A list of useful commands for the FFmpeg command line tool.
Download FFmpeg: https://www.ffmpeg.org/download.html
Full documentation: https://www.ffmpeg.org/ffmpeg.html
A list of useful commands for the FFmpeg command line tool.
Download FFmpeg: https://www.ffmpeg.org/download.html
Full documentation: https://www.ffmpeg.org/ffmpeg.html
#include <functional> | |
#include <chrono> | |
#include <iostream> | |
template <class> struct ExeTime; | |
// Execution time decorator | |
template <class R, class... Args> | |
struct ExeTime<R(Args ...)> { | |
public: |
You could always use Smart HTTP.
For read-only (git:) urls, install corkscrew.
Download git-proxy.sh. Put it somewhere and make it executable.
Run git config --global core.gitproxy '/usr/local/bin/git-proxy.sh'
To clone, push, pull over ssh, add the contents of ssh_config to your ~/.ssh/config file.
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
// Use Gists to store code you would like to remember later on | |
console.log(window); // log the "window" object to the console |