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ABCNet: An attention-based method for particle tagging.This is the main repository for the ABCNet paper.The implementation uses a modified version of GAPNet to suit the High Energy Physics needs.This repository is divided into two main folders: classification and segmentation, for the quark-gluon tagging and pileup mitigation applications, respectively.The input .h5 files are expected to have the following structure:data: [N,P,F],label:[N,P]pid: [N]global: [N,G]N = Number of eventsF = Number of features per pointP = Number of pointsG = Number of global featuresFor classification, only the pid is required, while for segmentation only label is required.The files to be used for the training (train_files.txt), test (test_files.txt) and evaluation (evaluate_files.txt) are required to be listed in the respective text files.RequirementsTensorflowh5pyClassificationTo train use:cd classificationpython train.py --data_dir ../data/QG/ --log_dir qg_testA logs folder will be created with the training results under the main directory.To evaluate the training use:python evaluate.py --data_dir ../data/QG --model_path ../logs/qg_test --batch 500 --name qg_test --modeln 1SegmentationTo train use:cd segmentationpython train.py --data_dir ../data/PU/ --log_dir pu_testTo evaluate the training use:python evaluate.py --data_dir ../data/PU --model_path ../logs/ou_test --batch 500 --name pu_test LicenseMIT LicenseAcknowledgementsABCNet uses a modified version of GAPNet and PointNet.

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Author = {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, journal = {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)}, year = {2021}}@inproceedings{chen2020blendmask, title = {{BlendMask}: Top-Down Meets Bottom-Up for Instance Segmentation}, author = {Chen, Hao and Sun, Kunyang and Tian, Zhi and Shen, Chunhua and Huang, Yongming and Yan, Youliang}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@inproceedings{zhang2020MEInst, title = {Mask Encoding for Single Shot Instance Segmentation}, author = {Zhang, Rufeng and Tian, Zhi and Shen, Chunhua and You, Mingyu and Yan, Youliang}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@inproceedings{liu2020abcnet, title = {{ABCNet}: Real-time Scene Text Spotting with Adaptive {B}ezier-Curve Network}, author = {Liu, Yuliang and Chen, Hao and Shen, Chunhua and He, Tong and Jin, Lianwen and Wang, Liangwei}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@ARTICLE{9525302, author={Liu, Yuliang and Shen, Chunhua and Jin, Lianwen and He, Tong and Chen, Peng and Liu, Chongyu and Chen, Hao}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TPAMI.2021.3107437}} @inproceedings{wang2020solo, title = {{SOLO}: Segmenting Objects by Locations}, author = {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei}, booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)}, year = {2020}}@inproceedings{wang2020solov2, title = {{SOLOv2}: Dynamic and Fast Instance Segmentation}, author = {Wang, Xinlong and Zhang, Rufeng and Kong, Tao and Li, Lei and Shen, Chunhua}, booktitle = {Proc. Advances in Neural Information Processing Systems (NeurIPS)}, year = {2020}}@article{wang2021solo, title = {{SOLO}: A Simple Framework for Instance Segmentation}, author = {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei}, journal = {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)}, year = {2021}}@article{tian2019directpose, title = {{DirectPose}: Direct End-to-End Multi-Person Pose Estimation}, author = {Tian, Zhi and Chen, Hao and Shen, Chunhua}, journal = {arXiv preprint arXiv:1911.07451}, year = {2019}}@inproceedings{tian2020conditional, title = {Conditional Convolutions for Instance Segmentation}, author = {Tian, Zhi and Shen, Chunhua and Chen, Hao}, booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)}, year = {2020}}@inproceedings{tian2021boxinst, title = {{BoxInst}: High-Performance Instance Segmentation with Box Annotations}, author = {Tian, Zhi and Shen, Chunhua and Wang, Xinlong and Chen, Hao}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}@inproceedings{wang2021densecl, title = {Dense Contrastive Learning for Self-Supervised Visual Pre-Training}, author = {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}@inproceedings{Mao2021pose, title = {{FCPose}: Fully Convolutional Multi-Person Pose Estimation With Dynamic Instance-Aware Convolutions}, author = {Mao, Weian and Tian, Zhi and Wang, Xinlong and Shen, Chunhua}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}LicenseFor academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact Chunhua Shen.

LCM1.3.1 downloadOpen Source Navigation: Home \ Development \ Help Tools \ Fast File Finder by Autosofted Software Info Best Vista Download periodically updates pricing and software information of Fast File Finder by Autosofted full version from the publisher, but some information may be out-of-date. You should confirm all information. Software piracy is theft, using crack, warez passwords, patches, serial numbers, registration codes, key generator, keymaker or keygen for Fast File Finder by Autosofted license key is illegal and prevent future development of Fast File Finder by Autosofted. Download links are directly from our mirrors or publisher's website, Fast File Finder by Autosofted torrent files or shared files from rapidshare, yousendit or megaupload are not allowed! Released: June 14, 2014 Filesize: 1.18 MB Language: English Platform: WinOther, Win2000, Windows XP, Windows 7 x32, Windows 7 x64, Windows 8, Windows RT, WinServer, Windows Vista, Windows Vista x64 Requirements: A functional computer. Install Install Only Add Your Review or Windows Vista Compatibility Report Fast File Finder by Autosofted - Releases History Software: Fast File Finder by Autosofted 1.0 Date Released: Jun 14, 2014 Status: New Release Most popular freeze 5 1 in Help Tools downloads for Vista

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