Video

A fantastic work in Video-level Anomaly Detection

This is my codes that can visualize the psnr image in testing videos. Future Frame Prediction for Anomaly Detection – A New Baseline This is a fantastic work in Video-level Anomaly Detection, published in CVPR2018. ShanghaiTech svip-lab has given their work in [Github]. Moreover, this work also have an interesting video in [YouTube]. And we can see that when anomaly examples happened, PSNR images will have a low response. Such is an example in avenue dataset. Testing images through PSNR […]

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Analysis

Domain Connectivity Analysis Tool In Python

DomainCAT (Domain Connectivity Analysis Tool) “See Connections Between Domains Right Meow” The Domain Connectivity Analysis Tool is used to analyze aggregate connectivity patterns across a set of domains during security investigations This project was a collaborative effort between myself and Matthew Pahl Introduction When analyzing pivots during threat hunting, most people approach it from the perspective of “what can a singlepivot tell you?” But often actors will set their domains up to use commodity hosting infrastructure, so the number ofentities […]

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Video

Real world Anomaly Detection in Surveillance Videos

This repository is a re-implementation of “Real-world Anomaly Detection in Surveillance Videos” with pytorch. As a result of our re-implementation, we achieved a much higher AUC than the original implementation. Datasets Download following data link and unzip under your $DATA_ROOT_DIR./workspace/DATA/UCF-Crime/all_rgbs Directory tree DATA/ UCF-Crime/ ../all_rgbs ../~.npy ../all_flows ../~.npy train_anomaly.txt train_normal.txt test_anomaly.txt test_normal.txt train-test script python main.py Reslut METHOD DATASET AUC Original paper(C3D two stream) UCF-Crimes 75.41 RTFM (I3D RGB) UCF-Crimes 84.03 Ours Re-implementation (I3D two stream) UCF-Crimes 84.45 Visualization Acknowledgment […]

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Discord

Discord optional detailed weather bot

Discord opsiyonel detaylı hava durumu botu önümüzdeki Perşembe ──► önümüzdeki Çarşamba ┌─────────┐┌─────────┐┌─────────┐┌─────────┐ │ Perşembe││ Cuma ││Cumartesi││ Pazar │ ├─────────┤├─────────┤├─────────┤├─────────┤ │°C: 16.7││°C: 5.82││°C: 4.45││°C: 2.39│ │ Clouds ││ Snow ││ Snow ││ Snow │ │hPa: 1009││hPa: 1018││hPa: 1018││hPa: 1019│ │Nem: 34││Nem: 54││Nem: 54││Nem: 99│ └─────────┘└─────────┘└─────────┘└─────────┘ ┌─────────┐┌─────────┐┌─────────┐ │Pazartesi││ Salı ││ Çarşamba│ ├─────────┤├─────────┤├─────────┤ │°C: 3.24││°C: 9.02││°C: 15.87│ │ Snow ││ Rain ││ Clouds │ │hPa: 1024││hPa: 1022││hPa: 1018│ │Nem: 97││Nem: 75││Nem: 49│ └─────────┘└─────────┘└─────────┘ önümüzdeki 7 günlük hava durumu tahminleri Olay Detaylı Bilgi Örnek […]

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Segmentation

Hypercorrelation Squeeze for Few-Shot Segmentation

Hypercorrelation Squeeze for Few-Shot Segmentation This is the implementation of the paper “Hypercorrelation Squeeze for Few-Shot Segmentation” by Juhong Min, Dahyun Kang, and Minsu Cho. Implemented on Python 3.7 and Pytorch 1.5.1. For more information, check out project [website] and the paper on [arXiv]. Requirements Python 3.7 PyTorch 1.5.1 cuda 10.1 tensorboard 1.14 Conda environment settings: conda create -n hsnet python=3.7 conda activate hsnet conda install pytorch=1.5.1 torchvision cudatoolkit=10.1 -c pytorch conda install -c conda-forge tensorflow pip install tensorboardX Preparing […]

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Semi-supervised

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang, Jiaya Jia This is the official PyTorch implementation of our paper Semi-supervised Semantic Segmentation with Directional Context-aware Consistency that has been accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). [Paper] Our method achives the state-of-the-art performance on semi-supervised semantic segmentation. Based on CCT, this Repository also supports efficient distributed training with multiple GPUs. Environment The […]

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Scripts

Generate direct m3u playlist for all the channels subscribed in the Tata Sky portal

A script to generate the m3u playlist containing direct streamable file (.mpd or MPEG-DASH or DASH) based on the channels that the user has subscribed on the Tata Sky portal. You just have to login using your password or otp that’s it A working brain Knowledge of basic python A working Tata Sky account Channels that you want to watch, already subscribed (I’m sorry, no freebies) You can simply use the android app in your mobile phone or TV, login […]

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JAX

A Gaussian process (GP) library built in JAX (with objax)

Newt Newt is a Gaussian process (GP) library built in JAX (with objax), built and actively maintained by Will Wilkinson. Newt provides a unifying view of approximate Bayesian inference for GPs, and allows for the combination of many models (e.g. GPs, sparse GPs, Markov GPs, sparse Markov GPs) with the inference method of your choice (VI, EP, Laplace, Linearisation). For a full list of the methods implemented scroll down to the bottom of this page. Installation In the top directory […]

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Transformer

A fast and easy implementation of Transformer with PyTorch

FasySeq FasySeq is a shorthand as a Fast and easy sequential modeling toolkit. It aims to provide a seq2seq model to researchers and developers, which can be trained efficiently and modified easily. This toolkit is based on Transformer(Vaswani et al.), and will add more seq2seq models in the future. Dependency PyTorch >= 1.4 NLTK Result … Structure … To Be Updated top-k and top-p sampling multi-GPU inference length penalty in beam search … Preprocess Build Vocabulary createVocab.py NamedArguments Description -f/–file […]

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Detection

Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

QueryDet-PyTorch This repository is the official implementation of our paper: QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection Requirement a. Install Pytorch 1.4 b. Install APEX for mixed precision training c. Install our Pytorch based sparse convolution toolkit d. Install the detectron2 toolkit. Note we build our approach based on version 0.2.1, you may follow the instructions to set environment configs e. Install the Detectron2_Backbone for usage of MobileNet and ShuffleNet f. Clone our repository and have fun […]

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