Deep Learning, NLP, NMT, AI, ML
Nmap automated port scanner written in Python. USE: Clone the module Import the module: from portscanModule import portscanner Use: portscanner.scan(“TARGET IP ADDRESS”, UPPER PORT LIMIT) DISCLAIMER: Much slower than a standalone nmap scan, but convenient 🙂 GitHub View Github
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なりすまし検出(anti-spoof-mn3)のWebカメラ向けデモです。モデルはPINTO_model_zoo/191_anti-spoof-mn3からONNX形式のモデルを使用しています。 mediapipe 0.8.8 or later OpenCV 3.4.2 or later onnxruntime 1.8.1 or later デモの実行方法は以下です。 python demo_anti_spoof.py –deviceカメラデバイス番号の指定デフォルト：0 –widthカメラキャプチャ時の横幅デフォルト：1280 –heightカメラキャプチャ時の縦幅デフォルト：720 –model_selectionモデル選択(0：2m以内の検出に最適なモデル、1：5m以内の検出に最適なモデル)デフォルト：0 –min_detection_confidence検出信頼値の閾値デフォルト：0.7 –as_modelなりすまし検出モデルの格納パスデフォルト：anti-spoof-mn3/model_float32.onnx –as_input_sizeなりすまし検出モデルの入力サイズデフォルト：128,128 高橋かずひと(https://twitter.com/KzhtTkhs) FaceDetection-Anti-Spoof-Demo is under MIT License. GitHub View Github
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Script to calculate estimated delegator epoch returns for all Pillars, so you can delegate to the best one. You can find me on TG: @vibeznn If you’d like to donate for future scripts and data science projects on/around NoM please send to:z1qpn04kmqdn9qyx45znwr2stnerqm8rg0y2vdm8 Thanks! Usage You can run this script from an IDE or from commandline with “python3 delegator_calculator.py”.*When you run this script it defaults to 100 znn delegation size, so the epochRewardsForMe columns is equal to daily APR. You can […]
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This repo is the official implementation of our paper “Sample Prior Guided Robust Model Learning to Suppress Noisy Labels“. Citation If you use this code/data for your research, please cite our paper “Sample Prior Guided Robust Model Learning to Suppress Noisy Labels“. @misc{chen2021sample, title={Sample Prior Guided Robust Model Learning to Suppress Noisy Labels}, author={Wenkai Chen and Chuang Zhu and Yi Chen}, year={2021}, eprint={2112.01197}, archivePrefix={arXiv}, primaryClass={cs.CV} } Training Take CIFAR-10 with 50% symmetric noise as an example: First, please modify the […]
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Acknowledgement We implement our model, BtcDet, based on [OpenPcdet 0.3.0]. Installation Requirements All the codes are tested in the following environment: Install b. Install the dependent libraries as follows: Install the dependent python libraries: pip install -r requirements.txt Install the SparseConv library, we use the implementation from [spconv]. If you use PyTorch 1.1, then make sure you install the spconv v1.0 with (commit 8da6f96) instead of the latest one. If you use PyTorch 1.3+, then you need to install the […]
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Deploying a Text Summarization NLP use case on Docker Container Utilizing Nvidia GPU, to setup the enviroment on linux machine follow up the below process, make sure you should have a good configuration system, my system specs are listed below(I am utilizing DataCrunch Servers) : GPU : 2xV100.10V Image : Ubuntu 20.04 + CUDA 11.1 Some Insights/Explorations If you’re a proper linux user make sure to setup it CUDA, cudaNN and Cuda Toolkit If you’re a WSL2 user then you […]
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Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar [arXiv] [Project] [BibTeX] Features A single architecture for panoptic, instance and semantic segmentation. Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas. Installation See installation instructions. Getting Started See Preparing Datasets for Mask2Former. See Getting Started with Mask2Former. Advanced usage See Advanced Usage of Mask2Former. Model Zoo and Baselines We provide a large set of baseline results and
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A simple python stock Predictor Demo Run Locally Clone the project git clone https://github.com/yashraj-n/stock-price-predictor Go to the project directory Install dependencies pip install autots numpy pandas matplotlib seaborn Run Usage/Examples To download dataset,Visit Yahoo Finance, search for your desired stock/cryptocurrency Then, goto historical dataset and click on download
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