A tool to prepare websites grabbed with wget for local viewing.

Last update: Apr 23, 2022

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Deep Learning makelocal

Overview

makelocal

A tool to prepare websites grabbed with wget for local viewing.

exapmples

After fetching xkcd.com with:

wget -r -no-remove-listing -r -N --page-requisites --adjust-extension -HD xkcd.com xkcd.com

You can run this command convert links in the HTML documents to refrence your local version.

find xkcd.com | grep '\.html' | grep -v '\.bak'| python3 convert.py -b `pwd`

Usage

usage: convert.py [-h] [-b] [-o] directory

Convert links in html files for local viewing. file names are read from stdin.

positional arguments:
  directory       The absolute location of the files.

options:
  -h, --help      show this help message and exit
  -b, --backup    Backup origninal files, recommended for first run
  -o, --original  Used backed up files, recommended for later runs

-b save the original file to [filename].bak this is usefull to avoid having to grab a fresh tree if you mess something up or move the files.

Owner

aka: BuggyBugs

GitHub Repository

Official Pytorch Code for the paper TransWeather

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A tool to prepare websites grabbed with wget for local viewing.

Related tags

Overview

makelocal

exapmples

Usage

Owner

Official Pytorch Code for the paper TransWeather

Make a Turtlebot3 follow a figure 8 trajectory and create a robot arm and make it follow a trajectory

PyTorch code to run synthetic experiments.

Implementing SYNTHESIZER: Rethinking Self-Attention in Transformer Models using Pytorch

NVIDIA Deep Learning Examples for Tensor Cores

Implementation of 'X-Linear Attention Networks for Image Captioning' [CVPR 2020]

Convert Table data to approximate values with GUI

You Only Look Once for Panopitic Driving Perception

This is an open solution to the Home Credit Default Risk challenge 🏡

[CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation

Deep Learning for Time Series Forecasting.

Code release for NeRF (Neural Radiance Fields)

PyTorch implementation of: Michieli U. and Zanuttigh P., "Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations", CVPR 2021.

Deep learning with TensorFlow and earth observation data.

Simple image captioning model - CLIP prefix captioning.

Code for ICCV2021 paper SPEC: Seeing People in the Wild with an Estimated Camera

Uncertain natural language inference

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

[ICCV 2021] Official Tensorflow Implementation for "Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions"