ReCoin - Restoring our environment and businesses in parallel

Related tags

Text Data & NLPReCoin
Overview

Shashank Ojha, Sabrina Button, Abdellah Ghassel, Joshua Gonzales

"Reduce Reuse Recoin"

Theme Covered:

The themes covered in this project include post pandemic restoration for both the environment, small buisnesses, and personal finance! The app pitched uses an extensivly trained AI system to detect trash and sort it to the proper bin from your smartphone. While using the app, users will be incentivized to use the app and recover the environment through the opportunity to earn points, which will be redeemable in partnering stores.

Problem Statment:

As our actions continue to damage the environment, it is important that we invest in solutions that help restore our community in more sustainable practices. Moreover, an average person creates over 4 pounds of trash a day, and the EPA has found that over 75% of the waste we create are recyclable. As garbage sorting is so niche from town-to-town, students have reportable agreed to the difficulty of accurately sorting garbage, thus causing this significant misplacement of garbage.

Our passion to make our community globally and locally more sustainable has fueled us to use artificial intelligence to develop an app that not only makes sorting garbage as easy as using Snapchat, but also rewards individuals for sorting their garbage properly.

For this reason, we would like to introduce Recoin. This intuitive app allows a person to scan any product and easily find the bin that the trash belongs based off their location. Furthermore, if they attempt to sell their product, or use our app, they will earn points which will be redeemable in partnering stores that advocate for the environment. The more the user uses the app, the more points they receive, resulting in better items to redeem in stores. With this app we will not only help recover the environment, but also increase sales in small businesses which struggled during the pandemic to recover.

About the App:

Incentive Breakdown:

Please note that these expenses are estimated expectations for potential benefit packages but are not defined yet.

We are proposing a $1 discount for participating small businesses when 100 coffee/drink cups are returned to participating restaurants. This will be easy for small companies to uphold financially, while providing a motivation for individuals to use our scanner.

Amazon costs around $0.5 to $2 on packaging, so we are proposing that Amazon provides a $15 gift card per 100 packages returned to Amazon. As the 100 packages can cost from $50 to $200, this incentive will save Amazon resources by 5 to 100 times the amount, while providing positive public perception for reusing.

As recycling plastic for 3D filament is an up-and-coming technology that can revolutionize environment sustainability, we would like to create a system where providing materials for such causes can give the individuals benefits.

Lastly, as metals become more valuable, we hope to provide recyclable metals to companies to reduce their expenses through our platform.

The next steps to this endeavor will be to provide benefits for individuals that provide batteries and electronics with some sort of incentive as well.

User Interface:

#add user stuff!!!!!!!!!!!1

Technological Specifics and Next Steps:

Frontend

----ADDDDDDDDDDDD GRAPHHHHHHHHHHHHHHHHHHHHHHHH____ We used to React.JS to develop components for the webcam footage and capture screen shots. It was also utilized to create the rest of the overall UI design.

Backend

Trash Detection AI:

On Pytorch, we utilized an open-source trash detection AI software and data, to train the trash detection system originally developed by IamAbhinav03. The system utilizes over 2500 images to train, test, and validate the system. To improve the system, we increased the number of epochs to 8 rather than 5 (number of passes the training system has completed) to train it for an additional four hours than required. This allowed the accuracy to increase by 4% more than the original system. We also modified the test train and split amounts to 70%, 10%, and 20% respectively, as more prominent AI studies have found this distribution to receive the best results.

Currently, the system is predicted to have a 94% accuracy, but in the future, we plan on using reinforcement learning in our beta testing to continuously improve our algorithm. Reinforcement learning allows for the data to be more accurate, through learning from user correction. This will allow AI to become more precise as it gains more popularity.

Other Systems:

By using Matbox API and the Google Suite/API, we will be creating maps to find recycling locations and an extensively thorough Recoin currency system that can easily be transferred to real time money for consumers and businesses.

Stakeholders:

After the completion of this project, we intend to continue to pursue the app to improve our communities’ sustainability. After looking at the demographic of interest in our school itself, we know that students will be interested in this app, not only from convenience but also through the reward system. Local cafes and Starbucks already have initiatives to improve public perspective and support the environment (i.e., using paper straws and cups), therefore supporting this new endeavor will be an interest to them. As branding is everything in a business, having a positive public perspective will increase sales.

Amazon:

As Amazon continues to be the leading online marketplace, more packages will continue to be made, which can be detrimental to the world's limited resources. We will be training the UI to track packages that are Amazon based. With such training, we would like to be able to implement a system where the packaging can be sent back to Amazon to be reused for credit. This will allow Amazon to form a more environmentally friendly corporate image, while also saving on resources.

Small Businesses:

As the pandemic has caused a significant decline in small business revenue, we intend to mainly partner with small businesses in this project. The software will also help increase small business sales as by supporting the app, students will be more inclined to go to their store due to a positive public image, and the additive discounts will attract more customers. In the future, we wish to train AI to also detect trash of value (i.e.. Broken smartphones, precious metals), so that consumers can sell it in a bundle to local companies that can benefit from the material (ex: 3D-printing companies that convert used plastic to filament)

Timeline:

The following timeline will be used to ensure that our project will be on the market as soon as possible:

Code Refrences

https://medium.datadriveninvestor.com/deploy-your-pytorch-model-to-production-f69460192217

https://narainsreehith.medium.com/upload-image-video-to-flask-backend-from-react-native-app-expo-app-1aac5653d344

https://pytorch.org/tutorials/beginner/saving_loading_models.html

https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html

https://pytorch.org/get-started/locally/

https://www.kdnuggets.com/2019/03/deploy-pytorch-model-production.html

Refrences for Information

https://www.rubicon.com/blog/trash-reason-statistics-facts/

https://www.dosomething.org/us/facts/11-facts-about-recycling

https://www.forbes.com/sites/forbesagencycouncil/2016/10/31/why-brand-image-matters-more-than-you-think/?sh=6a4b462e10b8

https://www.channelreply.com/blog/view/ebay-amazon-packaging-costs

Owner
sabrina button
First Year Engineering Student at Queen's University (she/her)
sabrina button
NLP applications using deep learning.

