High degree of structural complexity and practical imaging constraints make retrieval ofmacromolecular structures from cryo-ET is very challenging. For image classification oflarge-scale systematic macro-molecular structure from cryo-ET data. For image classification of large-scale systematic macro-molecular structure from cryo-ET data, adeep learning-based image classification approach has been employed to improve theaccuracy for a small range of SNR values where the present models have fallen short.Here, a novel SEC3 model for macro-molecule separation has been used. The model comprises 3D convolutional blocks […]

This readme will guide you through the use of the code in this repository. The code in this repository is for nonparametric prior-free and likelihood-free posterior inference. We named this method: Inference with consonant structures via data peeling As the name suggests, this method construct consonant confidence structures directly from data using a procedure name data peeling. When to use this code? The probability distribution of the data-generating mechanism, $P_{X}$ is multivariate (d>2) The distribution family (e.g. lognormal) of $P_{X}$ […]

Hi welcome to my new deep learning project “Yoga Asana Classifier / pose classifier “. This project as the name suggests can predict the yoga pose which you are doing in front of the webcam.This project comprise of three python scripts namely,Data CollectionData TrainingAnd finally Inference script.As all of the name suggest do there respective work. For this project I used mediapipe pose detection to detect the human body pose and after that I made model with simple Dense network […]

To reproduce the results from paper, do the following steps. install pytorch and sparceconov. The website for sparse convolution is here Download the processed dataset from here and unzip it. You would have a folder structure like the following: . +– data +– src +– readme.md +– data.zip cd src ./sh/ad/train00.sh Try different script in the folder sh to reproduce the results in the paper. GitHub View Github    

Task Training huge unsupervised deep neural networks yields to strong progress in the field of Natural Language Processing (NLP). Using these extensively pre-trained networks for particular NLP applications is the current state-of-the-art approach. In this project, we approach the task of ranking possible clarifying questions for a given query. We fine-tuned a pre-trained BERT model to rank the possible clarifying questions in a classification manner. The achieved model scores a top-5 accuracy of 0.4565 on the provided benchmark dataset. Installation […]

Component for deep integration LedFx from Home Assistant. FAQ Q. What versions were tested on? A. So far only 0.10.7 Q. Does it support audio settings? A. Yes, it supports Q. Can I change the effect settings? A. You can, for this, enable the appropriate mode from the [PRO] section. The required objects will only be available when supported by the effect. Install Installed through the custom repository HACS – dmamontov/hass-ledfx Or by copying the ledfx folder from the latest […]

This package provides an implementation of a trainable, Transformer-based deep protein folding model. We modified the open-source code of DeepMind AlphaFold v2.0 and provided code to train the model from scratch. See the reference and the repository of DeepMind AlphaFold v2.0. To train your own Uni-Fold models, please follow the steps below: 1. Install the environment. Run the following code to install the dependencies of Uni-Fold: conda create -n unifold python=3.8.10 -y conda activate unifold ./install_dependencies.sh Uni-Fold has been tested […]

TLoL-py is the Python component of the TLoL League of Legends deep learning library.It provides a set of utility methods and classes to deal with League of Legendsgame playing, deep learning datasets and provides a library to build a deep learningagent which can play League of Legends. This module is currently updated to patch 11.23. About Disclaimer: This project is not affiliated with Riot Games in any way. If you are interested in using this project or are just curious, […]

Setting up a python environment Follow the instruction in https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html for downloading and installing Miniconda Open a terminal in the code directory Create an environment using the .yml file: conda env create -f deepsatmodels_env.yml Activate the environment: source activate deepsatmodels Install required version of torch: conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch-nightly Datasets MTLCC dataset (Germany) Download the dataset (.tfrecords) The data for Germany can be downloaded from: https://github.com/TUM-LMF/MTLCC clone the repository in a separate directory: git clone https://github.com/TUM-LMF/MTLCC […]

О чем репозиторий В этом репозитории представлен способ файтюнить оригинальный CLIP на новый язык Почему модель не видит женщину и откуда на картинке с текстом слон? Основные особенности: Используются оригинальные картиночные и текстовые трансформеры; Поэтому есть возможность использовать оригинальные эмбединги картинок, а тексты обучать или дообучать на требуемый язык. Что ожидалось? Для обучения трансформера русскому языку будет достаточно 3.7 млн пар картинка-текст; Будет использована вся сила исходных картиночных эмбедингов, обученных на сотнях миллионов пар картинка-текст; Сохранится скорость и качество работы […]