From d836e5ba27c525442ca63671ea29f0b7186749f2 Mon Sep 17 00:00:00 2001 From: Rosna Thomas Date: Fri, 26 Jul 2019 15:34:23 -0700 Subject: [PATCH] added contributor field in data/csv --- data/citation.csv | 615 ++++---- data/description.csv | 674 ++++----- data/installation.csv | 1860 +++++++++++------------ data/invocation.csv | 2272 ++++++++++++++-------------- data/none.csv | 742 ++++----- helper_scripts/splitcsvcategory.py | 20 +- 6 files changed, 3101 insertions(+), 3082 deletions(-) diff --git a/data/citation.csv b/data/citation.csv index 4591fb7c..c2464648 100644 --- a/data/citation.csv +++ b/data/citation.csv @@ -1,298 +1,317 @@ -URL,excerpt -https://github.com/JimmySuen/integral-human-pose,"If you find Integral Regression useful in your research, please consider citing:" -https://github.com/JimmySuen/integral-human-pose,"@article{sun2017integral," -https://github.com/JimmySuen/integral-human-pose,"title={Integral human pose regression}," -https://github.com/JimmySuen/integral-human-pose,"author={Sun, Xiao and Xiao, Bin and Liang, Shuang and Wei, Yichen}," -https://github.com/JimmySuen/integral-human-pose,"journal={arXiv preprint arXiv:1711.08229}," -https://github.com/JimmySuen/integral-human-pose,year={2017} -https://github.com/JimmySuen/integral-human-pose,} -https://github.com/JimmySuen/integral-human-pose,"@article{sun2018integral," -https://github.com/JimmySuen/integral-human-pose,"title={An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge}," -https://github.com/JimmySuen/integral-human-pose,"author={Sun, Xiao and Li, Chuankang and Lin, Stephen}," -https://github.com/JimmySuen/integral-human-pose,"journal={arXiv preprint arXiv:1809.06079}," -https://github.com/JimmySuen/integral-human-pose,year={2018} -https://github.com/LMescheder/GAN_stability,"@INPROCEEDINGS{Mescheder2018ICML," -https://github.com/LMescheder/GAN_stability,"author = {Lars Mescheder and Sebastian Nowozin and Andreas Geiger}," -https://github.com/LMescheder/GAN_stability,"title = {Which Training Methods for GANs do actually Converge?}," -https://github.com/LMescheder/GAN_stability,"booktitle = {International Conference on Machine Learning (ICML)}," -https://github.com/LMescheder/GAN_stability,year = {2018} -https://github.com/NVIDIA/vid2vid,"If you find this useful for your research, please cite the following paper." -https://github.com/NVIDIA/vid2vid, -https://github.com/NVIDIA/vid2vid,"@inproceedings{wang2018vid2vid," -https://github.com/NVIDIA/vid2vid,author = {Ting-Chun Wang and Ming-Yu Liu and Jun-Yan Zhu and Guilin Liu -https://github.com/NVIDIA/vid2vid,"and Andrew Tao and Jan Kautz and Bryan Catanzaro}," -https://github.com/NVIDIA/vid2vid,"title = {Video-to-Video Synthesis}," -https://github.com/NVIDIA/vid2vid,"booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}," -https://github.com/NVIDIA/vid2vid,"year = {2018}," -https://github.com/NVIDIA/vid2vid,Video-to-Video Synthesis -https://github.com/NVIDIA/vid2vid,"Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Guilin Liu1, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1" -https://github.com/NVIDIA/vid2vid,"1NVIDIA Corporation, 2MIT CSAIL" -https://github.com/NVIDIA/vid2vid,In Neural Information Processing Systems (NeurIPS) 2018 -https://github.com/OpenGeoVis/PVGeo,"The PVGeo code library was created and is managed by Bane Sullivan, graduate student in the Hydrological Science and Engineering interdisciplinary program at the Colorado School of Mines under Whitney Trainor-Guitton. If you would like to contact us, inquire with info@pvgeo.org." -https://github.com/XiaLiPKU/RESCAN,"Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, Hongbin Zha" -https://github.com/XiaLiPKU/RESCAN,"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University" -https://github.com/XiaLiPKU/RESCAN,"Key Laboratory of Machine Perception (MOE), School of EECS, Peking University" -https://github.com/XiaLiPKU/RESCAN,"Cooperative Medianet Innovation Center, Shanghai Jiao Tong University" -https://github.com/XiaLiPKU/RESCAN,"{ethanlee, jlwu1992, zlin, hongliu}@pku.edu.cn, zha@cis.pku.edu.cn" -https://github.com/XiaLiPKU/RESCAN,"@inproceedings{li2018recurrent," -https://github.com/XiaLiPKU/RESCAN,"title={Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining}," -https://github.com/XiaLiPKU/RESCAN,"author={Li, Xia and Wu, Jianlong and Lin, Zhouchen and Liu, Hong and Zha, Hongbin}," -https://github.com/XiaLiPKU/RESCAN,"booktitle={European Conference on Computer Vision}," -https://github.com/XiaLiPKU/RESCAN,"pages={262--277}," -https://github.com/XiaLiPKU/RESCAN,"year={2018}," -https://github.com/XiaLiPKU/RESCAN,organization={Springer} -https://github.com/ZhouYanzhao/PRM,Citation -https://github.com/ZhouYanzhao/PRM,"If you find the code useful for your research, please cite:" -https://github.com/ZhouYanzhao/PRM,"@INPROCEEDINGS{Zhou2018PRM," -https://github.com/ZhouYanzhao/PRM,"author = {Zhou, Yanzhao and Zhu, Yi and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin}," -https://github.com/ZhouYanzhao/PRM,"title = {Weakly Supervised Instance Segmentation using Class Peak Response}," -https://github.com/ZhouYanzhao/PRM,"booktitle = {CVPR}," -https://github.com/akanazawa/hmr,"Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018" -https://github.com/akanazawa/hmr,"@inProceedings{kanazawaHMR18," -https://github.com/akanazawa/hmr,"title={End-to-end Recovery of Human Shape and Pose}," -https://github.com/akanazawa/hmr,author = {Angjoo Kanazawa -https://github.com/akanazawa/hmr,and Michael J. Black -https://github.com/akanazawa/hmr,and David W. Jacobs -https://github.com/akanazawa/hmr,"and Jitendra Malik}," -https://github.com/akanazawa/hmr,"booktitle={Computer Vision and Pattern Regognition (CVPR)}," -https://github.com/albertpumarola/GANimation,"If you use this code or ideas from the paper for your research, please cite our paper:" -https://github.com/albertpumarola/GANimation,"@inproceedings{pumarola2018ganimation," -https://github.com/albertpumarola/GANimation,"title={GANimation: Anatomically-aware Facial Animation from a Single Image}," -https://github.com/albertpumarola/GANimation,"author={A. Pumarola and A. Agudo and A.M. Martinez and A. Sanfeliu and F. Moreno-Noguer}," -https://github.com/albertpumarola/GANimation,"booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}," -https://github.com/cgre-aachen/gempy,"For a more detailed elaboration of the theory behind GemPy, take a look at the upcoming scientific publication ""GemPy 1.0: open-source stochastic geological modeling and inversion"" by de la Varga et al. (2018)." -https://github.com/cgre-aachen/gempy,References -https://github.com/cgre-aachen/gempy,"de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1-32, https://doi.org/10.5194/gmd-12-1-2019, 2019" -https://github.com/cgre-aachen/gempy,"Calcagno, P., Chilès, J. P., Courrioux, G., & Guillen, A. (2008). Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules. Physics of the Earth and Planetary Interiors, 171(1-4), 147-157." -https://github.com/cgre-aachen/gempy,"Lajaunie, C., Courrioux, G., & Manuel, L. (1997). Foliation fields and 3D cartography in geology: principles of a method based on potential interpolation. Mathematical Geology, 29(4), 571-584." -https://github.com/driftingtides/hyvr,"HyVR can be attributed by citing the following journal article: Bennett, J. P., Haslauer, C. P., Ross, M., & Cirpka, O. A. (2018). An open, object-based framework for generating anisotropy in sedimentary subsurface models. Groundwater. DOI: 10.1111/gwat.12803." -https://github.com/driving-behavior/DBNet,"DBNet was developed by MVIG, Shanghai Jiao Tong University* and SCSC Lab, Xiamen University* (alphabetical order)." -https://github.com/driving-behavior/DBNet,"If you find our work useful in your research, please consider citing:" -https://github.com/driving-behavior/DBNet,"@InProceedings{DBNet2018," -https://github.com/driving-behavior/DBNet,"author = {Yiping Chen and Jingkang Wang and Jonathan Li and Cewu Lu and Zhipeng Luo and HanXue and Cheng Wang}," -https://github.com/driving-behavior/DBNet,"title = {LiDAR-Video Driving Dataset: Learning Driving Policies Effectively}," -https://github.com/driving-behavior/DBNet,"booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," -https://github.com/driving-behavior/DBNet,"month = {June}," -https://github.com/empymod/empymod,"If you publish results for which you used empymod, please give credit by citing Werthmüller (2017):" -https://github.com/empymod/empymod,"Werthmüller, D., 2017, An open-source full 3D electromagnetic modeler for 1D VTI media in Python: empymod: Geophysics, 82(6), WB9--WB19; DOI: 10.1190/geo2016-0626.1." -https://github.com/empymod/empymod,"All releases have a Zenodo-DOI, provided on the release-page. Also consider citing Hunziker et al. (2015) and Key (2012), without which empymod would not exist." -https://github.com/endernewton/iter-reason,"@inproceedings{chen18iterative," -https://github.com/endernewton/iter-reason,"author = {Xinlei Chen and Li-Jia Li and Li Fei-Fei and Abhinav Gupta}," -https://github.com/endernewton/iter-reason,"title = {Iterative Visual Reasoning Beyond Convolutions}," -https://github.com/endernewton/iter-reason,"booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}," -https://github.com/endernewton/iter-reason,Year = {2018} -https://github.com/endernewton/iter-reason,"@inproceedings{chen2017spatial," -https://github.com/endernewton/iter-reason,"author = {Xinlei Chen and Abhinav Gupta}," -https://github.com/endernewton/iter-reason,"title = {Spatial Memory for Context Reasoning in Object Detection}," -https://github.com/endernewton/iter-reason,"booktitle = {Proceedings of the International Conference on Computer Vision}," -https://github.com/endernewton/iter-reason,Year = {2017} -https://github.com/equinor/pylops,Contributors -https://github.com/equinor/pylops,"Matteo Ravasi, mrava87" -https://github.com/equinor/pylops,"Carlos da Costa, cako" -https://github.com/equinor/pylops,"Dieter Werthmüller, prisae" -https://github.com/equinor/pylops,"Tristan van Leeuwen, TristanvanLeeuwen" -https://github.com/facebookresearch/Detectron/,"If you use Detectron in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry." -https://github.com/facebookresearch/Detectron/,"@misc{Detectron2018," -https://github.com/facebookresearch/Detectron/,author = {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and -https://github.com/facebookresearch/Detectron/,"Piotr Doll\'{a}r and Kaiming He}," -https://github.com/facebookresearch/Detectron/,"title = {Detectron}," -https://github.com/facebookresearch/Detectron/,"howpublished = {\url{https://github.com/facebookresearch/detectron}}," -https://github.com/facebookresearch/Detectron/,year = {2018} -https://github.com/foolwood/DaSiamRPN,"Zheng Zhu*, Qiang Wang*, Bo Li*, Wei Wu, Junjie Yan, and Weiming Hu" -https://github.com/foolwood/DaSiamRPN,"European Conference on Computer Vision (ECCV), 2018" -https://github.com/foolwood/DaSiamRPN,Citing DaSiamRPN -https://github.com/foolwood/DaSiamRPN,"If you find DaSiamRPN and SiamRPN useful in your research, please consider citing:" -https://github.com/foolwood/DaSiamRPN,"@inproceedings{Zhu_2018_ECCV," -https://github.com/foolwood/DaSiamRPN,"title={Distractor-aware Siamese Networks for Visual Object Tracking}," -https://github.com/foolwood/DaSiamRPN,"author={Zhu, Zheng and Wang, Qiang and Bo, Li and Wu, Wei and Yan, Junjie and Hu, Weiming}," -https://github.com/foolwood/DaSiamRPN,"@InProceedings{Li_2018_CVPR," -https://github.com/foolwood/DaSiamRPN,"title = {High Performance Visual Tracking With Siamese Region Proposal Network}," -https://github.com/foolwood/DaSiamRPN,"author = {Li, Bo and Yan, Junjie and Wu, Wei and Zhu, Zheng and Hu, Xiaolin}," -https://github.com/google/sg2im/,"@inproceedings{johnson2018image," -https://github.com/google/sg2im/,"title={Image Generation from Scene Graphs}," -https://github.com/google/sg2im/,"author={Johnson, Justin and Gupta, Agrim and Fei-Fei, Li}," -https://github.com/google/sg2im/,"booktitle={CVPR}," -https://github.com/google/sg2im/,Image Generation from Scene Graphs -https://github.com/google/sg2im/,"Justin Johnson, Agrim Gupta, Li Fei-Fei" -https://github.com/google/sg2im/,Presented at CVPR 2018 -https://github.com/gprMax/gprMax,Using gprMax? Cite us -https://github.com/gprMax/gprMax,If you use gprMax and publish your work we would be grateful if you could cite our work using: -https://github.com/gprMax/gprMax,"Warren, C., Giannopoulos, A., & Giannakis I. (2016). gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar, Computer Physics Communications (http://dx.doi.org/10.1016/j.cpc.2016.08.020)" -https://github.com/hezhangsprinter/DCPDN,"He Zhang, Vishal M. Patel" -https://github.com/hezhangsprinter/DCPDN,[Paper Link] (CVPR'18) -https://github.com/hezhangsprinter/DID-MDN,"@inproceedings{derain_zhang_2018," -https://github.com/hezhangsprinter/DID-MDN,"title={Density-aware Single Image De-raining using a Multi-stream Dense Network}," -https://github.com/hezhangsprinter/DID-MDN,"author={Zhang, He and Patel, Vishal M}," -https://github.com/hiroharu-kato/neural_renderer,@InProceedings{kato2018renderer -https://github.com/hiroharu-kato/neural_renderer,"title={Neural 3D Mesh Renderer}," -https://github.com/hiroharu-kato/neural_renderer,"author={Kato, Hiroharu and Ushiku, Yoshitaka and Harada, Tatsuya}," -https://github.com/hiroharu-kato/neural_renderer,"booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," -https://github.com/iannesbitt/readgssi,"Ian M. Nesbitt, François-Xavier Simon, Thomas Paulin, 2018. readgssi - an open-source tool to read and plot GSSI ground-penetrating radar data. doi:10.5281/zenodo.1439119" -https://github.com/jiangsutx/SRN-Deblur,"Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, Jiaya Jia." -https://github.com/jiangsutx/SRN-Deblur,"@inproceedings{tao2018srndeblur," -https://github.com/jiangsutx/SRN-Deblur,"title={Scale-recurrent Network for Deep Image Deblurring}," -https://github.com/jiangsutx/SRN-Deblur,"author={Tao, Xin and Gao, Hongyun and Shen, Xiaoyong and Wang, Jue and Jia, Jiaya}," -https://github.com/jiangsutx/SRN-Deblur,"booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," -https://github.com/joferkington/mplstereonet,"[Kamb1956]Kamb, 1959. Ice Petrofabric Observations from Blue Glacier, Washington, in Relation to Theory and Experiment. Journal of Geophysical Research, Vol. 64, No. 11, pp. 1891--1909." -https://github.com/joferkington/mplstereonet,"[Vollmer1995]Vollmer, 1995. C Program for Automatic Contouring of Spherical Orientation Data Using a Modified Kamb Method. Computers & Geosciences, Vol. 21, No. 1, pp. 31--49." -https://github.com/kenshohara/3D-ResNets-PyTorch,"If you use this code or pre-trained models, please cite the following:" -https://github.com/kenshohara/3D-ResNets-PyTorch,"@inproceedings{hara3dcnns," -https://github.com/kenshohara/3D-ResNets-PyTorch,"author={Kensho Hara and Hirokatsu Kataoka and Yutaka Satoh}," -https://github.com/kenshohara/3D-ResNets-PyTorch,"title={Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?}," -https://github.com/kenshohara/3D-ResNets-PyTorch,"booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," -https://github.com/kenshohara/3D-ResNets-PyTorch,"pages={6546--6555}," -https://github.com/kenshohara/3D-ResNets-PyTorch,"Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh," -https://github.com/kenshohara/3D-ResNets-PyTorch,"Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?""," -https://github.com/kenshohara/3D-ResNets-PyTorch,"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6546-6555, 2018." -https://github.com/kenshohara/3D-ResNets-PyTorch,"Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition""," -https://github.com/kenshohara/3D-ResNets-PyTorch,"Proceedings of the ICCV Workshop on Action, Gesture, and Emotion Recognition, 2017." -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"@inproceedings{zhu17fgfa," -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Author = {Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei}," -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Title = {Flow-Guided Feature Aggregation for Video Object Detection}," -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Conference = {ICCV}," -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"@inproceedings{dai16rfcn," -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun}," -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks}," -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Conference = {NIPS}," -https://github.com/msracver/Flow-Guided-Feature-Aggregation,Year = {2016} -https://github.com/nypl-spacetime/map-vectorizer,Author: Mauricio Giraldo Arteaga @mgiraldo / NYPL Labs @nypl_labs -https://github.com/nypl-spacetime/map-vectorizer,Additional contributor: Thomas Levine @thomaslevine -https://github.com/phoenix104104/LapSRN,"Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang" -https://github.com/phoenix104104/LapSRN,"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017" -https://github.com/phoenix104104/LapSRN,"If you find the code and datasets useful in your research, please cite:" -https://github.com/phoenix104104/LapSRN,"@inproceedings{LapSRN," -https://github.com/phoenix104104/LapSRN,"author = {Lai, Wei-Sheng and Huang, Jia-Bin and Ahuja, Narendra and Yang, Ming-Hsuan}," -https://github.com/phoenix104104/LapSRN,"title = {Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution}," -https://github.com/phoenix104104/LapSRN,"booktitle = {IEEE Conferene on Computer Vision and Pattern Recognition}," -https://github.com/phoenix104104/LapSRN,year = {2017} -https://github.com/phuang17/DeepMVS,"@inproceedings{DeepMVS," -https://github.com/phuang17/DeepMVS,"author = ""Huang, Po-Han and Matzen, Kevin and Kopf, Johannes and Ahuja, Narendra and Huang, Jia-Bin""," -https://github.com/phuang17/DeepMVS,"title = ""DeepMVS: Learning Multi-View Stereopsis""," -https://github.com/phuang17/DeepMVS,"booktitle = ""IEEE Conference on Computer Vision and Pattern Recognition (CVPR)""," -https://github.com/phuang17/DeepMVS,"year = ""2018""" -https://github.com/pyvista/pymeshfix,Algorithm and Citation Policy -https://github.com/pyvista/pymeshfix,"To better understand how the algorithm works, please refer to the following paper:" -https://github.com/pyvista/pymeshfix,"M. Attene. A lightweight approach to repairing digitized polygon meshes. The Visual Computer, 2010. (c) Springer. DOI: 10.1007/s00371-010-0416-3" -https://github.com/pyvista/pymeshfix,This software is based on ideas published therein. If you use MeshFix for research purposes you should cite the above paper in your published results. MeshFix cannot be used for commercial purposes without a proper licensing contract. -https://github.com/pyvista/pyvista,Citing PyVista -https://github.com/pyvista/pyvista,There is a paper about PyVista! -https://github.com/pyvista/pyvista,"If you are using PyVista in your scientific research, please help our scientific visibility by citing our work!" -https://github.com/pyvista/pyvista,"Sullivan et al., (2019). PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). Journal of Open Source Software, 4(37), 1450, https://doi.org/10.21105/joss.01450" -https://github.com/pyvista/pyvista,BibTex: -https://github.com/pyvista/pyvista,"@article{sullivan2019pyvista," -https://github.com/pyvista/pyvista,"doi = {10.21105/joss.01450}," -https://github.com/pyvista/pyvista,"url = {https://doi.org/10.21105/joss.01450}," -https://github.com/pyvista/pyvista,"year = {2019}," -https://github.com/pyvista/pyvista,"month = {may}," -https://github.com/pyvista/pyvista,"publisher = {The Open Journal}," -https://github.com/pyvista/pyvista,"volume = {4}," -https://github.com/pyvista/pyvista,"number = {37}," -https://github.com/pyvista/pyvista,"pages = {1450}," -https://github.com/pyvista/pyvista,"author = {C. Bane Sullivan and Alexander Kaszynski}," -https://github.com/pyvista/pyvista,"title = {{PyVista}: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit ({VTK})}," -https://github.com/pyvista/pyvista,journal = {Journal of Open Source Software} -https://github.com/rowanz/neural-motifs,Bibtex -https://github.com/rowanz/neural-motifs,"@inproceedings{zellers2018scenegraphs," -https://github.com/rowanz/neural-motifs,"title={Neural Motifs: Scene Graph Parsing with Global Context}," -https://github.com/rowanz/neural-motifs,"author={Zellers, Rowan and Yatskar, Mark and Thomson, Sam and Choi, Yejin}," -https://github.com/rowanz/neural-motifs,"booktitle = ""Conference on Computer Vision and Pattern Recognition""," -https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"@inproceedings{tesfaldet2018," -https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"author = {Matthew Tesfaldet and Marcus A. Brubaker and Konstantinos G. Derpanis}," -https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"title = {Two-Stream Convolutional Networks for Dynamic Texture Synthesis}," -https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," -https://github.com/salihkaragoz/pose-residual-network-pytorch,"Muhammed Kocabas, Salih Karagoz, Emre Akbas. MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network. In ECCV, 2018. arxiv" -https://github.com/salihkaragoz/pose-residual-network-pytorch,"If you find this code useful for your research, please consider citing our paper:" -https://github.com/salihkaragoz/pose-residual-network-pytorch,"@Inproceedings{kocabas18prn," -https://github.com/salihkaragoz/pose-residual-network-pytorch,"Title = {Multi{P}ose{N}et: Fast Multi-Person Pose Estimation using Pose Residual Network}," -https://github.com/salihkaragoz/pose-residual-network-pytorch,"Author = {Kocabas, Muhammed and Karagoz, Salih and Akbas, Emre}," -https://github.com/salihkaragoz/pose-residual-network-pytorch,"Booktitle = {European Conference on Computer Vision (ECCV)}," -https://github.com/salihkaragoz/pose-residual-network-pytorch,Year = {2018} -https://github.com/whimian/pyGeoPressure,Cite pyGeoPressure as: -https://github.com/whimian/pyGeoPressure,"Yu, (2018). PyGeoPressure: Geopressure Prediction in Python. Journal of Open Source Software, 3(30), 992, https://doi.org/10.21105/joss.00992" -https://github.com/whimian/pyGeoPressure,"@article{yu2018pygeopressure," -https://github.com/whimian/pyGeoPressure,"title = {{PyGeoPressure}: {Geopressure} {Prediction} in {Python}}," -https://github.com/whimian/pyGeoPressure,"author = {Yu, Hao}," -https://github.com/whimian/pyGeoPressure,"journal = {Journal of Open Source Software}," -https://github.com/whimian/pyGeoPressure,"volume = {3}," -https://github.com/whimian/pyGeoPressure,pages = {922} -https://github.com/whimian/pyGeoPressure,"number = {30}," -https://github.com/whimian/pyGeoPressure,"year = {2018}," -https://github.com/whimian/pyGeoPressure,"doi = {10.21105/joss.00992}," -https://github.com/wuhuikai/DeepGuidedFilter,Fast End-to-End Trainable Guided Filter -https://github.com/wuhuikai/DeepGuidedFilter,"Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang" -https://github.com/wuhuikai/DeepGuidedFilter,CVPR 2018 -https://github.com/wuhuikai/DeepGuidedFilter,"@inproceedings{wu2017fast," -https://github.com/wuhuikai/DeepGuidedFilter,"title = {Fast End-to-End Trainable Guided Filter}," -https://github.com/wuhuikai/DeepGuidedFilter,"author = {Wu, Huikai and Zheng, Shuai and Zhang, Junge and Huang, Kaiqi}," -https://github.com/yuhuayc/da-faster-rcnn,"If you find it helpful for your research, please consider citing:" -https://github.com/yuhuayc/da-faster-rcnn,"@inproceedings{chen2018domain," -https://github.com/yuhuayc/da-faster-rcnn,"title={Domain Adaptive Faster R-CNN for Object Detection in the Wild}," -https://github.com/yuhuayc/da-faster-rcnn,"author={Chen, Yuhua and Li, Wen and Sakaridis, Christos and Dai, Dengxin and Van Gool, Luc}," -https://github.com/yuhuayc/da-faster-rcnn,"booktitle = {Computer Vision and Pattern Recognition (CVPR)}," -https://github.com/yulunzhang/RDN,"Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu, ""Residual Dense Network for Image Super-Resolution"", CVPR 2018 (spotlight), [arXiv]" -https://github.com/yulunzhang/RDN,"Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu, ""Residual Dense Network for Image Restoration"", arXiv 2018, [arXiv]" -https://github.com/yulunzhang/RDN,"@InProceedings{Lim_2017_CVPR_Workshops," -https://github.com/yulunzhang/RDN,"author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu}," -https://github.com/yulunzhang/RDN,"title = {Enhanced Deep Residual Networks for Single Image Super-Resolution}," -https://github.com/yulunzhang/RDN,"booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}," -https://github.com/yulunzhang/RDN,"month = {July}," -https://github.com/yulunzhang/RDN,year = {2017} -https://github.com/yulunzhang/RDN,"@inproceedings{zhang2018residual," -https://github.com/yulunzhang/RDN,"title={Residual Dense Network for Image Super-Resolution}," -https://github.com/yulunzhang/RDN,"author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun}," -https://github.com/yulunzhang/RDN,"@article{zhang2018rdnir," -https://github.com/yulunzhang/RDN,"title={Residual Dense Network for Image Restoration}," -https://github.com/yulunzhang/RDN,"booktitle={arXiv}," -https://github.com/zhiqiangdon/CU-Net,"@inproceedings{tang2018quantized," -https://github.com/zhiqiangdon/CU-Net,"title={Quantized densely connected U-Nets for efficient landmark localization}," -https://github.com/zhiqiangdon/CU-Net,"author={Tang, Zhiqiang and Peng, Xi and Geng, Shijie and Wu, Lingfei and Zhang, Shaoting and Metaxas, Dimitris}," -https://github.com/zhiqiangdon/CU-Net,"booktitle={ECCV}," -https://github.com/zhiqiangdon/CU-Net,"@inproceedings{tang2018cu," -https://github.com/zhiqiangdon/CU-Net,"title={CU-Net: Coupled U-Nets}," -https://github.com/zhiqiangdon/CU-Net,"author={Tang, Zhiqiang and Peng, Xi and Geng, Shijie and Zhu, Yizhe and Metaxas, Dimitris}," -https://github.com/zhiqiangdon/CU-Net,"booktitle={BMVC}," -https://github.com/cltk/cltk,"Each major release of the CLTK is given a DOI, a type of unique identity for digital documents. This DOI ought to be included in your citation, as it will allow researchers to reproduce your results should the CLTK's API or codebase change. To find the CLTK's current DOI, observe the blue DOI button in the repository's home on GitHub. To the end of your bibliographic entry, append DOI plus the current identifier. You may also add version/release number, located in the pypi button at the project's GitHub repository homepage." -https://github.com/cltk/cltk,"Thus, please cite core software as something like:" -https://github.com/cltk/cltk,Kyle P. Johnson et al.. (2014-2019). CLTK: The Classical Language Toolkit. DOI 10.5281/zenodo.<current_release_id> -https://github.com/cltk/cltk,A style-neutral BibTeX entry would look like this: -https://github.com/cltk/cltk,"@Misc{johnson2014," -https://github.com/cltk/cltk,"author = {Kyle P. Johnson et al.}," -https://github.com/cltk/cltk,"title = {CLTK: The Classical Language Toolkit}," -https://github.com/cltk/cltk,"howpublished = {\url{https://github.com/cltk/cltk}}," -https://github.com/cltk/cltk,"note = {{DOI} 10.5281/zenodo.<current_release_id>}," -https://github.com/cltk/cltk,"year = {2014--2019}," -https://github.com/facebookresearch/DensePose,"Citing DensePose" -https://github.com/facebookresearch/DensePose,"If you use Densepose, please use the following BibTeX entry." -https://github.com/facebookresearch/DensePose,"@InProceedings{Guler2018DensePose," -https://github.com/facebookresearch/DensePose," title={DensePose: Dense Human Pose Estimation In The Wild}," -https://github.com/facebookresearch/DensePose," author={R\{i}za Alp G\""uler, Natalia Neverova, Iasonas Kokkinos}," -https://github.com/facebookresearch/DensePose," journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," -https://github.com/facebookresearch/DensePose, year={2018} -https://github.com/facebookresearch/DensePose, } -https://github.com/facebookresearch/ResNeXt,"If you use ResNeXt in your research, please cite the paper:" -https://github.com/facebookresearch/ResNeXt,"@article{Xie2016," -https://github.com/facebookresearch/ResNeXt," title={Aggregated Residual Transformations for Deep Neural Networks}," -https://github.com/facebookresearch/ResNeXt," author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He}," -https://github.com/facebookresearch/ResNeXt," journal={arXiv preprint arXiv:1611.05431}," -https://github.com/facebookresearch/ResNeXt, year={2016} -https://github.com/harismuneer/Ultimate-Facebook-Scraper,"If you use this tool for your research, then kindly cite it. Click the above badge for more information regarding the complete citation for this tool and diffferent citation formats like IEEE, APA etc." -https://github.com/microsoft/malmo,Citations -https://github.com/microsoft/malmo,Please cite Malmo as: -https://github.com/microsoft/malmo,"Johnson M., Hofmann K., Hutton T., Bignell D. (2016) The Malmo Platform for Artificial Intelligence Experimentation. Proc. 25th International Joint Conference on Artificial Intelligence, Ed. Kambhampati S., p. 4246. AAAI Press, Palo Alto, California USA. https://github.com/Microsoft/malmo" -https://github.com/nextflow-io/nextflow,"If you use Nextflow in your research, please cite:" -https://github.com/nextflow-io/nextflow,"P. Di Tommaso, et al. Nextflow enables reproducible computational workflows. Nature Biotechnology 35, 316–319 (2017) doi:10.1038/nbt.3820" -https://github.com/pyro-ppl/pyro,"If you use Pyro, please consider citing:" -https://github.com/pyro-ppl/pyro,"@article{bingham2018pyro," -https://github.com/pyro-ppl/pyro," author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and" -https://github.com/pyro-ppl/pyro," Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and" -https://github.com/pyro-ppl/pyro," Horsfall, Paul and Goodman, Noah D.