mlorant

REGIONS = {
    'Auvergne-Rhône-Alpes': ['01', '03', '07', '15', '26', '38', '42', '43', '63', '69', '73', '74'],
    'Bourgogne-Franche-Comté': ['21', '25', '39', '58', '70', '71', '89', '90'],
    'Bretagne': ['35', '22', '56', '29'],
    'Centre-Val de Loire': ['18', '28', '36', '37', '41', '45'],
    'Corse': ['2A', '2B'],
    'Grand Est': ['08', '10', '51', '52', '54', '55', '57', '67', '68', '88'],
    'Guadeloupe': ['971'],
    'Guyane': ['973'],
    'Hauts-de-France': ['02', '59', '60', '62', '80'],

benagricola

from PyInstaller.utils.hooks import collect_submodules, collect_data_files

# This hooks the scrapy project 'cot' to import all submodules, change name to match scrapy project
hiddenimports = (collect_submodules('cot'))

BIGBALLON

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

antoinebrl

Preparation of ImageNet (ILSVRC2012)

The dataset can be found on the official website if you are affiliated with a research organization. It is also available on Academic torrents.

This script extracts all the images and group them so that folders contain images that belong to the same class.

1. Download the ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar
2. Download the script wget https://gist.githubusercontent.com/antoinebrl/7d00d5cb6c95ef194c737392ef7e476a/raw/dc53ad5fcb69dcde2b3e0b9d6f8f99d000ead696/prepare.sh
3. Run it ./prepare.sh

veekaybee

Anti-hype LLM reading list

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.

Foundational Concepts

Pre-Transformer Models