richzhang/colorization
richzhang/colorization has 3,472 GitHub stars and 923 forks. GitHot measured 0 stars in 24 hours and +7 in 7 days. Last checked 2026-11-08 02:28 UTC. Last push 2023-11-27.
Colorización automática mediante redes neuronales profundas. «Colorful Image Colorization». En ECCV, 2016.
Crecimiento de estrellas medido
- in 24h
- 0
- in 7 days
- +7
- in 15 days
- Actualización retrasada
- in 1 month
- Actualización retrasada
Cambios netos en períodos que se solapan, no totales diarios. Los datos no disponibles se dejan en blanco. Las estrellas indican interés, no calidad.
README.md
OriginalColorful Image Colorization [Project Page]
Richard Zhang, Phillip Isola, Alexei A. Efros. In ECCV, 2016.
+ automatic colorization functionality for Real-Time User-Guided Image Colorization with Learned Deep Priors, SIGGRAPH 2017!
[Sept20 Update] Since it has been 3-4 years, I converted this repo to support minimal test-time usage in PyTorch. I also added our SIGGRAPH 2017 (it's an interactive method but can also do automatic). See the Caffe branch for the original release.

Clone the repository; install dependencies
git clone https://github.com/richzhang/colorization.git
pip install requirements.txt
Colorize! This script will colorize an image. The results should match the images in the imgs_out folder.
python demo_release.py -i imgs/ansel_adams3.jpg
Model loading in Python The following loads pretrained colorizers. See demo_release.py for some details on how to run the model. There are some pre and post-processing steps: convert to Lab space, resize to 256x256, colorize, and concatenate to the original full resolution, and convert to RGB.
import colorizers
colorizer_eccv16 = colorizers.eccv16().eval()
colorizer_siggraph17 = colorizers.siggraph17().eval()
Original implementation (Caffe branch)
The original implementation contained train and testing, our network and AlexNet (for representation learning tests), as well as representation learning tests. It is in Caffe and is no longer supported. Please see the caffe branch for it.
Citation
If you find these models useful for your resesarch, please cite with these bibtexs.
@inproceedings{zhang2016colorful,
title={Colorful Image Colorization},
author={Zhang, Richard and Isola, Phillip and Efros, Alexei A},
booktitle={ECCV},
year={2016}
}
@article{zhang2017real,
title={Real-Time User-Guided Image Colorization with Learned Deep Priors},
author={Zhang, Richard and Zhu, Jun-Yan and Isola, Phillip and Geng, Xinyang and Lin, Angela S and Yu, Tianhe and Efros, Alexei A},
journal={ACM Transactions on Graphics (TOG)},
volume={9},
number={4},
year={2017},
publisher={ACM}
}
Misc
Contact Richard Zhang at rich.zhang at eecs.berkeley.edu for any questions or comments.
Explorar proyectos relacionados
tensorflow/tensorflow
Un framework de código abierto de aprendizaje automático para todos
201,388 estrellashuggingface/transformers
🤗 Transformers: el framework de definición de modelos para modelos de aprendizaje automático de vanguardia en texto, visión, audio y multimodal, tanto para inferencia como para entrenamiento.
168,636 estrellasAUTOMATIC1111/stable-diffusion-webui
Interfaz web de Stable Diffusion
165,251 estrellasComfy-Org/ComfyUI
La GUI, la API y el backend más potentes y modulares para modelos de difusión, con una interfaz de grafos y nodos. El motor de inferencia local más rápido del mundo.
145,629 estrellas