Bot

An open-source multi-purpose Discord bot built with Discord.py and PostgreSQL

Wavy Wavy is an open-source multi-purpose Discord bot built with Discord.py and PostgreSQL. How to use Requirements: Then copy the .env.example file to .env and edit it using your preferred text editor. Afterwards install the required dependencies and start the bot using python3 -m pip install -r requirements.txt python3 main.py The aforementioned however is only to run the bot. You wil also need some way to configure it, like a web dashboard. Wavy’s web dashboard is located here. GitHub https://github.com/Wavy-Bot/bot […]

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Scripts

Python code for working with NFL play by play data

nfl_data_py nfl_data_py is a Python library for interacting with NFL data sourced from nflfastR (https://github.com/nflverse/nflfastR-data/) and nfldata (https://github.com/nflverse/nfldata/). Installation Use the package manager pip to install nfl_data_py. pip install nfl_data_py Usage import nfl_data_py as nfl Working with play-by-play data nfl.import_pbp_data(years, columns) Returns play-by-play data for the years and columns specified years : required, list of years to pull data for (earliest available is 1999) columns : optional, list of columns to pull data for nfl.see_pbp_cols() returns list of columns available […]

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tensorflow

Minimal implementation of PAWS in TensorFlow

PAWS-TF Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS) in TensorFlow (2.4.1). PAWS introduces a simple way to combine a very small fraction of labeled data with a comparatively larger corpus of unlabeled data during pre-training. With its approach, it sets the state-of-the-art in semi-supervised learning (as of May 2021) beating methods like SimCLRV2, Meta Pseudo Labels that too with fewer parameters and a smaller pre-training schedule. For details, I recommend checking […]

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Machine Learning

Image Cropping on Twitter: Fairness Metrics, their Limitations

Image Crop Analysis Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency This is a repo for the code used for reproducing our Image Crop Analysis paper as shared on our blog post. If you plan to use this code please cite our paper as follows: @ARTICLE{TwitterImageCrop2021, author = {{Yee}, Kyra and {Tantipongpipat}, Uthaipon and {Mishra}, Shubhanshu}, title = “{Image Cropping on Twitter: Fairness […]

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Text

Vision Transformer for Fast and Efficient Scene Text Recognition

deep-text-recognition-benchmark ViTSTR is a simple single-stage model that uses a pre-trained Vision Transformer (ViT) to perform Scene Text Recognition (ViTSTR). It has a comparable accuracy with state-of-the-art STR models although it uses significantly less number of parameters and FLOPS. ViTSTR is also fast due to the parallel computation inherent to ViT architecture. ViTSTR is built using a fork of CLOVA AI Deep Text Recognition Benchmark whose original documentation is at the bottom. Below we document how to train and evaluate […]

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Text

A Simple Strong Baseline for TextVQA and TextCaps

Simple is not Easy Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021] Citation If you use ssbaseline in your work, please cite: @article{zhu2020simple, title={Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps}, author={Zhu, Qi and Gao, Chenyu and Wang, Peng and Wu, Qi}, journal={arXiv preprint arXiv:2012.05153}, year={2020} } Installation First install the repo using git clone https://github.com/ZephyrZhuQi/ssbaseline.git ~/ssbaseline cd ~/ssbaseline python setup.py build develop Getting Data We provide SBD-Trans OCR for TextVQA and […]

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Machine Learning

End-to-End Pre-training for Vision-Language Representation Learning

Seeing Out of tHe bOx End-to-End Pre-training for Vision-Language Representation Learning [CVPR’21, Oral]By Zhicheng Huang*, Zhaoyang Zeng*, Yupan Huang*, Bei Liu, Dongmei Fu and Jianlong Fu arxiv: https://arxiv.org/pdf/2104.03135.pdf This is the official implementation of the paper. In this paper, we propose SOHO to “See Out of tHe bOx” that takes a whole image as input, and learns vision-language representation in an end-to-end manner. SOHO does not require bounding box annotations which enables inference 10 times faster than region-based approaches. Architecture […]

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Images

A public available dataset for road boundary detection in aerial images

Topo-boundary This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving. Project page. Topo-boundary is a publicly available benchmark dataset for topological road-boundary detection in aerial images. With an aerial image as the input, the evaluated method should predict the topological structure of road boundaries in the form of a graph. This dataset is based on NYC Planimetric Database. Topo-boundary consists of 25,297 4-channel aerial images, and each […]

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Visualization

Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest

Stock-market-forecasting Forecasting directional movements of stock-prices for intraday trading using LSTM and random-foresthttps://arxiv.org/abs/2004.10178Pushpendu Ghosh, Ariel Neufeld, Jajati K Sahoo We design a highly profitable trading stratergy and employ random forests and LSTM networks (more precisely CuDNNLSTM) to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500, for intraday trading, from January 1993 till December 2018. Bibtex @article{ghosh2021forecasting, title={Forecasting directional movements of stock prices for intraday trading using LSTM and random forests}, author={Ghosh, Pushpendu and […]

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PyTorch

Face Identity Disentanglement via Latent Space Mapping

ID-disentanglement-Pytorch Pytorch implementation of the paper Face Identity Disentanglement via Latent Space Mapping for both training and evaluation, with StyleGAN 2. Changes from original paper instead of using a Discriminator loss for the mapper. We have used several other losses such as: LPIPS Loss (The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, Zhang el al, 2018) MSE Loss Different ID Loss Different landmark detector The reason for those changes resides in the fact that the training procedure with […]

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