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ABCnet/README.md at main cljun27/ABCnet - GitHub

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. 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.

ABCNet/ABCNet.py at main lironui/ABCNet - GitHub

Quantization version of AdelaiDetThis project requires the Dedicated version of Detectron2. For instructions, refer to detectron2.mdFor quantization performance, refer performance results.The following is from the original project.AdelaiDetAdelaiDet is an open source toolbox for multiple instance-level recognition tasks on top of Detectron2.All instance-level recognition works from our group are open-sourced here.To date, AdelaiDet implements the following algorithms:FCOSBlendMaskMEInstABCNetABCNetv2CondInstSOLO (mmdet version)SOLOv2BoxInst (video demo)DenseCLFCPoseDirectPose to be releasedModelsCOCO Object Detecton Baselines with FCOSNameinf. timebox APdownloadFCOS_R_50_1x16 FPS38.7modelFCOS_MS_R_101_2x12 FPS43.1modelFCOS_MS_X_101_32x8d_2x6.6 FPS43.9modelFCOS_MS_X_101_32x8d_dcnv2_2x4.6 FPS46.6modelFCOS_RT_MS_DLA_34_4x_shtw52 FPS39.1modelMore models can be found in FCOS README.md.COCO Instance Segmentation Baselines with BlendMaskModelNameinf. timebox APmask APdownloadMask R-CNNR_101_3x10 FPS42.938.6BlendMaskR_101_3x11 FPS44.839.5modelBlendMaskR_101_dcni3_5x10 FPS46.841.1modelFor more models and information, please refer to BlendMask README.md.COCO Instance Segmentation Baselines with MEInstNameinf. timebox APmask APdownloadMEInst_R_50_3x12 FPS43.634.5modelFor more models and information, please refer to MEInst README.md.Total_Text results with ABCNetNameinf. timee2e-hmeandet-hmeandownloadv1-totaltext11 FPS67.186.0modelv2-totaltext7.7 FPS71.887.2modelFor more models and information, please refer to ABCNet README.md.COCO Instance Segmentation Baselines with CondInstNameinf. timebox APmask APdownloadCondInst_MS_R_50_1x14 FPS39.735.7modelCondInst_MS_R_50_BiFPN_3x_sem13 FPS44.739.4modelCondInst_MS_R_101_3x11 FPS43.338.6modelCondInst_MS_R_101_BiFPN_3x_sem10 FPS45.740.2modelFor more models and information, please refer to CondInst README.md.Note that:Inference time for all projects is measured on a NVIDIA 1080Ti with batch size 1.APs are evaluated on COCO2017 val split unless specified.InstallationFirst install Detectron2 following the official guide: INSTALL.md.Please use Detectron2 with commit id 9eb4831 if you have any issues related to Detectron2.Then build AdelaiDet with:git clone AdelaiDetpython setup.py build developIf you are using docker, a pre-built image can be pulled with:docker pull tianzhi0549/adet:latestSome projects may require special setup, please follow their own README.md in configs.Quick StartInference with Pre-trained ModelsPick a model and its config file, for example, fcos_R_50_1x.yaml.Download the model wget -O fcos_R_50_1x.pthRun the demo withpython demo/demo.py \ --config-file configs/FCOS-Detection/R_50_1x.yaml \ --input input1.jpg input2.jpg \ --opts MODEL.WEIGHTS fcos_R_50_1x.pthTrain Your Own ModelsTo train a model with "train_net.py", firstsetup the corresponding datasets followingdatasets/README.md,then run:OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/FCOS-Detection/R_50_1x.yaml \ --num-gpus 8 \ OUTPUT_DIR training_dir/fcos_R_50_1xTo evaluate the model after training, run:OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/FCOS-Detection/R_50_1x.yaml \ --eval-only \ --num-gpus 8 \ OUTPUT_DIR training_dir/fcos_R_50_1x \ MODEL.WEIGHTS training_dir/fcos_R_50_1x/model_final.pthNote that:The configs are made for 8-GPU training. To train on another number of GPUs, change the --num-gpus.If you want to measure the inference time, please change --num-gpus to 1.We set OMP_NUM_THREADS=1 by default, which achieves the best speed on our machines, please change it as needed.This quick start is made for FCOS. If you are using other projects, please check the projects' own README.md in configs.AcknowledgementsThe authors are grateful toNvidia, Huawei Noah's Ark Lab, ByteDance, Adobe who generously donated GPU computing in the past a few years.Citing AdelaiDetIf you use this toolbox in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:@misc{tian2019adelaidet, author = {Tian, Zhi and Chen, Hao and Wang, Xinlong and Liu, Yuliang and Shen, Chunhua}, title = {{AdelaiDet}: A Toolbox for Instance-level Recognition Tasks}, howpublished = {\url{ year = {2019}}and relevant publications:@inproceedings{tian2019fcos, title = {{FCOS}: Fully Convolutional One-Stage Object Detection}, author = {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, booktitle = {Proc. Int. Conf. Computer Vision (ICCV)}, year = {2019}}@article{tian2021fcos, title = {{FCOS}: A Simple and Strong Anchor-free Object Detector},. AS-ABCNET. Database RIPE Full Name AS-ABCNET Overview; Reverse; Raw; Total Size 7 ASNs 31 v4 Prefixes 1 v6 Prefix

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