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Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Model Development
Subcategory
Architecture
Keywords
Image production
Stars
1,249
Forks
221
Contributors
1
Created date
2017-07-28
Updated date
2026-10-13
Stars in 1 day
0
Stars in 7 days
1
Rank
10,965
Previous rank
10,930
Rank change
-35
7-day stars rank
10,022
Previous 7-day stars rank
10,063
7-day stars rank change
41
Total stars rank
9,380
Previous total stars rank
9,370
Total stars rank change
-10
README summary
Photographic Image Synthesis with Cascaded Refinement Networks This is a Tensorflow implementation of cascaded refinement networks to synthesize photographic images from semantic layouts. Setup Requirement Required python libraries: Tensorflow (>=1. Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8. CPU mode should also work with minor changes. Download the pretrained models from Google Drive by running "python download_models. It takes several minutes to download all the models. py" (requires large GPU memory) to synthesize images
. The synthesized images are saved in "result_512p/final" or "result_1024p/final". Training To train a model at 256p resolution, please set "is_training=True" and change the file paths for training and test sets accordingly in "demo_256p. To train a model at 512p resolution, we fine-tune the pretrained model at 256p using "demo_512p. Also change "is_training=True" and file paths accordingly. To train a model at 1024p resolution, we fine-tune the pretrained model
at 512p using "demo_1024p. Also change "is_training=True" and file paths accordingly. Video Citation If you use our code for research, please cite our paper: Qifeng Chen and Vladlen Koltun. Photographic Image Synthesis with Cascaded Refinement Networks. Amazon Turk Scripts The scripts are put in the folder "mturk_scripts". Add the code and models for the GTA dataset. Question If you have any question or request about the code and data, please email me at chenqifeng22@gmail. If
you need the pretrained model on NYU, please send an email to me.
Frequently asked questions
How many stars and forks are recorded?
CQFIO/PhotographicImageSynthesis has 1,249 stars and 221 forks.
What is the repository rank?
CQFIO/PhotographicImageSynthesis is ranked 10,965.
What language and category are recorded?
CQFIO/PhotographicImageSynthesis has 1,249 stars, uses Python and belongs to Model Development.
Which repository is closest in rank?
CQFIO/PhotographicImageSynthesis is compared with kamalkraj/BERT-NER, ranked 10,964 with 1,250 stars.
README
Photographic Image Synthesis with Cascaded Refinement Networks
This is a Tensorflow implementation of cascaded refinement networks to synthesize photographic images from semantic layouts.
Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8.0) and CuDNN (>=5.0). CPU mode should also work with minor changes.
Quick Start (Testing)
Clone this repository.
Download the pretrained models from Google Drive by running "python download_models.py". It takes several minutes to download all the models.
Run "python demo_512p.py" or "python demo_1024p.py" (requires large GPU memory) to synthesize images.
The synthesized images are saved in "result_512p/final" or "result_1024p/final".
Training
To train a model at 256p resolution, please set "is_training=True" and change the file paths for training and test sets accordingly in "demo_256p.py". Then run "demo_256p.py".
To train a model at 512p resolution, we fine-tune the pretrained model at 256p using "demo_512p.py". Also change "is_training=True" and file paths accordingly.
To train a model at 1024p resolution, we fine-tune the pretrained model at 512p using "demo_1024p.py". Also change "is_training=True" and file paths accordingly.
If you use our code for research, please cite our paper:
Qifeng Chen and Vladlen Koltun. Photographic Image Synthesis with Cascaded Refinement Networks. In ICCV 2017.
Amazon Turk Scripts
The scripts are put in the folder "mturk_scripts".
Todo List
Add the code and models for the GTA dataset.
Question
If you have any question or request about the code and data, please email me at chenqifeng22@gmail.com. If you need the pretrained model on NYU, please send an email to me.
