Tud/fudan

.gitignore

@@ -106,3 +106,6 @@ venv.bak/
 *.pyc
 exp/
 backup_exp/
+
+# MacOS
+.DS_Store

README_bay.md

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+# Bayesian-Crowd-Counting （ICCV 2019 oral）
+[Arxiv](https://arxiv.org/abs/1908.03684) | [CVF](http://openaccess.thecvf.com/content_ICCV_2019/papers/Ma_Bayesian_Loss_for_Crowd_Count_Estimation_With_Point_Supervision_ICCV_2019_paper.pdf) 
+###  Official Implement of ICCV 2019 oral paper "Bayesian Loss for Crowd Count Estimation with Point Supervision"
+
+## Visualization
+### Bayesian
+
+![](imgs/bayesian.png)
+
+### Bayesian+
+
+![](imgs/bayesian+.png)
+
+### Density
+
+![](imgs/density.png)
+
+## Citation
+If you use this code for your research, please cite our paper:
+
+```
+@inproceedings{ma2019bayesian,
+  title={Bayesian loss for crowd count estimation with point supervision},
+  author={Ma, Zhiheng and Wei, Xing and Hong, Xiaopeng and Gong, Yihong},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={6142--6151},
+  year={2019}
+}
+```
+
+## Code
+
+### Install dependencies
+
+torch >= 1.0 torchvision opencv numpy scipy, all the dependencies can be easily installed by pip or conda
+
+This code was tested with python 3.6  
+
+###  Train and Test
+
+1、 Dowload Dataset UCF-QNRF [Link](https://www.crcv.ucf.edu/data/ucf-qnrf/)
+
+2、 Pre-Process Data (resize image and split train/validation)
+
+```
+python preprocess_dataset.py --origin_dir <directory of original data> --data_dir <directory of processed data>
+```
+
+3、 Train model (validate on single GTX Titan X)
+
+```
+python train.py --data_dir <directory of processed data> --save_dir <directory of log and model>
+```
+
+4、 Test Model
+```
+python test.py --data_dir <directory of processed data> --save_dir <directory of log and model>
+```
+The result is slightly influenced by the random seed, but fixing the random seed (have to set cuda_benchmark to False) will make training time extrodinary long, so sometimes you can get a slightly worse result than the reported result, but most of time you can get a better result than the reported one. If you find this code is useful, please give us a star and cite our paper, have fun.
+
+5、 Training on ShanghaiTech Dataset
+
+Change dataloader to crowd_sh.py
+
+For shanghaitech a, you should set learning rate to 1e-6, and bg_ratio to 0.1
+
+### Pretrain Weight
+#### UCF-QNRF
+
+Baidu Yun [Link](https://pan.baidu.com/s/1Evxxu1skHni3Iv3VxdcZvA) extract code: x9wc
+
+Google Drive [Link](https://drive.google.com/file/d/1i22E7_zigkSm7nBnqMaEv00MD3CPhIDk/view?usp=sharing)
+
+#### ShanghaiTech A
+
+Baidu Yun [Link](https://pan.baidu.com/s/1GlaxGzFI8qFCHbqu56qSRw) extract code: tx0m
+
+Goodle Drive [Link](https://drive.google.com/file/d/13bEdshBY-brUvLSwTCOqDlK5QKcZIAAH/view?usp=sharing)
+
+#### ShanghaiTech B
+
+Baidu Yun [Link](https://pan.baidu.com/s/1YYg-a-sdhBAHZRJzZOU-6Q) extract code: a15u
+
+Goodle Drive [Link](https://drive.google.com/file/d/1woK-bI_JyeY9wZL2pXsWgPzQqhD8Qy0u/view?usp=sharing)
+
+### License
+
+GNU GENERAL PUBLIC LICENSE 
+Version 3, 29 June 2007
+Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/>
+