Deep Learning, NLP, NMT, AI, ML
___ _ ___ _ _ ___ | __|__| |_ ___| | | / __| | _|/ _| ‘ / _ |) | .` __ |_____|_||____/___/|_|_|___/ v0.0.1 Analyze DNS traffic. EchoDNS shows all requested DNS traffic. It will forward the original packet to a real DNS server. You can choose the target DNS. Default is 1.1.1.1.The endpoint won’t notice this “FakeDNS” at all. With the usage of multithreading, EchoDNS will perfom requests very efficient. A builtin garbage collector takes care of […]
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Introduction This repository contains PyTorch image dataloaders and utility functions to load datasets for supervised continual learning. Currently supported datasets: MNIST Pairwise-MNIST Fashion-MNIST not-MNIST (letters version of MNIST, see EMNIST for more detail) CIFAR-10 CIFAR-100 German Traffic Signs Street View House Numbers (SVHN) Incremental CIFAR-100 Incremental TinyImageNet Features The provided interface simplifies typical data loading for supervised continual learning scenarios. Dataset order, additional training data (for replay buffers) and test data (for global metrics computation) can all be specified. A […]
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Creation of an application in django on music albums Before Before you must verify that you have the latest python version downloaded In your terminalfirst create a virtual environment In windows In Unix o MacOS run source envScriptsactivate.bat Run the command pip install -r requirements.txt Create an .env file in the same location as the manage.py file inside the .env file – DJANGO_SETTINGS_MODULE =
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Bad Blood is an exploit for CVE-2021-20038, a stack-based buffer overflow in the httpd binary of SMA-100 series systems using firmware versions 10.2.1.x. I’ve written a lot of the technical details here: The exploit, as written, will open up a telnet bind shell on port 1270. An attacker that connects to the shell will achieve execution as nobody. Example Output
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PYTHON Identação Escopo Pai Escopo filho Escopo neto Variaveis int ==> Inteiro (São números inteiros) str ==> String (São textos, sepre dentro de aspas) float ==> Decimais (Números com “virgula”/usar o ponto) bool ==> Booleano (True ou False) Output print(“Hello World”) print(f”Meu IMC é: {imc}”) Input input(“Digite o dado: “) seuDado = float(input(“Digite o dado: “)) GitHub View Github
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0. Flask Rest API – in SunnyWeb : 이미지 변환 웹의 Flask Rest API 1. 구성 flaskRestAPI01 : 이미지 변환 기능 중 유명 화가풍으로 변환, 안경과 동물들의 특징을 이미지에 추가하는 기능을 담당하는 flask Rest API a) imgs : 안경, 돼지(코), 쥐(코, 귀) 이미지 b) models b-1) instance_norm b-2) eccv16 c) api_test01.py : json으로 request를 주었을 때를 테스트하는 모듈 d) app01.py : flask로 웹 서버를 올려 json request를 json response를 보내주는 flask Rest API e) img_download.py : s3에서 이미지를 다운 받아서 […]
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Requirements and Installation More details see fairseq. Briefly, python == 3.6.4 pytorch == 1.7.1 Installing fairseq and other requirements git clone https://github.com/MUGE-2021/image-caption-baseline cd muge_baseline/ pip install -r requirements.txt cd fairseq/ pip install –editable . Downloading data and place to dataset/ directory,file structure is text2image-baseline – dataset – ECommerce-T2I – T2I_train.img.tsv – T2I_train.text.tsv – … Getting Started The model is a BART-like model with vqgan as a image tokenizer, please see models/t2i_baseline.py for detailed model structure. Training
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This is the official code of Retinal Vessel Segmentation with Pixel-wise Adaptive Filters and Consistency Training (ISBI 2022). We evaluate our methods on three datasets, DRIVE, CHASE_DB1 and STARE. You can download the three datasets from Google drive.Of course, you can download the dataset from DRIVE, CHASE_DB1 and STARE respectively. Requirement Refer to Pytorch to install Pytorch >= 1.1. pip install -r requirements.txt Config file
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