Stable Diffusion web UI
- deep-learning
- diffusion
- image-generation
- image2image
Repository Index
47,133 repositories indexed
1–15 of 15 · Most stars
Image augmentation for machine learning experiments.
- image-augmentation
- machine-learning
- augmentation
- deep-learning
🐍 Geometric Computer Vision Library for Spatial AI
CUDA Templates and Python DSLs for High-Performance Linear Algebra
- cuda
- deep-learning
- deep-learning-library
- cpp
Optimized primitives for collective multi-GPU communication
- communications
- cpp
- cuda
- deep-learning
A PyTorch Library for Accelerating 3D Deep Learning Research
- pytorch
- 3d-deep-learning
- neural-networks
- differentiable-rendering
Image augmentation library in Python for machine learning.
- augmentation
- machine-learning
- deep-learning
- neural-networks
General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
- vulkan
- vulkan-compute
- vulkan-example
- vulkan-tutorial
:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification
- convolutional-neural-networks
- deep-learning
- speaker-recognition
- 3d
Go's answer to pandas + scikit-learn: a batteries-included data analysis library with dataframes, statistics, machine learning, quant, and GPU acceleration. Run Python with seamless two-way variable sharing, or explore data straight from the Insyra CLI.
Machine learning compiler in Go. A from-scratch tinygrad port: graph-rewrite IR, autodiff as a compiler pass, zero-CGO WebGPU backend.
- autograd
- compiler
- deep-learning
- golang
HistoClean is a tool for the preprocessing and augmentation of images used in deep learning models. This easy to use application brings together the most popular image processing packages from across the python universe, meaning no more looking at documentation! HistoClean provides real time feedback to augmentations and preprocessing options. This allows users to evaluate their steps before implementation.
- image-processing
- images
- image-analysis
- deep-learning
3D shape analysis using deep learning
- cancer-biology
- cell-biology
- deep-learning
- geometric-deep-learning
Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.
- microscopy
- image-processing
- segmentation
- biology
Panoptic segmentation algorithms for 2D and 3D electron microscopy images
- deep-learning
- semantic-segmentation
- segmentation
- pytorch