DOI: 10.5281/zenodo.18364275
title: MMoRWaD - The Multi Modal Recyclable Waste Management Dataset

description: A multimodal dataset for automated municipal solid waste classification, featuring synchronized RGB images, depth data, video, and impact audio from recyclable household items. It includes 3,300 annotated samples with detailed material, color, and physical attributes, plus COCO annotations for detection and segmentation tasks.
description: Automated classification of municipal solid waste is challenging due to large variability in object appearance, deformation, contamination, and disposal behavior. To address this, we present a multimodal dataset collected using a custom reverse-vending-machine station equipped with an RGB-D sensor and a microphone, capturing synchronized RGB images, depth data, video, and impact audio from recyclable household waste. The dataset contains 3,300 multimodal samples from 722 distinct items and is fully annotated with recycling-relevant attributes, including material type, color, and the presence of caps, labels, contamination, and deformation. COCO annotations are also provided to support object detection and segmentation tasks. This dataset is intended to support the development and evaluation of robust machine-learning models for automated waste classification.
description: The directory structure is organized by modality into six main components: RGB images, depth images, RGB videos, depth videos, audio recordings, and metadata files. Each modality is stored in a dedicated directory following a consistent naming convention.

In addition to the raw data, three annotation files are provided:• a CSV file containing all classification attributes (material, color, cap, label, contamination, deformation),• a COCO-format JSON file with instance segmentation masks, bounding boxes, categories, and item names,• a supplementary JSONL file recording the number of captures per item along with the corresponding item name, ensuring consistent integration of new samples into the dataset.

All RGB and depth images are stored in PNG format at a resolution of 848 × 480 pixels. RGB and depth videos share the same resolution, are recorded at 30 fps, have an average duration of approximately 2 seconds, and are stored in AVI format. Audio recordings are provided in WAV format, captured as mono signals at 48 kHz and 16-bit resolution.
description: Naming Convention

A structured file-naming scheme encodes the main properties of each waste item directly in the filename, enabling fast identification and filtering without inspecting file contents.

Filename format:

<ID><data_type>_MxxHxxTxLxCxDx

Where:





ID: incremental numerical identifier




data_type: data modality (ic, id, a, vc, vd)




Mxx: material code




Hxx: hue/color code




Tx: cap presence




Lx: label presence




Cx: contamination




Dx: deformation



ic = image color, id = image depth, a = audio, vc = video color, vd = video depth.

Material Codes (Mxx)

The material codes follow standard recycling identification symbols commonly used on consumer packaging. These codes allow material type to be inferred directly from the filename and align with widely adopted sorting practices.








Code
Description
Category




01
Polyethylene Terephthalate (PET)
Plastic


02
High Density Polyethylene (HDPE)
Plastic


03
Polyvinyl Chloride (PVC)
Plastic


04
Low Density Polyethylene (LDPE)
Plastic


05
Polypropylene (PP)
Plastic


06
Polystyrene (PS)
Plastic


07
Other Plastic (O)
Plastic


20
Cardboard (PAP)
Paper/Cardboard


21
Paperboard (PAP)
Paper/Cardboard


22
Paper (PAP)
Paper


40
Steel (FE)
Metal


41
Aluminium (ALU)
Metal


70
Clear Glass (GL)
Glass


71
Green Glass (GL)
Glass


72
Brown Glass (GL)
Glass


81
Paper/Cardboard + Plastic (C/PAP)
Composite


84
Paper/Cardboard + Plastic + Aluminium (C/PAP)
Composite


90
Plastic + Aluminium (C/PAP)
Composite






Hue Codes (Hxx)








Code
Category
Code
Category




00
Clear
20
Green


01
Opaque
21
Light Green


02
White
30
Blue


03
Gray
31
Light Blue


04
Black
40
Yellow


10
Red
41
Brown


11
Pink
42
Orange


12
Purple
99
Other






Binary Attribute Codes








Attribute
Meaning
Codes




T
Cap presence
0 = No, 1 = Yes


L
Label presence
0 = No, 1 = Yes


C
Contamination
0 = No, 1 = Yes


D
Deformation
0 = No, 1 = Yes




 



Dataset Distributions

This section provides a concise visual overview of the dataset composition, focusing on material balance and key physical attributes relevant to recycling scenarios.

Material Distribution

type is summarized below.

Plastic





Polyethylene Terephthalate (PET): 1,146 samples (34.8%)




High Density Polyethylene (HDPE): 488 samples (14.8%)




Polypropylene (PP): 432 samples (13.1%)




Other Plastics: 103 samples (3.1%)




Low Density Polyethylene (LDPE): 35 samples (1.1%)




Polystyrene (PS): 14 samples (0.4%)




Polyvinyl Chloride (PVC): 5 samples (0.2%)



Paper and Cardboard





Paperboard (PAP): 375 samples (11.4%)




Cardboard (PAP): 61 samples (1.8%)




Paper (PAP): 38 samples (1.2%)



Metal





Aluminium (ALU): 167 samples (5.1%)




Steel (FE): 77 samples (2.3%)



Glass





Clear Glass (GL): 109 samples (3.3%)




Brown Glass (GL): 36 samples (1.1%)




Green Glass (GL): 10 samples (0.3%)



Composite Materials





Paper/Cardboard + Plastic + Aluminium (C/PAP): 96 samples (2.9%)




Paper/Cardboard + Plastic (C/PAP): 76 samples (2.3%)




Plastic + Aluminium (C/PE): 32 samples (1.0%)



Color Distribution

Color information is included to capture variations relevant to material identification and recycling. Plastic items exhibit the widest color diversity, ranging from transparent PET to highly saturated and multicolored packaging. Paper items are predominantly annotated as “other” due to printed surfaces, with brown used mainly for cardboard. Metal items are mostly gray or multicolored, while glass colors are directly tied to material type and limited to clear, brown, and green.

Binary Attribute Distribution

Binary attributes include cap presence, label presence, contamination, and deformation. Plastic and composite items show the highest variability across these attributes, particularly due to caps and removable labels. Paper and metal items rarely include caps or separate labels, as information is typically printed directly on the surface. Glass items frequently include caps and labels but show no deformation, while contamination is generally low across all materials.

 

Dataset Limitations





Multiple samples come from the same object under different conditions. Dataset splits should be done at the object level (using the CSV) for evaluation requiring independence.




Data were collected in a controlled environment with fixed lighting and background. While this ensures consistency and reliable annotations, it may limit generalization to unconstrained real-world settings.




Certain attribute combinations are underrepresented. Contaminated samples without caps were not recorded to protect the acquisition equipment. Labels printed on surfaces were annotated as absent, as they do not affect recycling processes.




Minor geometric distortion was observed in a portion of the depth images, which can be mitigated through standard post-processing techniques.




Visually similar items may belong to different material categories. This is a problem inherent to recycling.




The dataset reflects household waste in Greece. Regional differences in packaging may affect generalization. It provides a good representation of European waste.
description: Please cite the original paper of this work as well as this repository!DOI: [DOI will be inserted here when the paper is published]

The above paper also serves as the reference for additional information about the dataset and related resources.