Masaki Saito (rezoo)

rezoo

Geek Repo

Company:Preferred Networks Inc.

Location:Tokyo, Japan

Home Page:https://scholar.google.com/citations?user=KIxHWVMAAAAJ

Github PK Tool:Github PK Tool


Organizations
chainer

Masaki Saito's repositories

illustration2vec

A simple deep learning library for estimating a set of tags and extracting semantic feature vectors from given illustrations.

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movis

Python library for video editing, presentation video generation, motion graphics, shader art coding, and other video production tasks

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cvutil

Convenient algorithms for OpenCV

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imagenet-python

imagenet-python is a python library to download images from ImageNet

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boost-BP

The belief propagation algorithms for boost.Graph

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twopy

2ch python library

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pyrealsense

A thin wrapper for communicating realsense via python

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selective_search_py

Python-based implementation of the Selective Search for Object Recognition.

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chainer

A flexible framework of neural networks for deep learning

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chainer_computational_cost

Estimate theoretical computational cost of a chainer-based neural network

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chainercv

ChainerCV: a Library for Computer Vision in Deep Learning

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cupy

NumPy-like API accelerated with CUDA

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giraffe

This repository will contain the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

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imageio

Python library for reading and writing image data

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maxim-pytorch

[CVPR 2022 Oral] PyTorch re-implementation for "MAXIM: Multi-Axis MLP for Image Processing", with *training code*. Official Jax repo: https://github.com/google-research/maxim

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multibit

MultiBit is a desktop bitcoin client, powered by bitcoinj

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onnx-chainer

Add-on package for ONNX format support in Chainer

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pynvvl

A Python wrapper of NVIDIA Video Loader (NVVL) with CuPy for fast video loading with Python

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python-bitcoin

A collection of types, crypto functions, script interpreter, and utility functions needed to implement the bitcoin protocol.

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stable-diffusion-webui

Stable Diffusion web UI

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tgan2

The official implementation of "Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GAN"

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