A flexible framework of neural networks for deep learning
A simplified implemention of Faster R-CNN that replicate performance from origin paper
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
ChainerCV: a Library for Deep Learning in Computer Vision
TensorLy: Tensor Learning in Python.
an implementation of 3D Ken Burns Effect from a Single Image using PyTorch
an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch
a reimplementation of PWC-Net in PyTorch that matches the official Caffe version
a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version
an implementation of softmax splatting for differentiable forward warping using PyTorch
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19)
Library for faster pinned CPU <-> GPU transfer in Pytorch
Deep learning framework realized by Numpy purely, supports for both Dynamic Graph and Static Graph with GPU acceleration
The code for multi-channel source separation and dereverberation such as FastMNMF1, FastMNMF2, and AR-FastMNMF2.
Computer vision container that includes Jupyter notebooks with built-in code hinting, Anaconda, CUDA 11.8, TensorRT inference accelerator for Tensor cores, CuPy (GPU drop in replacement for Numpy), PyTorch, PyTorch geometric for Graph Neural Networks, TF2, Tensorboard, and OpenCV for accelerated workloads on NVIDIA Tensor cores and GPUs.
Official source code for our paper "AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation" (CVPR 2020)
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