Official Pytorch implementation of Meta Internal Learning

Last update: Aug 24, 2022

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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

Automatically measure the facial Width-To-Height ratio and get facial analysis results provided by Microsoft Azure

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Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

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Implementation of SwinTransformerV2 in TensorFlow.

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[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

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My personal Home Assistant configuration.

Lava-DL, but with PyTorch-Lightning flavour

Official Pytorch implementation of Meta Internal Learning

Related tags

Overview

Meta Internal Learning

Requirements

Training on a dataset

Examples from paper -

Applications

Owner

Automatically measure the facial Width-To-Height ratio and get facial analysis results provided by Microsoft Azure

[ICCV2021] Learning to Track Objects from Unlabeled Videos

Automated Attendance Project Using Face Recognition

DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

Serve TensorFlow ML models with TF-Serving and then create a Streamlit UI to use them

CenterPoint 3D Object Detection and Tracking using center points in the bird-eye view.

[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch

Simple Tensorflow implementation of "Adaptive Convolutions for Structure-Aware Style Transfer" (CVPR 2021)

Collection of sports betting AI tools.

Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

Mixed Transformer UNet for Medical Image Segmentation

Implementation of SwinTransformerV2 in TensorFlow.

Implementation of the paper "Generating Symbolic Reasoning Problems with Transformer GANs"

A very short and easy implementation of Quantile Regression DQN

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

The source code for the Cutoff data augmentation approach proposed in this paper: "A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation".

My personal Home Assistant configuration.

Lava-DL, but with PyTorch-Lightning flavour

NuPIC Studio is an all-in-one tool that allows users create a HTM neural network from scratch