}," -https://github.com/pyro-ppl/pyro," title = {{Pyro: Deep Universal Probabilistic Programming}}," -https://github.com/pyro-ppl/pyro," journal = {arXiv preprint arXiv:1810.09538}," -https://github.com/pyro-ppl/pyro, year = {2018} -https://github.com/scikit-image/scikit-image,"If you find this project useful, please cite:" -https://github.com/scikit-image/scikit-image,"Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias," -https://github.com/scikit-image/scikit-image,"François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle" -https://github.com/scikit-image/scikit-image,"Gouillart, Tony Yu, and the scikit-image contributors." -https://github.com/scikit-image/scikit-image,scikit-image: Image processing in Python. PeerJ 2:e453 (2014) -https://github.com/scikit-image/scikit-image,https://doi.org/10.7717/peerj.453 -https://github.com/scikit-learn/scikit-learn,"If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn" +URL,contributor,excerpt +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"If you find Integral Regression useful in your research, please consider citing:" +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"@article{sun2017integral," +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"title={Integral human pose regression}," +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"author={Sun, Xiao and Xiao, Bin and Liang, Shuang and Wei, Yichen}," +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"journal={arXiv preprint arXiv:1711.08229}," +https://github.com/JimmySuen/integral-human-pose,Allen Mao,year={2017} +https://github.com/JimmySuen/integral-human-pose,Allen Mao,} +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"@article{sun2018integral," +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"title={An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge}," +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"author={Sun, Xiao and Li, Chuankang and Lin, Stephen}," +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"journal={arXiv preprint arXiv:1809.06079}," +https://github.com/JimmySuen/integral-human-pose,Allen Mao,year={2018} +https://github.com/LMescheder/GAN_stability,Allen Mao,"@INPROCEEDINGS{Mescheder2018ICML," +https://github.com/LMescheder/GAN_stability,Allen Mao,"author = {Lars Mescheder and Sebastian Nowozin and Andreas Geiger}," +https://github.com/LMescheder/GAN_stability,Allen Mao,"title = {Which Training Methods for GANs do actually Converge?}," +https://github.com/LMescheder/GAN_stability,Allen Mao,"booktitle = {International Conference on Machine Learning (ICML)}," +https://github.com/LMescheder/GAN_stability,Allen Mao,year = {2018} +https://github.com/NVIDIA/vid2vid,Allen Mao,"If you find this useful for your research, please cite the following paper." +https://github.com/NVIDIA/vid2vid,Allen Mao, +https://github.com/NVIDIA/vid2vid,Allen Mao,"@inproceedings{wang2018vid2vid," +https://github.com/NVIDIA/vid2vid,Allen Mao,author = {Ting-Chun Wang and Ming-Yu Liu and Jun-Yan Zhu and Guilin Liu +https://github.com/NVIDIA/vid2vid,Allen Mao,"and Andrew Tao and Jan Kautz and Bryan Catanzaro}," +https://github.com/NVIDIA/vid2vid,Allen Mao,"title = {Video-to-Video Synthesis}," +https://github.com/NVIDIA/vid2vid,Allen Mao,"booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}," +https://github.com/NVIDIA/vid2vid,Allen Mao,"year = {2018}," +https://github.com/NVIDIA/vid2vid,Allen Mao,Video-to-Video Synthesis +https://github.com/NVIDIA/vid2vid,Allen Mao,"Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Guilin Liu1, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1" +https://github.com/NVIDIA/vid2vid,Allen Mao,"1NVIDIA Corporation, 2MIT CSAIL" +https://github.com/NVIDIA/vid2vid,Allen Mao,In Neural Information Processing Systems (NeurIPS) 2018 +https://github.com/OpenGeoVis/PVGeo,Allen Mao,"The PVGeo code library was created and is managed by Bane Sullivan, graduate student in the Hydrological Science and Engineering interdisciplinary program at the Colorado School of Mines under Whitney Trainor-Guitton. If you would like to contact us, inquire with info@pvgeo.org." +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, Hongbin Zha" +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University" +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"Key Laboratory of Machine Perception (MOE), School of EECS, Peking University" +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"Cooperative Medianet Innovation Center, Shanghai Jiao Tong University" +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"{ethanlee, jlwu1992, zlin, hongliu}@pku.edu.cn, zha@cis.pku.edu.cn" +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"@inproceedings{li2018recurrent," +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"title={Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining}," +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"author={Li, Xia and Wu, Jianlong and Lin, Zhouchen and Liu, Hong and Zha, Hongbin}," +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"booktitle={European Conference on Computer Vision}," +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"pages={262--277}," +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"year={2018}," +https://github.com/XiaLiPKU/RESCAN,Allen Mao,organization={Springer} +https://github.com/ZhouYanzhao/PRM,Allen Mao,Citation +https://github.com/ZhouYanzhao/PRM,Allen Mao,"If you find the code useful for your research, please cite:" +https://github.com/ZhouYanzhao/PRM,Allen Mao,"@INPROCEEDINGS{Zhou2018PRM," +https://github.com/ZhouYanzhao/PRM,Allen Mao,"author = {Zhou, Yanzhao and Zhu, Yi and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin}," +https://github.com/ZhouYanzhao/PRM,Allen Mao,"title = {Weakly Supervised Instance Segmentation using Class Peak Response}," +https://github.com/ZhouYanzhao/PRM,Allen Mao,"booktitle = {CVPR}," +https://github.com/akanazawa/hmr,Allen Mao,"Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018" +https://github.com/akanazawa/hmr,Allen Mao,"@inProceedings{kanazawaHMR18," +https://github.com/akanazawa/hmr,Allen Mao,"title={End-to-end Recovery of Human Shape and Pose}," +https://github.com/akanazawa/hmr,Allen Mao,author = {Angjoo Kanazawa +https://github.com/akanazawa/hmr,Allen Mao,and Michael J. Black +https://github.com/akanazawa/hmr,Allen Mao,and David W. Jacobs +https://github.com/akanazawa/hmr,Allen Mao,"and Jitendra Malik}," +https://github.com/akanazawa/hmr,Allen Mao,"booktitle={Computer Vision and Pattern Regognition (CVPR)}," +https://github.com/albertpumarola/GANimation,Allen Mao,"If you use this code or ideas from the paper for your research, please cite our paper:" +https://github.com/albertpumarola/GANimation,Allen Mao,"@inproceedings{pumarola2018ganimation," +https://github.com/albertpumarola/GANimation,Allen Mao,"title={GANimation: Anatomically-aware Facial Animation from a Single Image}," +https://github.com/albertpumarola/GANimation,Allen Mao,"author={A. Pumarola and A. Agudo and A.M. Martinez and A. Sanfeliu and F. Moreno-Noguer}," +https://github.com/albertpumarola/GANimation,Allen Mao,"booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}," +https://github.com/cgre-aachen/gempy,Allen Mao,"For a more detailed elaboration of the theory behind GemPy, take a look at the upcoming scientific publication ""GemPy 1.0: open-source stochastic geological modeling and inversion"" by de la Varga et al. (2018)." +https://github.com/cgre-aachen/gempy,Allen Mao,References +https://github.com/cgre-aachen/gempy,Allen Mao,"de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1-32, https://doi.org/10.5194/gmd-12-1-2019, 2019" +https://github.com/cgre-aachen/gempy,Allen Mao,"Calcagno, P., Chilès, J. P., Courrioux, G., & Guillen, A. (2008). Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules. Physics of the Earth and Planetary Interiors, 171(1-4), 147-157." +https://github.com/cgre-aachen/gempy,Allen Mao,"Lajaunie, C., Courrioux, G., & Manuel, L. (1997). Foliation fields and 3D cartography in geology: principles of a method based on potential interpolation. Mathematical Geology, 29(4), 571-584." +https://github.com/driftingtides/hyvr,Allen Mao,"HyVR can be attributed by citing the following journal article: Bennett, J. P., Haslauer, C. P., Ross, M., & Cirpka, O. A. (2018). An open, object-based framework for generating anisotropy in sedimentary subsurface models. Groundwater. DOI: 10.1111/gwat.12803." +https://github.com/driving-behavior/DBNet,Allen Mao,"DBNet was developed by MVIG, Shanghai Jiao Tong University* and SCSC Lab, Xiamen University* (alphabetical order)." +https://github.com/driving-behavior/DBNet,Allen Mao,"If you find our work useful in your research, please consider citing:" +https://github.com/driving-behavior/DBNet,Allen Mao,"@InProceedings{DBNet2018," +https://github.com/driving-behavior/DBNet,Allen Mao,"author = {Yiping Chen and Jingkang Wang and Jonathan Li and Cewu Lu and Zhipeng Luo and HanXue and Cheng Wang}," +https://github.com/driving-behavior/DBNet,Allen Mao,"title = {LiDAR-Video Driving Dataset: Learning Driving Policies Effectively}," +https://github.com/driving-behavior/DBNet,Allen Mao,"booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," +https://github.com/driving-behavior/DBNet,Allen Mao,"month = {June}," +https://github.com/empymod/empymod,Allen Mao,"If you publish results for which you used empymod, please give credit by citing Werthmüller (2017):" +https://github.com/empymod/empymod,Allen Mao,"Werthmüller, D., 2017, An open-source full 3D electromagnetic modeler for 1D VTI media in Python: empymod: Geophysics, 82(6), WB9--WB19; DOI: 10.1190/geo2016-0626.1." +https://github.com/empymod/empymod,Allen Mao,"All releases have a Zenodo-DOI, provided on the release-page. Also consider citing Hunziker et al. (2015) and Key (2012), without which empymod would not exist." +https://github.com/endernewton/iter-reason,Allen Mao,"@inproceedings{chen18iterative," +https://github.com/endernewton/iter-reason,Allen Mao,"author = {Xinlei Chen and Li-Jia Li and Li Fei-Fei and Abhinav Gupta}," +https://github.com/endernewton/iter-reason,Allen Mao,"title = {Iterative Visual Reasoning Beyond Convolutions}," +https://github.com/endernewton/iter-reason,Allen Mao,"booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}," +https://github.com/endernewton/iter-reason,Allen Mao,Year = {2018} +https://github.com/endernewton/iter-reason,Allen Mao,"@inproceedings{chen2017spatial," +https://github.com/endernewton/iter-reason,Allen Mao,"author = {Xinlei Chen and Abhinav Gupta}," +https://github.com/endernewton/iter-reason,Allen Mao,"title = {Spatial Memory for Context Reasoning in Object Detection}," +https://github.com/endernewton/iter-reason,Allen Mao,"booktitle = {Proceedings of the International Conference on Computer Vision}," +https://github.com/endernewton/iter-reason,Allen Mao,Year = {2017} +https://github.com/equinor/pylops,Allen Mao,Contributors +https://github.com/equinor/pylops,Allen Mao,"Matteo Ravasi, mrava87" +https://github.com/equinor/pylops,Allen Mao,"Carlos da Costa, cako" +https://github.com/equinor/pylops,Allen Mao,"Dieter Werthmüller, prisae" +https://github.com/equinor/pylops,Allen Mao,"Tristan van Leeuwen, TristanvanLeeuwen" +https://github.com/facebookresearch/Detectron/,Allen Mao,"If you use Detectron in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry." +https://github.com/facebookresearch/Detectron/,Allen Mao,"@misc{Detectron2018," +https://github.com/facebookresearch/Detectron/,Allen Mao,author = {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and +https://github.com/facebookresearch/Detectron/,Allen Mao,"Piotr Doll\'{a}r and Kaiming He}," +https://github.com/facebookresearch/Detectron/,Allen Mao,"title = {Detectron}," +https://github.com/facebookresearch/Detectron/,Allen Mao,"howpublished = {\url{https://github.com/facebookresearch/detectron}}," +https://github.com/facebookresearch/Detectron/,Allen Mao,year = {2018} +https://github.com/foolwood/DaSiamRPN,Allen Mao,"Zheng Zhu*, Qiang Wang*, Bo Li*, Wei Wu, Junjie Yan, and Weiming Hu" +https://github.com/foolwood/DaSiamRPN,Allen Mao,"European Conference on Computer Vision (ECCV), 2018" +https://github.com/foolwood/DaSiamRPN,Allen Mao,Citing DaSiamRPN +https://github.com/foolwood/DaSiamRPN,Allen Mao,"If you find DaSiamRPN and SiamRPN useful in your research, please consider citing:" +https://github.com/foolwood/DaSiamRPN,Allen Mao,"@inproceedings{Zhu_2018_ECCV," +https://github.com/foolwood/DaSiamRPN,Allen Mao,"title={Distractor-aware Siamese Networks for Visual Object Tracking}," +https://github.com/foolwood/DaSiamRPN,Allen Mao,"author={Zhu, Zheng and Wang, Qiang and Bo, Li and Wu, Wei and Yan, Junjie and Hu, Weiming}," +https://github.com/foolwood/DaSiamRPN,Allen Mao,"@InProceedings{Li_2018_CVPR," +https://github.com/foolwood/DaSiamRPN,Allen Mao,"title = {High Performance Visual Tracking With Siamese Region Proposal Network}," +https://github.com/foolwood/DaSiamRPN,Allen Mao,"author = {Li, Bo and Yan, Junjie and Wu, Wei and Zhu, Zheng and Hu, Xiaolin}," +https://github.com/google/sg2im/,Allen Mao,"@inproceedings{johnson2018image," +https://github.com/google/sg2im/,Allen Mao,"title={Image Generation from Scene Graphs}," +https://github.com/google/sg2im/,Allen Mao,"author={Johnson, Justin and Gupta, Agrim and Fei-Fei, Li}," +https://github.com/google/sg2im/,Allen Mao,"booktitle={CVPR}," +https://github.com/google/sg2im/,Allen Mao,Image Generation from Scene Graphs +https://github.com/google/sg2im/,Allen Mao,"Justin Johnson, Agrim Gupta, Li Fei-Fei" +https://github.com/google/sg2im/,Allen Mao,Presented at CVPR 2018 +https://github.com/gprMax/gprMax,Allen Mao,Using gprMax? Cite us +https://github.com/gprMax/gprMax,Allen Mao,If you use gprMax and publish your work we would be grateful if you could cite our work using: +https://github.com/gprMax/gprMax,Allen Mao,"Warren, C., Giannopoulos, A., & Giannakis I. (2016). gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar, Computer Physics Communications (http://dx.doi.org/10.1016/j.cpc.2016.08.020)" +https://github.com/hezhangsprinter/DCPDN,Allen Mao,"He Zhang, Vishal M. Patel" +https://github.com/hezhangsprinter/DCPDN,Allen Mao,[Paper Link] (CVPR'18) +https://github.com/hezhangsprinter/DID-MDN,Allen Mao,"@inproceedings{derain_zhang_2018," +https://github.com/hezhangsprinter/DID-MDN,Allen Mao,"title={Density-aware Single Image De-raining using a Multi-stream Dense Network}," +https://github.com/hezhangsprinter/DID-MDN,Allen Mao,"author={Zhang, He and Patel, Vishal M}," +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,@InProceedings{kato2018renderer +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,"title={Neural 3D Mesh Renderer}," +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,"author={Kato, Hiroharu and Ushiku, Yoshitaka and Harada, Tatsuya}," +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,"booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," +https://github.com/iannesbitt/readgssi,Allen Mao,"Ian M. Nesbitt, François-Xavier Simon, Thomas Paulin, 2018. readgssi - an open-source tool to read and plot GSSI ground-penetrating radar data. doi:10.5281/zenodo.1439119" +https://github.com/jiangsutx/SRN-Deblur,Allen Mao,"Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, Jiaya Jia." +https://github.com/jiangsutx/SRN-Deblur,Allen Mao,"@inproceedings{tao2018srndeblur," +https://github.com/jiangsutx/SRN-Deblur,Allen Mao,"title={Scale-recurrent Network for Deep Image Deblurring}," +https://github.com/jiangsutx/SRN-Deblur,Allen Mao,"author={Tao, Xin and Gao, Hongyun and Shen, Xiaoyong and Wang, Jue and Jia, Jiaya}," +https://github.com/jiangsutx/SRN-Deblur,Allen Mao,"booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," +https://github.com/joferkington/mplstereonet,Allen Mao,"[Kamb1956]Kamb, 1959. Ice Petrofabric Observations from Blue Glacier, Washington, in Relation to Theory and Experiment. Journal of Geophysical Research, Vol. 64, No. 11, pp. 1891--1909." +https://github.com/joferkington/mplstereonet,Allen Mao,"[Vollmer1995]Vollmer, 1995. C Program for Automatic Contouring of Spherical Orientation Data Using a Modified Kamb Method. Computers & Geosciences, Vol. 21, No. 1, pp. 31--49." +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"If you use this code or pre-trained models, please cite the following:" +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"@inproceedings{hara3dcnns," +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"author={Kensho Hara and Hirokatsu Kataoka and Yutaka Satoh}," +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"title={Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?}," +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"pages={6546--6555}," +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh," +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?""," +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6546-6555, 2018." +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition""," +https://github.com/kenshohara/3D-ResNets-PyTorch,Allen Mao,"Proceedings of the ICCV Workshop on Action, Gesture, and Emotion Recognition, 2017." +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"@inproceedings{zhu17fgfa," +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"Author = {Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei}," +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"Title = {Flow-Guided Feature Aggregation for Video Object Detection}," +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"Conference = {ICCV}," +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"@inproceedings{dai16rfcn," +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun}," +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks}," +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"Conference = {NIPS}," +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,Year = {2016} +https://github.com/nypl-spacetime/map-vectorizer,Allen Mao,Author: Mauricio Giraldo Arteaga @mgiraldo / NYPL Labs @nypl_labs +https://github.com/nypl-spacetime/map-vectorizer,Allen Mao,Additional contributor: Thomas Levine @thomaslevine +https://github.com/phoenix104104/LapSRN,Allen Mao,"Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang" +https://github.com/phoenix104104/LapSRN,Allen Mao,"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017" +https://github.com/phoenix104104/LapSRN,Allen Mao,"If you find the code and datasets useful in your research, please cite:" +https://github.com/phoenix104104/LapSRN,Allen Mao,"@inproceedings{LapSRN," +https://github.com/phoenix104104/LapSRN,Allen Mao,"author = {Lai, Wei-Sheng and Huang, Jia-Bin and Ahuja, Narendra and Yang, Ming-Hsuan}," +https://github.com/phoenix104104/LapSRN,Allen Mao,"title = {Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution}," +https://github.com/phoenix104104/LapSRN,Allen Mao,"booktitle = {IEEE Conferene on Computer Vision and Pattern Recognition}," +https://github.com/phoenix104104/LapSRN,Allen Mao,year = {2017} +https://github.com/phuang17/DeepMVS,Allen Mao,"@inproceedings{DeepMVS," +https://github.com/phuang17/DeepMVS,Allen Mao,"author = ""Huang, Po-Han and Matzen, Kevin and Kopf, Johannes and Ahuja, Narendra and Huang, Jia-Bin""," +https://github.com/phuang17/DeepMVS,Allen Mao,"title = ""DeepMVS: Learning Multi-View Stereopsis""," +https://github.com/phuang17/DeepMVS,Allen Mao,"booktitle = ""IEEE Conference on Computer Vision and Pattern Recognition (CVPR)""," +https://github.com/phuang17/DeepMVS,Allen Mao,"year = ""2018""" +https://github.com/pyvista/pymeshfix,Allen Mao,Algorithm and Citation Policy +https://github.com/pyvista/pymeshfix,Allen Mao,"To better understand how the algorithm works, please refer to the following paper:" +https://github.com/pyvista/pymeshfix,Allen Mao,"M. Attene. A lightweight approach to repairing digitized polygon meshes. The Visual Computer, 2010. (c) Springer. DOI: 10.1007/s00371-010-0416-3" +https://github.com/pyvista/pymeshfix,Allen Mao,This software is based on ideas published therein. If you use MeshFix for research purposes you should cite the above paper in your published results. MeshFix cannot be used for commercial purposes without a proper licensing contract. +https://github.com/pyvista/pyvista,Allen Mao,Citing PyVista +https://github.com/pyvista/pyvista,Allen Mao,There is a paper about PyVista! +https://github.com/pyvista/pyvista,Allen Mao,"If you are using PyVista in your scientific research, please help our scientific visibility by citing our work!" +https://github.com/pyvista/pyvista,Allen Mao,"Sullivan et al., (2019). PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). Journal of Open Source Software, 4(37), 1450, https://doi.org/10.21105/joss.01450" +https://github.com/pyvista/pyvista,Allen Mao,BibTex: +https://github.com/pyvista/pyvista,Allen Mao,"@article{sullivan2019pyvista," +https://github.com/pyvista/pyvista,Allen Mao,"doi = {10.21105/joss.01450}," +https://github.com/pyvista/pyvista,Allen Mao,"url = {https://doi.org/10.21105/joss.01450}," +https://github.com/pyvista/pyvista,Allen Mao,"year = {2019}," +https://github.com/pyvista/pyvista,Allen Mao,"month = {may}," +https://github.com/pyvista/pyvista,Allen Mao,"publisher = {The Open Journal}," +https://github.com/pyvista/pyvista,Allen Mao,"volume = {4}," +https://github.com/pyvista/pyvista,Allen Mao,"number = {37}," +https://github.com/pyvista/pyvista,Allen Mao,"pages = {1450}," +https://github.com/pyvista/pyvista,Allen Mao,"author = {C. Bane Sullivan and Alexander Kaszynski}," +https://github.com/pyvista/pyvista,Allen Mao,"title = {{PyVista}: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit ({VTK})}," +https://github.com/pyvista/pyvista,Allen Mao,journal = {Journal of Open Source Software} +https://github.com/rowanz/neural-motifs,Allen Mao,Bibtex +https://github.com/rowanz/neural-motifs,Allen Mao,"@inproceedings{zellers2018scenegraphs," +https://github.com/rowanz/neural-motifs,Allen Mao,"title={Neural Motifs: Scene Graph Parsing with Global Context}," +https://github.com/rowanz/neural-motifs,Allen Mao,"author={Zellers, Rowan and Yatskar, Mark and Thomson, Sam and Choi, Yejin}," +https://github.com/rowanz/neural-motifs,Allen Mao,"booktitle = ""Conference on Computer Vision and Pattern Recognition""," +https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Allen Mao,"@inproceedings{tesfaldet2018," +https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Allen Mao,"author = {Matthew Tesfaldet and Marcus A. Brubaker and Konstantinos G. Derpanis}," +https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Allen Mao,"title = {Two-Stream Convolutional Networks for Dynamic Texture Synthesis}," +https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Allen Mao,"booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," +https://github.com/salihkaragoz/pose-residual-network-pytorch,Allen Mao,"Muhammed Kocabas, Salih Karagoz, Emre Akbas. MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network. In ECCV, 2018. arxiv" +https://github.com/salihkaragoz/pose-residual-network-pytorch,Allen Mao,"If you find this code useful for your research, please consider citing our paper:" +https://github.com/salihkaragoz/pose-residual-network-pytorch,Allen Mao,"@Inproceedings{kocabas18prn," +https://github.com/salihkaragoz/pose-residual-network-pytorch,Allen Mao,"Title = {Multi{P}ose{N}et: Fast Multi-Person Pose Estimation using Pose Residual Network}," +https://github.com/salihkaragoz/pose-residual-network-pytorch,Allen Mao,"Author = {Kocabas, Muhammed and Karagoz, Salih and Akbas, Emre}," +https://github.com/salihkaragoz/pose-residual-network-pytorch,Allen Mao,"Booktitle = {European Conference on Computer Vision (ECCV)}," +https://github.com/salihkaragoz/pose-residual-network-pytorch,Allen Mao,Year = {2018} +https://github.com/whimian/pyGeoPressure,Allen Mao,Cite pyGeoPressure as: +https://github.com/whimian/pyGeoPressure,Allen Mao,"Yu, (2018). PyGeoPressure: Geopressure Prediction in Python. Journal of Open Source Software, 3(30), 992, https://doi.org/10.21105/joss.00992" +https://github.com/whimian/pyGeoPressure,Allen Mao,"@article{yu2018pygeopressure," +https://github.com/whimian/pyGeoPressure,Allen Mao,"title = {{PyGeoPressure}: {Geopressure} {Prediction} in {Python}}," +https://github.com/whimian/pyGeoPressure,Allen Mao,"author = {Yu, Hao}," +https://github.com/whimian/pyGeoPressure,Allen Mao,"journal = {Journal of Open Source Software}," +https://github.com/whimian/pyGeoPressure,Allen Mao,"volume = {3}," +https://github.com/whimian/pyGeoPressure,Allen Mao,pages = {922} +https://github.com/whimian/pyGeoPressure,Allen Mao,"number = {30}," +https://github.com/whimian/pyGeoPressure,Allen Mao,"year = {2018}," +https://github.com/whimian/pyGeoPressure,Allen Mao,"doi = {10.21105/joss.00992}," +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,Fast End-to-End Trainable Guided Filter +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,"Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang" +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,CVPR 2018 +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,"@inproceedings{wu2017fast," +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,"title = {Fast End-to-End Trainable Guided Filter}," +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,"author = {Wu, Huikai and Zheng, Shuai and Zhang, Junge and Huang, Kaiqi}," +https://github.com/yuhuayc/da-faster-rcnn,Allen Mao,"If you find it helpful for your research, please consider citing:" +https://github.com/yuhuayc/da-faster-rcnn,Allen Mao,"@inproceedings{chen2018domain," +https://github.com/yuhuayc/da-faster-rcnn,Allen Mao,"title={Domain Adaptive Faster R-CNN for Object Detection in the Wild}," +https://github.com/yuhuayc/da-faster-rcnn,Allen Mao,"author={Chen, Yuhua and Li, Wen and Sakaridis, Christos and Dai, Dengxin and Van Gool, Luc}," +https://github.com/yuhuayc/da-faster-rcnn,Allen Mao,"booktitle = {Computer Vision and Pattern Recognition (CVPR)}," +https://github.com/yulunzhang/RDN,Allen Mao,"Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu, ""Residual Dense Network for Image Super-Resolution"", CVPR 2018 (spotlight), [arXiv]" +https://github.com/yulunzhang/RDN,Allen Mao,"Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu, ""Residual Dense Network for Image Restoration"", arXiv 2018, [arXiv]" +https://github.com/yulunzhang/RDN,Allen Mao,"@InProceedings{Lim_2017_CVPR_Workshops," +https://github.com/yulunzhang/RDN,Allen Mao,"author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu}," +https://github.com/yulunzhang/RDN,Allen Mao,"title = {Enhanced Deep Residual Networks for Single Image Super-Resolution}," +https://github.com/yulunzhang/RDN,Allen Mao,"booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}," +https://github.com/yulunzhang/RDN,Allen Mao,"month = {July}," +https://github.com/yulunzhang/RDN,Allen Mao,year = {2017} +https://github.com/yulunzhang/RDN,Allen Mao,"@inproceedings{zhang2018residual," +https://github.com/yulunzhang/RDN,Allen Mao,"title={Residual Dense Network for Image Super-Resolution}," +https://github.com/yulunzhang/RDN,Allen Mao,"author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun}," +https://github.com/yulunzhang/RDN,Allen Mao,"@article{zhang2018rdnir," +https://github.com/yulunzhang/RDN,Allen Mao,"title={Residual Dense Network for Image Restoration}," +https://github.com/yulunzhang/RDN,Allen Mao,"booktitle={arXiv}," +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"@inproceedings{tang2018quantized," +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"title={Quantized densely connected U-Nets for efficient landmark localization}," +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"author={Tang, Zhiqiang and Peng, Xi and Geng, Shijie and Wu, Lingfei and Zhang, Shaoting and Metaxas, Dimitris}," +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"booktitle={ECCV}," +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"@inproceedings{tang2018cu," +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"title={CU-Net: Coupled U-Nets}," +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"author={Tang, Zhiqiang and Peng, Xi and Geng, Shijie and Zhu, Yizhe and Metaxas, Dimitris}," +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"booktitle={BMVC}," +https://github.com/cltk/cltk,Rosna Thomas,"Each major release of the CLTK is given a DOI, a type of unique identity for digital documents. This DOI ought to be included in your citation, as it will allow researchers to reproduce your results should the CLTK's API or codebase change. To find the CLTK's current DOI, observe the blue DOI button in the repository's home on GitHub. To the end of your bibliographic entry, append DOI plus the current identifier. You may also add version/release number, located in the pypi button at the project's GitHub repository homepage." +https://github.com/cltk/cltk,Rosna Thomas,"Thus, please cite core software as something like:" +https://github.com/cltk/cltk,Rosna Thomas,Kyle P. Johnson et al.. (2014-2019). CLTK: The Classical Language Toolkit. DOI 10.5281/zenodo.<current_release_id> +https://github.com/cltk/cltk,Rosna Thomas,A style-neutral BibTeX entry would look like this: +https://github.com/cltk/cltk,Rosna Thomas,"@Misc{johnson2014," +https://github.com/cltk/cltk,Rosna Thomas,"author = {Kyle P. Johnson et al.}," +https://github.com/cltk/cltk,Rosna Thomas,"title = {CLTK: The Classical Language Toolkit}," +https://github.com/cltk/cltk,Rosna Thomas,"howpublished = {\url{https://github.com/cltk/cltk}}," +https://github.com/cltk/cltk,Rosna Thomas,"note = {{DOI} 10.5281/zenodo.<current_release_id>}," +https://github.com/cltk/cltk,Rosna Thomas,"year = {2014--2019}," +https://github.com/facebookresearch/DensePose,Rosna Thomas,"Citing DensePose" +https://github.com/facebookresearch/DensePose,Rosna Thomas,"If you use Densepose, please use the following BibTeX entry." +https://github.com/facebookresearch/DensePose,Rosna Thomas,"@InProceedings{Guler2018DensePose," +https://github.com/facebookresearch/DensePose,Rosna Thomas," title={DensePose: Dense Human Pose Estimation In The Wild}," +https://github.com/facebookresearch/DensePose,Rosna Thomas," author={R\{i}za Alp G\""uler, Natalia Neverova, Iasonas Kokkinos}," +https://github.com/facebookresearch/DensePose,Rosna Thomas," journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}," +https://github.com/facebookresearch/DensePose,Rosna Thomas, year={2018} +https://github.com/facebookresearch/DensePose,Rosna Thomas, } +https://github.com/facebookresearch/ResNeXt,Rosna Thomas,"If you use ResNeXt in your research, please cite the paper:" +https://github.com/facebookresearch/ResNeXt,Rosna Thomas,"@article{Xie2016," +https://github.com/facebookresearch/ResNeXt,Rosna Thomas," title={Aggregated Residual Transformations for Deep Neural Networks}," +https://github.com/facebookresearch/ResNeXt,Rosna Thomas," author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He}," +https://github.com/facebookresearch/ResNeXt,Rosna Thomas," journal={arXiv preprint arXiv:1611.05431}," +https://github.com/facebookresearch/ResNeXt,Rosna Thomas, year={2016} +https://github.com/harismuneer/Ultimate-Facebook-Scraper,Rosna Thomas,"If you use this tool for your research, then kindly cite it. Click the above badge for more information regarding the complete citation for this tool and diffferent citation formats like IEEE, APA etc." +https://github.com/microsoft/malmo,Rosna Thomas,Citations +https://github.com/microsoft/malmo,Rosna Thomas,Please cite Malmo as: +https://github.com/microsoft/malmo,Rosna Thomas,"Johnson M., Hofmann K., Hutton T., Bignell D. (2016) The Malmo Platform for Artificial Intelligence Experimentation. Proc. 25th International Joint Conference on Artificial Intelligence, Ed. Kambhampati S., p. 4246. AAAI Press, Palo Alto, California USA. https://github.com/Microsoft/malmo" +https://github.com/nextflow-io/nextflow,Rosna Thomas,"If you use Nextflow in your research, please cite:" +https://github.com/nextflow-io/nextflow,Rosna Thomas,"P. Di Tommaso, et al. Nextflow enables reproducible computational workflows. Nature Biotechnology 35, 316–319 (2017) doi:10.1038/nbt.3820" +https://github.com/pyro-ppl/pyro,Rosna Thomas,"If you use Pyro, please consider citing:" +https://github.com/pyro-ppl/pyro,Rosna Thomas,"@article{bingham2018pyro," +https://github.com/pyro-ppl/pyro,Rosna Thomas," author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and" +https://github.com/pyro-ppl/pyro,Rosna Thomas," Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and" +https://github.com/pyro-ppl/pyro,Rosna Thomas," Horsfall, Paul and Goodman, Noah D.