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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.

Figure 1 from ABCNet: A comprehensive highway visibility

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.

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