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"
关键参数与规格
关键参数与规格
参数
值
语言
Python
类别
Models
子类别
Vision models
关键词
Computer vision, Datasets, Model architecture
星标数
527
分叉数
143
贡献者
1
创建日期
2018-09-11
更新日期
2026-10-09
1 天星标数
0
7 天星标数
-1
排名
17,707
上次排名
17,485
排名变化
-222
7 天星标排名
18,023
上次 7 天星标排名
17,965
7 天星标排名变化
-58
总星标排名
18,592
上次总星标排名
18,567
总星标排名变化
-25
国家
China
README 摘录
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
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.
