NumPy & SciPy for GPU

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

A flexible framework of neural networks for deep learning

A simplified implemention of Faster R-CNN that replicate performance from origin paper

TensorLy: Tensor Learning in Python.

an implementation of 3D Ken Burns Effect from a Single Image using PyTorch

ChainerCV: a Library for Deep Learning in Computer Vision

an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

Library for faster pinned CPU <-> GPU transfer in Pytorch

a reimplementation of PWC-Net in PyTorch that matches the official Caffe version

an implementation of softmax splatting for differentiable forward warping using PyTorch

a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version

A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python 🚀

CuPy fused PyTorch neural networks ops

Deep learning framework realized by Numpy purely, supports for both Dynamic Graph and Static Graph with GPU acceleration

Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19)

The code for multi-channel source separation and dereverberation such as FastMNMF1, FastMNMF2, and AR-FastMNMF2.

Official source code for our paper "AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation" (CVPR 2020)

Chainer implementation of waifu2x

A Simple & Flexible Cross Framework Operators Toolkit