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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Image-to-image translation with conditional adversarial nets
| Date | Stars |
|---|---|
| 2026-07-24 | 10650 |
| 2026-07-25 | 10651 |
| 2026-07-28 | 10653 |
| 2026-07-30 | 10653 |
| 2026-08-06 | 10653 |
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# pix2pix [Project](https://phillipi.github.io/pix2pix/) | [Arxiv](https://arxiv.org/abs/1611.07004) | [PyTorch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) Torch implementation for learning a mapping from input images to output images, for example: <img src="imgs/examples.jpg" width="900px"/> Image-to-Image Translation with Conditional Adversarial Networks [Phillip Isola](http://web.mit.edu/phillipi/), [Jun-Yan Zhu](https://www.cs.cmu.edu/~junyanz/), [Tinghui Zhou](https://people.eecs.berkeley.edu/~tinghuiz/), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/) CVPR, 2017. On some tasks, decent results can be obtained fairly quickly and on small datasets. For example, to learn to generate facades (example shown above), we trained on just 400 images for about 2 hours (on a single Pascal Titan X GPU). However, for harder problems it may be important to train on far larger datasets, and for many hours or even days. **Note**: Please check out our [PyTorch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation for pix2pix and CycleGAN. The PyTorch version is under active development and can produce results comparable to or better than this Torch version. ## Setup ### Prerequisites - Linux or OSX - NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN may work with minimal modification, but untested) ### Getting Started - Install torch and dependencies from https://github.com/torch/distro - Install torch packages `nngraph` and `display` ```bash luarocks install nngraph luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec ``` - Clone this repo: ```bash git clone [email protected]:phillipi/pix2pix.git cd pix2pix ``` - Download the dataset (e.g., [CMP Facades](http://cmp.felk.cvut.cz/~tylecr1/facade/)): ```bash bash ./datasets/download_dataset.sh facades ``` - Train the model ```bash DATA_ROOT=./datasets/facades name=facades_generation which_direction=BtoA th train.lua ``` - (CPU only) The same training command without using a GPU or CUDNN. Setting the environment variables ```gpu=0 cudnn=0``` forces CPU only ```bash DATA_ROOT=./datasets/facades name=facades_generation which_direction=BtoA gpu=0 cudnn=0 batchSize=10 save_epoch_freq=5 th train.lua ``` - (Optionally) start the display server to view results as the model trains. ( See [Display UI](#display-ui) for more details): ```bash th -ldisplay.start 8000 0.0.0.0 ``` - Finally, test the model: ```bash DATA_ROOT=./datasets/facades name=facades_generation which_direction=BtoA phase=val th test.lua ``` The test results will be saved to an html file here: `./results/facades_generation/latest_net_G_val/index.html`. ## Train ```bash DATA_ROOT=/path/to/data/ name=expt_name which_direction=AtoB th train.lua ``` Switch `AtoB` to `BtoA` to train translation in opposite direction. Models are saved to `./checkpoints/expt_name` (can be changed by passing `checkpoint_dir=your_dir` in train.lua). See `opt` in train.lua for additional training options. ## Test ```bash DATA_ROOT=/path/to/data/ name=expt_name which_direction=AtoB phase=val th test.lua ``` This will run the model named `expt_name` in direction `AtoB` on all images in `/path/to/data/val`. Result images, and a webpage to view them, are saved to `./results/expt_name` (can be changed by passing `results_dir=your_dir` in test.lua). See `opt` in test.lua for additional testing options. ## Datasets Download the datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data. ```bash bash ./datasets/download_dataset.sh dataset_name ``` - `facades`: 400 images from [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/). [[Citation](datasets/bibtex/facades.tex)] - `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com/). [[Citation](datasets/bibtex/cityscapes.tex)] - `maps`: 1096 training images scraped from Google M
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Phillip Isola
42
Jun-Yan Zhu · Carnegie Mellon University: · United States
36
Tinghui Zhou
12
Brannon Dorsey · @runwayml · United States
3
Hungryof
2
Kyle McDonald
1
Yulong Wang · China
1
1
1
Salisbury
1
1
Dex Hunter · @WecoAI
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9ea08365c854b013, topic:image-generation, topic:gan
matched fp:9ea08365c854b013, topic:deep-learning
matched fp:9ea08365c854b013, topic:computer-vision