CLI tool to interface with Google Translate's text-to-speech API
- speech
- python
- tts
- text-to-speech
Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
- computer-vision
- annotate-images
- entity-recognition
- desktop
A uniform interface to run deep learning models from multiple frameworks
TavernGym and TavernBench: a faithful, full-content Hearthstone Battlegrounds simulator with an RL interface, and a 960-task decision benchmark. Code and data for "Build, Don't Play".
- game-ai
- hearthstone
- reinforcement-learning
- rust
Cumo (pronounced like "koomo") is CUDA aware numerical library whose interface is highly compatible with Ruby Numo
- ruby
- cuda
- numo
- scicentific-computing
A graphical user interface to annotate point clouds and 3D data.
- point-cloud
- lidar
- computer-vision
- cpp
MCP server for spatial transcriptomics analysis through natural language interfaces.
- bioinformatics
- cell-communication
- deconvolution
- machine-learning
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.
A Ruby interface to the Timbl machine-learning library