}," +https://github.com/pyro-ppl/pyro,Rosna Thomas," title = {{Pyro: Deep Universal Probabilistic Programming}}," +https://github.com/pyro-ppl/pyro,Rosna Thomas," journal = {arXiv preprint arXiv:1810.09538}," +https://github.com/pyro-ppl/pyro,Rosna Thomas, year = {2018} +https://github.com/scikit-image/scikit-image,Rosna Thomas,"If you find this project useful, please cite:" +https://github.com/scikit-image/scikit-image,Rosna Thomas,"Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias," +https://github.com/scikit-image/scikit-image,Rosna Thomas,"François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle" +https://github.com/scikit-image/scikit-image,Rosna Thomas,"Gouillart, Tony Yu, and the scikit-image contributors." +https://github.com/scikit-image/scikit-image,Rosna Thomas,scikit-image: Image processing in Python. PeerJ 2:e453 (2014) +https://github.com/scikit-image/scikit-image,Rosna Thomas,https://doi.org/10.7717/peerj.453 +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,"If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn" +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, +,, \ No newline at end of file diff --git a/data/description.csv b/data/description.csv index 4f4c1411..649d278b 100644 --- a/data/description.csv +++ b/data/description.csv @@ -1,337 +1,337 @@ -URL,excerpt -https://github.com/GoogleChrome/puppeteer,"Puppeteer is a Node library which provides a high-level API to control Chrome or Chromium over the DevTools Protocol. Puppeteer runs headless by default, but can be configured to run full (non-headless) Chrome or Chromium." -https://github.com/JimmySuen/integral-human-pose,"The major contributors of this repository include Xiao Sun, Chuankang Li, Bin Xiao, Fangyin Wei, Yichen Wei." -https://github.com/JimmySuen/integral-human-pose,Integral Regression is initially described in an ECCV 2018 paper. (Slides). -https://github.com/JimmySuen/integral-human-pose,"We build a 3D pose estimation system based mainly on the Integral Regression, placing second in the ECCV2018 3D Human Pose Estimation Challenge. Note that, the winner Sarandi et al. also uses the Integral Regression (or soft-argmax) with a better augmented 3D dataset in their method indicating the Integral Regression is the currently state-of-the-art 3D human pose estimation method." -https://github.com/JimmySuen/integral-human-pose,The Integral Regression is also known as soft-argmax. Please refer to two contemporary works (Luvizon et al. and Nibali et al.) for a better comparision and more comprehensive understanding. -https://github.com/JimmySuen/integral-human-pose,This is an official implementation for Integral Human Pose Regression based on Pytorch. It is worth noticing that: -https://github.com/JimmySuen/integral-human-pose,The original implementation is based on our internal Mxnet version. There are slight differences in the final accuracy and running time due to the plenty details in platform switch. -https://github.com/JuliaGeo/LibGEOS.jl,"LibGEOS is a LGPL-licensed package for manipulation and analysis of planar geometric objects, based on the libraries GEOS (the engine of PostGIS) and JTS (from which GEOS is ported)." -https://github.com/JuliaGeo/LibGEOS.jl,"Among other things, it allows you to parse Well-known Text (WKT)" -https://github.com/LMescheder/GAN_stability,This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converge?. -https://github.com/LMescheder/GAN_stability,"For the results presented in the paper, we did not use a moving average over the weights. However, using a moving average helps to reduce noise and we therefore recommend its usage. Indeed, we found that using a moving average leads to much better inception scores on Imagenet." -https://github.com/LMescheder/GAN_stability,"Batch normalization is currently not supported when using an exponential running average, as the running average is only computed over the parameters of the models and not the other buffers of the model." -https://github.com/NSGeophysics/GPRPy,Open-source Ground Penetrating Radar processing and visualization software. -https://github.com/NVIDIA/vid2vid,"Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic video-to-video translation. It can be used for turning semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses. The core of video-to-video translation is image-to-image translation. Some of our work in that space can be found in pix2pixHD and SPADE. " -https://github.com/OpenGeoVis/PVGeo,The PVGeo Python package contains VTK powered tools for data visualization in geophysics which are wrapped for direct use within the application ParaView by Kitware or in a Python environment with PyVista. These tools are tailored to data visualization in the geosciences with a heavy focus on structured data sets like 2D or 3D time-varying grids. -https://github.com/OpenGeoVis/omfvista,A PyVista (and VTK) interface for the Open Mining Format package (omf) providing Python 3D visualization and useable mesh data structures for processing datasets in the OMF specification. -https://github.com/OpenGeoscience/geonotebook/,"GeoNotebook is an application that provides client/server environment with interactive visualization and analysis capabilities using Jupyter, GeoJS and other open source tools. Jointly developed by Kitware and NASA Ames." -https://github.com/Toblerity/Fiona/,Fiona is OGR's neat and nimble API for Python programmers. -https://github.com/Toblerity/Fiona/,"Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as pyproj, Rtree, and Shapely. Fiona is supported only on CPython versions 2.7 and 3.4+." -https://github.com/Toblerity/Shapely,"Shapely is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployed GEOS (the engine of PostGIS) and JTS (from which GEOS is ported) libraries. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are." -https://github.com/XiaLiPKU/RESCAN,"Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics." -https://github.com/ZhouYanzhao/PRM,The pytorch branch contains: -https://github.com/ZhouYanzhao/PRM,the pytorch implementation of Peak Response Mapping (Stimulation and Backprop). -https://github.com/ZhouYanzhao/PRM,"the PASCAL-VOC demo (training, inference, and visualization)." -https://github.com/agile-geoscience/striplog/,Lithology and stratigraphic logs for wells and outcrop. -https://github.com/akaszynski/pyansys,This Python module allows you to: -https://github.com/akaszynski/pyansys,"Interactively control an instance of ANSYS v14.5 + using Python on Linux, >=17.0 on Windows." -https://github.com/akaszynski/pyansys,Extract data directly from binary ANSYS v14.5+ files and to display or animate them. -https://github.com/akaszynski/pyansys,"Rapidly read in binary result (.rst), binary mass and stiffness (.full), and ASCII block archive (.cdb) files." -https://github.com/albertpumarola/GANimation,"Official implementation of GANimation. In this work we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describe in a continuous manifold the anatomical facial movements defining a human expression. Our approach permits controlling the magnitude of activation of each AU and combine several of them. For more information please refer to the paper." -https://github.com/albertpumarola/GANimation,This code was made public to share our research for the benefit of the scientific community. Do NOT use it for immoral purposes. -https://github.com/cgre-aachen/gempy,What is it -https://github.com/cgre-aachen/gempy,"GemPy is a Python-based, open-source library for implicitly generating 3D structural geological models. It is capable of constructing complex 3D geological models of folded structures, fault networks and unconformities. It was designed from the ground up to support easy embedding in probabilistic frameworks for the uncertainty analysis of subsurface structures." -https://github.com/cgre-aachen/gempy,Features -https://github.com/cgre-aachen/gempy,The core algorithm of GemPy is based on a universal cokriging interpolation method devised by Lajaunie et al. (1997) and extended by Calcagno et al. (2008). Its implicit nature allows the user to automatically generate complex 3D structural geological models through the interpolation of input data: -https://github.com/cgre-aachen/gempy,"Surface contact points: 3D coordinates of points marking the boundaries between different features (e.g. layer interfaces, fault planes, unconformities)." -https://github.com/cgre-aachen/gempy,Orientation measurements: Orientation of the poles perpendicular to the dipping of surfaces at any point in the 3D space. -https://github.com/cgre-aachen/gempy,GemPy also allows for the definition of topological elements such as combining multiple stratigraphic sequences and complex fault networks to be considered in the modeling process. -https://github.com/cgre-aachen/gempy,"GemPy itself offers direct visualization of 2D model sections via matplotlib and in full, interactive 3D using the Visualization Toolkit (VTK). The VTK support also allow to the real time maniulation of the 3-D model, allowing for the exact modification of data. Models can also easily be exportes in VTK file format for further visualization and processing in other software such as ParaView." -https://github.com/cgre-aachen/gempy,"GemPy was designed from the beginning to support stochastic geological modeling for uncertainty analysis (e.g. Monte Carlo simulations, Bayesian inference). This was achieved by writing GemPy's core architecture using the numerical computation library Theano to couple it with the probabilistic programming framework PyMC3. This enables the use of advanced sampling methods (e.g. Hamiltonian Monte Carlo) and is of particular relevance when considering uncertainties in the model input data and making use of additional secondary information in a Bayesian inference framework." -https://github.com/cgre-aachen/gempy,"We can, for example, include uncertainties with respect to the z-position of layer boundaries in the model space. Simple Monte Carlo simulation via PyMC will then result in different model realizations:" -https://github.com/cgre-aachen/gempy,"Theano allows the automated computation of gradients opening the door to the use of advanced gradient-based sampling methods coupling GeMpy and PyMC3 for advanced stochastic modeling. Also, the use of Theano allows making use of GPUs through cuda (see the Theano documentation for more information." -https://github.com/cgre-aachen/gempy,Making use of vtk interactivity and Qgrid (https://github.com/quantopian/qgrid) GemPy provides a functional interface to interact with input data and models. -https://github.com/cgre-aachen/gempy,Sandbox -https://github.com/cgre-aachen/gempy,"New developments in the field of augmented reality, i.e. the superimposition of real and digital objects, offer interesting and diverse possibilities that have hardly been exploited to date. The aim of the project is therefore the development and realization of an augmented reality sandbox for interaction with geoscientific data and models. In this project, methods are to be developed to project geoscientific data (such as the outcrop of a geological layer surface or geophysical measurement data) onto real surfaces." -https://github.com/cgre-aachen/gempy,"The AR Sandbox is based on a container filled with sand, the surface of which can be shaped as required. The topography of the sand surface is continuously scanned by a 3D sensor and a camera. In the computer the scanned surface is now blended with a digital geological 3D model (or other data) in real time and an image is calculated, which is projected onto the sand surface by means of a beamer. This results in an interactive model with which the user can interact in an intuitive way and which visualizes and comprehend complex three-dimensional facts in an accessible way." -https://github.com/cgre-aachen/gempy,"In addition to applications in teaching and research, this development offers great potential as an interactive exhibit with high outreach for the geosciences thanks to its intuitive operation. The finished sandbox can be used in numerous lectures and public events , but is mainly used as an interface to GemPy software and for rapid prototyping of implicit geological models." -https://github.com/cgre-aachen/gempy,Remote Geomod: From GoogleEarth to 3-D Geology -https://github.com/cgre-aachen/gempy,"We support this effort here with a full 3-D geomodeling exercise on the basis of the excellent possibilities offered by open global data sets, implemented in GoogleEarth, and dedicated geoscientific open-source software and motivate the use of 3-D geomodeling to address specific geological questions. Initial steps include the selection of relevant geological surfaces in GoogleEarth and the analysis of determined orientation values for a selected region This information is subsequently used to construct a full 3-D geological model with a state-of-the-art interpolation algorithm. Fi- nally, the generated model is intersected with a digital elevation model to obtain a geological map, which can then be reimported into GoogleEarth." -https://github.com/cgre-aachen/gempy,New in GemPy 2.0: Docker image -https://github.com/cgre-aachen/gempy,Finally e also provide precompiled Docker images hosted on Docker Hub with all necessary dependencies to get GemPy up and running (except vtk). -https://github.com/cgre-aachen/gempy,"ocker is an operating-system-level-visualization software, meaning that we can package a tiny operating system with pre-installed software into a Docker image. This Docker image can then be shared with and run by others, enabling them to use intricate dependencies with just a few commands. For this to work the user needs to have a working Docker installation." -https://github.com/d3/d3,"D3 (or D3.js) is a JavaScript library for visualizing data using web standards. D3 helps you bring data to life using SVG, Canvas and HTML. D3 combines powerful visualization and interaction techniques with a data-driven approach to DOM manipulation, giving you the full capabilities of modern browsers and the freedom to design the right visual interface for your data." -https://github.com/driftingtides/hyvr,Introduction -https://github.com/driftingtides/hyvr,HyVR: Turning your geofantasy into reality! -https://github.com/driftingtides/hyvr,"The Hydrogeological Virtual Reality simulation package (HyVR) is a Python module that helps researchers and practitioners generate subsurface models with multiple scales of heterogeneity that are based on geological concepts. The simulation outputs can then be used to explore groundwater flow and solute transport behaviour. This is facilitated by HyVR outputs in common flow simulation packages' input formats. As each site is unique, HyVR has been designed that users can take the code and extend it to suit their particular simulation needs." -https://github.com/driftingtides/hyvr,"The original motivation for HyVR was the lack of tools for modelling sedimentary deposits that include bedding structure model outputs (i.e., dip and azimuth). Such bedding parameters were required to approximate full hydraulic-conductivity tensors for groundwater flow modelling. HyVR is able to simulate these bedding parameters and generate spatially distributed parameter fields, including full hydraulic-conductivity tensors. More information about HyVR is available in the online technical documentation." -https://github.com/driftingtides/hyvr,I hope you enjoy using HyVR much more than I enjoyed putting it together! I look forward to seeing what kind of funky fields you created in the course of your work. -https://github.com/driving-behavior/DBNet,"This work is based on our research paper, which appears in CVPR 2018. We propose a large-scale dataset for driving behavior learning, namely, DBNet. You can also check our dataset webpage for a deeper introduction." -https://github.com/driving-behavior/DBNet,"In this repository, we release demo code and partial prepared data for training with only images, as well as leveraging feature maps or point clouds. The prepared data are accessible here. (More demo models and scripts are released soon!)" -https://github.com/driving-behavior/DBNet,"This baseline is run on dbnet-2018 challenge data and only nvidia_pn is tested. To measure difficult architectures comprehensively, several metrics are set, including accuracy under different thresholds, area under curve (AUC), max error (ME), mean error (AE) and mean of max errors (AME)." -https://github.com/driving-behavior/DBNet,The implementations of these metrics could be found in evaluate.py. -https://github.com/empymod/empymod,"The electromagnetic modeller empymod can model electric or magnetic responses due to a three-dimensional electric or magnetic source in a layered-earth model with vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, from very low frequencies (DC) to very high frequencies (GPR). The calculation is carried out in the wavenumber-frequency domain, and various Hankel- and Fourier-transform methods are included to transform the responses into the space-frequency and space-time domains." -https://github.com/empymod/empymod,"Calculates the complete (diffusion and wave phenomena) 3D electromagnetic field in a layered-earth model including vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, for electric and magnetic sources as well as electric and magnetic receivers." -https://github.com/empymod/empymod,Modelling routines: -https://github.com/empymod/empymod,"bipole: arbitrary oriented, finite length bipoles with given source strength; space-frequency and space-time domains." -https://github.com/empymod/empymod,"dipole: infinitesimal small dipoles oriented along the principal axes, normalized field; space-frequency and space-time domains." -https://github.com/empymod/empymod,"wavenumber: as dipole, but returns the wavenumber-frequency domain response." -https://github.com/empymod/empymod,"gpr: calculates the ground-penetrating radar response for given central frequency, using a Ricker wavelet (experimental)." -https://github.com/empymod/empymod,"analytical: interface to the analytical, space-frequency and space-time domain solutions." -https://github.com/empymod/empymod,Hankel transforms (wavenumber-frequency to space-frequency transform): -https://github.com/empymod/empymod,Digital Linear Filters DLF (using included filters or providing own ones) -https://github.com/empymod/empymod,Quadrature with extrapolation QWE -https://github.com/empymod/empymod,Adaptive quadrature QUAD -https://github.com/empymod/empymod,Fourier transforms (space-frequency to space-time transform): - Digital Linear Filters DLF (using included filters or providing own ones) - Quadrature with extrapolation QWE - Logarithmic Fast Fourier Transform FFTLog - Fast Fourier Transform FFT -https://github.com/empymod/empymod,"Analytical, space-frequency and space-time domain solutions:" -https://github.com/empymod/empymod,Complete full-space (electric and magnetic sources and receivers); space-frequency domain -https://github.com/empymod/empymod,Diffusive half-space (electric sources and receivers); space-frequency and space-time domains: -https://github.com/empymod/empymod,Direct wave (= diffusive full-space solution) -https://github.com/empymod/empymod,Reflected wave -https://github.com/empymod/empymod,Airwave (semi-analytical in the case of step responses) -https://github.com/empymod/empymod,Add-ons (empymod.scripts): -https://github.com/empymod/empymod,"The add-ons for empymod provide some very specific, additional functionalities:" -https://github.com/empymod/empymod,"tmtemod: Return up- and down-going TM/TE-mode contributions for x-directed electric sources and receivers, which are located in the same layer." -https://github.com/empymod/empymod,fdesign: Design digital linear filters for the Hankel and Fourier transforms. -https://github.com/empymod/empymod,"printinfo: Can be used to show date, time, and package version information at the end of a notebook or a script." -https://github.com/equinor/pylops,"Linear operators and inverse problems are at the core of many of the most used algorithms in signal processing, image processing, and remote sensing. When dealing with small-scale problems, the Python numerical scientific libraries numpy and scipy allow to perform many of the underlying matrix operations (e.g., computation of matrix-vector products and manipulation of matrices) in a simple and compact way." -https://github.com/equinor/pylops,"Many useful operators, however, do not lend themselves to an explicit matrix representation when used to solve large-scale problems. PyLops operators, on the other hand, still represent a matrix and can be treated in a similar way, but do not rely on the explicit creation of a dense (or sparse) matrix itself. Conversely, the forward and adjoint operators are represented by small pieces of codes that mimic the effect of the matrix on a vector or another matrix." -https://github.com/equinor/pylops,"Luckily, many iterative methods (e.g. cg, lsqr) do not need to know the individual entries of a matrix to solve a linear system. Such solvers only require the computation of forward and adjoint matrix-vector products as done for any of the PyLops operators." -https://github.com/equinor/segyio,"Segyio is a small LGPL licensed C library for easy interaction with SEG-Y and Seismic Unix formatted seismic data, with language bindings for Python and Matlab. Segyio is an attempt to create an easy-to-use, embeddable, community-oriented library for seismic applications. Features are added as they are needed; suggestions and contributions of all kinds are very welcome." -https://github.com/equinor/segyio,Project goals -https://github.com/equinor/segyio,"Segyio does not necessarily attempt to be the end-all of SEG-Y interactions; rather, we aim to lower the barrier to interacting with SEG-Y files for embedding, new applications or free-standing programs." -https://github.com/equinor/segyio,"Additionally, the aim is not to support the full standard or all exotic (but standard compliant) formatted files out there. Some assumptions are made, such as:" -https://github.com/equinor/segyio,All traces in a file are assumed to be of the same size -https://github.com/equinor/segyio,"Currently, segyio supports:" -https://github.com/equinor/segyio,"Post-stack 3D volumes, sorted with respect to two header words (generally INLINE and CROSSLINE)" -https://github.com/equinor/segyio,"Pre-stack 4D volumes, sorted with respect to three header words (generally INLINE, CROSSLINE, and OFFSET)" -https://github.com/equinor/segyio,"Unstructured data, i.e. a collection of traces" -https://github.com/equinor/segyio,"Most numerical formats (including IEEE 4- and 8-byte float, IBM float, 2- and 4-byte integers)" -https://github.com/equinor/segyio,"The writing functionality in segyio is largely meant to modify or adapt files. A file created from scratch is not necessarily a to-spec SEG-Y file, as we only necessarily write the header fields segyio needs to make sense of the geometry. It is still highly recommended that SEG-Y files are maintained and written according to specification, but segyio does not enforce this." -https://github.com/equinor/segyio,SEG-Y Revisions -https://github.com/equinor/segyio,"Segyio can handle a lot of files that are SEG-Y-like, i.e. segyio handles files that don't strictly conform to the SEG-Y standard. Segyio also does not discriminate between the revisions, but instead tries to use information available in the file. For an actual standard's reference, please see the publications by SEG:" -https://github.com/facebook/react,React is a JavaScript library for building user interfaces. -https://github.com/facebook/react,"Declarative: React makes it painless to create interactive UIs. Design simple views for each state in your application, and React will efficiently update and render just the right components when your data changes. Declarative views make your code more predictable, simpler to understand, and easier to debug." -https://github.com/facebook/react,"Component-Based: Build encapsulated components that manage their own state, then compose them to make complex UIs. Since component logic is written in JavaScript instead of templates, you can easily pass rich data through your app and keep state out of the DOM." -https://github.com/facebook/react,"Learn Once, Write Anywhere: We don't make assumptions about the rest of your technology stack, so you can develop new features in React without rewriting existing code. React can also render on the server using Node and power mobile apps using React Native." -https://github.com/facebookresearch/Detectron/,"Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework." -https://github.com/facebookresearch/Detectron/,"At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, Data Distillation: Towards Omni-Supervised Learning, DensePose: Dense Human Pose Estimation In The Wild, and Group Normalization." -https://github.com/facebookresearch/Detectron/,"The goal of Detectron is to provide a high-quality, high-performance codebase for object detection research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research. Detectron includes implementations of the following object detection algorithms:" -https://github.com/facebookresearch/Detectron/,Mask R-CNN -https://github.com/facebookresearch/Detectron/,RetinaNet -https://github.com/facebookresearch/Detectron/,Faster R-CNN -https://github.com/facebookresearch/Detectron/,RPN -https://github.com/facebookresearch/Detectron/,Fast R-CNN -https://github.com/facebookresearch/Detectron/,R-FCN -https://github.com/facebookresearch/Detectron/,using the following backbone network architectures: -https://github.com/facebookresearch/Detectron/,"ResNeXt{50,101,152}" -https://github.com/facebookresearch/Detectron/,"ResNet{50,101,152}" -https://github.com/facebookresearch/Detectron/,Feature Pyramid Networks (with ResNet/ResNeXt) -https://github.com/facebookresearch/Detectron/,VGG16 -https://github.com/facebookresearch/Detectron/,"Additional backbone architectures may be easily implemented. For more details about these models, please see References below." -https://github.com/foolwood/DaSiamRPN,This repository includes PyTorch code for reproducing the results on VOT2018. -https://github.com/foolwood/DaSiamRPN,"SiamRPN formulates the task of visual tracking as a task of localization and identification simultaneously, initially described in an CVPR2018 spotlight paper. (Slides at CVPR 2018 Spotlight)" -https://github.com/foolwood/DaSiamRPN,"DaSiamRPN improves the performances of SiamRPN by (1) introducing an effective sampling strategy to control the imbalanced sample distribution, (2) designing a novel distractor-aware module to perform incremental learning, (3) making a long-term tracking extension. ECCV2018. (Slides at VOT-18 Real-time challenge winners talk)" -https://github.com/geo-data/gdal-docker,This is an Ubuntu derived image containing the Geospatial Data Abstraction Library (GDAL) compiled with a broad range of drivers. The build process is based on that defined in the GDAL TravisCI tests. -https://github.com/geo-data/gdal-docker,Each branch in the git repository corresponds to a supported GDAL version (e.g. 1.11.2) with the master branch following GDAL master. These branch names are reflected in the image tags on the Docker Index (e.g. branch 1.11.2 corresponds to the image geodata/gdal:1.11.2). -https://github.com/geopandas/geopandas/,Python tools for geographic data -https://github.com/geopandas/geopandas/,GeoPandas is a project to add support for geographic data to pandas objects. It currently implements GeoSeries and GeoDataFrame types which are subclasses of pandas.Series and pandas.DataFrame respectively. GeoPandas objects can act on shapely geometry objects and perform geometric operations. -https://github.com/geopandas/geopandas/,"GeoPandas geometry operations are cartesian. The coordinate reference system (crs) can be stored as an attribute on an object, and is automatically set when loading from a file. Objects may be transformed to new coordinate systems with the to_crs() method. There is currently no enforcement of like coordinates for operations, but that may change in the future." -https://github.com/google/sg2im/,This is the code for the paper -https://github.com/google/sg2im/,Please note that this is not an officially supported Google product. -https://github.com/google/sg2im/,A scene graph is a structured representation of a visual scene where nodes represent objects in the scene and edges represent relationships between objects. In this paper we present and end-to-end neural network model that inputs a scene graph and outputs an image. -https://github.com/google/sg2im/,Below we show some example scene graphs along with images generated from those scene graphs using our model. By modifying the input scene graph we can exercise fine-grained control over the objects in the generated image. -https://github.com/google/sg2im/,Model -https://github.com/google/sg2im/,"The input scene graph is processed with a graph convolution network which passes information along edges to compute embedding vectors for all objects. These vectors are used to predict bounding boxes and segmentation masks for all objects, which are combined to form a coarse scene layout. The layout is passed to a cascaded refinement network (Chen an Koltun, ICCV 2017) which generates an output image at increasing spatial scales. The model is trained adversarially against a pair of discriminator networks which ensure that output images look realistic." -https://github.com/gprMax/gprMax,gprMax is open source software that simulates electromagnetic wave propagation. It solves Maxwell's equations in 3D using the Finite-Difference Time-Domain (FDTD) method. gprMax was designed for modelling Ground Penetrating Radar (GPR) but can also be used to model electromagnetic wave propagation for many other applications. -https://github.com/gprMax/gprMax,"gprMax is principally written in Python 3 with performance-critical parts written in Cython. It includes a CPU-based solver parallelised using OpenMP, and a GPU-based solver written using the NVIDIA CUDA programming model." -https://github.com/haoliangyu/node-qa-masker,"This is a NodeJS port of pymasker. It provides a convenient way to produce masks from the Quality Assessment band of Landsat 8 OLI images, as well as MODIS land products." -https://github.com/hezhangsprinter/DCPDN,"We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together. The end-to-end learning is achieved by directly embedding the atmospheric scattering model into the network, thereby ensuring that the proposed method strictly follows the physics-driven scattering model for dehazing. Inspired by the dense network that can maximize the information flow along features from different levels, we propose a new edge-preserving densely connected encoder-decoder structure with multi-level pyramid pooling module for estimating the transmission map. This network is optimized using a newly introduced edge-preserving loss function. To further incorporate the mutual structural information between the estimated transmission map and the dehazed result, we propose a joint-discriminator based on generative adversarial network framework to decide whether the corresponding dehazed image and the estimated transmission map are real or fake. An ablation study is conducted to demonstrate the effectiveness of each module evaluated at both estimated transmission map and dehazed result. