Month: October 2021

Sentiment

Docker Container wallstreetbets-sentiment-analysis

A docker container using restful endpoints exposed on port 5000 “/analyze” to gather sentiment analysis on the wsb subreddit. Getting Started These instructions will cover usage information and for the docker container Prerequisities In order to run this container you’ll need docker installed. Usage Container Startup List the different parameters available to your container docker run -p 5000:5000 -e CLIENT_ID= -e CLIENT_SECRET= -e USERNAME= -e PASSWORD= restfulleo/wallstreetbets-sentiment-analysis Environment Variables CLIENT_ID – A client_id from valid reddit account with a developer […]

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Segmentation

Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation

Introduction In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss functionis utilized to learn to produce pixel-wise feature embeddings such that pixels from the same object are close to each other and pixels from different objects are separated in the embedding space. With the learned feature embeddings, a mean shift clustering algorithm can be applied to discover and segment unseen objects. We further improve […]

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Teachers

Implementation of momentum^2 teacher

All experiments are done with python3.6, torch==1.5.0; torchvision==0.6.0 Data Preparation Prepare the ImageNet data in ${root_of_your_clone}/data/imagenet_train, ${root_of_your_clone}/data/imagenet_val. Since we have an internal platform(storage) to read imagenet, I have not tried the local mode. You may need to do some modification in momentum_teacher/data/dataset.py to support the local mode. Training Before training, ensure the path (namely ${root_of_clone}) is added in your PYTHONPATH, e.g. export PYTHONPATH=$PYTHONPATH:${root_of_clone} To do unsupervised pre-training of a ResNet-50 model on ImageNet in an 8-gpu machine, run: using -d […]

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AWS

An AWS Pentesting tool that lets you use one-liner commands to backdoor an AWS account’s resources with a rogue AWS account

An AWS Pentesting tool that lets you use one-liner commands to backdoor an AWS account’s resources with a rogue AWS account – or share the resources with the entire internet? TL;DR: endgame smash –service all to create backdoors across your entire AWS account – by sharing resources either with a rogue IAM user/role or with the entire Internet. Endgame abuses AWS’s resource permission model to grant rogue users (or the Internet) access to an AWS account’s resources with a single […]

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Motion

Motion and Shape Capture from Sparse Markers

This repository contains the official chumpy implementation of mocap body solver used for AMASS: AMASS: Archive of Motion Capture as Surface ShapesNaureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. BlackFull paper |Video |Project website |Poster Description This repository holds the code for MoSh++, introduced in AMASS, ICCV’19.MoSh++ is the upgraded version of MoSh, Sig.Asia’2014.Given a labeled marker-based motion capture (mocap) c3d file and the correspondencesof the marker labels to the locations on the body, MoSh canreturn model […]

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Bot

The Few-Shot Bot: Prompt-Based Learning for Dialogue Systems

This repository includes the dataset, experiments results, and code for the paper: Few-Shot Bot: Prompt-Based Learning for Dialogue Systems PDF. Authors: Andrea Madotto, Zhaojiang Lin, Genta Indra Winata, Pascale Fung Abstract Learning to converse using only a few examples is a grand challenge in Conversational AI. The current best conversational models, which are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL), are language models (LMs) fine-tuned on large conversational datasets. Training these models is expensive, both in terms […]

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Detection

Morphable Detector for Object Detection on Demand

(ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. If our project is helpful for your research, please consider citing: @inproceedings{zhaomorph, author = {Xiangyun Zhao, Xu Zou, Ying Wu}, title = {Morphable Detector for Object Detection on Demand}, booktitle = {ICCV}, Year = {2021} } Install First, install PyTorch and torchvision. We have tested on version of 1.8.0 with CUDA 11.0, but the other versions should also be working. Our code is    

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Transformer

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

This repository is the official implementation of CARE paper. Updates (09/10/2021) Our paper is accepted by NeurIPS 2021. (14/10/2021) Our code is available for ssl pretraining and image classification evaluation. Comming Provide resnet_50(100e) models and logs. Complete the pretrained models and logs. Requirements To install requirements: conda create -n care python=3.6 conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1    

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Docker

A simple docker-compose app for orchestrating a fastapi application, a celery queue with rabbitmq(broker) and redis(backend)

A simple docker-compose app for orchestrating a fastapi application, a celery queue with rabbitmq(broker) and redis(backend) build and run containers docker-compose up -d –build This will expose fastapi application on 5000 and celery flower on 5555 swagger docs – http://localhost:5000/ redoc – http://localhost:5000/redoc celery flower – http://localhost:5555 GitHub https://github.com/karthikasasanka/fastapi-celery-redis-rabbitmq    

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