Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
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Updated
Sep 17, 2024 - Python
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 🚀
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)
A Simple & Flexible Cross Framework Operators Toolkit
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