Repository for the CVPR 2026 paper MeshFlow Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer by Weiyu Li, Antoine Toisoul, Tom Monnier, Roman Shapovalov, Rakesh Ranjan, Ping Tan and Andrea Vedaldi.
Pytorch code of for our CVPR 2018 paper "Neural Baby Talk".
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Models
Subcategory
Vision models
Keywords
Computer vision, Datasets, Model architecture
Stars
527
Forks
143
Contributors
1
Created date
2018-09-11
Updated date
2026-10-09
Stars in 1 day
0
Stars in 7 days
-1
Rank
17,707
Previous rank
17,485
Rank change
-222
7-day stars rank
18,023
Previous 7-day stars rank
17,965
7-day stars rank change
-58
Total stars rank
18,592
Previous total stars rank
18,567
Total stars rank change
-25
Country
China
README summary
Richer Convolutional Features for Edge Detection Thanks to yun-liu's help. Created by XuanyiLi, if you have any problem in using it, please contact:xuanyili. The best result of my pytorch model is 0. my model result the following are the side outputs and the prediction example Citation If you find our work useful in your research, please consider citing: @article{RcfEdgePami2019, author = {Yun Liu and Ming-Ming Cheng and Xiaowei Hu and Jia-Wang Bian and Le Zhang and
Xiang Bai and Jinhui Tang}, title = {Richer Convolutional Features for Edge Detection}, year = {2019}, journal= {IEEE Trans. }, volume={}, number={}, pages={}, doi = {}, } @inproceedings{RCFEdgeCVPR2017, title={Richer Convolutional Features for Edge Detection}, author={Yun Liu and Ming-Ming Cheng, Xiaowei Hu and K Wang and X Bai}, booktitle={IEEE CVPR}, year={2017}, } online demo(upload your own image):😋 online demo link Video demo:😋 this is the edge version of movie Titanic: youtube video link Introduction I implement the edge detection model according
to the RCF model in pytorch. 811 | *** | Installation Install pytorch . There are also some dependencies for a few Python libraries for data processing and visualizations like etc. It's highly recommended that you have access to GPUs. Usage image edge detection To train a RCF model on BSDS500: After training, to evaluate: Side Note: Hello mingyang, I love you License Our code is released under MIT License (see LICENSE file for details). Updates To do Add support for multi-gpu training
for the edge detetion task. Add a gpu version of edge-eval code to accelerate the evaluation process. source： To download the pretrained model, please click (This pretrained model is not the best model, just for communicating) To download the vgg16 pretrained model which is used for the backbone. please click Related Projects [1] Richer Convolutional Features for Edge Detection [2] HED [3] HED created by zeakey's [4] ContourNet
Frequently asked questions
How many stars and forks are recorded?
meteorshowers/RCF-pytorch has 527 stars and 143 forks.
What is the repository rank?
meteorshowers/RCF-pytorch is ranked 17,707.
What language and category are recorded?
meteorshowers/RCF-pytorch has 527 stars, uses Python and belongs to Models.
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meteorshowers/RCF-pytorch is compared with facebookresearch/meshflow, ranked 17,700 with 527 stars.
README
Richer Convolutional Features for Edge Detection
Thanks to yun-liu's help.
Created by XuanyiLi, if you have any problem in using it, please contact:xuanyili.edu@gmail.com.
The best result of my pytorch model is 0.808 ODS F-score now.
my model result
the following are the side outputs and the prediction example
Citation
If you find our work useful in your research, please consider citing:
@article{RcfEdgePami2019,
author = {Yun Liu and Ming-Ming Cheng and Xiaowei Hu and Jia-Wang Bian and Le Zhang and Xiang Bai and Jinhui Tang},
title = {Richer Convolutional Features for Edge Detection},
year = {2019},
journal= {IEEE Trans. Pattern Anal. Mach. Intell.},
volume={},
number={},
pages={},
doi = {},
}
@inproceedings{RCFEdgeCVPR2017,
title={Richer Convolutional Features for Edge Detection},
author={Yun Liu and Ming-Ming Cheng, Xiaowei Hu and K Wang and X Bai},
booktitle={IEEE CVPR},
year={2017},
}
I implement the edge detection model according to the RCF model in pytorch.
the result of my pytorch model will be released in the future
Method
ODS F-score on BSDS500 dataset
ODS F-score on NYU Depth dataset
ours
0.808
***
Reference[1]
0.811
***
Installation
Install pytorch. The code is tested under 0.4.1 GPU version and Python 3.6 on Ubuntu 16.04. There are also some dependencies for a few Python libraries for data processing and visualizations like cv2 etc. It's highly recommended that you have access to GPUs.
Usage
image edge detection
To train a RCF model on BSDS500:
python train_RCF.py
After training, to evaluate:
python evaluate.py (for further work)
Side Note: Hello mingyang, I love you
License
Our code is released under MIT License (see LICENSE file for details).
Updates
To do
Add support for multi-gpu training for the edge detetion task.
Improve the performance to 0.806/0.811 in the original paper.
Add a gpu version of edge-eval code to accelerate the evaluation process.
