The problem of effective disposal of the trash generated by people has rightfully attracted major interest from various sections of society in recent times. Recently, deep learning solutions have been proposed to design automated mechanisms to segregate waste. However, most datasets used for this purpose are not adequate. In this paper, we introduce a new dataset, TrashBox, containing 17,785 images across seven different classes, including medical and e-waste classes which are not included in any other existing dataset.

Dataset Files

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Documentation: 
[1] Nikhil Kumsetty, Amith Nekkare, "TrashBox", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.21227/csg6-h017. Accessed: Dec. 06, 2024.
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doi = {10.21227/csg6-h017},
url = {http://dx.doi.org/10.21227/csg6-h017},
author = {Nikhil Kumsetty; Amith Nekkare },
publisher = {IEEE Dataport},
title = {TrashBox},
year = {2022} }
TY - DATA
T1 - TrashBox
AU - Nikhil Kumsetty; Amith Nekkare
PY - 2022
PB - IEEE Dataport
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Nikhil Kumsetty, Amith Nekkare. (2022). TrashBox. IEEE Dataport. http://dx.doi.org/10.21227/csg6-h017
Nikhil Kumsetty, Amith Nekkare, 2022. TrashBox. Available at: http://dx.doi.org/10.21227/csg6-h017.
Nikhil Kumsetty, Amith Nekkare. (2022). "TrashBox." Web.
1. Nikhil Kumsetty, Amith Nekkare. TrashBox [Internet]. IEEE Dataport; 2022. Available from : http://dx.doi.org/10.21227/csg6-h017
Nikhil Kumsetty, Amith Nekkare. "TrashBox." doi: 10.21227/csg6-h017