OpenCV

Multi-Scale Geometric Consistency Guided Multi-View Stereo

ACMM ACMM is a multi-scale geometric consistency guided multi-view stereo method for efficient and accurate depth map estimation. If you find this project useful for your research, please cite: @article{Xu2019ACMM, title={Multi-Scale Geometric Consistency Guided Multi-View Stereo}, author={Xu, Qingshan and Tao, Wenbing}, journal={Computer Vision and Pattern Recognition (CVPR)}, year={2019} } Dependencies The code has been tested on Ubuntu 14.04 with GTX Titan X. Usage cmake . make Use script colmap2mvsnet_acm.py to convert COLMAP SfM result to ACMM input Run ./ACMM $data_folder […]

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Transformer

Rethinking Spatial Dimensions of Vision Transformers

Rethinking Spatial Dimensions of Vision Transformers Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, Seong Joon Oh | Paper NAVER AI LAB News Mar 30, 2021: Code & paper released Apr 2, 2021: PiT models with pretrained weights are added to timm repo. You can directly use PiT models with timm>=0.4.7. Jul 23, 2021: Accepted to ICCV 2021 as a poster session Abstract Vision Transformer (ViT) extends the application range of transformers from language processing to computer vision […]

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Machine Learning

Adversarial Training Against Location-Optimized Adversarial Patches

Adversarial-Patch-Training Code for the paper: Sukrut Rao, David Stutz, Bernt Schiele. (2020) Adversarial Training Against Location-Optimized Adversarial Patches. In: Bartoli A., Fusiello A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12539. Springer, Cham. https://doi.org/10.1007/978-3-030-68238-5_32 Setup Requirements Python 3.7 or above PyTorch scipy h5py scikit-image scikit-learn Optional requirements To use script to convert data to HDF5 format torchvision Pillow pandas To use Tensorboard logging With the exception of Python and PyTorch, all requirements […]

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Neural Network

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

StemGNN This repository is the official implementation of Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Requirements Recommended version of OS & Python: To install python dependencies, virtualenv is recommended, sudo apt install python3.7-venv to install virtualenv for python3.7. All the python dependencies are verified for pip==20.1.1 and setuptools==41.2.0. Run the following commands to create a venv and install python dependencies: python3.7 -m venv venv source venv/bin/activate pip install –upgrade pip pip install -r requirements.txt Datasets PEMS03,PEMS04,PEMS07,PEMS08,METR-LA,PEMS-BAY,Solar,Electricity,ECG5000,COVID-19 We can […]

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Computer Vision

CvT: Introducing Convolutions to Vision Transformers

convolution-vision-transformers Pytorch implementation of CvT: Introducing Convolutions to Vision Transformers , for official repo please visit here. Usage: img = torch.ones([1, 3, 224, 224]) model = CvT(224, 3, 1000) parameters = filter(lambda p: p.requires_grad, model.parameters()) parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 print(‘Trainable Parameters: %.3fM’ % parameters) out = model(img) print(“Shape of out :”, out.shape) # [B, num_classes] Citation: @misc{wu2021cvt, title={CvT: Introducing Convolutions to Vision Transformers}, author={Haiping Wu and Bin Xiao and Noel Codella and Mengchen Liu and […]

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Deep Learning

Efficient Deep Neural Network Training via Cyclic Precision

CPT Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin Accepted at ICLR 2021 (Spotlight) [Paper Link]. Overview Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs’ training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs’ precision might have a similar […]

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Deep Learning

Implementation of lightweight GAN proposed in ICLR 2021 in Pytorch

lightweight-gan 512×512 flowers after 12 hours of training, 1 gpu 256×256 flowers after 12 hours of training, 1 gpu Pizza ‘Lightweight’ GAN Implementation of ‘lightweight’ GAN proposed in ICLR 2021, in Pytorch. The main contributions of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary “converge on single gpu with few hours’ training, on 1024 resolution sub-hundred images”. Install $ pip install lightweight-gan Use One command $ lightweight_gan […]

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Tool

All In One Tools For Cryptology in python

Written by TMRSWRRVersion 1.0.0All in one tools for CRYPTOLOGY. Screenshots 📹 How to use 📹 Click on the image… Features This tool include: :round_pushpin: HASH :round_pushpin: RSA :round_pushpin: XOR :round_pushpin: AES (ECC) :round_pushpin: AES (CBC) :round_pushpin: DES (ECB) :round_pushpin: FERNET :round_pushpin: RC2 :round_pushpin: RC4 :round_pushpin: CHACHA20POLY1305 :round_pushpin: TRANSPOSITION :round_pushpin: DIFFIE HELMAN :round_pushpin: IMAGE ENCRYPT/DECRYPT :round_pushpin: FILE ENCRYPT/DECRYPT 📀 Installation 📀 Installation with requirements.txt git clone https://github.com/capture0x/cypher cd cypher pip3 install -r requirements.txt Usage python3 cryptot.py HASH Encryption and decryption algorithms […]

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HTTP

A screaming-fast, scalable, asynchronous Python 3.5+ HTTP toolkit integrated

Japronto There is no new project development happening at the moment, but it’s not abandoned either. Pull requests and new maintainers are welcome. If you are a novice Python programmer, you don’t like plumbing yourself or you don’t have basic understanding of C, this project is not probably what you are looking for. Japronto (from Portuguese “já pronto” /ˈʒa pɾõtu/ meaning “already done”) is a screaming-fast, scalable, asynchronous Python 3.5+ HTTP toolkit integrated with pipelining HTTP server based on uvloop […]

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Json

Declaratively specify how to extract elements from a JSON document

JMESPath JMESPath (pronounced “james path”) allows you to declaratively specify how to extract elements from a JSON document. For example, given this document: {“foo”: {“bar”: “baz”}} The jmespath expression foo.bar will return “baz”. JMESPath also supports: Referencing elements in a list. Given the data: {“foo”: {“bar”: [“one”, “two”]}} The expression: foo.bar[0] will return “one”. You can also reference all the items in a list using the * syntax: {“foo”: {“bar”: [{“name”: “one”}, {“name”: “two”}]}} The expression: foo.bar[*].name will return [“one”, […]

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