Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details

All Projects → sangwoomo → Instagan

sangwoomo / Instagan

Licence: other

InstaGAN: Instance-aware Image Translation (ICLR 2019)

Programming Languages

139335 projects - #7 most used programming language

Labels

gan generative-adversarial-network

Projects that are alternatives of or similar to Instagan

Cartoongan Tensorflow

Generate your own cartoon-style images with CartoonGAN (CVPR 2018), powered by TensorFlow 2.0 Alpha.

Stars: ✭ 587 (-22.86%)

Mutual labels: gan, generative-adversarial-network

Adversarial video generation

A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Stars: ✭ 662 (-13.01%)

Mutual labels: gan, generative-adversarial-network

A simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)

Stars: ✭ 502 (-34.03%)

Mutual labels: gan, generative-adversarial-network

All About The Gan

All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

Stars: ✭ 630 (-17.21%)

Mutual labels: gan, generative-adversarial-network

Fewshot Face Translation Gan

Generative adversarial networks integrating modules from FUNIT and SPADE for face-swapping.

Stars: ✭ 705 (-7.36%)

Mutual labels: gan, generative-adversarial-network

Ssgan Tensorflow

A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).

Stars: ✭ 496 (-34.82%)

Mutual labels: gan, generative-adversarial-network

Awesome Generative Adversarial Networks with tensorflow

Stars: ✭ 585 (-23.13%)

Mutual labels: gan, generative-adversarial-network

Cool Fashion Papers

👔👗🕶️🎩 Cool resources about Fashion + AI! (papers, datasets, workshops, companies, ...) (constantly updating)

Stars: ✭ 464 (-39.03%)

Mutual labels: gan, generative-adversarial-network

Context Encoder

[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

Stars: ✭ 731 (-3.94%)

Mutual labels: gan, generative-adversarial-network

Official repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)

Stars: ✭ 513 (-32.59%)

Mutual labels: gan, generative-adversarial-network

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Stars: ✭ 641 (-15.77%)

Mutual labels: gan, generative-adversarial-network

Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

Stars: ✭ 748 (-1.71%)

Mutual labels: gan, generative-adversarial-network

T2F: text to face generation using Deep Learning

Stars: ✭ 494 (-35.09%)

Mutual labels: gan, generative-adversarial-network

Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model.

Stars: ✭ 605 (-20.5%)

Mutual labels: gan, generative-adversarial-network

Textgan Pytorch

TextGAN is a PyTorch framework for Generative Adversarial Networks (GANs) based text generation models.

Stars: ✭ 479 (-37.06%)

Mutual labels: gan, generative-adversarial-network

Synthesizing and manipulating 2048x1024 images with conditional GANs

Stars: ✭ 5,553 (+629.7%)

Mutual labels: gan, generative-adversarial-network

Generative Compression

TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression

Stars: ✭ 428 (-43.76%)

Mutual labels: gan, generative-adversarial-network

Generative Models

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Stars: ✭ 438 (-42.44%)

Mutual labels: gan, generative-adversarial-network

Generative Adversarial Networks

Introduction to generative adversarial networks, with code to accompany the O'Reilly tutorial on GANs

Stars: ✭ 505 (-33.64%)

Mutual labels: gan, generative-adversarial-network

Anime Inpainting

An application tool of edge-connect, which can do anime inpainting and drawing. 动漫人物图片自动修复，去马赛克，填补，去瑕疵

Stars: ✭ 761 (+0%)

Mutual labels: gan, generative-adversarial-network

View All Similar Projects ➔

InstaGAN: Instance-aware Image-to-Image Translation

PyTorch implementation of "InstaGAN: Instance-aware Image-to-Image Translation" (ICLR 2019). The implementation is based on the official CycleGAN code. Our major contributions are in ./models/insta_gan_model.py and ./models/networks.py.

Getting Started

Installation

Clone this repository

git clone https://github.com/sangwoomo/instagan

Install PyTorch 0.4+ and torchvision from http://pytorch.org and other dependencies (e.g., visdom and dominate). You can install all the dependencies by

pip install -r requirements.txt

For Conda users, you can use a script ./scripts/conda_deps.sh to install PyTorch and other libraries.
Acknowledgment: Installation scripts are from the official CycleGAN code.

