Implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

Related tags

Deep LearningSinGAN
Overview

SinGAN

This is an unofficial implementation of SinGAN from someone who's been sitting right next to SinGAN's creator for almost five years.

Please refer the project's page for more details.

Citation

If you use this code for your research, please cite the paper:

@inproceedings{shaham2019singan,
  title={Singan: Learning a generative model from a single natural image},
  author={Shaham, Tamar Rott and Dekel, Tali and Michaeli, Tomer},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4570--4580},
  year={2019}
}

Code

Clone repository

Clone this repository into any place you want.

git clone https://github.com/kligvasser/SinGAN
cd ./SinGAN/generation/

Install dependencies

python -m pip install -r requirements.txt

This code tested in PyTorch 1.8.1.

Training

To train SinGAN model on your own image:

python3 main.py --root <path-to-image>

Evaluating

For evaluating, run the following command:

python3 main.py --root <path-to-image> --evaluation --model-to-load <path-to-model-pt> --amps-to-load <path-to-amp-pt> --num-steps <number-of-samples> --batch-size <number-of-images-in-batch>
Repository of Vision Transformer with Deformable Attention

Vision Transformer with Deformable Attention This repository contains the code for the paper Vision Transformer with Deformable Attention [arXiv]. Int

410 Jan 03, 2023
Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Optimizing Dense Retrieval Model Training with Hard Negatives Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma 🔥 News 2021-10

Jingtao Zhan 99 Dec 27, 2022
[NeurIPS 2020] Official Implementation: "SMYRF: Efficient Attention using Asymmetric Clustering".

SMYRF: Efficient attention using asymmetric clustering Get started: Abstract We propose a novel type of balanced clustering algorithm to approximate a

Giannis Daras 46 Dec 22, 2022
Research - dataset and code for 2016 paper Learning a Driving Simulator

the people's comma the paper Learning a Driving Simulator the comma.ai driving dataset 7 and a quarter hours of largely highway driving. Enough to tra

comma.ai 4.1k Jan 02, 2023
Code for 'Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning', ICCV 2021

CMIC-Retrieval Code for Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning. ICCV 2021. Introduction In this wo

42 Nov 17, 2022
Image augmentation library in Python for machine learning.

Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independe

Marcus D. Bloice 4.8k Jan 07, 2023
Fastshap: A fast, approximate shap kernel

fastshap: A fast, approximate shap kernel fastshap was designed to be: Fast Calculating shap values can take an extremely long time. fastshap utilizes

Samuel Wilson 22 Sep 24, 2022
Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction Requirements The code has been tested running under Python 3.7.4, with the foll

zshicode 84 Jan 01, 2023
Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

BEVNet Datasets Datasets should be put inside data/. For example, data/semantic_kitti_4class_100x100. Training BEVNet-S Example: cd experiments bash t

(Brian) JoonHo Lee 24 Dec 12, 2022
Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Codes-for-Algorithms Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Tracy (Shengmin) Tao 1 Apr 12, 2022
Use AI to generate a optimized stock portfolio

Use AI, Modern Portfolio Theory, and Monte Carlo simulation's to generate a optimized stock portfolio that minimizes risk while maximizing returns. Ho

Greg James 30 Dec 22, 2022
Sequential Model-based Algorithm Configuration

SMAC v3 Project Copyright (C) 2016-2018 AutoML Group Attention: This package is a reimplementation of the original SMAC tool (see reference below). Ho

AutoML-Freiburg-Hannover 778 Jan 05, 2023
Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

Tracking Code for the winner of track1 in MMP-Trakcing challenge This repository contains our tracking code for the Multi-camera Multiple People Track

DamoCV 29 Nov 13, 2022
TensorFlow ROCm port

Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a

ROCm Software Platform 622 Jan 09, 2023
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation

Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation The code of: Cross-Image Region Mining with Region Proto

LiuWeide 16 Nov 26, 2022
Based on the given clinical dataset, Predict whether the patient having Heart Disease or Not having Heart Disease

Heart_Disease_Classification Based on the given clinical dataset, Predict whether the patient having Heart Disease or Not having Heart Disease Dataset

Ashish 1 Jan 30, 2022
Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

StackGAN-v2 StackGAN-v1: Tensorflow implementation StackGAN-v1: Pytorch implementation Inception score evaluation Pytorch implementation for reproduci

Han Zhang 809 Dec 16, 2022
PyTorch implementation of Memory-based semantic segmentation for off-road unstructured natural environments.

MemSeg: Memory-based semantic segmentation for off-road unstructured natural environments Introduction This repository is a PyTorch implementation of

11 Nov 28, 2022
Code for Motion Representations for Articulated Animation paper

Motion Representations for Articulated Animation This repository contains the source code for the CVPR'2021 paper Motion Representations for Articulat

Snap Research 851 Jan 09, 2023
Split your patch similarly to `git add -p` but supporting multiple buckets

split-patch.py This is git add -p on steroids for patches. Given a my.patch you can run ./split-patch.py my.patch You can choose in which bucket to p

102 Oct 06, 2022