WasteNet

The world’s largest dataset for waste, with pioneering research driving innovation in waste sorting technologies

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About WasteNet

Our pioneering research has been developed in partnership with academics at leading universities to create WasteNet: the world’s largest dataset for waste. 

It boasts over 3 million training images created by deep learning and computer vision, refined by weight and brand-level detection. 

WasteNet is underpinned by 4 unique pillars: waste image datasets, research papers, our data exploratory tool and waste taxonomy.

Waste Image DatasetsResearch papersVIZ-EDAWaste Taxonomy

Waste Image Datasets

Through Recycleye Vision, we have analysed over 3 million images of waste items in MRFs (and counting!). Collated by our expert team of machine learning engineers and academic research partners, our databases are available for academic and non-commercial purposes.

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Research Papers

Machine Learning

WasteNet

Single Shot 2D Image to 3D Model

This research consists of 2 papers offering differing reconstruction approaches to obtain high accuracy inferences…
WasteNet

Deep Learning: Unsupervised Domain Adaptation

The goal of this WasteNet series is to engineer a Deep Learning model that will…
Logo detection
WasteNet

An Application of Machine Learning for Brand-Level Waste Management

Computer vision for brand-level logo detection of waste in real-time was tested. The brand recognition…
WasteNet
Recycleye Bin: A Human-in-the-Loop Approach to Computer Vision Waste Disposal
WasteNet
Instance Segmentation of Novel Objects in a Conveyer Setting
ai and waste management
WasteNet
A Generative Adversarial Network (GAN) to Generate Transparent Objects
ai and waste management
WasteNet
Synthetic Generation of Augmented 3D Data for Waste Classification

Waste Management Industry

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WasteNet

Modelling of a modern MRF

Paloma's paper has a dual focus on two of the challenges faced by MRFs today:…
WasteNet

Decentralised and Digitised Mini Material Recovery Facilities

This research explores the disruption of centralised waste facilities to accommodate a decentralised model, known as the…
recycling regulations
WasteNet

Regulated Bans in Waste Management

Amandine looks at how we can form healthier consumer habits and increased recycling rates by…

Viz-EDA

We know that datasets are complex and hard to visualise, so we created Viz-EDA: an exploratory data analysis tool that helps to see through the data. It is completely open-source and available for use.

Learn MoreView GitHub

Our Waste Taxonomy

At Recycleye, we’re constructing a global standard for waste classification so that industry players across the world can speak a common language, establishing clarity between markets.

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WasteNet Access

Click below if you are a member of an academic institution, and are interested in exploring one of our datasets within your research

Request access