The World’s largest dataset for waste

About WasteNet

Recycleye has partnered with academics at leading universities to create WasteNet; the world’s largest dataset for waste, holding over 2.7 million training images created by deep learning and computer vision.
These datasets are refined by weight and brand-level detection enabled through Recycleye’s vision system. This technology holds world-leading accuracy that has disrupted the waste industry, and is revolutionising the current waste infrastructure.

Databases and Sub-databases

wastenetObject and Material-level Database in Industrial Setting

wastenetObject and Material-level Database in Lab Setting

Brand Level Waste

Synthetic Waste

Research Papers

Machine Learning


Single Shot 2D Image to 3D Model

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

Deep Learning: Unsupervised Domain Adaptation

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

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…
Recycleye Bin: A Human-in-the-Loop Approach to Computer Vision Waste Disposal
Instance Segmentation of Novel Objects in a Conveyer Setting
ai and waste management
A Generative Adversarial Network (GAN) to Generate Transparent Objects
ai and waste management
Synthetic Generation of Augmented 3D Data for Waste Classification

Waste Management


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

Regulated Bans in Waste Management

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


Datasets are complex and hard to visualise – Viz-EDA is a exploratory data analysis tool that helps to see through the data.

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Our Waste Taxonomy

Recycleye's waste taxonomy