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods." -https://github.com/hezhangsprinter/DID-MDN,"We present a novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining. The proposed method enables the network itself to automatically determine the rain-density information and then efficiently remove the corresponding rain-streaks guided by the estimated rain-density label. To better characterize rain-streaks with dif- ferent scales and shapes, a multi-stream densely connected de-raining network is proposed which efficiently leverages features from different scales. Furthermore, a new dataset containing images with rain-density labels is created and used to train the proposed density-aware network." -https://github.com/hezhangsprinter/DID-MDN,"To reproduce the quantitative results shown in the paper, please save both generated and target using python demo.py into the .png format and then test using offline tool such as the PNSR and SSIM measurement in Python or Matlab. In addition, please use netG.train() for testing since the batch for training is 1." -https://github.com/hiroharu-kato/neural_renderer,"This is code for the paper Neural 3D Mesh Renderer by Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada." -https://github.com/hiroharu-kato/neural_renderer,"For more details, please visit project page." -https://github.com/hiroharu-kato/neural_renderer,This repository only contains the core component and simple examples. Related repositories are: -https://github.com/hiroharu-kato/neural_renderer,Neural Renderer (this repository) -https://github.com/hiroharu-kato/neural_renderer,Single-image 3D mesh reconstruction -https://github.com/hiroharu-kato/neural_renderer,2D-to-3D style transfer -https://github.com/hiroharu-kato/neural_renderer,3D DeepDream -https://github.com/hiroharu-kato/neural_renderer,For PyTorch users -https://github.com/hiroharu-kato/neural_renderer,"This code is written in Chainer. For PyTorch users, there are two options." -https://github.com/hiroharu-kato/neural_renderer,"Angjoo Kanazawa & Shubham Tulsiani provides PyTorch wrapper of our renderer used in their work ""Learning Category-Specific Mesh Reconstruction from Image Collections"" (ECCV 2018)." -https://github.com/hiroharu-kato/neural_renderer,"Nikos Kolotouros provides PyTorch re-implementation of our renderer, which does not require installation of Chainer / CuPy." -https://github.com/iannesbitt/readgssi,"readgssi is a tool intended for use as an open-source reader and preprocessing module for subsurface data collected with Geophysical Survey Systems Incorporated (GSSI) ground-penetrating georadar (GPR) devices. It has the capability to read DZT and DZG files with the same pre-extension name and plot the data contained in those files. readgssi is also currently able to translate most DZT files to CSV and will be able to translate to other output formats including HDF5 (see future). Matlab code donated by Gabe Lewis, Dartmouth College Department of Earth Sciences. Python adaptation written with permission by Ian Nesbitt, University of Maine School of Earth and Climate Sciences." -https://github.com/iannesbitt/readgssi,"The file read parameters are based on GSSI's DZT file description, similar to the ones available on pages 55-57 of the SIR-3000 manual. File structure is, unfortunately, prone to change at any time, and although I've been able to test with files from several systems, I have not encountered every iteration of file header yet. If you run into trouble, please create a github issue." -https://github.com/imfunniee/gitfolio,personal website + blog for every github user -https://github.com/imfunniee/gitfolio,Gitfolio will help you get started with a portfolio website where you could showcase your work + a blog that will help you spread your ideas into real world. -https://github.com/joferkington/mplstereonet,mplstereonet provides lower-hemisphere equal-area and equal-angle stereonets for matplotlib. -https://github.com/joferkington/mplstereonet,"All planar measurements are expected to follow the right-hand-rule to indicate dip direction. As an example, 315/30S would be 135/30 following the right-hand rule." -https://github.com/joferkington/mplstereonet,"By default, a modified Kamb method with exponential smoothing [Vollmer1995] is used to estimate the orientation density distribution. Other methods (such as the ""traditional"" Kamb [Kamb1956] and ""Schmidt"" (a.k.a. 1%) methods) are available as well. The method and expected count (in standard deviations) can be controlled by the method and sigma keyword arguments, respectively." -https://github.com/joferkington/mplstereonet,mplstereonet also includes a number of utilities to parse structural measurements in either quadrant or azimuth form such that they follow the right-hand-rule. -https://github.com/jupyter-widgets/ipyleaflet,A Jupyter / Leaflet bridge enabling interactive maps in the Jupyter notebook. -https://github.com/jwass/mplleaflet,mplleaflet -https://github.com/jwass/mplleaflet,"mplleaflet is a Python library that converts a matplotlib plot into a webpage containing a pannable, zoomable Leaflet map. It can also embed the Leaflet map in an IPython notebook. The goal of mplleaflet is to enable use of Python and matplotlib for visualizing geographic data on slippy maps without having to write any Javascript or HTML. You also don't need to worry about choosing the base map content i.e., coastlines, roads, etc." -https://github.com/jwass/mplleaflet,"Normally, displaying data as longitude, latitude will cause a cartographer to cry. That's totally fine with mplleaflet, Leaflet will project your data properly." -https://github.com/jwass/mplleaflet,"Other Python libraries, basemap and folium, exist to create maps in Python. However mplleaflet allows you to leverage all matplotlib capability without having to set up the background basemap. You can use plot() to style points and lines, and you can also use more complex functions like contour(), quiver(), etc. Furthermore, with mplleaflet you no longer have to worry about setting up the basemap. Displaying continents or roads is determined automatically by the zoom level required to view the physical size of the data. You should use a different library if you need fine control over the basemap, or need a geographic projection other than spherical mercator." -https://github.com/kinverarity1/lasio/,"This is a Python 2.7 and 3.3+ package to read and write Log ASCII Standard (LAS) files, used for borehole data such as geophysical, geological, or petrophysical logs. It's compatible with versions 1.2 and 2.0 of the LAS file specification, published by the Canadian Well Logging Society. Support for LAS 3 is being worked on. In principle it is designed to read as many types of LAS files as possible, including ones containing common errors or non-compliant formatting." -https://github.com/kinverarity1/lasio/,"Depending on your particular application you may also want to check out striplog for stratigraphic/lithological data, or welly for dealing with data at the well level. lasio is primarily for reading & writing LAS files." -https://github.com/kinverarity1/lasio/,"Note this is not a package for reading LiDAR data (also called ""LAS files"")" -https://github.com/kosmtik/kosmtik,Very lite but extendable mapping framework to create Mapnik ready maps with OpenStreetMap data (and more). -https://github.com/kosmtik/kosmtik,"For now, only Carto based projects are supported (with .mml or .yml config), but in the future we hope to plug in MapCSS too." -https://github.com/kosmtik/kosmtik,Lite -https://github.com/kosmtik/kosmtik,Only the core needs: -https://github.com/kosmtik/kosmtik,project loading -https://github.com/kosmtik/kosmtik,local configuration management -https://github.com/kosmtik/kosmtik,tiles server for live feedback when coding -https://github.com/kosmtik/kosmtik,"exports to common formats (Mapnik XML, PNG…)" -https://github.com/kosmtik/kosmtik,hooks everywhere to make easy to extend it with plugins -https://github.com/mapbox/geojson-vt,"A highly efficient JavaScript library for slicing GeoJSON data into vector tiles on the fly, primarily designed to enable rendering and interacting with large geospatial datasets on the browser side (without a server)." -https://github.com/mapbox/geojson-vt,"Created to power GeoJSON in Mapbox GL JS, but can be useful in other visualization platforms like Leaflet and d3, as well as Node.js server applications." -https://github.com/mapbox/geojson-vt,"Resulting tiles conform to the JSON equivalent of the vector tile specification. To make data rendering and interaction fast, the tiles are simplified, retaining the minimum level of detail appropriate for each zoom level (simplifying shapes, filtering out tiny polygons and polylines)." -https://github.com/mapbox/geojson-vt,Read more on how the library works on the Mapbox blog. -https://github.com/mapbox/geojson-vt,There's a C++11 port: geojson-vt-cpp -https://github.com/mapbox/rasterio,Rasterio reads and writes geospatial raster data. -https://github.com/mapbox/rasterio,"Geographic information systems use GeoTIFF and other formats to organize and store gridded, or raster, datasets. Rasterio reads and writes these formats and provides a Python API based on N-D arrays." -https://github.com/mapbox/rasterio,"Rasterio 1.0.x works with Python versions 2.7.x and 3.5.0 through 3.7.x, and GDAL versions 1.11.x through 2.4.x. Official binary packages for Linux and Mac OS X are available on PyPI. Unofficial binary packages for Windows are available through other channels." -https://github.com/mapbox/rasterio,Rasterio 1.0.x is not compatible with GDAL versions 3.0.0 or greater. -https://github.com/mapbox/tilelive-mapnik,Renderer backend for tilelive.js that uses node-mapnik to render tiles and grids from a Mapnik XML file. tilelive-mapnik implements the Tilesource API. -https://github.com/mapbox/tippecanoe,"Builds vector tilesets from large (or small) collections of GeoJSON, Geobuf, or CSV features, like these." -https://github.com/mapbox/tippecanoe,Intent -https://github.com/mapbox/tippecanoe,"The goal of Tippecanoe is to enable making a scale-independent view of your data, so that at any level from the entire world to a single building, you can see the density and texture of the data rather than a simplification from dropping supposedly unimportant features or clustering or aggregating them." -https://github.com/mapbox/tippecanoe,"If you give it all of OpenStreetMap and zoom out, it should give you back something that looks like ""All Streets"" rather than something that looks like an Interstate road atlas." -https://github.com/mapbox/tippecanoe,"If you give it all the building footprints in Los Angeles and zoom out far enough that most individual buildings are no longer discernable, you should still be able to see the extent and variety of development in every neighborhood, not just the largest downtown buildings." -https://github.com/mapbox/tippecanoe,"If you give it a collection of years of tweet locations, you should be able to see the shape and relative popularity of every point of interest and every significant travel corridor." -https://github.com/mbloch/mapshaper,"Mapshaper is software for editing Shapefile, GeoJSON, TopoJSON, CSV and several other data formats, written in JavaScript." -https://github.com/mbloch/mapshaper,"The mapshaper command line program supports essential map making tasks like simplifying shapes, editing attribute data, clipping, erasing, dissolving, filtering and more." -https://github.com/mbloch/mapshaper,"The web UI supports interactive simplification, attribute data editing, and running cli commands in a built-in console. Visit the public website at www.mapshaper.org or use the web UI locally via the mapshaper-gui script." -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"This repository is implemented by Yuqing Zhu, Shuhao Fu, and Xizhou Zhu, when they are interns at MSRA." -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Flow-Guided Feature Aggregation (FGFA) is initially described in an ICCV 2017 paper. It provides an accurate and end-to-end learning framework for video object detection. The proposed FGFA method, together with our previous work of Deep Feature Flow, powered the winning entry of ImageNet VID 2017. It is worth noting that:" -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"FGFA improves the per-frame features by aggregating nearby frame features along the motion paths. It significantly improves the object detection accuracy in videos, especially for fast moving objects." -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"FGFA is end-to-end trainable for the task of video object detection, which is vital for improving the recognition accuracy." -https://github.com/msracver/Flow-Guided-Feature-Aggregation,"We proposed to evaluate the detection accuracy for slow, medium and fast moving objects respectively, for better understanding and analysis of video object detection. The motion-specific evaluation code is included in this repository." -https://github.com/nypl-spacetime/map-vectorizer,"This project aims to automate the manual process of geographic polygon and attribute data extraction from maps (i.e. georectified images) including those from insurance atlases published in the 19th and early 20th centuries. Here is some background on why we're doing this and here is one of the maps we're extracting polygons from. This example map layer shows what these atlases look like once geo-rectified, i.e. geographically normalized." -https://github.com/nypl-spacetime/map-vectorizer,The New York Public Library has hundreds of atlases with tens of thousands of these sheets and there is no way we can extract data manually in a reasonable amount of time. -https://github.com/nypl-spacetime/map-vectorizer,"Just so you get an idea, it took NYPL staff coordinating a small army of volunteers three years to produce 170,000 polygons with attributes (from just four of hundreds of atlases at NYPL)." -https://github.com/nypl-spacetime/map-vectorizer,It now takes a period of time closer to 24 hours to generate a comparable number of polygons with some basic metadata. -https://github.com/odoe/generator-arcgis-js-app,This is a yeoman generator for ArcGIS API for JavaScript applications. -https://github.com/odoe/generator-arcgis-js-app,"Basically, he wears a top hat, lives in your computer, and waits for you to tell him what kind of application you wish to create." -https://github.com/odoe/generator-arcgis-js-app,"Not every new computer comes with a Yeoman pre-installed. He lives in the npm package repository. You only have to ask for him once, then he packs up and moves into your hard drive. Make sure you clean up, he likes new and shiny things." -https://github.com/odoe/generator-arcgis-js-app,"Yeoman travels light. He didn't pack any generators when he moved in. You can think of a generator like a plug-in. You get to choose what type of application you wish to create, such as a Backbone application or even a Chrome extension." -https://github.com/odoe/generator-arcgis-js-app,"Yeoman has a heart of gold. He's a person with feelings and opinions, but he's very easy to work with. If you think he's too opinionated, he can be easily convinced." -https://github.com/ondrolexa/apsg,"APSG defines several new python classes to easily manage, analyze and visualize orientational structural geology data." -https://github.com/phoenix104104/LapSRN,"The Laplacian Pyramid Super-Resolution Network (LapSRN) is a progressive super-resolution model that super-resolves an low-resolution images in a coarse-to-fine Laplacian pyramid framework. Our method is fast and achieves state-of-the-art performance on five benchmark datasets for 4x and 8x SR. For more details and evaluation results, please check out our project webpage and paper." -https://github.com/phuang17/DeepMVS,DeepMVS is a Deep Convolutional Neural Network which learns to estimate pixel-wise disparity maps from a sequence of an arbitrary number of unordered images with the camera poses already known or estimated. -https://github.com/phuang17/DeepMVS,"If you use our codes or datasets in your work, please cite:" -https://github.com/pysal/pysal/,"PySAL, the Python spatial analysis library, is an open source cross-platform library for geospatial data science with an emphasis on geospatial vector data written in Python. It supports the development of high level applications for spatial analysis, such as" -https://github.com/pysal/pysal/,"detection of spatial clusters, hot-spots, and outliers" -https://github.com/pysal/pysal/,construction of graphs from spatial data -https://github.com/pysal/pysal/,spatial regression and statistical modeling on geographically embedded networks -https://github.com/pysal/pysal/,spatial econometrics -https://github.com/pysal/pysal/,exploratory spatio-temporal data analysis -https://github.com/pysal/pysal/,PySAL Components -https://github.com/pysal/pysal/,"explore - modules to conduct exploratory analysis of spatial and spatio-temporal data, including statistical testing on points, networks, and polygonal lattices. Also includes methods for spatial inequality and distributional dynamics." -https://github.com/pysal/pysal/,"viz - visualize patterns in spatial data to detect clusters, outliers, and hot-spots." -https://github.com/pysal/pysal/,"model - model spatial relationships in data with a variety of linear, generalized-linear, generalized-additive, and nonlinear models." -https://github.com/pysal/pysal/,lib - solve a wide variety of computational geometry problems: -https://github.com/pysal/pysal/,"graph construction from polygonal lattices, lines, and points." -https://github.com/pysal/pysal/,construction and interactive editing of spatial weights matrices & graphs -https://github.com/pysal/pysal/,"computation of alpha shapes, spatial indices, and spatial-topological relationships" -https://github.com/pysal/pysal/,"reading and writing of sparse graph data, as well as pure python readers of spatial vector data." -https://github.com/pyvista/pymeshfix,"Python/Cython wrapper of Marco Attene's wonderful, award-winning MeshFix software. This module brings the speed of C++ with the portability and ease of installation of Python." -https://github.com/pyvista/pymeshfix,"This software takes as input a polygon mesh and produces a copy of the input where all the occurrences of a specific set of ""defects"" are corrected. MeshFix has been designed to correct typical flaws present in raw digitized mesh models, thus it might fail or produce coarse results if run on other sorts of input meshes (e.g. tessellated CAD models)." -https://github.com/pyvista/pymeshfix,"The input is assumed to represent a single closed solid object, thus the output will be a single watertight triangle mesh bounding a polyhedron. All the singularities, self-intersections and degenerate elements are removed from the input, while regions of the surface without defects are left unmodified." -https://github.com/pyvista/pymeshfix,"One of the main reasons to bring MeshFix to Python is to allow the library to communicate to other python programs without having to use the hard drive. Therefore, this example assumes that you have a mesh within memory and wish to repair it using MeshFix." -https://github.com/pyvista/pyvista,"PyVista is a helper module for the Visualization Toolkit (VTK) that takes a different approach on interfacing with VTK through NumPy and direct array access. This package provides a Pythonic, well-documented interface exposing VTK's powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets." -https://github.com/pyvista/pyvista,This module can be used for scientific plotting for presentations and research papers as well as a supporting module for other mesh 3D rendering dependent Python modules; see Connections for a list of projects that leverage PyVista. -https://github.com/pyvista/pyvista,Overview of Features -https://github.com/pyvista/pyvista,Embeddable rendering in Jupyter Notebooks -https://github.com/pyvista/pyvista,Filtering/plotting tools built for interactivity in Jupyter notebooks (see IPython Tools) -https://github.com/pyvista/pyvista,Direct access to mesh analysis and transformation routines (see Filters) -https://github.com/pyvista/pyvista,Intuitive plotting routines with matplotlib similar syntax (see Plotting) -https://github.com/pyvista/pyvista,Import meshes from many common formats (use pyvista.read()) -https://github.com/pyvista/pyvista,"Export meshes as VTK, STL, OBJ, or PLY file types" -https://github.com/pyvista/tetgen,This Python module is an interface to Hang Si's TetGen C++ software. This module combines speed of C++ with the portability and ease of installation of Python along with integration to PyVista for 3D visualization and analysis. See the TetGen GitHub page for more details on the original creator. -https://github.com/pyvista/tetgen,"The last update to the original C++ software was on 19 January 2011, but the software remains relevant today. Brief description from Weierstrass Institute Software:" -https://github.com/pyvista/tetgen,"TetGen is a program to generate tetrahedral meshes of any 3D polyhedral domains. TetGen generates exact constrained Delaunay tetrahedralization, boundary conforming Delaunay meshes, and Voronoi partitions." -https://github.com/pyvista/tetgen,"TetGen provides various features to generate good quality and adaptive tetrahedral meshes suitable for numerical methods, such as finite element or finite volume methods. For more information of TetGen, please take a look at a list of features." -https://github.com/rowanz/neural-motifs,"This repository contains data and code for the paper Neural Motifs: Scene Graph Parsing with Global Context (CVPR 2018) For the project page (as well as links to the baseline checkpoints), check out rowanzellers.com/neuralmotifs." -https://github.com/salihkaragoz/pose-residual-network-pytorch,This repository contains a PyTorch implementation of the Pose Residual Network (PRN) presented in our ECCV 2018 paper: -https://github.com/sentinelsat/sentinelsat,"Sentinelsat makes searching, downloading and retrieving the metadata of Sentinel satellite images from the Copernicus Open Access Hub easy." -https://github.com/tensorflow/tensorflow,"TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit." -https://github.com/tensorflow/tensorflow,"TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well." -https://github.com/tensorflow/tensorflow,"TensorFlow provides stable Python and C APIs as well as non-guaranteed backwards compatible API's for C++, Go, Java, JavaScript, and Swift." -https://github.com/twbs/bootstrap,"Sleek, intuitive, and powerful front-end framework for faster and easier web development. " -https://github.com/ungarj/tilematrix,Tilematrix handles geographic web tiles and tile pyramids. -https://github.com/ungarj/tilematrix,"The module is designed to translate between tile indices (zoom, row, column) and map coordinates (e.g. latitute, longitude)." -https://github.com/ungarj/tilematrix,Tilematrix supports metatiling and tile buffers. Furthermore it makes heavy use of shapely and it can also generate Affine objects per tile which facilitates working with rasterio for tile based data reading and writing. -https://github.com/ungarj/tilematrix,"It is very similar to mercantile but besides of supporting spherical mercator tile pyramids, it also supports geodetic (WGS84) tile pyramids." -https://github.com/vuejs/vue,"Vue (pronounced /vjuː/, like view) is a progressive framework for building user interfaces. It is designed from the ground up to be incrementally adoptable, and can easily scale between a library and a framework depending on different use cases. It consists of an approachable core library that focuses on the view layer only, and an ecosystem of supporting libraries that helps you tackle complexity in large Single-Page Applications." -https://github.com/whimian/pyGeoPressure,A Python package for pore pressure prediction using well log data and seismic velocity data. -https://github.com/whimian/pyGeoPressure,Overburden (or Lithostatic) Pressure Calculation -https://github.com/whimian/pyGeoPressure,Eaton's method and Parameter Optimization -https://github.com/whimian/pyGeoPressure,Bowers' method and Parameter Optimization -https://github.com/whimian/pyGeoPressure,Multivariate method and Parameter Optimization -https://github.com/wuhuikai/DeepGuidedFilter,Official implementation of Fast End-to-End Trainable Guided Filter. -https://github.com/wuhuikai/DeepGuidedFilter,"Faster, Better and Lighter for image processing and dense prediction." -https://github.com/wuhuikai/DeepGuidedFilter,Overview -https://github.com/wuhuikai/DeepGuidedFilter,DeepGuidedFilter is the author's implementation of the deep learning building block for joint upsampling described in: -https://github.com/wuhuikai/DeepGuidedFilter,"Given a reference image pair in high-resolution and low-resolution, our algorithm generates high-resolution target from the low-resolution input. Through joint training with CNNs, our algorithm achieves the state-of-the-art performance while runs 10-100 times faster." -https://github.com/yuhuayc/da-faster-rcnn,"This is the implementation of our CVPR 2018 work 'Domain Adaptive Faster R-CNN for Object Detection in the Wild'. The aim is to improve the cross-domain robustness of object detection, in the screnario where training and test data are drawn from different distributions. The original paper can be found here." -https://github.com/yulunzhang/RDN,This repository is for RDN introduced in the following paper -https://github.com/yulunzhang/RDN,"The code is built on EDSR (Torch) and tested on Ubuntu 14.04 environment (Torch7, CUDA8.0, cuDNN5.1) with Titan X/1080Ti/Xp GPUs." -https://github.com/yulunzhang/RDN,"A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via dense connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. Experiments on benchmark datasets with different degradation models show that our RDN achieves favorable performance against state-of-the-art methods." -https://github.com/zhiqiangdon/CU-Net,"The follwoing figure gives an illustration of naive dense U-Net, stacked U-Nets and coupled U-Nets (CU-Net). The naive dense U-Net and stacked U-Nets have shortcut connections only inside each U-Net. In contrast, the coupled U-Nets also have connections for semantic blocks across U-Nets. The CU-Net is a hybrid of naive dense U-Net and stacked U-Net, integrating the merits of both dense connectivity, intermediate supervisions and multi-stage top-down and bottom-up refinement. The resulted CU-Net could save ~70% parameters of the previous stacked U-Nets but with comparable accuracy." -https://github.com/zhiqiangdon/CU-Net,"If we couple each U-Net pair in multiple U-Nets, the coupling connections would have quadratic growth with respect to the U-Net number. To make the model more parameter efficient, we propose the order-K coupling to trim off the long-distance coupling connections." -https://github.com/zhiqiangdon/CU-Net,"For simplicity, each dot represents one U-Net. The red and blue lines are the shortcut connections of inside semantic blocks and outside inputs. Order-0 connectivity (Top) strings U-Nets together only by their inputs and outputs, i.e. stacked U-Nets. Order-1 connectivity (Middle) has shortcut connections for adjacent U-Nets. Similarly, order-2 connectivity (Bottom) has shortcut connections for 3 nearby U-Nets." -https://github.com/cltk/cltk,The Classical Language Toolkit -https://github.com/cltk/cltk,"The Classical Language Toolkit (CLTK) offers natural language processing (NLP) support for the languages of Ancient, Classical, and Medieval Eurasia. Greek, Latin, Akkadian, and the Germanic languages are currently most complete. The goals of the CLTK are to:" -https://github.com/cltk/cltk,* compile analysis-friendly corpora; -https://github.com/cltk/cltk,* collect and generate linguistic data; -https://github.com/cltk/cltk,* act as a free and open platform for generating scientific research. -https://github.com/facebookresearch/DensePose,Dense Human Pose Estimation In The Wild -https://github.com/facebookresearch/DensePose,"Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos" -https://github.com/facebookresearch/DensePose,Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. -https://github.com/facebookresearch/DensePose,DensePose-RCNN is implemented in the Detectron framework and is powered by Caffe2. -https://github.com/facebookresearch/DensePose,"In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model." -https://github.com/facebookresearch/ResNeXt,ResNeXt: Aggregated Residual Transformations for Deep Neural Networks -https://github.com/facebookresearch/ResNeXt,ResNeXt is the foundation of their new SENet architecture (a ResNeXt-152 (64 x 4d) with the Squeeze-and-Excitation module)! -https://github.com/facebookresearch/ResNeXt,This repository contains a Torch implementation for the ResNeXt algorithm for image classification. The code is based on fb.resnet.torch. -https://github.com/facebookresearch/ResNeXt,"ResNeXt is a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width." -https://github.com/facebookresearch/pyrobot,"PyRobot is a light weight, high-level interface which provides hardware independent APIs for robotic manipulation and navigation. This repository also contains the low-level stack for LoCoBot, a low cost mobile manipulator hardware platform." -https://github.com/gitbucket/gitbucket,GitBucket is a Git web platform powered by Scala offering: -https://github.com/gitbucket/gitbucket,"You can try an online demo (ID: root / Pass: root) of GitBucket, and also get the latest information at GitBucket News." -https://github.com/harismuneer/Ultimate-Facebook-Scraper,🔥 Ultimate Facebook