Official Pytorch implementation of Meta Internal Learning

Last update: Aug 24, 2022

Related tags

Overview

Meta Internal Learning

This repository is the official implementation of Meta Internal Learning by Raphael Bensadoun, Shir Gur, Tomer Galanti, Lior Wolf.

Project | arXiv | Code

Requirements

To install requirements:

pip install -r requirements.txt

Training on a dataset

Create a folder X containing the images. (see structure in data folder)
Determine how many iterations Y to train by scale (depends mostly on the size of the dataset, you may refer to the appendix for reference).
Run

python train.py --image-path X --batch-size 16 --visualize --niter Y --min-size 25 --checkname X_result --SAVE-MODEL --SAVE-IMGS

Generated images, tensorboard logs and trained models are stored in MetaInternalLearning/run/X_result.

The default input format is jpg. Use '--file-suffix png' for .png files.

Examples from paper -

Places-50 -

python train.py --image-path data/places_50 --batch-size 16 --visualize --niter 4000  --min-size 28 --checkname places_50_result --SAVE-MODEL --SAVE-IMGS

LSUN-50

python train.py --image-path data/lsun_50 --batch-size 16 --visualize --niter 5000  --checkname lsun_50_result --SAVE-MODEL --SAVE-IMGS

Valley dataset can be downloaded here - http://places2.csail.mit.edu/download.html (256x256 small images) and can be divided into subsets as mentioned in the paper.

V500 -

python train.py --image-path data/V500 --batch-size 16 --visualize --niter 25000 --min-size 25 --checkname v500_result --ar 1 --SAVE-MODEL

V2500 -

python train_dataset_parallel.py --image-path data/V2500 --batch-size 16 --niter 100000 --rec-weight 50 --min-size 25 --checkname v2500_result --ar 1 --SAVE-MODEL

V5000 -

python train_dataset_parallel.py --image-path data/V5000 --batch-size 16 --niter 150000 --rec-weight 50 --min-size 25 --checkname v5000_result --ar 1 --SAVE-MODEL

Applications

Coming soon!

Official Pytorch implementation of Meta Internal Learning

Related tags

Overview

Meta Internal Learning

Requirements

Training on a dataset

Examples from paper -

Applications

Owner

code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology

Simple keras FCN Encoder/Decoder model for MS-COCO (food subset) segmentation

DLFlow is a deep learning framework.

Towards Implicit Text-Guided 3D Shape Generation (CVPR2022)

IRON Kaggle project done while doing IRONHACK Bootcamp where we had to analyze and use a Machine Learning Project to predict future sales

Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Repo for Photon-Starved Scene Inference using Single Photon Cameras, ICCV 2021

Semantically Contrastive Learning for Low-light Image Enhancement

YOLOX Win10 Project

ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

Complete* list of autonomous driving related datasets

[CVPR 2021] Monocular depth estimation using wavelets for efficiency

Learning to Initialize Neural Networks for Stable and Efficient Training

Combine Tacotron2 and Hifi GAN to generate speech from text

Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019

Heat transfer problemas solved using python

NeRD: Neural Reflectance Decomposition from Image Collections

This repository includes code of my study about Asynchronous in Frequency domain of GAN images.

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.