ASCII Art

Convert any image into greyscale ASCII art in python

Image-to-ASCII Convert any image into greyscale ASCII art. Setup 1. Clone Repo git clonehttps://github.com/BSmith156/Image-to-ASCII.git 2. Install Requirements pip install -r requirements.txt Usage image_to_ascii.py input_file output_file [-i] [-max n] Arguments Argument Description input_file The image file being converted. output_file The file to store the ASCII art. -i Optional. Inverts the image colour, useful when displaying the output using a light font on a dark background. -max n Optional. Sets the maximum width/height of the output to n, 0 for no maximum. […]

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Natural Language Processing

Learning Dense Representations of Phrases at Scale

DensePhrases DensePhrases is an extractive phrase search tool based on your natural language inputs. From 5 million Wikipedia articles, it can search phrase-level answers to your questions or find related entities to (subject, relation) pairs in real-time. Due to the extractive nature of DensePhrases, it always provides an evidence passage for each phrase. Please see our paper Learning Dense Representations of Phrases at Scale (Lee et al., 2021) for more details. Installation # Install torch with conda (please check your […]

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Analysis

Hyperbolic Dimensionality Reduction via Horospherical Projections

HoroPCA This code is the official PyTorch implementation of the ICML 2021 paper: HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsInes Chami*, Albert Gu*, Dat Nguyen*, Christopher RéStanford UniversityPaper: https://arxiv.org/abs/2106.03306 Abstract. This paper studies Principal Component Analysis (PCA) for data lying in hyperbolic spaces. Given directions, PCA relies on: (1) a parameterization of subspaces spanned by these directions, (2) a method of projection onto subspaces that preserves information in these directions, and (3) an objective to optimize, namely the variance explained […]

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Graph

Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

DeepCDR Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network This work has been accepted to ECCB2020 and was also published in the journal Bioinformatics. DeepCDR is a hybrid graph convolutional network for cancer drug response prediction. It takes both multi-omics data of cancer cell lines and drug structure as inputs and predicts the drug sensitivity (binary or contineous IC50 value). Keras==2.1.4 TensorFlow==1.13.1 hickle >= 2.1.0 DeepCDR can be downloaded by git clone https://github.com/kimmo1019/DeepCDR Installation has been tested in […]

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Machine Learning

Flexible data structures for complex machine learning datasets

meerkat Meerkat provides fast and flexible data structures for working with complex machine learning datasets. Getting started pip install meerkat-ml Note: some parts of Meerkat rely on optional dependencies. If you know which optional dependencies you’d like to install, you can do so using something like pip install meerkat-ml[dev,text] instead. See setup.py for a full list of optional dependencies. Load your dataset into a DataPanel and get going! import meerkat as mk dp = mk.DataPanel.from_csv(“…”) What is Meerkat? Meerkat makes […]

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Machine Learning

3D Animal Reconstruction with Expectation Maximization in the Loop

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop. Install Clone the repository with submodules: git clone –recurse-submoduleshttps://github.com/benjiebob/WLDO For segmentation decoding, install pycocotools python -m pip install “git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI” Datasets To use the StanfordExtra dataset, you will need to download the .json file via the repository. Please ensure you have StanfordExtra_v12 installed, which we released 1 Feb 2021. You may also wish […]

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Video

Implementation of FitVid video prediction model in JAX/Flax

FitVid Video Prediction Model Implementation of FitVid video prediction model in JAX/Flax. If you find this code useful, please cite it in your paper: @article{babaeizadeh2021fitvid, title={FitVid: Overfitting in Pixel-Level Video Prediction}, author= {Babaeizadeh, Mohammad and Saffar, Mohammad Taghi and Nair, Suraj and Levine, Sergey and Finn, Chelsea and Erhan, Dumitru}, journal={arXiv preprint arXiv:2106.13195}, year={2020} } Method FitVid is a new architecture for conditional variational video prediction. It has ~300 million parameters and can be trained with minimal training tricks. Sample […]

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Machine Learning

Complete U-net Implementation with keras in python

U Net Lowered with Keras Complete U-net Implementation with keras The model is implemented using the original paper. But I have changed the number of filters of the layers. The implemented number of layers are reduced to 25% of the original paper. Original Model Architecture : Dataset : The dataset has been taken from kaggle . It had a specific directory tree, but it was tough to execute dataset building from it, so I prepared an usable dat directory. Link […]

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Dataset

A dataset for online Arabic calligraphy

Calliar is a dataset for Arabic calligraphy. The dataset consists of 2500 json files that contain strokes manually annotated for Arabic calligraphy. This repository contains the dataset for the following paper : Calliar: An Online Handwritten Dataset for Arabic Calligraphy Zaid Alyafeai, Maged S. Al-shaibani, Mustafa Ghaleb, Yousif Ahmed Al-Wajih https://arxiv.org/abs/2106.10745 Abstract: Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy […]

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tensorflow

Attention mechanism with MNIST dataset

MNIST_AttentionMap [TensorFlow] Attention mechanism with MNIST dataset Usage $ python run.py Result Training Loss graph. Test Each figure shows input digit, attention map, and overlapped image sequentially. Further usage The further usages. Detecting the location of digits can be conducted using an attention map. Requirements TensorFlow 2.3.0 Numpy 1.18.5 GitHub https://github.com/YeongHyeon/MNIST_AttentionMap    

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