Scrapper -https://github.com/harismuneer/Ultimate-Facebook-Scraper,A bot which scrapes almost everything about a facebook user's profile including -https://github.com/harismuneer/Ultimate-Facebook-Scraper,"The best thing about this scraper is that the data is scraped in an organized format so that it can be used for educational/research purpose by researchers. Moreover, this scraper does not use Facebook's Graph API so there are no rate limiting issues as such. " -https://github.com/nextflow-io/nextflow,Nextflow is a bioinformatics workflow manager that enables the development of portable and reproducible workflows. -https://github.com/nextflow-io/nextflow,"It supports deploying workflows on a variety of execution platforms including local, HPC schedulers, AWS Batch," -https://github.com/nextflow-io/nextflow,"Google Genomics Pipelines, and Kubernetes. Additionally, it provides support for manage your workflow dependencies" -https://github.com/nextflow-io/nextflow,"through built-in support for Conda, Docker, Singularity, and Modules." -https://github.com/nextflow-io/nextflow,"Nextflow framework is based on the dataflow programming model, which greatly simplifies writing parallel and distributed pipelines without adding unnecessary complexity and letting you concentrate on the flow of data, i.e. the functional logic of the application/algorithm." -https://github.com/nextflow-io/nextflow,"It doesn't aim to be another pipeline scripting language yet, but it is built around the idea that the Linux platform is the lingua franca of data science, since it provides many simple command line and scripting tools, which by themselves are powerful, but when chained together facilitate complex data manipulations." -https://github.com/nextflow-io/nextflow,"In practice, this means that a Nextflow script is defined by composing many different processes. Each process can execute a given bioinformatics tool or scripting language, to which is added the ability to coordinate and synchronize the processes execution by simply specifying their inputs and outputs." -https://github.com/nextflow-io/nextflow,"Nextflow also supports running workflows across various clouds and cloud technologies. Nextflow can create AWS EC2 or Google GCE clusters and deploy your workflow. Managed solutions from both Amazon and Google are also supported through AWS Batch and Google Genomics Pipelines. Additionally, Nextflow can run workflows on either on-prem or managed cloud Kubernetes clusters. " -https://github.com/nextflow-io/nextflow,"Nextflow is built on two great pieces of open source software, namely Groovy" -https://github.com/pyro-ppl/pyro,"Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:" -https://github.com/pyro-ppl/pyro,Universal: Pyro is a universal PPL - it can represent any computable probability distribution. -https://github.com/pyro-ppl/pyro,Scalable: Pyro scales to large data sets with little overhead compared to hand-written code. -https://github.com/pyro-ppl/pyro,"Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions." -https://github.com/pyro-ppl/pyro,"Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference." -https://github.com/pyro-ppl/pyro,Pyro is in a beta release. It is developed and maintained by Uber AI Labs and community contributors. -https://github.com/reduxjs/react-redux,Official React bindings for Redux. -https://github.com/reduxjs/react-redux,Performant and flexible. -https://github.com/scikit-image/scikit-image,scikit-image: Image processing in Python -https://github.com/scikit-learn/scikit-learn,scikit-learn is a Python module for machine learning built on top of -https://github.com/scikit-learn/scikit-learn,SciPy and is distributed under the 3-Clause BSD license. -https://github.com/scikit-learn/scikit-learn,The project was started in 2007 by David Cournapeau as a Google Summer -https://github.com/scikit-learn/scikit-learn,"of Code project, and since then many volunteers have contributed. See" -https://github.com/scikit-learn/scikit-learn,the About us <http://scikit-learn.org/dev/about.html#authors>_ page -https://github.com/scikit-learn/scikit-learn,for a list of core contributors. -https://github.com/scikit-learn/scikit-learn,It is currently maintained by a team of volunteers. -https://github.com/scikit-learn/scikit-learn,Website: http://scikit-learn.org -https://github.com/tensorflow/magenta,Magenta is a research project exploring the role of machine learning -https://github.com/tensorflow/magenta,in the process of creating art and music. Primarily this -https://github.com/tensorflow/magenta,involves developing new deep learning and reinforcement learning -https://github.com/tensorflow/magenta,"algorithms for generating songs, images, drawings, and other materials. But it's also" -https://github.com/tensorflow/magenta,an exploration in building smart tools and interfaces that allow -https://github.com/tensorflow/magenta,artists and musicians to extend (not replace!) their processes using -https://github.com/tensorflow/magenta,these models. Magenta was started by some researchers and engineers -https://github.com/tensorflow/magenta,"from the Google Brain team," -https://github.com/tensorflow/magenta,but many others have contributed significantly to the project. We use -https://github.com/tensorflow/magenta,TensorFlow and release our models and -https://github.com/tensorflow/magenta,tools in open source on this GitHub. If you’d like to learn more -https://github.com/tensorflow/magenta,"about Magenta, check out our blog," -https://github.com/tensorflow/magenta,where we post technical details. You can also join our discussion -https://github.com/tensorflow/magenta,group. -https://github.com/tensorflow/magenta,"This is the home for our Python TensorFlow library. To use our models in the browser with TensorFlow.js, head to the Magenta.js repository." +URL,contributor,excerpt +https://github.com/GoogleChrome/puppeteer,Allen Mao,"Puppeteer is a Node library which provides a high-level API to control Chrome or Chromium over the DevTools Protocol. Puppeteer runs headless by default, but can be configured to run full (non-headless) Chrome or Chromium." +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"The major contributors of this repository include Xiao Sun, Chuankang Li, Bin Xiao, Fangyin Wei, Yichen Wei." +https://github.com/JimmySuen/integral-human-pose,Allen Mao,Integral Regression is initially described in an ECCV 2018 paper. (Slides). +https://github.com/JimmySuen/integral-human-pose,Allen Mao,"We build a 3D pose estimation system based mainly on the Integral Regression, placing second in the ECCV2018 3D Human Pose Estimation Challenge. Note that, the winner Sarandi et al. also uses the Integral Regression (or soft-argmax) with a better augmented 3D dataset in their method indicating the Integral Regression is the currently state-of-the-art 3D human pose estimation method." +https://github.com/JimmySuen/integral-human-pose,Allen Mao,The Integral Regression is also known as soft-argmax. Please refer to two contemporary works (Luvizon et al. and Nibali et al.) for a better comparision and more comprehensive understanding. +https://github.com/JimmySuen/integral-human-pose,Allen Mao,This is an official implementation for Integral Human Pose Regression based on Pytorch. It is worth noticing that: +https://github.com/JimmySuen/integral-human-pose,Allen Mao,The original implementation is based on our internal Mxnet version. There are slight differences in the final accuracy and running time due to the plenty details in platform switch. +https://github.com/JuliaGeo/LibGEOS.jl,Allen Mao,"LibGEOS is a LGPL-licensed package for manipulation and analysis of planar geometric objects, based on the libraries GEOS (the engine of PostGIS) and JTS (from which GEOS is ported)." +https://github.com/JuliaGeo/LibGEOS.jl,Allen Mao,"Among other things, it allows you to parse Well-known Text (WKT)" +https://github.com/LMescheder/GAN_stability,Allen Mao,This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converge?. +https://github.com/LMescheder/GAN_stability,Allen Mao,"For the results presented in the paper, we did not use a moving average over the weights. However, using a moving average helps to reduce noise and we therefore recommend its usage. Indeed, we found that using a moving average leads to much better inception scores on Imagenet." +https://github.com/LMescheder/GAN_stability,Allen Mao,"Batch normalization is currently not supported when using an exponential running average, as the running average is only computed over the parameters of the models and not the other buffers of the model." +https://github.com/NSGeophysics/GPRPy,Allen Mao,Open-source Ground Penetrating Radar processing and visualization software. +https://github.com/NVIDIA/vid2vid,Allen Mao,"Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic video-to-video translation. It can be used for turning semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses. The core of video-to-video translation is image-to-image translation. Some of our work in that space can be found in pix2pixHD and SPADE. " +https://github.com/OpenGeoVis/PVGeo,Allen Mao,The PVGeo Python package contains VTK powered tools for data visualization in geophysics which are wrapped for direct use within the application ParaView by Kitware or in a Python environment with PyVista. These tools are tailored to data visualization in the geosciences with a heavy focus on structured data sets like 2D or 3D time-varying grids. +https://github.com/OpenGeoVis/omfvista,Allen Mao,A PyVista (and VTK) interface for the Open Mining Format package (omf) providing Python 3D visualization and useable mesh data structures for processing datasets in the OMF specification. +https://github.com/OpenGeoscience/geonotebook/,Allen Mao,"GeoNotebook is an application that provides client/server environment with interactive visualization and analysis capabilities using Jupyter, GeoJS and other open source tools. Jointly developed by Kitware and NASA Ames." +https://github.com/Toblerity/Fiona/,Allen Mao,Fiona is OGR's neat and nimble API for Python programmers. +https://github.com/Toblerity/Fiona/,Allen Mao,"Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as pyproj, Rtree, and Shapely. Fiona is supported only on CPython versions 2.7 and 3.4+." +https://github.com/Toblerity/Shapely,Allen Mao,"Shapely is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployed GEOS (the engine of PostGIS) and JTS (from which GEOS is ported) libraries. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are." +https://github.com/XiaLiPKU/RESCAN,Allen Mao,"Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics." +https://github.com/ZhouYanzhao/PRM,Allen Mao,The pytorch branch contains: +https://github.com/ZhouYanzhao/PRM,Allen Mao,the pytorch implementation of Peak Response Mapping (Stimulation and Backprop). +https://github.com/ZhouYanzhao/PRM,Allen Mao,"the PASCAL-VOC demo (training, inference, and visualization)." +https://github.com/agile-geoscience/striplog/,Allen Mao,Lithology and stratigraphic logs for wells and outcrop. +https://github.com/akaszynski/pyansys,Allen Mao,This Python module allows you to: +https://github.com/akaszynski/pyansys,Allen Mao,"Interactively control an instance of ANSYS v14.5 + using Python on Linux, >=17.0 on Windows." +https://github.com/akaszynski/pyansys,Allen Mao,Extract data directly from binary ANSYS v14.5+ files and to display or animate them. +https://github.com/akaszynski/pyansys,Allen Mao,"Rapidly read in binary result (.rst), binary mass and stiffness (.full), and ASCII block archive (.cdb) files." +https://github.com/albertpumarola/GANimation,Allen Mao,"Official implementation of GANimation. In this work we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describe in a continuous manifold the anatomical facial movements defining a human expression. Our approach permits controlling the magnitude of activation of each AU and combine several of them. For more information please refer to the paper." +https://github.com/albertpumarola/GANimation,Allen Mao,This code was made public to share our research for the benefit of the scientific community. Do NOT use it for immoral purposes. +https://github.com/cgre-aachen/gempy,Allen Mao,What is it +https://github.com/cgre-aachen/gempy,Allen Mao,"GemPy is a Python-based, open-source library for implicitly generating 3D structural geological models. It is capable of constructing complex 3D geological models of folded structures, fault networks and unconformities. It was designed from the ground up to support easy embedding in probabilistic frameworks for the uncertainty analysis of subsurface structures." +https://github.com/cgre-aachen/gempy,Allen Mao,Features +https://github.com/cgre-aachen/gempy,Allen Mao,The core algorithm of GemPy is based on a universal cokriging interpolation method devised by Lajaunie et al. (1997) and extended by Calcagno et al. (2008). Its implicit nature allows the user to automatically generate complex 3D structural geological models through the interpolation of input data: +https://github.com/cgre-aachen/gempy,Allen Mao,"Surface contact points: 3D coordinates of points marking the boundaries between different features (e.g. layer interfaces, fault planes, unconformities)." +https://github.com/cgre-aachen/gempy,Allen Mao,Orientation measurements: Orientation of the poles perpendicular to the dipping of surfaces at any point in the 3D space. +https://github.com/cgre-aachen/gempy,Allen Mao,GemPy also allows for the definition of topological elements such as combining multiple stratigraphic sequences and complex fault networks to be considered in the modeling process. +https://github.com/cgre-aachen/gempy,Allen Mao,"GemPy itself offers direct visualization of 2D model sections via matplotlib and in full, interactive 3D using the Visualization Toolkit (VTK). The VTK support also allow to the real time maniulation of the 3-D model, allowing for the exact modification of data. Models can also easily be exportes in VTK file format for further visualization and processing in other software such as ParaView." +https://github.com/cgre-aachen/gempy,Allen Mao,"GemPy was designed from the beginning to support stochastic geological modeling for uncertainty analysis (e.g. Monte Carlo simulations, Bayesian inference). This was achieved by writing GemPy's core architecture using the numerical computation library Theano to couple it with the probabilistic programming framework PyMC3. This enables the use of advanced sampling methods (e.g. Hamiltonian Monte Carlo) and is of particular relevance when considering uncertainties in the model input data and making use of additional secondary information in a Bayesian inference framework." +https://github.com/cgre-aachen/gempy,Allen Mao,"We can, for example, include uncertainties with respect to the z-position of layer boundaries in the model space. Simple Monte Carlo simulation via PyMC will then result in different model realizations:" +https://github.com/cgre-aachen/gempy,Allen Mao,"Theano allows the automated computation of gradients opening the door to the use of advanced gradient-based sampling methods coupling GeMpy and PyMC3 for advanced stochastic modeling. Also, the use of Theano allows making use of GPUs through cuda (see the Theano documentation for more information." +https://github.com/cgre-aachen/gempy,Allen Mao,Making use of vtk interactivity and Qgrid (https://github.com/quantopian/qgrid) GemPy provides a functional interface to interact with input data and models. +https://github.com/cgre-aachen/gempy,Allen Mao,Sandbox +https://github.com/cgre-aachen/gempy,Allen Mao,"New developments in the field of augmented reality, i.e. the superimposition of real and digital objects, offer interesting and diverse possibilities that have hardly been exploited to date. The aim of the project is therefore the development and realization of an augmented reality sandbox for interaction with geoscientific data and models. In this project, methods are to be developed to project geoscientific data (such as the outcrop of a geological layer surface or geophysical measurement data) onto real surfaces." +https://github.com/cgre-aachen/gempy,Allen Mao,"The AR Sandbox is based on a container filled with sand, the surface of which can be shaped as required. The topography of the sand surface is continuously scanned by a 3D sensor and a camera. In the computer the scanned surface is now blended with a digital geological 3D model (or other data) in real time and an image is calculated, which is projected onto the sand surface by means of a beamer. This results in an interactive model with which the user can interact in an intuitive way and which visualizes and comprehend complex three-dimensional facts in an accessible way." +https://github.com/cgre-aachen/gempy,Allen Mao,"In addition to applications in teaching and research, this development offers great potential as an interactive exhibit with high outreach for the geosciences thanks to its intuitive operation. The finished sandbox can be used in numerous lectures and public events , but is mainly used as an interface to GemPy software and for rapid prototyping of implicit geological models." +https://github.com/cgre-aachen/gempy,Allen Mao,Remote Geomod: From GoogleEarth to 3-D Geology +https://github.com/cgre-aachen/gempy,Allen Mao,"We support this effort here with a full 3-D geomodeling exercise on the basis of the excellent possibilities offered by open global data sets, implemented in GoogleEarth, and dedicated geoscientific open-source software and motivate the use of 3-D geomodeling to address specific geological questions. Initial steps include the selection of relevant geological surfaces in GoogleEarth and the analysis of determined orientation values for a selected region This information is subsequently used to construct a full 3-D geological model with a state-of-the-art interpolation algorithm. Fi- nally, the generated model is intersected with a digital elevation model to obtain a geological map, which can then be reimported into GoogleEarth." +https://github.com/cgre-aachen/gempy,Allen Mao,New in GemPy 2.0: Docker image +https://github.com/cgre-aachen/gempy,Allen Mao,Finally e also provide precompiled Docker images hosted on Docker Hub with all necessary dependencies to get GemPy up and running (except vtk). +https://github.com/cgre-aachen/gempy,Allen Mao,"ocker is an operating-system-level-visualization software, meaning that we can package a tiny operating system with pre-installed software into a Docker image. This Docker image can then be shared with and run by others, enabling them to use intricate dependencies with just a few commands. For this to work the user needs to have a working Docker installation." +https://github.com/d3/d3,Allen Mao,"D3 (or D3.js) is a JavaScript library for visualizing data using web standards. D3 helps you bring data to life using SVG, Canvas and HTML. D3 combines powerful visualization and interaction techniques with a data-driven approach to DOM manipulation, giving you the full capabilities of modern browsers and the freedom to design the right visual interface for your data." +https://github.com/driftingtides/hyvr,Allen Mao,Introduction +https://github.com/driftingtides/hyvr,Allen Mao,HyVR: Turning your geofantasy into reality! +https://github.com/driftingtides/hyvr,Allen Mao,"The Hydrogeological Virtual Reality simulation package (HyVR) is a Python module that helps researchers and practitioners generate subsurface models with multiple scales of heterogeneity that are based on geological concepts. The simulation outputs can then be used to explore groundwater flow and solute transport behaviour. This is facilitated by HyVR outputs in common flow simulation packages' input formats. As each site is unique, HyVR has been designed that users can take the code and extend it to suit their particular simulation needs." +https://github.com/driftingtides/hyvr,Allen Mao,"The original motivation for HyVR was the lack of tools for modelling sedimentary deposits that include bedding structure model outputs (i.e., dip and azimuth). Such bedding parameters were required to approximate full hydraulic-conductivity tensors for groundwater flow modelling. HyVR is able to simulate these bedding parameters and generate spatially distributed parameter fields, including full hydraulic-conductivity tensors. More information about HyVR is available in the online technical documentation." +https://github.com/driftingtides/hyvr,Allen Mao,I hope you enjoy using HyVR much more than I enjoyed putting it together! I look forward to seeing what kind of funky fields you created in the course of your work. +https://github.com/driving-behavior/DBNet,Allen Mao,"This work is based on our research paper, which appears in CVPR 2018. We propose a large-scale dataset for driving behavior learning, namely, DBNet. You can also check our dataset webpage for a deeper introduction." +https://github.com/driving-behavior/DBNet,Allen Mao,"In this repository, we release demo code and partial prepared data for training with only images, as well as leveraging feature maps or point clouds. The prepared data are accessible here. (More demo models and scripts are released soon!)" +https://github.com/driving-behavior/DBNet,Allen Mao,"This baseline is run on dbnet-2018 challenge data and only nvidia_pn is tested. To measure difficult architectures comprehensively, several metrics are set, including accuracy under different thresholds, area under curve (AUC), max error (ME), mean error (AE) and mean of max errors (AME)." +https://github.com/driving-behavior/DBNet,Allen Mao,The implementations of these metrics could be found in evaluate.py. +https://github.com/empymod/empymod,Allen Mao,"The electromagnetic modeller empymod can model electric or magnetic responses due to a three-dimensional electric or magnetic source in a layered-earth model with vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, from very low frequencies (DC) to very high frequencies (GPR). The calculation is carried out in the wavenumber-frequency domain, and various Hankel- and Fourier-transform methods are included to transform the responses into the space-frequency and space-time domains." +https://github.com/empymod/empymod,Allen Mao,"Calculates the complete (diffusion and wave phenomena) 3D electromagnetic field in a layered-earth model including vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, for electric and magnetic sources as well as electric and magnetic receivers." +https://github.com/empymod/empymod,Allen Mao,Modelling routines: +https://github.com/empymod/empymod,Allen Mao,"bipole: arbitrary oriented, finite length bipoles with given source strength; space-frequency and space-time domains." +https://github.com/empymod/empymod,Allen Mao,"dipole: infinitesimal small dipoles oriented along the principal axes, normalized field; space-frequency and space-time domains." +https://github.com/empymod/empymod,Allen Mao,"wavenumber: as dipole, but returns the wavenumber-frequency domain response." +https://github.com/empymod/empymod,Allen Mao,"gpr: calculates the ground-penetrating radar response for given central frequency, using a Ricker wavelet (experimental)." +https://github.com/empymod/empymod,Allen Mao,"analytical: interface to the analytical, space-frequency and space-time domain solutions." +https://github.com/empymod/empymod,Allen Mao,Hankel transforms (wavenumber-frequency to space-frequency transform): +https://github.com/empymod/empymod,Allen Mao,Digital Linear Filters DLF (using included filters or providing own ones) +https://github.com/empymod/empymod,Allen Mao,Quadrature with extrapolation QWE +https://github.com/empymod/empymod,Allen Mao,Adaptive quadrature QUAD +https://github.com/empymod/empymod,Allen Mao,Fourier transforms (space-frequency to space-time transform): - Digital Linear Filters DLF (using included filters or providing own ones) - Quadrature with extrapolation QWE - Logarithmic Fast Fourier Transform FFTLog - Fast Fourier Transform FFT +https://github.com/empymod/empymod,Allen Mao,"Analytical, space-frequency and space-time domain solutions:" +https://github.com/empymod/empymod,Allen Mao,Complete full-space (electric and magnetic sources and receivers); space-frequency domain +https://github.com/empymod/empymod,Allen Mao,Diffusive half-space (electric sources and receivers); space-frequency and space-time domains: +https://github.com/empymod/empymod,Allen Mao,Direct wave (= diffusive full-space solution) +https://github.com/empymod/empymod,Allen Mao,Reflected wave +https://github.com/empymod/empymod,Allen Mao,Airwave (semi-analytical in the case of step responses) +https://github.com/empymod/empymod,Allen Mao,Add-ons (empymod.scripts): +https://github.com/empymod/empymod,Allen Mao,"The add-ons for empymod provide some very specific, additional functionalities:" +https://github.com/empymod/empymod,Allen Mao,"tmtemod: Return up- and down-going TM/TE-mode contributions for x-directed electric sources and receivers, which are located in the same layer." +https://github.com/empymod/empymod,Allen Mao,fdesign: Design digital linear filters for the Hankel and Fourier transforms. +https://github.com/empymod/empymod,Allen Mao,"printinfo: Can be used to show date, time, and package version information at the end of a notebook or a script." +https://github.com/equinor/pylops,Allen Mao,"Linear operators and inverse problems are at the core of many of the most used algorithms in signal processing, image processing, and remote sensing. When dealing with small-scale problems, the Python numerical scientific libraries numpy and scipy allow to perform many of the underlying matrix operations (e.g., computation of matrix-vector products and manipulation of matrices) in a simple and compact way." +https://github.com/equinor/pylops,Allen Mao,"Many useful operators, however, do not lend themselves to an explicit matrix representation when used to solve large-scale problems. PyLops operators, on the other hand, still represent a matrix and can be treated in a similar way, but do not rely on the explicit creation of a dense (or sparse) matrix itself. Conversely, the forward and adjoint operators are represented by small pieces of codes that mimic the effect of the matrix on a vector or another matrix." +https://github.com/equinor/pylops,Allen Mao,"Luckily, many iterative methods (e.g. cg, lsqr) do not need to know the individual entries of a matrix to solve a linear system. Such solvers only require the computation of forward and adjoint matrix-vector products as done for any of the PyLops operators." +https://github.com/equinor/segyio,Allen Mao,"Segyio is a small LGPL licensed C library for easy interaction with SEG-Y and Seismic Unix formatted seismic data, with language bindings for Python and Matlab. Segyio is an attempt to create an easy-to-use, embeddable, community-oriented library for seismic applications. Features are added as they are needed; suggestions and contributions of all kinds are very welcome." +https://github.com/equinor/segyio,Allen Mao,Project goals +https://github.com/equinor/segyio,Allen Mao,"Segyio does not necessarily attempt to be the end-all of SEG-Y interactions; rather, we aim to lower the barrier to interacting with SEG-Y files for embedding, new applications or free-standing programs." +https://github.com/equinor/segyio,Allen Mao,"Additionally, the aim is not to support the full standard or all exotic (but standard compliant) formatted files out there. Some assumptions are made, such as:" +https://github.com/equinor/segyio,Allen Mao,All traces in a file are assumed to be of the same size +https://github.com/equinor/segyio,Allen Mao,"Currently, segyio supports:" +https://github.com/equinor/segyio,Allen Mao,"Post-stack 3D volumes, sorted with respect to two header words (generally INLINE and CROSSLINE)" +https://github.com/equinor/segyio,Allen Mao,"Pre-stack 4D volumes, sorted with respect to three header words (generally INLINE, CROSSLINE, and OFFSET)" +https://github.com/equinor/segyio,Allen Mao,"Unstructured data, i.e. a collection of traces" +https://github.com/equinor/segyio,Allen Mao,"Most numerical formats (including IEEE 4- and 8-byte float, IBM float, 2- and 4-byte integers)" +https://github.com/equinor/segyio,Allen Mao,"The writing functionality in segyio is largely meant to modify or adapt files. A file created from scratch is not necessarily a to-spec SEG-Y file, as we only necessarily write the header fields segyio needs to make sense of the geometry. It is still highly recommended that SEG-Y files are maintained and written according to specification, but segyio does not enforce this." +https://github.com/equinor/segyio,Allen Mao,SEG-Y Revisions +https://github.com/equinor/segyio,Allen Mao,"Segyio can handle a lot of files that are SEG-Y-like, i.e. segyio handles files that don't strictly conform to the SEG-Y standard. Segyio also does not discriminate between the revisions, but instead tries to use information available in the file. For an actual standard's reference, please see the publications by SEG:" +https://github.com/facebook/react,Allen Mao,React is a JavaScript library for building user interfaces. +https://github.com/facebook/react,Allen Mao,"Declarative: React makes it painless to create interactive UIs. Design simple views for each state in your application, and React will efficiently update and render just the right components when your data changes. Declarative views make your code more predictable, simpler to understand, and easier to debug." +https://github.com/facebook/react,Allen Mao,"Component-Based: Build encapsulated components that manage their own state, then compose them to make complex UIs. Since component logic is written in JavaScript instead of templates, you can easily pass rich data through your app and keep state out of the DOM." +https://github.com/facebook/react,Allen Mao,"Learn Once, Write Anywhere: We don't make assumptions about the rest of your technology stack, so you can develop new features in React without rewriting existing code. React can also render on the server using Node and power mobile apps using React Native." +https://github.com/facebookresearch/Detectron/,Allen Mao,"Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework." +https://github.com/facebookresearch/Detectron/,Allen Mao,"At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, Data Distillation: Towards Omni-Supervised Learning, DensePose: Dense Human Pose Estimation In The Wild, and Group Normalization." +https://github.com/facebookresearch/Detectron/,Allen Mao,"The goal of Detectron is to provide a high-quality, high-performance codebase for object detection research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research. Detectron includes implementations of the following object detection algorithms:" +https://github.com/facebookresearch/Detectron/,Allen