We are recruiting interns, please send your resume to my email zhangruiqi@megvii.com.
[实习招聘]旷视Face++ 研究院video组 见习研究员
工作职责： 负责实际场景中图像相关深度学习算法研究与应用（检测，识别等方向）
期望要求： 1、具有计算机视觉，图形学等相关专业在读本科或以上学历 2、熟悉深度学习基础算法 3、熟悉常用的目标检测算法，如Faster-RCNN、 RetinaNet等 4、有一定编程基础，擅长python编程 5、熟悉caffe、Tensorflow、PyTorch等深度学习框架
加分项： 1、具备ACM等大赛奖牌 2、具有实际深度学习相关项目经验或实习经历 3、在ICCV、CVPR、ECCV、NIPS等相关会议取得过优秀成绩或发表论文 4、熟悉文字检测与识别相关算法，如EAST，textboxes，CRNN等 5、能够长期实习（六个月或以上）
实习时间： 每周4天以上（工作日），能够实习三个月以上
实习地点： 北京市海淀区融科资讯中心
联系方式： shiyuxuan@megvii.com
This is a PyTorch implementation of SiameseRPN. This project is mainly based on SiamFC-PyTorch and DaSiamRPN.
For more details about siameseRPN please refer to the paper : High Performance Visual Tracking with Siamese Region Proposal Network by Bo Li, Junjie Yan,Wei Wu, Zheng Zhu, Xiaolin Hu.
This repository includes training and tracking codes.
This project can get 0.626 AUC on OTB100, and can get better result than the DaSiamRPN on 46 videos. Test results of 50 trained models on OTB100 are available in the eval_result.json. The best is the 38 epoch.
You should first get VID dataset and youtube-bb dataset. This process is a little troublesome. The part of code has not been formatted by now. If any one do this, please give a git pull request.
python bin/create_dataset_ytbid.py --vid-dir /PATH/TO/ILSVRC2015 --ytb-dir /PATH/TO/YT-BB --output-dir /PATH/TO/SAVE_DATA --num_threads 6
The command above will get a dataset, I put the dataset in the baiduyundisk. Use this data to create lmdb. 链接:https://pan.baidu.com/s/1QnQEM_jtc3alX8RyZ3i4-g 密码:myq4
python bin/create_lmdb.py --data-dir /PATH/TO/SAVE_DATA --output-dir /PATH/TO/RESULT.lmdb --num_threads 12
python bin/train_siamrpn.py --data_dir /PATH/TO/SAVE_DATA
Change the data_path first in the test_OTB.py, then run:
python bin/test_OTB.py -ms /PATH/TO/MODEL -v cvpr2013
python version == 3.6.5
pytorch version == 1.0.0
Pretrained model on Imagenet: https://drive.google.com/drive/folders/1HJOvl_irX3KFbtfj88_FVLtukMI1GTCR
Model with 0.626 AUC: https://pan.baidu.com/s/1vSvTqxaFwgmZdS00U3YIzQ keyword:v91k
[1] Li B , Yan J , Wu W , et al. High Performance Visual Tracking with Siamese Region Proposal Network[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018.