Download base datasets

Download clothing-co-parsing (CCP) dataset:

git clone https://github.com/bearpaw/clothing-co-parsing ./datasets/clothing-co-parsing

Download multi-human parsing (MHP) dataset:

# Download "LV-MHP-v1" from the link and locate in ./datasets

Download MS COCO dataset:

./datasets/download_coco.sh

Generate two-domain datasets

Generate two-domain dataset for experiments:

python ./datasets/generate_ccp_dataset.py --save_root ./datasets/jeans2skirt_ccp --cat1 jeans --cat2 skirt
python ./datasets/generate_mhp_dataset.py --save_root ./datasets/pants2skirt_mhp --cat1 pants --cat2 skirt
python ./datasets/generate_coco_dataset.py --save_root ./datasets/shp2gir_coco --cat1 sheep --cat2 giraffe

Note: Generated dataset contains images and corresponding masks, which are located in image folders (e.g., 'trainA') and mask folders (e.g., 'trainA_seg'), respectively. For each image (e.g., '0001.png'), corresponding masks for each instance (e.g., '0001_0.png', '0001_1.png', ...) are provided.

Run experiments

Train a model:

python train.py --dataroot ./datasets/jeans2skirt_ccp --model insta_gan --name jeans2skirt_ccp_instagan --loadSizeH 330 --loadSizeW 220 --fineSizeH 300 --fineSizeW 200 --niter 400 --niter_decay 200
python train.py --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 270 --loadSizeW 180 --fineSizeH 240 --fineSizeW 160
python train.py --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 220 --loadSizeW 220 --fineSizeH 200 --fineSizeW 200

To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. To see more intermediate results, check out ./checkpoints/experiment_name/web/index.html.
For faster experiment, increase batch size and use more gpus:

python train.py --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 220 --loadSizeW 220 --fineSizeH 200 --fineSizeW 200 --batch_size 4 --gpu_ids 0,1,2,3

Test the model:

python test.py --dataroot ./datasets/jeans2skirt_ccp --model insta_gan --name jeans2skirt_ccp_instagan --loadSizeH 300 --loadSizeW 200 --fineSizeH 300 --fineSizeW 200
python test.py --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 240 --loadSizeW 160 --fineSizeH 240 --fineSizeW 160 --ins_per 2 --ins_max 20
python test.py --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 200 --loadSizeW 200 --fineSizeH 200 --fineSizeW 200 --ins_per 2 --ins_max 20

The test results will be saved to a html file here: ./results/experiment_name/latest_test/index.html.

Apply a pre-trained model

You can download a pre-trained model (pants->skirt and/or sheep->giraffe) from the following Google drive link. Save the pretrained model in ./checkpoints/ directory.
We provide samples of two datasets (pants->skirt and sheep->giraffe) in this repository. To test the model:

python test.py --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 240 --loadSizeW 160 --fineSizeH 240 --fineSizeW 160 --ins_per 2 --ins_max 20 --phase sample --epoch 200
python test.py --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 200 --loadSizeW 200 --fineSizeH 200 --fineSizeW 200 --ins_per 2 --ins_max 20 --phase sample --epoch 200

Results

We provide some translation results of our model. See the link for more translation results.

1. Fashion dataset (pants->skirt)

2. COCO dataset (sheep->giraffe)

3. Results on Google-searched images (pants->skirt)

4. Results on YouTube-searched videos (pants->skirt)

Citation

If you use this code for your research, please cite our papers.

@inproceedings{
    mo2019instagan,
    title={InstaGAN: Instance-aware Image-to-Image Translation},
    author={Sangwoo Mo and Minsu Cho and Jinwoo Shin},
    booktitle={International Conference on Learning Representations},
    year={2019},
    url={https://openreview.net/forum?id=ryxwJhC9YX},
}

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].

Stars: ✭ 761

Visit Git Page 🔗Visit User Page 🔗Visit Issues Page (5) 🔗