Mao,Mask R-CNN +https://github.com/facebookresearch/Detectron/,Allen Mao,RetinaNet +https://github.com/facebookresearch/Detectron/,Allen Mao,Faster R-CNN +https://github.com/facebookresearch/Detectron/,Allen Mao,RPN +https://github.com/facebookresearch/Detectron/,Allen Mao,Fast R-CNN +https://github.com/facebookresearch/Detectron/,Allen Mao,R-FCN +https://github.com/facebookresearch/Detectron/,Allen Mao,using the following backbone network architectures: +https://github.com/facebookresearch/Detectron/,Allen Mao,"ResNeXt{50,101,152}" +https://github.com/facebookresearch/Detectron/,Allen Mao,"ResNet{50,101,152}" +https://github.com/facebookresearch/Detectron/,Allen Mao,Feature Pyramid Networks (with ResNet/ResNeXt) +https://github.com/facebookresearch/Detectron/,Allen Mao,VGG16 +https://github.com/facebookresearch/Detectron/,Allen Mao,"Additional backbone architectures may be easily implemented. For more details about these models, please see References below." +https://github.com/foolwood/DaSiamRPN,Allen Mao,This repository includes PyTorch code for reproducing the results on VOT2018. +https://github.com/foolwood/DaSiamRPN,Allen Mao,"SiamRPN formulates the task of visual tracking as a task of localization and identification simultaneously, initially described in an CVPR2018 spotlight paper. (Slides at CVPR 2018 Spotlight)" +https://github.com/foolwood/DaSiamRPN,Allen Mao,"DaSiamRPN improves the performances of SiamRPN by (1) introducing an effective sampling strategy to control the imbalanced sample distribution, (2) designing a novel distractor-aware module to perform incremental learning, (3) making a long-term tracking extension. ECCV2018. (Slides at VOT-18 Real-time challenge winners talk)" +https://github.com/geo-data/gdal-docker,Allen Mao,This is an Ubuntu derived image containing the Geospatial Data Abstraction Library (GDAL) compiled with a broad range of drivers. The build process is based on that defined in the GDAL TravisCI tests. +https://github.com/geo-data/gdal-docker,Allen Mao,Each branch in the git repository corresponds to a supported GDAL version (e.g. 1.11.2) with the master branch following GDAL master. These branch names are reflected in the image tags on the Docker Index (e.g. branch 1.11.2 corresponds to the image geodata/gdal:1.11.2). +https://github.com/geopandas/geopandas/,Allen Mao,Python tools for geographic data +https://github.com/geopandas/geopandas/,Allen Mao,GeoPandas is a project to add support for geographic data to pandas objects. It currently implements GeoSeries and GeoDataFrame types which are subclasses of pandas.Series and pandas.DataFrame respectively. GeoPandas objects can act on shapely geometry objects and perform geometric operations. +https://github.com/geopandas/geopandas/,Allen Mao,"GeoPandas geometry operations are cartesian. The coordinate reference system (crs) can be stored as an attribute on an object, and is automatically set when loading from a file. Objects may be transformed to new coordinate systems with the to_crs() method. There is currently no enforcement of like coordinates for operations, but that may change in the future." +https://github.com/google/sg2im/,Allen Mao,This is the code for the paper +https://github.com/google/sg2im/,Allen Mao,Please note that this is not an officially supported Google product. +https://github.com/google/sg2im/,Allen Mao,A scene graph is a structured representation of a visual scene where nodes represent objects in the scene and edges represent relationships between objects. In this paper we present and end-to-end neural network model that inputs a scene graph and outputs an image. +https://github.com/google/sg2im/,Allen Mao,Below we show some example scene graphs along with images generated from those scene graphs using our model. By modifying the input scene graph we can exercise fine-grained control over the objects in the generated image. +https://github.com/google/sg2im/,Allen Mao,Model +https://github.com/google/sg2im/,Allen Mao,"The input scene graph is processed with a graph convolution network which passes information along edges to compute embedding vectors for all objects. These vectors are used to predict bounding boxes and segmentation masks for all objects, which are combined to form a coarse scene layout. The layout is passed to a cascaded refinement network (Chen an Koltun, ICCV 2017) which generates an output image at increasing spatial scales. The model is trained adversarially against a pair of discriminator networks which ensure that output images look realistic." +https://github.com/gprMax/gprMax,Allen Mao,gprMax is open source software that simulates electromagnetic wave propagation. It solves Maxwell's equations in 3D using the Finite-Difference Time-Domain (FDTD) method. gprMax was designed for modelling Ground Penetrating Radar (GPR) but can also be used to model electromagnetic wave propagation for many other applications. +https://github.com/gprMax/gprMax,Allen Mao,"gprMax is principally written in Python 3 with performance-critical parts written in Cython. It includes a CPU-based solver parallelised using OpenMP, and a GPU-based solver written using the NVIDIA CUDA programming model." +https://github.com/haoliangyu/node-qa-masker,Allen Mao,"This is a NodeJS port of pymasker. It provides a convenient way to produce masks from the Quality Assessment band of Landsat 8 OLI images, as well as MODIS land products." +https://github.com/hezhangsprinter/DCPDN,Allen Mao,"We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together. The end-to-end learning is achieved by directly embedding the atmospheric scattering model into the network, thereby ensuring that the proposed method strictly follows the physics-driven scattering model for dehazing. Inspired by the dense network that can maximize the information flow along features from different levels, we propose a new edge-preserving densely connected encoder-decoder structure with multi-level pyramid pooling module for estimating the transmission map. This network is optimized using a newly introduced edge-preserving loss function. To further incorporate the mutual structural information between the estimated transmission map and the dehazed result, we propose a joint-discriminator based on generative adversarial network framework to decide whether the corresponding dehazed image and the estimated transmission map are real or fake. An ablation study is conducted to demonstrate the effectiveness of each module evaluated at both estimated transmission map and dehazed result. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods." +https://github.com/hezhangsprinter/DID-MDN,Allen Mao,"We present a novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining. The proposed method enables the network itself to automatically determine the rain-density information and then efficiently remove the corresponding rain-streaks guided by the estimated rain-density label. To better characterize rain-streaks with dif- ferent scales and shapes, a multi-stream densely connected de-raining network is proposed which efficiently leverages features from different scales. Furthermore, a new dataset containing images with rain-density labels is created and used to train the proposed density-aware network." +https://github.com/hezhangsprinter/DID-MDN,Allen Mao,"To reproduce the quantitative results shown in the paper, please save both generated and target using python demo.py into the .png format and then test using offline tool such as the PNSR and SSIM measurement in Python or Matlab. In addition, please use netG.train() for testing since the batch for training is 1." +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,"This is code for the paper Neural 3D Mesh Renderer by Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada." +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,"For more details, please visit project page." +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,This repository only contains the core component and simple examples. Related repositories are: +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,Neural Renderer (this repository) +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,Single-image 3D mesh reconstruction +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,2D-to-3D style transfer +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,3D DeepDream +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,For PyTorch users +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,"This code is written in Chainer. For PyTorch users, there are two options." +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,"Angjoo Kanazawa & Shubham Tulsiani provides PyTorch wrapper of our renderer used in their work ""Learning Category-Specific Mesh Reconstruction from Image Collections"" (ECCV 2018)." +https://github.com/hiroharu-kato/neural_renderer,Allen Mao,"Nikos Kolotouros provides PyTorch re-implementation of our renderer, which does not require installation of Chainer / CuPy." +https://github.com/iannesbitt/readgssi,Allen Mao,"readgssi is a tool intended for use as an open-source reader and preprocessing module for subsurface data collected with Geophysical Survey Systems Incorporated (GSSI) ground-penetrating georadar (GPR) devices. It has the capability to read DZT and DZG files with the same pre-extension name and plot the data contained in those files. readgssi is also currently able to translate most DZT files to CSV and will be able to translate to other output formats including HDF5 (see future). Matlab code donated by Gabe Lewis, Dartmouth College Department of Earth Sciences. Python adaptation written with permission by Ian Nesbitt, University of Maine School of Earth and Climate Sciences." +https://github.com/iannesbitt/readgssi,Allen Mao,"The file read parameters are based on GSSI's DZT file description, similar to the ones available on pages 55-57 of the SIR-3000 manual. File structure is, unfortunately, prone to change at any time, and although I've been able to test with files from several systems, I have not encountered every iteration of file header yet. If you run into trouble, please create a github issue." +https://github.com/imfunniee/gitfolio,Allen Mao,personal website + blog for every github user +https://github.com/imfunniee/gitfolio,Allen Mao,Gitfolio will help you get started with a portfolio website where you could showcase your work + a blog that will help you spread your ideas into real world. +https://github.com/joferkington/mplstereonet,Allen Mao,mplstereonet provides lower-hemisphere equal-area and equal-angle stereonets for matplotlib. +https://github.com/joferkington/mplstereonet,Allen Mao,"All planar measurements are expected to follow the right-hand-rule to indicate dip direction. As an example, 315/30S would be 135/30 following the right-hand rule." +https://github.com/joferkington/mplstereonet,Allen Mao,"By default, a modified Kamb method with exponential smoothing [Vollmer1995] is used to estimate the orientation density distribution. Other methods (such as the ""traditional"" Kamb [Kamb1956] and ""Schmidt"" (a.k.a. 1%) methods) are available as well. The method and expected count (in standard deviations) can be controlled by the method and sigma keyword arguments, respectively." +https://github.com/joferkington/mplstereonet,Allen Mao,mplstereonet also includes a number of utilities to parse structural measurements in either quadrant or azimuth form such that they follow the right-hand-rule. +https://github.com/jupyter-widgets/ipyleaflet,Allen Mao,A Jupyter / Leaflet bridge enabling interactive maps in the Jupyter notebook. +https://github.com/jwass/mplleaflet,Allen Mao,mplleaflet +https://github.com/jwass/mplleaflet,Allen Mao,"mplleaflet is a Python library that converts a matplotlib plot into a webpage containing a pannable, zoomable Leaflet map. It can also embed the Leaflet map in an IPython notebook. The goal of mplleaflet is to enable use of Python and matplotlib for visualizing geographic data on slippy maps without having to write any Javascript or HTML. You also don't need to worry about choosing the base map content i.e., coastlines, roads, etc." +https://github.com/jwass/mplleaflet,Allen Mao,"Normally, displaying data as longitude, latitude will cause a cartographer to cry. That's totally fine with mplleaflet, Leaflet will project your data properly." +https://github.com/jwass/mplleaflet,Allen Mao,"Other Python libraries, basemap and folium, exist to create maps in Python. However mplleaflet allows you to leverage all matplotlib capability without having to set up the background basemap. You can use plot() to style points and lines, and you can also use more complex functions like contour(), quiver(), etc. Furthermore, with mplleaflet you no longer have to worry about setting up the basemap. Displaying continents or roads is determined automatically by the zoom level required to view the physical size of the data. You should use a different library if you need fine control over the basemap, or need a geographic projection other than spherical mercator." +https://github.com/kinverarity1/lasio/,Allen Mao,"This is a Python 2.7 and 3.3+ package to read and write Log ASCII Standard (LAS) files, used for borehole data such as geophysical, geological, or petrophysical logs. It's compatible with versions 1.2 and 2.0 of the LAS file specification, published by the Canadian Well Logging Society. Support for LAS 3 is being worked on. In principle it is designed to read as many types of LAS files as possible, including ones containing common errors or non-compliant formatting." +https://github.com/kinverarity1/lasio/,Allen Mao,"Depending on your particular application you may also want to check out striplog for stratigraphic/lithological data, or welly for dealing with data at the well level. lasio is primarily for reading & writing LAS files." +https://github.com/kinverarity1/lasio/,Allen Mao,"Note this is not a package for reading LiDAR data (also called ""LAS files"")" +https://github.com/kosmtik/kosmtik,Allen Mao,Very lite but extendable mapping framework to create Mapnik ready maps with OpenStreetMap data (and more). +https://github.com/kosmtik/kosmtik,Allen Mao,"For now, only Carto based projects are supported (with .mml or .yml config), but in the future we hope to plug in MapCSS too." +https://github.com/kosmtik/kosmtik,Allen Mao,Lite +https://github.com/kosmtik/kosmtik,Allen Mao,Only the core needs: +https://github.com/kosmtik/kosmtik,Allen Mao,project loading +https://github.com/kosmtik/kosmtik,Allen Mao,local configuration management +https://github.com/kosmtik/kosmtik,Allen Mao,tiles server for live feedback when coding +https://github.com/kosmtik/kosmtik,Allen Mao,"exports to common formats (Mapnik XML, PNG…)" +https://github.com/kosmtik/kosmtik,Allen Mao,hooks everywhere to make easy to extend it with plugins +https://github.com/mapbox/geojson-vt,Allen Mao,"A highly efficient JavaScript library for slicing GeoJSON data into vector tiles on the fly, primarily designed to enable rendering and interacting with large geospatial datasets on the browser side (without a server)." +https://github.com/mapbox/geojson-vt,Allen Mao,"Created to power GeoJSON in Mapbox GL JS, but can be useful in other visualization platforms like Leaflet and d3, as well as Node.js server applications." +https://github.com/mapbox/geojson-vt,Allen Mao,"Resulting tiles conform to the JSON equivalent of the vector tile specification. To make data rendering and interaction fast, the tiles are simplified, retaining the minimum level of detail appropriate for each zoom level (simplifying shapes, filtering out tiny polygons and polylines)." +https://github.com/mapbox/geojson-vt,Allen Mao,Read more on how the library works on the Mapbox blog. +https://github.com/mapbox/geojson-vt,Allen Mao,There's a C++11 port: geojson-vt-cpp +https://github.com/mapbox/rasterio,Allen Mao,Rasterio reads and writes geospatial raster data. +https://github.com/mapbox/rasterio,Allen Mao,"Geographic information systems use GeoTIFF and other formats to organize and store gridded, or raster, datasets. Rasterio reads and writes these formats and provides a Python API based on N-D arrays." +https://github.com/mapbox/rasterio,Allen Mao,"Rasterio 1.0.x works with Python versions 2.7.x and 3.5.0 through 3.7.x, and GDAL versions 1.11.x through 2.4.x. Official binary packages for Linux and Mac OS X are available on PyPI. Unofficial binary packages for Windows are available through other channels." +https://github.com/mapbox/rasterio,Allen Mao,Rasterio 1.0.x is not compatible with GDAL versions 3.0.0 or greater. +https://github.com/mapbox/tilelive-mapnik,Allen Mao,Renderer backend for tilelive.js that uses node-mapnik to render tiles and grids from a Mapnik XML file. tilelive-mapnik implements the Tilesource API. +https://github.com/mapbox/tippecanoe,Allen Mao,"Builds vector tilesets from large (or small) collections of GeoJSON, Geobuf, or CSV features, like these." +https://github.com/mapbox/tippecanoe,Allen Mao,Intent +https://github.com/mapbox/tippecanoe,Allen Mao,"The goal of Tippecanoe is to enable making a scale-independent view of your data, so that at any level from the entire world to a single building, you can see the density and texture of the data rather than a simplification from dropping supposedly unimportant features or clustering or aggregating them." +https://github.com/mapbox/tippecanoe,Allen Mao,"If you give it all of OpenStreetMap and zoom out, it should give you back something that looks like ""All Streets"" rather than something that looks like an Interstate road atlas." +https://github.com/mapbox/tippecanoe,Allen Mao,"If you give it all the building footprints in Los Angeles and zoom out far enough that most individual buildings are no longer discernable, you should still be able to see the extent and variety of development in every neighborhood, not just the largest downtown buildings." +https://github.com/mapbox/tippecanoe,Allen Mao,"If you give it a collection of years of tweet locations, you should be able to see the shape and relative popularity of every point of interest and every significant travel corridor." +https://github.com/mbloch/mapshaper,Allen Mao,"Mapshaper is software for editing Shapefile, GeoJSON, TopoJSON, CSV and several other data formats, written in JavaScript." +https://github.com/mbloch/mapshaper,Allen Mao,"The mapshaper command line program supports essential map making tasks like simplifying shapes, editing attribute data, clipping, erasing, dissolving, filtering and more." +https://github.com/mbloch/mapshaper,Allen Mao,"The web UI supports interactive simplification, attribute data editing, and running cli commands in a built-in console. Visit the public website at www.mapshaper.org or use the web UI locally via the mapshaper-gui script." +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"This repository is implemented by Yuqing Zhu, Shuhao Fu, and Xizhou Zhu, when they are interns at MSRA." +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"Flow-Guided Feature Aggregation (FGFA) is initially described in an ICCV 2017 paper. It provides an accurate and end-to-end learning framework for video object detection. The proposed FGFA method, together with our previous work of Deep Feature Flow, powered the winning entry of ImageNet VID 2017. It is worth noting that:" +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"FGFA improves the per-frame features by aggregating nearby frame features along the motion paths. It significantly improves the object detection accuracy in videos, especially for fast moving objects." +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"FGFA is end-to-end trainable for the task of video object detection, which is vital for improving the recognition accuracy." +https://github.com/msracver/Flow-Guided-Feature-Aggregation,Allen Mao,"We proposed to evaluate the detection accuracy for slow, medium and fast moving objects respectively, for better understanding and analysis of video object detection. The motion-specific evaluation code is included in this repository." +https://github.com/nypl-spacetime/map-vectorizer,Allen Mao,"This project aims to automate the manual process of geographic polygon and attribute data extraction from maps (i.e. georectified images) including those from insurance atlases published in the 19th and early 20th centuries. Here is some background on why we're doing this and here is one of the maps we're extracting polygons from. This example map layer shows what these atlases look like once geo-rectified, i.e. geographically normalized." +https://github.com/nypl-spacetime/map-vectorizer,Allen Mao,The New York Public Library has hundreds of atlases with tens of thousands of these sheets and there is no way we can extract data manually in a reasonable amount of time. +https://github.com/nypl-spacetime/map-vectorizer,Allen Mao,"Just so you get an idea, it took NYPL staff coordinating a small army of volunteers three years to produce 170,000 polygons with attributes (from just four of hundreds of atlases at NYPL)." +https://github.com/nypl-spacetime/map-vectorizer,Allen Mao,It now takes a period of time closer to 24 hours to generate a comparable number of polygons with some basic metadata. +https://github.com/odoe/generator-arcgis-js-app,Allen Mao,This is a yeoman generator for ArcGIS API for JavaScript applications. +https://github.com/odoe/generator-arcgis-js-app,Allen Mao,"Basically, he wears a top hat, lives in your computer, and waits for you to tell him what kind of application you wish to create." +https://github.com/odoe/generator-arcgis-js-app,Allen Mao,"Not every new computer comes with a Yeoman pre-installed. He lives in the npm package repository. You only have to ask for him once, then he packs up and moves into your hard drive. Make sure you clean up, he likes new and shiny things." +https://github.com/odoe/generator-arcgis-js-app,Allen Mao,"Yeoman travels light. He didn't pack any generators when he moved in. You can think of a generator like a plug-in. You get to choose what type of application you wish to create, such as a Backbone application or even a Chrome extension." +https://github.com/odoe/generator-arcgis-js-app,Allen Mao,"Yeoman has a heart of gold. He's a person with feelings and opinions, but he's very easy to work with. If you think he's too opinionated, he can be easily convinced." +https://github.com/ondrolexa/apsg,Allen Mao,"APSG defines several new python classes to easily manage, analyze and visualize orientational structural geology data." +https://github.com/phoenix104104/LapSRN,Allen Mao,"The Laplacian Pyramid Super-Resolution Network (LapSRN) is a progressive super-resolution model that super-resolves an low-resolution images in a coarse-to-fine Laplacian pyramid framework. Our method is fast and achieves state-of-the-art performance on five benchmark datasets for 4x and 8x SR. For more details and evaluation results, please check out our project webpage and paper." +https://github.com/phuang17/DeepMVS,Allen Mao,DeepMVS is a Deep Convolutional Neural Network which learns to estimate pixel-wise disparity maps from a sequence of an arbitrary number of unordered images with the camera poses already known or estimated. +https://github.com/phuang17/DeepMVS,Allen Mao,"If you use our codes or datasets in your work, please cite:" +https://github.com/pysal/pysal/,Allen Mao,"PySAL, the Python spatial analysis library, is an open source cross-platform library for geospatial data science with an emphasis on geospatial vector data written in Python. It supports the development of high level applications for spatial analysis, such as" +https://github.com/pysal/pysal/,Allen Mao,"detection of spatial clusters, hot-spots, and outliers" +https://github.com/pysal/pysal/,Allen Mao,construction of graphs from spatial data +https://github.com/pysal/pysal/,Allen Mao,spatial regression and statistical modeling on geographically embedded networks +https://github.com/pysal/pysal/,Allen Mao,spatial econometrics +https://github.com/pysal/pysal/,Allen Mao,exploratory spatio-temporal data analysis +https://github.com/pysal/pysal/,Allen Mao,PySAL Components +https://github.com/pysal/pysal/,Allen Mao,"explore - modules to conduct exploratory analysis of spatial and spatio-temporal data, including statistical testing on points, networks, and polygonal lattices. Also includes methods for spatial inequality and distributional dynamics." +https://github.com/pysal/pysal/,Allen Mao,"viz - visualize patterns in spatial data to detect clusters, outliers, and hot-spots." +https://github.com/pysal/pysal/,Allen Mao,"model - model spatial relationships in data with a variety of linear, generalized-linear, generalized-additive, and nonlinear models." +https://github.com/pysal/pysal/,Allen Mao,lib - solve a wide variety of computational geometry problems: +https://github.com/pysal/pysal/,Allen Mao,"graph construction from polygonal lattices, lines, and points." +https://github.com/pysal/pysal/,Allen Mao,construction and interactive editing of spatial weights matrices & graphs +https://github.com/pysal/pysal/,Allen Mao,"computation of alpha shapes, spatial indices, and spatial-topological relationships" +https://github.com/pysal/pysal/,Allen Mao,"reading and writing of sparse graph data, as well as pure python readers of spatial vector data." +https://github.com/pyvista/pymeshfix,Allen Mao,"Python/Cython wrapper of Marco Attene's wonderful, award-winning MeshFix software. This module brings the speed of C++ with the portability and ease of installation of Python." +https://github.com/pyvista/pymeshfix,Allen Mao,"This software takes as input a polygon mesh and produces a copy of the input where all the occurrences of a specific set of ""defects"" are corrected. MeshFix has been designed to correct typical flaws present in raw digitized mesh models, thus it might fail or produce coarse results if run on other sorts of input meshes (e.g. tessellated CAD models)." +https://github.com/pyvista/pymeshfix,Allen Mao,"The input is assumed to represent a single closed solid object, thus the output will be a single watertight triangle mesh bounding a polyhedron. All the singularities, self-intersections and degenerate elements are removed from the input, while regions of the surface without defects are left unmodified." +https://github.com/pyvista/pymeshfix,Allen Mao,"One of the main reasons to bring MeshFix to Python is to allow the library to communicate to other python programs without having to use the hard drive. Therefore, this example assumes that you have a mesh within memory and wish to repair it using MeshFix." +https://github.com/pyvista/pyvista,Allen Mao,"PyVista is a helper module for the Visualization Toolkit (VTK) that takes a different approach on interfacing with VTK through NumPy and direct array access. This package provides a Pythonic, well-documented interface exposing VTK's powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets." +https://github.com/pyvista/pyvista,Allen Mao,This module can be used for scientific plotting for presentations and research papers as well as a supporting module for other mesh 3D rendering dependent Python modules; see Connections for a list of projects that leverage PyVista. +https://github.com/pyvista/pyvista,Allen Mao,Overview of Features +https://github.com/pyvista/pyvista,Allen Mao,Embeddable rendering in Jupyter Notebooks +https://github.com/pyvista/pyvista,Allen Mao,Filtering/plotting tools built for interactivity in Jupyter notebooks (see IPython Tools) +https://github.com/pyvista/pyvista,Allen Mao,Direct access to mesh analysis and transformation routines (see Filters) +https://github.com/pyvista/pyvista,Allen Mao,Intuitive plotting routines with matplotlib similar syntax (see Plotting) +https://github.com/pyvista/pyvista,Allen Mao,Import meshes from many common formats (use pyvista.read()) +https://github.com/pyvista/pyvista,Allen Mao,"Export meshes as VTK, STL, OBJ, or PLY file types" +https://github.com/pyvista/tetgen,Allen Mao,This Python module is an interface to Hang Si's TetGen C++ software. This module combines speed of C++ with the portability and ease of installation of Python along with integration to PyVista for 3D visualization and analysis. See the TetGen GitHub page for more details on the original creator. +https://github.com/pyvista/tetgen,Allen Mao,"The last update to the original C++ software was on 19 January 2011, but the software remains relevant today. Brief description from Weierstrass Institute Software:" +https://github.com/pyvista/tetgen,Allen Mao,"TetGen is a program to generate tetrahedral meshes of any 3D polyhedral domains. TetGen generates exact constrained Delaunay tetrahedralization, boundary conforming Delaunay meshes, and Voronoi partitions." +https://github.com/pyvista/tetgen,Allen Mao,"TetGen provides various features to generate good quality and adaptive tetrahedral meshes suitable for numerical methods, such as finite element or finite volume methods. For more information of TetGen, please take a look at a list of features." +https://github.com/rowanz/neural-motifs,Allen Mao,"This repository contains data and code for the paper Neural Motifs: Scene Graph Parsing with Global Context (CVPR 2018) For the project page (as well as links to the baseline checkpoints), check out rowanzellers.com/neuralmotifs." +https://github.com/salihkaragoz/pose-residual-network-pytorch,Allen Mao,This repository contains a PyTorch implementation of the Pose Residual Network (PRN) presented in our ECCV 2018 paper: +https://github.com/sentinelsat/sentinelsat,Allen Mao,"Sentinelsat makes searching, downloading and retrieving the metadata of Sentinel satellite images from the Copernicus Open Access Hub easy." +https://github.com/tensorflow/tensorflow,Allen Mao,"TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit." +https://github.com/tensorflow/tensorflow,Allen Mao,"TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well." +https://github.com/tensorflow/tensorflow,Allen Mao,"TensorFlow provides stable Python and C APIs as well as non-guaranteed backwards compatible API's for C++, Go, Java, JavaScript, and Swift." +https://github.com/twbs/bootstrap,Allen Mao,"Sleek, intuitive, and powerful front-end framework for faster and easier web development. " +https://github.com/ungarj/tilematrix,Allen Mao,Tilematrix handles geographic web tiles and tile pyramids. +https://github.com/ungarj/tilematrix,Allen Mao,"The module is designed to translate between tile indices (zoom, row, column) and map coordinates (e.g. latitute, longitude)." +https://github.com/ungarj/tilematrix,Allen Mao,Tilematrix supports metatiling and tile buffers. Furthermore it makes heavy use of shapely and it can also generate Affine objects per tile which facilitates working with rasterio for tile based data reading and writing. +https://github.com/ungarj/tilematrix,Allen Mao,"It