Sergey Zagoruyko

pytorchviz. A small package to create visualizations of PyTorch execution graphs

attention-transfer. Improving Convolutional Networks via Attention Transfer (ICLR 2017)

wide-residual-networks. 3.8% and 18.3% on CIFAR-10 and CIFAR-100

diracnets. Training Very Deep Neural Networks Without Skip-Connections

functional-zoo. PyTorch and Tensorflow functional model definitions

loadcaffe. Load Caffe networks in Torch7

cvpr15deepcompare. Code and models for "Learning to Compare Image Patches via Convolutional Neural Networks"

pyinn. CuPy fused PyTorch neural networks ops

cifar.torch. 92.45% on CIFAR-10 in Torch

torch-opencv-demos. Torch7+OpenCV+ConvNets

binary-wide-resnet. PyTorch implementation of Wide Residual Networks with 1-bit weights by McDonnell (ICLR 2018)

imagine-nn. IMAGINE torch neural network routines

torch-caffe-binding. Use Caffe in Torch7

imagenet-validation.torch. Fast and easy testing of imagenet models

neural-style-autograd. autograd version of https://github.com/jcjohnson/neural-style

cunnproduction. easy embeddable Torch7 networks

nnpack.torch. Torch FFI-bindings for NNPACK

iterm.torch. Display images directly in iTerm2

openai-gemm.pytorch. PyTorch bindings for openai-gemm

fastrcnn-models.torch. Fast-RCNN models in Torch-7 format

cutorch-rtc. lua apply function for cutorch

idiap-tutorials. Jupyter Notebook

functional-style-transfer. minimal implementation of style transfer

imi-demos. live convolutional neural networks demos

nvrtc.torch. Torch7 bindings for CUDA NVRTC (runtime compilation) library

cunn-rtc. Runtime compiled Torch cunn modules

clipp.torch. Torch interface to OpenCLIPP

examples. Python

DeepDream.torch. Torch version for https://github.com/google/deepdream

imagenet-multiGPU.torch. an imagenet example in torch.

object-detection.torch. Lua

libclsvm. OpenCL optimized SVM library

quantile-regression-dqn-pytorch. Quantile Regression DQN a Minimal Working Example

infimnist.torch. Torch7 InfiMNIST ffi binding

apex. A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

cupy. NumPy-like API accelerated with CUDA

hub. Python