NLP-Natural-Language-Processing NLP applications using deep learning like text generation etc. 1- Poetry Generation: Using a collection of Irish Poem

KASHISH 1 Jan 27, 2022
A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"

A sample Python project A sample project that exists as an aid to the Python Packaging User Guide's Tutorial on Packaging and Distributing Projects. T

Python Packaging Authority 4.5k Dec 30, 2022
TalkNet: Audio-visual active speaker detection Model

Is someone talking? TalkNet: Audio-visual active speaker detection Model This repository contains the code for our ACM MM 2021 paper, TalkNet, an acti

142 Dec 14, 2022
This github repo is for Neurips 2021 paper, NORESQA A Framework for Speech Quality Assessment using Non-Matching References.

NORESQA: Speech Quality Assessment using Non-Matching References This is a Pytorch implementation for using NORESQA. It contains minimal code to predi

Meta Research 36 Dec 08, 2022
a CTF web challenge about making screenshots

screenshotter (web) A CTF web challenge about making screenshots. It is inspired by a bug found in real life. The challenge was created by @LiveOverfl

219 Jan 02, 2023
A pytorch implementation of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".

RE2 This is a pytorch implementation of the ACL 2019 paper "Simple and Effective Text Matching with Richer Alignment Features". The original Tensorflo

286 Jan 02, 2023
Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts

gpt-2-simple A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifical

Max Woolf 3.1k Jan 07, 2023
Text Classification Using LSTM

Text classification is the task of assigning a set of predefined categories to free text. Text classifiers can be used to organize, structure, and categorize pretty much anything. For example, new ar

KrishArul26 3 Jan 03, 2023
2021搜狐校园文本匹配算法大赛baseline

sohu2021-baseline 2021搜狐校园文本匹配算法大赛baseline 简介 分享了一个搜狐文本匹配的baseline,主要是通过条件LayerNorm来增加模型的多样性,以实现同一模型处理不同类型的数据、形成不同输出的目的。 线下验证集F1约0.74,线上测试集F1约0.73。

苏剑林(Jianlin Su) 45 Sep 06, 2022
Python library to make development of portfolio analysis faster and easier

Trafalgar Python library to make development of portfolio analysis faster and easier Installation 🔥 For the moment, Trafalgar is still in beta develo

Santosh Passoubady 641 Jan 01, 2023
FedNLP: A Benchmarking Framework for Federated Learning in Natural Language Processing

FedNLP is a research-oriented benchmarking framework for advancing federated learning (FL) in natural language processing (NLP). It uses FedML repository as the git submodule. In other words, FedNLP

FedML-AI 216 Nov 27, 2022
Spooky Skelly For Python

_____ _ _____ _ _ _ | __| ___ ___ ___ | |_ _ _ | __|| |_ ___ | || | _ _ |__ || . || . || . || '

Kur0R1uka 1 Dec 23, 2021
A Python 3.6+ package to run .many files, where many programs written in many languages may exist in one file.

RunMany Intro | Installation | VSCode Extension | Usage | Syntax | Settings | About A tool to run many programs written in many languages from one fil

6 May 22, 2022
Sentence Embeddings with BERT & XLNet

Sentence Transformers: Multilingual Sentence Embeddings using BERT / RoBERTa / XLM-RoBERTa & Co. with PyTorch This framework provides an easy method t

Ubiquitous Knowledge Processing Lab 9.1k Jan 02, 2023
A Fast Command Analyser based on Dict and Pydantic

Alconna Alconna 隶属于ArcletProject, 在Cesloi内有内置 Alconna 是 Cesloi-CommandAnalysis 的高级版,支持解析消息链 一般情况下请当作简易的消息链解析器/命令解析器 文档 暂时的文档 Example from arclet.alcon

19 Jan 03, 2023
Yet another Python binding for fastText

pyfasttext Warning! pyfasttext is no longer maintained: use the official Python binding from the fastText repository: https://github.com/facebookresea

Vincent Rasneur 230 Nov 16, 2022
A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

A Deep Learning NLP/NLU library by Intel® AI Lab Overview | Models | Installation | Examples | Documentation | Tutorials | Contributing NLP Architect

Intel Labs 2.9k Jan 02, 2023
Korean Simple Contrastive Learning of Sentence Embeddings using SKT KoBERT and kakaobrain KorNLU dataset

KoSimCSE Korean Simple Contrastive Learning of Sentence Embeddings implementation using pytorch SimCSE Installation git clone https://github.com/BM-K/

34 Nov 24, 2022
Code associated with the "Data Augmentation using Pre-trained Transformer Models" paper

Data Augmentation using Pre-trained Transformer Models Code associated with the Data Augmentation using Pre-trained Transformer Models paper Code cont

44 Dec 31, 2022
🐍 A hyper-fast Python module for reading/writing JSON data using Rust's serde-json.

A hyper-fast, safe Python module to read and write JSON data. Works as a drop-in replacement for Python's built-in json module. This is alpha software

Matthias 479 Jan 01, 2023