is very similar to mercantile but besides of supporting spherical mercator tile pyramids, it also supports geodetic (WGS84) tile pyramids." +https://github.com/vuejs/vue,Allen Mao,"Vue (pronounced /vjuː/, like view) is a progressive framework for building user interfaces. It is designed from the ground up to be incrementally adoptable, and can easily scale between a library and a framework depending on different use cases. It consists of an approachable core library that focuses on the view layer only, and an ecosystem of supporting libraries that helps you tackle complexity in large Single-Page Applications." +https://github.com/whimian/pyGeoPressure,Allen Mao,A Python package for pore pressure prediction using well log data and seismic velocity data. +https://github.com/whimian/pyGeoPressure,Allen Mao,Overburden (or Lithostatic) Pressure Calculation +https://github.com/whimian/pyGeoPressure,Allen Mao,Eaton's method and Parameter Optimization +https://github.com/whimian/pyGeoPressure,Allen Mao,Bowers' method and Parameter Optimization +https://github.com/whimian/pyGeoPressure,Allen Mao,Multivariate method and Parameter Optimization +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,Official implementation of Fast End-to-End Trainable Guided Filter. +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,"Faster, Better and Lighter for image processing and dense prediction." +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,Overview +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,DeepGuidedFilter is the author's implementation of the deep learning building block for joint upsampling described in: +https://github.com/wuhuikai/DeepGuidedFilter,Allen Mao,"Given a reference image pair in high-resolution and low-resolution, our algorithm generates high-resolution target from the low-resolution input. Through joint training with CNNs, our algorithm achieves the state-of-the-art performance while runs 10-100 times faster." +https://github.com/yuhuayc/da-faster-rcnn,Allen Mao,"This is the implementation of our CVPR 2018 work 'Domain Adaptive Faster R-CNN for Object Detection in the Wild'. The aim is to improve the cross-domain robustness of object detection, in the screnario where training and test data are drawn from different distributions. The original paper can be found here." +https://github.com/yulunzhang/RDN,Allen Mao,This repository is for RDN introduced in the following paper +https://github.com/yulunzhang/RDN,Allen Mao,"The code is built on EDSR (Torch) and tested on Ubuntu 14.04 environment (Torch7, CUDA8.0, cuDNN5.1) with Titan X/1080Ti/Xp GPUs." +https://github.com/yulunzhang/RDN,Allen Mao,"A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via dense connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. Experiments on benchmark datasets with different degradation models show that our RDN achieves favorable performance against state-of-the-art methods." +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"The follwoing figure gives an illustration of naive dense U-Net, stacked U-Nets and coupled U-Nets (CU-Net). The naive dense U-Net and stacked U-Nets have shortcut connections only inside each U-Net. In contrast, the coupled U-Nets also have connections for semantic blocks across U-Nets. The CU-Net is a hybrid of naive dense U-Net and stacked U-Net, integrating the merits of both dense connectivity, intermediate supervisions and multi-stage top-down and bottom-up refinement. The resulted CU-Net could save ~70% parameters of the previous stacked U-Nets but with comparable accuracy." +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"If we couple each U-Net pair in multiple U-Nets, the coupling connections would have quadratic growth with respect to the U-Net number. To make the model more parameter efficient, we propose the order-K coupling to trim off the long-distance coupling connections." +https://github.com/zhiqiangdon/CU-Net,Allen Mao,"For simplicity, each dot represents one U-Net. The red and blue lines are the shortcut connections of inside semantic blocks and outside inputs. Order-0 connectivity (Top) strings U-Nets together only by their inputs and outputs, i.e. stacked U-Nets. Order-1 connectivity (Middle) has shortcut connections for adjacent U-Nets. Similarly, order-2 connectivity (Bottom) has shortcut connections for 3 nearby U-Nets." +https://github.com/cltk/cltk,Rosna Thomas,The Classical Language Toolkit +https://github.com/cltk/cltk,Rosna Thomas,"The Classical Language Toolkit (CLTK) offers natural language processing (NLP) support for the languages of Ancient, Classical, and Medieval Eurasia. Greek, Latin, Akkadian, and the Germanic languages are currently most complete. The goals of the CLTK are to:" +https://github.com/cltk/cltk,Rosna Thomas,* compile analysis-friendly corpora; +https://github.com/cltk/cltk,Rosna Thomas,* collect and generate linguistic data; +https://github.com/cltk/cltk,Rosna Thomas,* act as a free and open platform for generating scientific research. +https://github.com/facebookresearch/DensePose,Rosna Thomas,Dense Human Pose Estimation In The Wild +https://github.com/facebookresearch/DensePose,Rosna Thomas,"Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos" +https://github.com/facebookresearch/DensePose,Rosna Thomas,Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. +https://github.com/facebookresearch/DensePose,Rosna Thomas,DensePose-RCNN is implemented in the Detectron framework and is powered by Caffe2. +https://github.com/facebookresearch/DensePose,Rosna Thomas,"In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model." +https://github.com/facebookresearch/ResNeXt,Rosna Thomas,ResNeXt: Aggregated Residual Transformations for Deep Neural Networks +https://github.com/facebookresearch/ResNeXt,Rosna Thomas,ResNeXt is the foundation of their new SENet architecture (a ResNeXt-152 (64 x 4d) with the Squeeze-and-Excitation module)! +https://github.com/facebookresearch/ResNeXt,Rosna Thomas,This repository contains a Torch implementation for the ResNeXt algorithm for image classification. The code is based on fb.resnet.torch. +https://github.com/facebookresearch/ResNeXt,Rosna Thomas,"ResNeXt is a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width." +https://github.com/facebookresearch/pyrobot,Rosna Thomas,"PyRobot is a light weight, high-level interface which provides hardware independent APIs for robotic manipulation and navigation. This repository also contains the low-level stack for LoCoBot, a low cost mobile manipulator hardware platform." +https://github.com/gitbucket/gitbucket,Rosna Thomas,GitBucket is a Git web platform powered by Scala offering: +https://github.com/gitbucket/gitbucket,Rosna Thomas,"You can try an online demo (ID: root / Pass: root) of GitBucket, and also get the latest information at GitBucket News." +https://github.com/harismuneer/Ultimate-Facebook-Scraper,Rosna Thomas,🔥 Ultimate Facebook Scrapper +https://github.com/harismuneer/Ultimate-Facebook-Scraper,Rosna Thomas,A bot which scrapes almost everything about a facebook user's profile including +https://github.com/harismuneer/Ultimate-Facebook-Scraper,Rosna Thomas,"The best thing about this scraper is that the data is scraped in an organized format so that it can be used for educational/research purpose by researchers. Moreover, this scraper does not use Facebook's Graph API so there are no rate limiting issues as such. " +https://github.com/nextflow-io/nextflow,Rosna Thomas,Nextflow is a bioinformatics workflow manager that enables the development of portable and reproducible workflows. +https://github.com/nextflow-io/nextflow,Rosna Thomas,"It supports deploying workflows on a variety of execution platforms including local, HPC schedulers, AWS Batch," +https://github.com/nextflow-io/nextflow,Rosna Thomas,"Google Genomics Pipelines, and Kubernetes. Additionally, it provides support for manage your workflow dependencies" +https://github.com/nextflow-io/nextflow,Rosna Thomas,"through built-in support for Conda, Docker, Singularity, and Modules." +https://github.com/nextflow-io/nextflow,Rosna Thomas,"Nextflow framework is based on the dataflow programming model, which greatly simplifies writing parallel and distributed pipelines without adding unnecessary complexity and letting you concentrate on the flow of data, i.e. the functional logic of the application/algorithm." +https://github.com/nextflow-io/nextflow,Rosna Thomas,"It doesn't aim to be another pipeline scripting language yet, but it is built around the idea that the Linux platform is the lingua franca of data science, since it provides many simple command line and scripting tools, which by themselves are powerful, but when chained together facilitate complex data manipulations." +https://github.com/nextflow-io/nextflow,Rosna Thomas,"In practice, this means that a Nextflow script is defined by composing many different processes. Each process can execute a given bioinformatics tool or scripting language, to which is added the ability to coordinate and synchronize the processes execution by simply specifying their inputs and outputs." +https://github.com/nextflow-io/nextflow,Rosna Thomas,"Nextflow also supports running workflows across various clouds and cloud technologies. Nextflow can create AWS EC2 or Google GCE clusters and deploy your workflow. Managed solutions from both Amazon and Google are also supported through AWS Batch and Google Genomics Pipelines. Additionally, Nextflow can run workflows on either on-prem or managed cloud Kubernetes clusters. " +https://github.com/nextflow-io/nextflow,Rosna Thomas,"Nextflow is built on two great pieces of open source software, namely Groovy" +https://github.com/pyro-ppl/pyro,Rosna Thomas,"Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:" +https://github.com/pyro-ppl/pyro,Rosna Thomas,Universal: Pyro is a universal PPL - it can represent any computable probability distribution. +https://github.com/pyro-ppl/pyro,Rosna Thomas,Scalable: Pyro scales to large data sets with little overhead compared to hand-written code. +https://github.com/pyro-ppl/pyro,Rosna Thomas,"Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions." +https://github.com/pyro-ppl/pyro,Rosna Thomas,"Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference." +https://github.com/pyro-ppl/pyro,Rosna Thomas,Pyro is in a beta release. It is developed and maintained by Uber AI Labs and community contributors. +https://github.com/reduxjs/react-redux,Rosna Thomas,Official React bindings for Redux. +https://github.com/reduxjs/react-redux,Rosna Thomas,Performant and flexible. +https://github.com/scikit-image/scikit-image,Rosna Thomas,scikit-image: Image processing in Python +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,scikit-learn is a Python module for machine learning built on top of +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,SciPy and is distributed under the 3-Clause BSD license. +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,The project was started in 2007 by David Cournapeau as a Google Summer +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,"of Code project, and since then many volunteers have contributed. See" +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,the About us <http://scikit-learn.org/dev/about.html#authors>_ page +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,for a list of core contributors. +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,It is currently maintained by a team of volunteers. +https://github.com/scikit-learn/scikit-learn,Rosna Thomas,Website: http://scikit-learn.org +https://github.com/tensorflow/magenta,Rosna Thomas,Magenta is a research project exploring the role of machine learning +https://github.com/tensorflow/magenta,Rosna Thomas,in the process of creating art and music. Primarily this +https://github.com/tensorflow/magenta,Rosna Thomas,involves developing new deep learning and reinforcement learning +https://github.com/tensorflow/magenta,Rosna Thomas,"algorithms for generating songs, images, drawings, and other materials. But it's also" +https://github.com/tensorflow/magenta,Rosna Thomas,an exploration in building smart tools and interfaces that allow +https://github.com/tensorflow/magenta,Rosna Thomas,artists and musicians to extend (not replace!) their processes using +https://github.com/tensorflow/magenta,Rosna Thomas,these models. Magenta was started by some researchers and engineers +https://github.com/tensorflow/magenta,Rosna Thomas,"from the Google Brain team," +https://github.com/tensorflow/magenta,Rosna Thomas,but many others have contributed significantly to the project. We use +https://github.com/tensorflow/magenta,Rosna Thomas,TensorFlow and release our models and +https://github.com/tensorflow/magenta,Rosna Thomas,tools in open source on this GitHub. If you’d like to learn more +https://github.com/tensorflow/magenta,Rosna Thomas,"about Magenta, check out our blog," +https://github.com/tensorflow/magenta,Rosna Thomas,where we post technical details. You can also join our discussion +https://github.com/tensorflow/magenta,Rosna Thomas,group. +https://github.com/tensorflow/magenta,Rosna Thomas,"This is the home for our Python TensorFlow library. To use our models in the browser with TensorFlow.js, head to the Magenta.js repository." \ No newline at end of file diff --git a/data/installation.csv b/data/installation.csv index 01d54273..933cb1e3 100644 --- a/data/installation.csv +++ b/data/installation.csv @@ -1,930 +1,930 @@ -URL,excerpt -https://github.com/GoogleChrome/puppeteer,Installation -https://github.com/GoogleChrome/puppeteer,"To use Puppeteer in your project, run:" -https://github.com/GoogleChrome/puppeteer,npm i puppeteer -https://github.com/GoogleChrome/puppeteer,"# or ""yarn add puppeteer""" -https://github.com/GoogleChrome/puppeteer,puppeteer-core -https://github.com/GoogleChrome/puppeteer,"Since version 1.7.0 we publish the puppeteer-core package, a version of Puppeteer that doesn't download Chromium by default." -https://github.com/GoogleChrome/puppeteer,npm i puppeteer-core -https://github.com/GoogleChrome/puppeteer,"# or ""yarn add puppeteer-core""" -https://github.com/JimmySuen/integral-human-pose,Environment -https://github.com/JimmySuen/integral-human-pose,Python Version: 3.6 -https://github.com/JimmySuen/integral-human-pose,OS: CentOs7 (Other Linux system is also OK) -https://github.com/JimmySuen/integral-human-pose,CUDA: 9.0 (least 8.0) -https://github.com/JimmySuen/integral-human-pose,PyTorch:0.4.0(see issue https://github.com/JimmySuen/integral-human-pose/issues/4) -https://github.com/JimmySuen/integral-human-pose,"We recommend installing python from Anaconda, installing pytorch following guide on PyTorch according to your specific CUDA & python version. In addition, you need to install dependencies below." -https://github.com/JimmySuen/integral-human-pose,pip install scipy -https://github.com/JimmySuen/integral-human-pose,pip install matplotlib -https://github.com/JimmySuen/integral-human-pose,pip install opencv-python -https://github.com/JimmySuen/integral-human-pose,pip install easydict -https://github.com/JimmySuen/integral-human-pose,pip install pyyaml -https://github.com/JuliaGeo/LibGEOS.jl,"At the Julia prompt, run" -https://github.com/JuliaGeo/LibGEOS.jl,"julia> Pkg.add(""LibGEOS"")" -https://github.com/JuliaGeo/LibGEOS.jl,"This will install both the Julia package and GEOS shared libraries together. To just reinstall the GEOS shared libraries, run Pkg.build(""LibGEOS"")." -https://github.com/JuliaGeo/LibGEOS.jl,Test that LibGEOS works by runnning -https://github.com/JuliaGeo/LibGEOS.jl,"julia> Pkg.test(""LibGEOS"")" -https://github.com/NSGeophysics/GPRPy,Simplemost installation -https://github.com/NSGeophysics/GPRPy,"In the following instructions, if you use Windows, use the comands python and pip. If you use Mac or Linux, use the commands python3 and pip3 instead." -https://github.com/NSGeophysics/GPRPy,Download the GPRPy software from https://github.com/NSGeophysics/GPRPy/archive/master.zip. -https://github.com/NSGeophysics/GPRPy,Save the file somewhere on your computer and extract the zip folder. -https://github.com/NSGeophysics/GPRPy,"As an alternative, you can install git from https://git-scm.com/, then run in a command prompt:" -https://github.com/NSGeophysics/GPRPy,git clone https://github.com/NSGeophysics/GPRPy.git -https://github.com/NSGeophysics/GPRPy,The advantage of the latter is that you can easily update your software by running from the GPRPy folder in a command prompt: -https://github.com/NSGeophysics/GPRPy,git pull origin master -https://github.com/NSGeophysics/GPRPy,Install Python 3.7 for example from https://conda.io/miniconda.html -https://github.com/NSGeophysics/GPRPy,"Once the installation finished, open a command prompt that can run Python" -https://github.com/NSGeophysics/GPRPy,"On Windows: click on Start, then enter ""Anaconda Prompt"", without the quotation marks into the ""Search programs and files"" field. On Mac or Linux, open the regular terminal." -https://github.com/NSGeophysics/GPRPy,"In the command prompt, change to the directory where you downloaded the GPRPy files. This is usually through a command like for example" -https://github.com/NSGeophysics/GPRPy,cd Desktop\GPRPy -https://github.com/NSGeophysics/GPRPy,if you downloaded GPRPy directly onto your desktop. Then type the following and press enter afterward: -https://github.com/NSGeophysics/GPRPy,python installMigration.py -https://github.com/NSGeophysics/GPRPy,Then type the following and press enter afterward: -https://github.com/NSGeophysics/GPRPy,pip install . -https://github.com/NSGeophysics/GPRPy,"don't forget the period ""."" at the end of the pip install command" -https://github.com/NVIDIA/vid2vid,Prerequisites -https://github.com/NVIDIA/vid2vid,Linux or macOS -https://github.com/NVIDIA/vid2vid,Python 3 -https://github.com/NVIDIA/vid2vid,NVIDIA GPU + CUDA cuDNN -https://github.com/NVIDIA/vid2vid,PyTorch 0.4 -https://github.com/NVIDIA/vid2vid,Install python libraries dominate and requests. -https://github.com/NVIDIA/vid2vid,pip install dominate requests -https://github.com/NVIDIA/vid2vid,"If you plan to train with face datasets, please install dlib." -https://github.com/NVIDIA/vid2vid,pip install dlib -https://github.com/NVIDIA/vid2vid,"If you plan to train with pose datasets, please install DensePose and/or OpenPose." -https://github.com/NVIDIA/vid2vid,Clone this repo: -https://github.com/NVIDIA/vid2vid,git clone https://github.com/NVIDIA/vid2vid -https://github.com/NVIDIA/vid2vid,cd vid2vid -https://github.com/NVIDIA/vid2vid,"Docker Image If you have difficulty building the repo, a docker image can be found in the docker folder." -https://github.com/OpenGeoVis/PVGeo,"To begin using the PVGeo Python package, create/activate your Python virtual environment (we highly recommend using anaconda) and install PVGeo through pip:" -https://github.com/OpenGeoVis/PVGeo,pip install PVGeo -https://github.com/OpenGeoVis/omfvista,Installation is simply: -https://github.com/OpenGeoVis/omfvista,pip install omfvista -https://github.com/OpenGeoVis/omfvista,All necessary dependencies will be installed alongside omfvista. Please note that this package heavily leverages the PyVista package. -https://github.com/OpenGeoscience/geonotebook/,System Prerequisites -https://github.com/OpenGeoscience/geonotebook/,For default tile serving -https://github.com/OpenGeoscience/geonotebook/,GDAL >= 2.1.0 -https://github.com/OpenGeoscience/geonotebook/,mapnik >= 3.1.0 -https://github.com/OpenGeoscience/geonotebook/,python-mapnik >= 0.1 -https://github.com/OpenGeoscience/geonotebook/,Clone the repo: -https://github.com/OpenGeoscience/geonotebook/,git clone https://github.com/OpenGeoscience/geonotebook.git -https://github.com/OpenGeoscience/geonotebook/,cd geonotebook -https://github.com/OpenGeoscience/geonotebook/,"Make a virtualenv, install jupyter[notebook], install geonotebook" -https://github.com/OpenGeoscience/geonotebook/,mkvirtualenv -a . geonotebook -https://github.com/OpenGeoscience/geonotebook/,# Numpy must be fully installed before rasterio -https://github.com/OpenGeoscience/geonotebook/,pip install -r prerequirements.txt -https://github.com/OpenGeoscience/geonotebook/,pip install -r requirements.txt -https://github.com/OpenGeoscience/geonotebook/,# Enable both the notebook and server extensions -https://github.com/OpenGeoscience/geonotebook/,jupyter serverextension enable --sys-prefix --py geonotebook -https://github.com/OpenGeoscience/geonotebook/,jupyter nbextension enable --sys-prefix --py geonotebook -https://github.com/OpenGeoscience/geonotebook/,Note The serverextension and nbextension commands accept flags that configure how and where the extensions are installed. See jupyter serverextension --help for more information. -https://github.com/OpenGeoscience/geonotebook/,Installing geonotebook for development -https://github.com/OpenGeoscience/geonotebook/,"When developing geonotebook, it is often helpful to install packages as a reference to the checked out repository rather than copying them to the system site-packages. A ""development install"" will allow you to make live changes to python or javascript without reinstalling the package." -https://github.com/OpenGeoscience/geonotebook/,"# Install the geonotebook python package as ""editable""" -https://github.com/OpenGeoscience/geonotebook/,pip install -e . -https://github.com/OpenGeoscience/geonotebook/,# Install the notebook extension as a symlink -https://github.com/OpenGeoscience/geonotebook/,jupyter nbextension install --sys-prefix --symlink --py geonotebook -https://github.com/OpenGeoscience/geonotebook/,# Enable the extension -https://github.com/OpenGeoscience/geonotebook/,# Start the javascript builder -https://github.com/OpenGeoscience/geonotebook/,cd js -https://github.com/OpenGeoscience/geonotebook/,npm run watch -https://github.com/Toblerity/Fiona/,"Fiona requires Python 2.7 or 3.4+ and GDAL/OGR 1.8+. To build from a source distribution you will need a C compiler and GDAL and Python development headers and libraries (libgdal1-dev for Debian/Ubuntu, gdal-dev for CentOS/Fedora)." -https://github.com/Toblerity/Fiona/,"To build from a repository copy, you will also need Cython to build C sources from the project's .pyx files. See the project's requirements-dev.txt file for guidance." -https://github.com/Toblerity/Fiona/,"The Kyngchaos GDAL frameworks will satisfy the GDAL/OGR dependency for OS X, as will Homebrew's GDAL Formula (brew install gdal)." -https://github.com/Toblerity/Fiona/,Python Requirements -https://github.com/Toblerity/Fiona/,"Fiona depends on the modules enum34, six, cligj, munch, argparse, and ordereddict (the two latter modules are standard in Python 2.7+). Pip will fetch these requirements for you, but users installing Fiona from a Windows installer must get them separately." -https://github.com/Toblerity/Fiona/,Unix-like systems -https://github.com/Toblerity/Fiona/,"Assuming you're using a virtualenv (if not, skip to the 4th command) and GDAL/OGR libraries, headers, and gdal-config program are installed to well known locations on your system via your system's package manager (brew install gdal using Homebrew on OS X), installation is this simple." -https://github.com/Toblerity/Fiona/,$ mkdir fiona_env -https://github.com/Toblerity/Fiona/,$ virtualenv fiona_env -https://github.com/Toblerity/Fiona/,$ source fiona_env/bin/activate -https://github.com/Toblerity/Fiona/,(fiona_env)$ pip install fiona -https://github.com/Toblerity/Fiona/,"If gdal-config is not available or if GDAL/OGR headers and libs aren't installed to a well known location, you must set include dirs, library dirs, and libraries options via the setup.cfg file or setup command line as shown below (using git). You must also specify the version of the GDAL API on the command line using the --gdalversion argument (see example below) or with the GDAL_VERSION environment variable (e.g. export GDAL_VERSION=2.1)." -https://github.com/Toblerity/Fiona/,(fiona_env)$ git clone git://github.com/Toblerity/Fiona.git -https://github.com/Toblerity/Fiona/,(fiona_env)$ cd Fiona -https://github.com/Toblerity/Fiona/,(fiona_env)$ python setup.py build_ext -I/path/to/gdal/include -L/path/to/gdal/lib -lgdal install --gdalversion 2.1 -https://github.com/Toblerity/Fiona/,Or specify that build options and GDAL API version should be provided by a particular gdal-config program. -https://github.com/Toblerity/Fiona/,(fiona_env)$ GDAL_CONFIG=/path/to/gdal-config pip install fiona -https://github.com/Toblerity/Fiona/,Windows -https://github.com/Toblerity/Fiona/,Binary installers are available at http://www.lfd.uci.edu/~gohlke/pythonlibs/#fiona and coming eventually to PyPI. -https://github.com/Toblerity/Fiona/,You can download a binary distribution of GDAL from here. You will also need to download the compiled libraries and headers (include files). -https://github.com/Toblerity/Fiona/,"When building from source on Windows, it is important to know that setup.py cannot rely on gdal-config, which is only present on UNIX systems, to discover the locations of header files and libraries that Fiona needs to compile its C extensions. On Windows, these paths need to be provided by the user. You will need to find the include files and the library files for gdal and use setup.py as follows. You must also specify the version of the GDAL API on the command line using the --gdalversion argument (see example below) or with the GDAL_VERSION environment variable (e.g. set GDAL_VERSION=2.1)." -https://github.com/Toblerity/Fiona/,$ python setup.py build_ext -I -lgdal_i -L install --gdalversion 2.1 -https://github.com/Toblerity/Fiona/,Note: The GDAL DLL (gdal111.dll or similar) and gdal-data directory need to be in your Windows PATH otherwise Fiona will fail to work. -https://github.com/Toblerity/Fiona/,"The Appveyor CI build uses the GISInternals GDAL binaries to build Fiona. This produces a binary wheel for successful builds, which includes GDAL and other dependencies, for users wanting to try an unstable development version. The Appveyor configuration file may be a useful example for users building from source on Windows." -https://github.com/Toblerity/Shapely,Requirements -https://github.com/Toblerity/Shapely,Shapely 1.6 requires -https://github.com/Toblerity/Shapely,"Python 2.7, >=3.4" -https://github.com/Toblerity/Shapely,GEOS >=3.3 -https://github.com/Toblerity/Shapely,Installing Shapely 1.6 -https://github.com/Toblerity/Shapely,Shapely may be installed from a source distribution or one of several kinds of built distribution. -https://github.com/Toblerity/Shapely,Built distributions -https://github.com/Toblerity/Shapely,Windows users have two good installation options: the wheels at http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely and the Anaconda platform's conda-forge channel. -https://github.com/Toblerity/Shapely,OS X and Linux users can get Shapely wheels with GEOS included from the Python Package Index with a recent version of pip (8+): -https://github.com/Toblerity/Shapely,$ pip install shapely -https://github.com/Toblerity/Shapely,A few extra speedups that require Numpy can be had by running -https://github.com/Toblerity/Shapely,$ pip install shapely[vectorized] -https://github.com/Toblerity/Shapely,"Shapely is available via system package management tools like apt, yum, and Homebrew, and is also provided by popular Python distributions like Canopy and Anaconda." -https://github.com/Toblerity/Shapely,Source distributions -https://github.com/Toblerity/Shapely,"If you want to build Shapely from source for compatibility with other modules that depend on GEOS (such as cartopy or osgeo.ogr) or want to use a different version of GEOS than the one included in the project wheels you should first install the GEOS library, Cython, and Numpy on your system (using apt, yum, brew, or other means) and then direct pip to ignore the binary wheels." -https://github.com/Toblerity/Shapely,$ pip install shapely --no-binary shapely -https://github.com/Toblerity/Shapely,"If you've installed GEOS to a standard location, the geos-config program will be used to get compiler and linker options. If geos-config is not on your executable, it can be specified with a GEOS_CONFIG environment variable, e.g.:" -https://github.com/Toblerity/Shapely,$ GEOS_CONFIG=/path/to/geos-config pip install shapely -https://github.com/XiaLiPKU/RESCAN,Prerequisite -https://github.com/XiaLiPKU/RESCAN,Python>=3.6 -https://github.com/XiaLiPKU/RESCAN,Pytorch>=4.1.0 -https://github.com/XiaLiPKU/RESCAN,Opencv>=3.1.0 -https://github.com/XiaLiPKU/RESCAN,tensorboardX -https://github.com/ZhouYanzhao/PRM,System (tested on Ubuntu 14.04LTS and Win10) -https://github.com/ZhouYanzhao/PRM,NVIDIA GPU + CUDA CuDNN (CPU mode is also supported but significantly slower) -https://github.com/ZhouYanzhao/PRM,Python>=3.5 -https://github.com/ZhouYanzhao/PRM,PyTorch>=0.4 -https://github.com/ZhouYanzhao/PRM,Jupyter Notebook and ipywidgets (required by the demo): -https://github.com/ZhouYanzhao/PRM,# enable the widgetsnbextension before you start the notebook server -https://github.com/ZhouYanzhao/PRM,jupyter nbextension enable --py --sys-prefix widgetsnbextension -https://github.com/ZhouYanzhao/PRM,"Install Nest, a flexible tool for building and sharing deep learning modules:" -https://github.com/ZhouYanzhao/PRM,I created Nest in the process of refactoring PRM's pytorch implementation. It aims at encouraging code reuse and ships with a bunch of useful features. PRM is now implemented as a set of Nest modules; thus you can easily install and use it as demonstrated below. -https://github.com/ZhouYanzhao/PRM,$ pip install git+https://github.com/ZhouYanzhao/Nest.git -https://github.com/ZhouYanzhao/PRM,Install PRM via Nest's CLI tool: -https://github.com/ZhouYanzhao/PRM,# note that data will be saved under your current path -https://github.com/ZhouYanzhao/PRM,$ nest module install github@ZhouYanzhao/PRM:pytorch prm -https://github.com/ZhouYanzhao/PRM,# verify the installation -https://github.com/ZhouYanzhao/PRM,$ nest module list --filter prm -https://github.com/ZhouYanzhao/PRM,# Output: -https://github.com/ZhouYanzhao/PRM,# 3 Nest modules found. -https://github.com/ZhouYanzhao/PRM,# [0] prm.fc_resnet50 (1.0.0) -https://github.com/ZhouYanzhao/PRM,# [1] prm.peak_response_mapping (1.0.0) -https://github.com/ZhouYanzhao/PRM,# [2] prm.prm_visualize (1.0.0) -https://github.com/agile-geoscience/striplog/,Dependencies -https://github.com/agile-geoscience/striplog/,"These are best installed with Anaconda, see Install, below." -https://github.com/agile-geoscience/striplog/,NumPy -https://github.com/agile-geoscience/striplog/,matplotlib -https://github.com/agile-geoscience/striplog/,Install -https://github.com/agile-geoscience/striplog/,pip install striplog -https://github.com/agile-geoscience/striplog/,I recommend setting up a virtual environment: -https://github.com/agile-geoscience/striplog/,Install