Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
![Screenshot 2023-12-18 at 10 40 27 PM](https://private-user-images.githubusercontent.com/3837836/291468646-4c30ad72-76ee-4939-a5fb-16b570d38cf2.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.Td5H0nudocqb8-pXE6BsZQxBOv-2zzVePP4zWVp7I3o)
/** | |
* Mariano Julio Vicario aka Ranu - TW: @el_ranu | |
* http://www.ranu.com.ar | |
* Licensed under the Apache License, Version 2.0 (the "License"); | |
* you may not use this file except in compliance with the License. | |
* You may obtain a copy of the License at | |
* | |
* http://www.apache.org/licenses/LICENSE-2.0 | |
* | |
* Unless required by applicable law or agreed to in writing, software |
// 4 spaces to 2 spaces | |
%s;^\(\s\+\);\=repeat(' ', len(submatch(0))/2);g | |
// Tab to 2 spaces | |
:%s/\t/ /g |
#!/bin/bash | |
# Anh Nguyen <anh.ng8@gmail.com> | |
# 2016-04-30 | |
# MIT License | |
# This script takes in same-size images from a folder and make a crossfade video from the images using ffmpeg. | |
# Make sure you have ffmpeg installed before running. | |
# The output command looks something like the below, but for as many images as you have in the folder. |
def image_name_to_netid(): | |
# Map imagenet names to their netids | |
input_f = open("./imagenet_validation_imagename_labels.txt") | |
label_map = {} | |
netid_map = {} | |
for line in input_f: | |
parts = line.strip().split(" ") | |
label_map[parts[0]] = parts[1] | |
netid_map[parts[0]] = parts[2] | |
return label_map, netid_map |