Anaconda if you don't have it already -https://github.com/agile-geoscience/striplog/,"Then do this to create an environment called myenv (or whatever you like), answering Yes to the confirmation question:" -https://github.com/agile-geoscience/striplog/,conda create -n myenv python=3.5 numpy matplotlib -https://github.com/agile-geoscience/striplog/,source activate myenv -https://github.com/agile-geoscience/striplog/,Then you can do: -https://github.com/akanazawa/hmr,Python 2.7 -https://github.com/akanazawa/hmr,"TensorFlow tested on version 1.3, demo alone runs with TF 1.12" -https://github.com/akanazawa/hmr,Setup virtualenv -https://github.com/akanazawa/hmr,virtualenv venv_hmr -https://github.com/akanazawa/hmr,source venv_hmr/bin/activate -https://github.com/akanazawa/hmr,pip install -U pip -https://github.com/akanazawa/hmr,deactivate -https://github.com/akanazawa/hmr,Install TensorFlow -https://github.com/akanazawa/hmr,With GPU: -https://github.com/akanazawa/hmr,pip install tensorflow-gpu==1.3.0 -https://github.com/akanazawa/hmr,Without GPU: -https://github.com/akanazawa/hmr,pip install tensorflow==1.3.0 -https://github.com/akaszynski/pyansys,Installation through pip: -https://github.com/akaszynski/pyansys,pip install pyansys -https://github.com/albertpumarola/GANimation,"Install PyTorch (version 0.3.1), Torch Vision and dependencies from http://pytorch.org" -https://github.com/albertpumarola/GANimation,Install requirements.txt (pip install -r requirements.txt) -https://github.com/cgre-aachen/gempy,We provide the latest release version of GemPy via the Conda and PyPi package services. We highly recommend using either PyPi as it will take care of automatically installing all dependencies. -https://github.com/cgre-aachen/gempy,PyPi -https://github.com/cgre-aachen/gempy,$ pip install gempy -https://github.com/cgre-aachen/gempy,Manual -https://github.com/cgre-aachen/gempy,Otherwise you can clone the current repository by downloading is manually or by using Git by calling -https://github.com/cgre-aachen/gempy,$ git clone https://github.com/cgre-aachen/gempy.git -https://github.com/cgre-aachen/gempy,and then manually install it using the provided Python install file by calling -https://github.com/cgre-aachen/gempy,$ python gempy/setup.py install -https://github.com/cgre-aachen/gempy,in the cloned or downloaded repository folder. Make sure you have installed all necessary dependencies listed above before using GemPy. -https://github.com/cgre-aachen/gempy,Windows installation guide (Jun 2019) -https://github.com/cgre-aachen/gempy,Install CUDA if you do not have it already. -https://github.com/cgre-aachen/gempy,Install Anaconda3 2019.03 with Python 3.7 (this is the last release). -https://github.com/cgre-aachen/gempy,"Install Theano and associated packages from the Anaconda prompt as administrator, and finally install GemPy 2.0:" -https://github.com/cgre-aachen/gempy,conda update --all -https://github.com/cgre-aachen/gempy,conda install libpython -https://github.com/cgre-aachen/gempy,conda install m2w64-toolchain -https://github.com/cgre-aachen/gempy,conda install git -https://github.com/cgre-aachen/gempy,conda install pygpu -https://github.com/cgre-aachen/gempy,pip install theano==1.0.4 -https://github.com/cgre-aachen/gempy,pip install gempy==2.0b0.dev2 -https://github.com/cgre-aachen/gempy,Note that: -https://github.com/cgre-aachen/gempy,"a) some other packages required by Theano are already included in Anaconda: numpy, scipy, mkl-service, nose, and sphinx." -https://github.com/cgre-aachen/gempy,b) pydot-ng (suggested on Theano web site) yields a lot of errors. I dropped this. It is needed to handle large picture for gif/images and probably it is not needed by GemPy. -https://github.com/cgre-aachen/gempy,"c) Trying to install all the packages in one go but it does not work, as well as doing the same in Anaconda Navigator, or installing an older Anaconda release with Python 3.5 (Anaconda3 4.2.0) as indicated in some tutorial on Theano." -https://github.com/d3/d3,Installing -https://github.com/d3/d3,"If you use npm, npm install d3. Otherwise, download the latest release. The released bundle supports anonymous AMD, CommonJS, and vanilla environments. You can load directly from d3js.org, CDNJS, or unpkg. For example:" -https://github.com/d3/d3,"" -https://github.com/d3/d3,For the minified version: -https://github.com/d3/d3,"" -https://github.com/d3/d3,"You can also use the standalone D3 microlibraries. For example, d3-selection:" -https://github.com/d3/d3,"" -https://github.com/driftingtides/hyvr,Installing the HyVR package -https://github.com/driftingtides/hyvr,Installing Python -https://github.com/driftingtides/hyvr,"If you are using Windows, we recommend installing the Anaconda distribution of Python 3. This distribution has the majority of dependencies that HyVR requires." -https://github.com/driftingtides/hyvr,It is also a good idea to install the HyVR package into a virtual environment. Do this by opening a command prompt window and typing the following: -https://github.com/driftingtides/hyvr,conda create --name hyvr_env -https://github.com/driftingtides/hyvr,You need to then activate this environment: -https://github.com/driftingtides/hyvr,conda activate hyvr_env -https://github.com/driftingtides/hyvr,Linux -https://github.com/driftingtides/hyvr,"Depending on your preferences you can either use the Anaconda/Miniconda distribution of python, or the version of your package manager. If you choose the former, follow the same steps as for Windows." -https://github.com/driftingtides/hyvr,"If you choose the latter, you probably already have Python 3 installed. If not, you can install it using your package manager (e.g. apt on Ubuntu/Debian)." -https://github.com/driftingtides/hyvr,In any way we recommend using a virtual environment. Non-conda users can use virtualenvwrapper or pipenv. -https://github.com/driftingtides/hyvr,Installing HyVR -https://github.com/driftingtides/hyvr,"Once you have activated your virtual environment, you can install HyVR from PyPI using pip:" -https://github.com/driftingtides/hyvr,pip install hyvr -https://github.com/driftingtides/hyvr,"The version on PyPI should always be up to date. If it's not, you can also install HyVR from github:" -https://github.com/driftingtides/hyvr,git clone https://github.com/driftingtides/hyvr.git -https://github.com/driftingtides/hyvr,To install from source you need a C compiler. -https://github.com/driftingtides/hyvr,Installation from conda-forge will (hopefully) be coming soon. -https://github.com/driftingtides/hyvr,Python -https://github.com/driftingtides/hyvr,"HyVR was developed for use with Python 3.4 or greater. It may be possible to use with earlier versions of Python 3, however this has not been tested." -https://github.com/driftingtides/hyvr,numpy <= 1.13.3 -https://github.com/driftingtides/hyvr,matplotlib <= 2.1.0 -https://github.com/driftingtides/hyvr,scipy = 1.0.0 -https://github.com/driftingtides/hyvr,pandas = 0.21.0 -https://github.com/driftingtides/hyvr,flopy == 3.2.9 (optional for modflow output) -https://github.com/driftingtides/hyvr,pyevtk = 1.1.0 (optional for VTK output) -https://github.com/driftingtides/hyvr,h5py (optional for HDF5 output) -https://github.com/driving-behavior/DBNet,Tensorflow 1.2.0 -https://github.com/driving-behavior/DBNet,CUDA 8.0+ (For GPU) -https://github.com/driving-behavior/DBNet,"Python Libraries: numpy, scipy and laspy" -https://github.com/driving-behavior/DBNet,"The code has been tested with Python 2.7, Tensorflow 1.2.0, CUDA 8.0 and cuDNN 5.1 on Ubuntu 14.04. But it may work on more machines (directly or through mini-modification), pull-requests or test report are well welcomed." -https://github.com/empymod/empymod,You can install empymod either via conda: -https://github.com/empymod/empymod,conda install -c prisae empymod -https://github.com/empymod/empymod,or via pip: -https://github.com/empymod/empymod,pip install empymod -https://github.com/empymod/empymod,Required are Python version 3.5 or higher and the modules NumPy and SciPy. Consult the installation notes in the manual for more information regarding installation and requirements. -https://github.com/endernewton/iter-reason,"Tensorflow, tested with version 1.6 with Ubuntu 16.04, installed with:" -https://github.com/endernewton/iter-reason,pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp27-none-linux_x86_64.whl -https://github.com/endernewton/iter-reason,Other packages needed can be installed with pip: -https://github.com/endernewton/iter-reason,pip install Cython easydict matplotlib opencv-python Pillow pyyaml scipy -https://github.com/endernewton/iter-reason,"For running COCO, the API can be installed globally:" -https://github.com/endernewton/iter-reason,# any path is okay -https://github.com/endernewton/iter-reason,mkdir ~/install && cd ~/install -https://github.com/endernewton/iter-reason,git clone https://github.com/cocodataset/cocoapi.git cocoapi -https://github.com/endernewton/iter-reason,cd cocoapi/PythonAPI -https://github.com/endernewton/iter-reason,python setup.py install --user -https://github.com/endernewton/iter-reason,git clone https://github.com/endernewton/iter-reason.git -https://github.com/endernewton/iter-reason,cd iter-reason -https://github.com/endernewton/iter-reason,"Set up data, here we use ADE20K as an example." -https://github.com/endernewton/iter-reason,mkdir -p data/ADE -https://github.com/endernewton/iter-reason,cd data/ADE -https://github.com/endernewton/iter-reason,wget -v http://groups.csail.mit.edu/vision/datasets/ADE20K/ADE20K_2016_07_26.zip -https://github.com/endernewton/iter-reason,tar -xzvf ADE20K_2016_07_26.zip -https://github.com/endernewton/iter-reason,mv ADE20K_2016_07_26/* ./ -https://github.com/endernewton/iter-reason,rmdir ADE20K_2016_07_26 -https://github.com/endernewton/iter-reason,# then get the train/val/test split -https://github.com/endernewton/iter-reason,wget -v http://xinleic.xyz/data/ADE_split.tar.gz -https://github.com/endernewton/iter-reason,tar -xzvf ADE_split.tar.gz -https://github.com/endernewton/iter-reason,rm -vf ADE_split.tar.gz -https://github.com/endernewton/iter-reason,cd ../.. -https://github.com/endernewton/iter-reason,Set up pre-trained ImageNet models. This is similarly done in tf-faster-rcnn. Here by default we use ResNet-50 as the backbone: -https://github.com/endernewton/iter-reason,mkdir -p data/imagenet_weights -https://github.com/endernewton/iter-reason,cd data/imagenet_weights -https://github.com/endernewton/iter-reason,wget -v http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz -https://github.com/endernewton/iter-reason,tar -xzvf resnet_v1_50_2016_08_28.tar.gz -https://github.com/endernewton/iter-reason,mv resnet_v1_50.ckpt res50.ckpt -https://github.com/endernewton/iter-reason,Compile the library (for computing bounding box overlaps). -https://github.com/endernewton/iter-reason,cd lib -https://github.com/endernewton/iter-reason,make -https://github.com/endernewton/iter-reason,cd .. -https://github.com/endernewton/iter-reason,"Now you are ready to run! For example, to train and test the baseline:" -https://github.com/endernewton/iter-reason,./experiments/scripts/train.sh [GPU_ID] [DATASET] [NET] [STEPS] [ITER] -https://github.com/endernewton/iter-reason,# GPU_ID is the GPU you want to test on -https://github.com/endernewton/iter-reason,"# DATASET in {ade, coco, vg} is the dataset to train/test on, defined in the script" -https://github.com/endernewton/iter-reason,"# NET in {res50, res101} is the backbone networks to choose from" -https://github.com/endernewton/iter-reason,"# STEPS (x10K) is the number of iterations before it reduces learning rate, can support multiple steps separated by character 'a'" -https://github.com/endernewton/iter-reason,# ITER (x10K) is the total number of iterations to run -https://github.com/endernewton/iter-reason,# Examples: -https://github.com/endernewton/iter-reason,"# train on ADE20K for 320K iterations, reducing learning rate at 280K." -https://github.com/endernewton/iter-reason,./experiments/scripts/train.sh 0 ade 28 32 -https://github.com/endernewton/iter-reason,"# train on COCO for 720K iterations, reducing at 320K and 560K." -https://github.com/endernewton/iter-reason,./experiments/scripts/train.sh 1 coco 32a56 72 -https://github.com/endernewton/iter-reason,To train and test the reasoning modules (based on ResNet-50): -https://github.com/endernewton/iter-reason,./experiments/scripts/train_memory.sh [GPU_ID] [DATASET] [MEM] [STEPS] [ITER] -https://github.com/endernewton/iter-reason,# MEM in {local} is the type of reasoning modules to use -https://github.com/endernewton/iter-reason,# train on ADE20K on the local spatial memory. -https://github.com/endernewton/iter-reason,./experiments/scripts/train_memory.sh 0 ade local 28 32 -https://github.com/endernewton/iter-reason,"Once the training is done, you can test the models separately with test.sh and test_memory.sh, we also provided a separate set of scripts to test on larger image inputs." -https://github.com/endernewton/iter-reason,"You can use tensorboard to visualize and track the progress, for example:" -https://github.com/endernewton/iter-reason,tensorboard --logdir=tensorboard/res50/ade_train_5/ --port=7002 & -https://github.com/equinor/pylops,From PyPi -https://github.com/equinor/pylops,"If you want to use PyLops within your codes, install it in your Python environment by typing the following command in your terminal:" -https://github.com/equinor/pylops,pip install pylops -https://github.com/equinor/pylops,Open a python terminal and type: -https://github.com/equinor/pylops,import pylops -https://github.com/equinor/pylops,"If you do not see any error, you should be good to go, enjoy!" -https://github.com/equinor/pylops,From Conda-forge -https://github.com/equinor/pylops,"Alternatively, you can install PyLops using the conda-forge distribution by typing the following command in your terminal:" -https://github.com/equinor/pylops,conda install -c conda-forge pylops -https://github.com/equinor/segyio,Get segyio -https://github.com/equinor/segyio,A copy of segyio is available both as pre-built binaries and source code: -https://github.com/equinor/segyio,In Debian unstable -https://github.com/equinor/segyio,apt install python3-segyio -https://github.com/equinor/segyio,Wheels for Python from PyPI -https://github.com/equinor/segyio,pip install segyio -https://github.com/equinor/segyio,Source code from github -https://github.com/equinor/segyio,git clone https://github.com/statoil/segyio -https://github.com/equinor/segyio,Source code in tarballs -https://github.com/equinor/segyio,Build segyio -https://github.com/equinor/segyio,To build segyio you need: -https://github.com/equinor/segyio,A C99 compatible C compiler (tested mostly on gcc and clang) -https://github.com/equinor/segyio,"A C++ compiler for the Python extension, and C++11 for the tests" -https://github.com/equinor/segyio,CMake version 2.8.12 or greater -https://github.com/equinor/segyio,Python 2.7 or 3.x. -https://github.com/equinor/segyio,numpy version 1.10 or greater -https://github.com/equinor/segyio,setuptools version 28 or greater -https://github.com/equinor/segyio,setuptools-scm -https://github.com/equinor/segyio,pytest -https://github.com/equinor/segyio,"To build the documentation, you also need sphinx" -https://github.com/equinor/segyio,"To build and install segyio, perform the following actions in your console:" -https://github.com/equinor/segyio,git clone https://github.com/equinor/segyio -https://github.com/equinor/segyio,mkdir segyio/build -https://github.com/equinor/segyio,cd segyio/build -https://github.com/equinor/segyio,cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON -https://github.com/equinor/segyio,make install -https://github.com/equinor/segyio,"make install must be done as root for a system install; if you want to install in your home directory, add -DCMAKE_INSTALL_PREFIX=~/ or some other appropriate directory, or make DESTDIR=~/ install. Please ensure your environment picks up on non-standard install locations (PYTHONPATH, LD_LIBRARY_PATH and PATH)." -https://github.com/equinor/segyio,"If you have multiple Python installations, or want to use some alternative interpreter, you can help cmake find the right one by passing -DPYTHON_EXECUTABLE=/opt/python/binary along with install prefix and build type." -https://github.com/equinor/segyio,"To build the matlab bindings, invoke CMake with the option -DBUILD_MEX=ON. In some environments the Matlab binaries are in a non-standard location, in which case you need to help CMake find the matlab binaries by passing -DMATLAB_ROOT=/path/to/matlab." -https://github.com/facebook/react,"React has been designed for gradual adoption from the start, and you can use as little or as much React as you need:" -https://github.com/facebook/react,Use Online Playgrounds to get a taste of React. -https://github.com/facebook/react,Add React to a Website as a " +https://github.com/d3/d3,Allen Mao,For the minified version: +https://github.com/d3/d3,Allen Mao,"" +https://github.com/d3/d3,Allen Mao,"You can also use the standalone D3 microlibraries. For example, d3-selection:" +https://github.com/d3/d3,Allen Mao,"" +https://github.com/driftingtides/hyvr,Allen Mao,Installing the HyVR package +https://github.com/driftingtides/hyvr,Allen Mao,Installing Python +https://github.com/driftingtides/hyvr,Allen Mao,"If you are using Windows, we recommend installing the Anaconda distribution of Python 3. This distribution has the majority of dependencies that HyVR requires." +https://github.com/driftingtides/hyvr,Allen Mao,It is also a good idea to install the HyVR package into a virtual environment. Do this by opening a command prompt window and typing the following: +https://github.com/driftingtides/hyvr,Allen Mao,conda create --name hyvr_env +https://github.com/driftingtides/hyvr,Allen Mao,You need to then activate this environment: +https://github.com/driftingtides/hyvr,Allen Mao,conda activate hyvr_env +https://github.com/driftingtides/hyvr,Allen Mao,Linux +https://github.com/driftingtides/hyvr,Allen Mao,"Depending on your preferences you can either use the Anaconda/Miniconda distribution of python, or the version of your package manager. If you choose the former, follow the same steps as for Windows." +https://github.com/driftingtides/hyvr,Allen Mao,"If you choose the latter, you probably already have Python 3 installed. If not, you can install it using your package manager (e.g. apt on Ubuntu/Debian)." +https://github.com/driftingtides/hyvr,Allen Mao,In any way we recommend using a virtual environment. Non-conda users can use virtualenvwrapper or pipenv. +https://github.com/driftingtides/hyvr,Allen Mao,Installing HyVR +https://github.com/driftingtides/hyvr,Allen Mao,"Once you have activated your virtual environment, you can install HyVR from PyPI using pip:" +https://github.com/driftingtides/hyvr,Allen Mao,pip install hyvr +https://github.com/driftingtides/hyvr,Allen Mao,"The version on PyPI should always be up to date. If it's not, you can also install HyVR from github:" +https://github.com/driftingtides/hyvr,Allen Mao,git clone https://github.com/driftingtides/hyvr.git +https://github.com/driftingtides/hyvr,Allen Mao,To install from source you need a C compiler. +https://github.com/driftingtides/hyvr,Allen Mao,Installation from conda-forge will (hopefully) be coming soon. +https://github.com/driftingtides/hyvr,Allen Mao,Python +https://github.com/driftingtides/hyvr,Allen Mao,"HyVR was developed for use with Python 3.4 or greater. It may be possible to use with earlier versions of Python 3, however this has not been tested." +https://github.com/driftingtides/hyvr,Allen Mao,numpy <= 1.13.3 +https://github.com/driftingtides/hyvr,Allen Mao,matplotlib <= 2.1.0 +https://github.com/driftingtides/hyvr,Allen Mao,scipy = 1.0.0 +https://github.com/driftingtides/hyvr,Allen Mao,pandas = 0.21.0 +https://github.com/driftingtides/hyvr,Allen Mao,flopy == 3.2.9 (optional for modflow output) +https://github.com/driftingtides/hyvr,Allen Mao,pyevtk = 1.1.0 (optional for VTK output) +https://github.com/driftingtides/hyvr,Allen Mao,h5py (optional for HDF5 output) +https://github.com/driving-behavior/DBNet,Allen Mao,Tensorflow 1.2.0 +https://github.com/driving-behavior/DBNet,Allen Mao,CUDA 8.0+ (For GPU) +https://github.com/driving-behavior/DBNet,Allen Mao,"Python Libraries: numpy, scipy and laspy" +https://github.com/driving-behavior/DBNet,Allen Mao,"The code has been tested with Python 2.7, Tensorflow 1.2.0, CUDA 8.0 and cuDNN 5.1 on Ubuntu 14.04. But it may work on more machines (directly or through mini-modification), pull-requests or test report are well welcomed." +https://github.com/empymod/empymod,Allen Mao,You can install empymod either via conda: +https://github.com/empymod/empymod,Allen Mao,conda install -c prisae empymod +https://github.com/empymod/empymod,Allen Mao,or via pip: +https://github.com/empymod/empymod,Allen Mao,pip install empymod +https://github.com/empymod/empymod,Allen Mao,Required are Python version 3.5 or higher and the modules NumPy and SciPy. Consult the installation notes in the manual for more information regarding installation and requirements. +https://github.com/endernewton/iter-reason,Allen Mao,"Tensorflow, tested with version 1.6 with Ubuntu 16.04, installed with:" +https://github.com/endernewton/iter-reason,Allen Mao,pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp27-none-linux_x86_64.whl +https://github.com/endernewton/iter-reason,Allen Mao,Other packages needed can be installed with pip: +https://github.com/endernewton/iter-reason,Allen Mao,pip install Cython easydict matplotlib opencv-python Pillow pyyaml scipy +https://github.com/endernewton/iter-reason,Allen Mao,"For running COCO, the API can be installed globally:" +https://github.com/endernewton/iter-reason,Allen Mao,# any path is okay +https://github.com/endernewton/iter-reason,Allen Mao,mkdir ~/install && cd ~/install +https://github.com/endernewton/iter-reason,Allen Mao,git clone https://github.com/cocodataset/cocoapi.git cocoapi +https://github.com/endernewton/iter-reason,Allen Mao,cd cocoapi/PythonAPI +https://github.com/endernewton/iter-reason,Allen Mao,python setup.py install --user +https://github.com/endernewton/iter-reason,Allen Mao,git clone https://github.com/endernewton/iter-reason.git +https://github.com/endernewton/iter-reason,Allen Mao,cd iter-reason +https://github.com/endernewton/iter-reason,Allen Mao,"Set up data, here we use ADE20K as an example." +https://github.com/endernewton/iter-reason,Allen Mao,mkdir -p data/ADE +https://github.com/endernewton/iter-reason,Allen Mao,cd data/ADE +https://github.com/endernewton/iter-reason,Allen Mao,wget -v http://groups.csail.mit.edu/vision/datasets/ADE20K/ADE20K_2016_07_26.zip +https://github.com/endernewton/iter-reason,Allen Mao,tar -xzvf ADE20K_2016_07_26.zip +https://github.com/endernewton/iter-reason,Allen Mao,mv ADE20K_2016_07_26/* ./ +https://github.com/endernewton/iter-reason,Allen Mao,rmdir ADE20K_2016_07_26 +https://github.com/endernewton/iter-reason,Allen Mao,# then get the train/val/test split +https://github.com/endernewton/iter-reason,Allen Mao,wget -v http://xinleic.xyz/data/ADE_split.tar.gz +https://github.com/endernewton/iter-reason,Allen Mao,tar -xzvf ADE_split.tar.gz +https://github.com/endernewton/iter-reason,Allen Mao,rm -vf ADE_split.tar.gz +https://github.com/endernewton/iter-reason,Allen Mao,cd ../.. +https://github.com/endernewton/iter-reason,Allen Mao,Set up pre-trained ImageNet models. This is similarly done in tf-faster-rcnn. Here by default we use ResNet-50 as the backbone: +https://github.com/endernewton/iter-reason,Allen Mao,mkdir -p data/imagenet_weights +https://github.com/endernewton/iter-reason,Allen Mao,cd data/imagenet_weights +https://github.com/endernewton/iter-reason,Allen Mao,wget -v http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz +https://github.com/endernewton/iter-reason,Allen Mao,tar -xzvf resnet_v1_50_2016_08_28.tar.gz +https://github.com/endernewton/iter-reason,Allen Mao,mv resnet_v1_50.ckpt res50.ckpt +https://github.com/endernewton/iter-reason,Allen Mao,Compile the library (for computing bounding box overlaps). +https://github.com/endernewton/iter-reason,Allen Mao,cd lib +https://github.com/endernewton/iter-reason,Allen Mao,make +https://github.com/endernewton/iter-reason,Allen Mao,cd .. +https://github.com/endernewton/iter-reason,Allen Mao,"Now you are ready to run! For example, to train and test the baseline:" +https://github.com/endernewton/iter-reason,Allen Mao,./experiments/scripts/train.sh [GPU_ID] [DATASET] [NET] [STEPS] [ITER] +https://github.com/endernewton/iter-reason,Allen Mao,# GPU_ID is the GPU you want to test on +https://github.com/endernewton/iter-reason,Allen Mao,"# DATASET in {ade, coco, vg} is the dataset to train/test on, defined in the script" +https://github.com/endernewton/iter-reason,Allen Mao,"# NET in {res50, res101} is the backbone networks to choose from" +https://github.com/endernewton/iter-reason,Allen Mao,"# STEPS (x10K) is the number of iterations before it reduces learning rate, can support multiple steps separated by character 'a'" +https://github.com/endernewton/iter-reason,Allen Mao,# ITER (x10K) is the total number of iterations to run +https://github.com/endernewton/iter-reason,Allen Mao,# Examples: +https://github.com/endernewton/iter-reason,Allen Mao,"# train on ADE20K for 320K iterations, reducing learning rate at 280K." +https://github.com/endernewton/iter-reason,Allen Mao,./experiments/scripts/train.sh 0 ade 28 32 +https://github.com/endernewton/iter-reason,Allen Mao,"# train on COCO for 720K iterations, reducing at 320K and 560K." +https://github.com/endernewton/iter-reason,Allen Mao,./experiments/scripts/train.sh 1 coco 32a56 72 +https://github.com/endernewton/iter-reason,Allen Mao,To train and test the reasoning modules (based on ResNet-50): +https://github.com/endernewton/iter-reason,Allen Mao,./experiments/scripts/train_memory.sh [GPU_ID] [DATASET] [MEM] [STEPS] [ITER] +https://github.com/endernewton/iter-reason,Allen Mao,# MEM in {local} is the type of reasoning modules to use +https://github.com/endernewton/iter-reason,Allen Mao,# train on ADE20K on the local spatial memory. +https://github.com/endernewton/iter-reason,Allen Mao,./experiments/scripts/train_memory.sh 0 ade local 28 32 +https://github.com/endernewton/iter-reason,Allen Mao,"Once the training is done, you can test the models separately with test.sh and test_memory.sh, we also provided a separate set of scripts to test on larger image inputs." +https://github.com/endernewton/iter-reason,Allen Mao,"You can use tensorboard to visualize and track the progress, for example:" +https://github.com/endernewton/iter-reason,Allen Mao,tensorboard --logdir=tensorboard/res50/ade_train_5/ --port=7002 & +https://github.com/equinor/pylops,Allen Mao,From PyPi +https://github.com/equinor/pylops,Allen Mao,"If you want to use PyLops within your codes, install it in your Python environment by typing the following command in your terminal:" +https://github.com/equinor/pylops,Allen Mao,pip install pylops +https://github.com/equinor/pylops,Allen Mao,Open a python terminal and type: +https://github.com/equinor/pylops,Allen Mao,import pylops +https://github.com/equinor/pylops,Allen Mao,"If you do not see any error, you should be good to go, enjoy!" +https://github.com/equinor/pylops,Allen Mao,From Conda-forge +https://github.com/equinor/pylops,Allen Mao,"Alternatively, you can install PyLops using the conda-forge distribution by typing the following command in your terminal:" +https://github.com/equinor/pylops,Allen Mao,conda install -c conda-forge pylops +https://github.com/equinor/segyio,Allen Mao,Get segyio +https://github.com/equinor/segyio,Allen Mao,A copy of segyio is available both as pre-built binaries and source code: +https://github.com/equinor/segyio,Allen Mao,In Debian unstable +https://github.com/equinor/segyio,Allen Mao,apt install python3-segyio +https://github.com/equinor/segyio,Allen Mao,Wheels for Python from PyPI +https://github.com/equinor/segyio,Allen Mao,pip install segyio +https://github.com/equinor/segyio,Allen Mao,Source code from github +https://github.com/equinor/segyio,Allen Mao,git clone https://github.com/statoil/segyio +https://github.com/equinor/segyio,Allen Mao,Source code in tarballs +https://github.com/equinor/segyio,Allen Mao,Build segyio +https://github.com/equinor/segyio,Allen Mao,To build segyio you need: +https://github.com/equinor/segyio,Allen Mao,A C99 compatible C compiler (tested mostly on gcc and clang) +https://github.com/equinor/segyio,Allen Mao,"A C++ compiler for the Python extension, and C++11 for the tests" +https://github.com/equinor/segyio,Allen Mao,CMake version 2.8.12 or greater +https://github.com/equinor/segyio,Allen Mao,Python 2.7 or 3.x. +https://github.com/equinor/segyio,Allen Mao,numpy version 1.10 or greater +https://github.com/equinor/segyio,Allen Mao,setuptools version 28 or greater +https://github.com/equinor/segyio,Allen Mao,setuptools-scm +https://github.com/equinor/segyio,Allen Mao,pytest +https://github.com/equinor/segyio,Allen Mao,"To build the documentation, you also need sphinx" +https://github.com/equinor/segyio,Allen Mao,"To build and install segyio, perform the following actions in your console:" +https://github.com/equinor/segyio,Allen Mao,git clone https://github.com/equinor/segyio +https://github.com/equinor/segyio,Allen Mao,mkdir segyio/build +https://github.com/equinor/segyio,Allen Mao,cd segyio/build +https://github.com/equinor/segyio,Allen Mao,cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON +https://github.com/equinor/segyio,Allen Mao,make install +https://github.com/equinor/segyio,Allen Mao,"make install must be done as root for a system install; if you want to install in your home directory, add -DCMAKE_INSTALL_PREFIX=~/ or some other appropriate directory, or make DESTDIR=~/ install. Please ensure your environment picks up on non-standard install locations (PYTHONPATH, LD_LIBRARY_PATH and PATH)." +https://github.com/equinor/segyio,Allen Mao,"If you have multiple Python installations, or want to use some alternative interpreter, you can help cmake find the right one by passing -DPYTHON_EXECUTABLE=/opt/python/binary along with install prefix and build type." +https://github.com/equinor/segyio,Allen Mao,"To build the matlab bindings, invoke CMake with the option -DBUILD_MEX=ON. In some environments the Matlab binaries are in a non-standard location, in which case you need to help CMake find the matlab binaries by passing -DMATLAB_ROOT=/path/to/matlab." +https://github.com/facebook/react,Allen Mao,"React has been designed for gradual adoption from the start, and you can use as little or as much React as you need:" +https://github.com/facebook/react,Allen Mao,Use Online Playgrounds to get a taste of React. +https://github.com/facebook/react,Allen Mao,Add React to a Website as a