Datasets:
image imagewidth (px) 524 524 | label int64 0 8 | label_name stringclasses 9
values |
|---|---|---|
8 | Vegetation | |
4 | Miscellaneous Trash | |
3 | Metal | |
2 | Glass | |
6 | Plastic | |
5 | Paper | |
2 | Glass | |
3 | Metal | |
4 | Miscellaneous Trash | |
5 | Paper | |
5 | Paper | |
1 | Food Organics | |
5 | Paper | |
1 | Food Organics | |
8 | Vegetation | |
1 | Food Organics | |
3 | Metal | |
8 | Vegetation | |
6 | Plastic | |
6 | Plastic | |
5 | Paper | |
6 | Plastic | |
8 | Vegetation | |
2 | Glass | |
5 | Paper | |
6 | Plastic | |
8 | Vegetation | |
4 | Miscellaneous Trash | |
3 | Metal | |
2 | Glass | |
1 | Food Organics | |
7 | Textile Trash | |
1 | Food Organics | |
0 | Cardboard | |
8 | Vegetation | |
6 | Plastic | |
0 | Cardboard | |
1 | Food Organics | |
8 | Vegetation | |
2 | Glass | |
3 | Metal | |
3 | Metal | |
6 | Plastic | |
4 | Miscellaneous Trash | |
6 | Plastic | |
8 | Vegetation | |
6 | Plastic | |
2 | Glass | |
8 | Vegetation | |
4 | Miscellaneous Trash | |
4 | Miscellaneous Trash | |
6 | Plastic | |
3 | Metal | |
6 | Plastic | |
4 | Miscellaneous Trash | |
0 | Cardboard | |
6 | Plastic | |
5 | Paper | |
8 | Vegetation | |
3 | Metal | |
7 | Textile Trash | |
5 | Paper | |
8 | Vegetation | |
8 | Vegetation | |
4 | Miscellaneous Trash | |
8 | Vegetation | |
5 | Paper | |
0 | Cardboard | |
2 | Glass | |
3 | Metal | |
6 | Plastic | |
7 | Textile Trash | |
7 | Textile Trash | |
6 | Plastic | |
8 | Vegetation | |
6 | Plastic | |
6 | Plastic | |
3 | Metal | |
3 | Metal | |
5 | Paper | |
0 | Cardboard | |
5 | Paper | |
3 | Metal | |
6 | Plastic | |
5 | Paper | |
3 | Metal | |
3 | Metal | |
1 | Food Organics | |
6 | Plastic | |
3 | Metal | |
6 | Plastic | |
3 | Metal | |
8 | Vegetation | |
6 | Plastic | |
6 | Plastic | |
3 | Metal | |
3 | Metal | |
6 | Plastic | |
3 | Metal | |
6 | Plastic |
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Waste Segregation Dataset
Descripción
Este dataset contiene imágenes de residuos clasificadas en 9 categorías para tareas de segregación automática de desechos. Fue preparado como parte de un proyecto de aprendizaje automático en el Instituto Tecnológico de Costa Rica (ITCR), con el objetivo de entrenar y evaluar modelos de clasificación de imágenes basados en Vision Transformers (ViT).
Información del Dataset
- Total de imágenes: ~4,765
- División: 80% entrenamiento / 20% validación (del conjunto train original) + prueba independiente
- Número de clases: 9
- Formatos: JPEG, PNG, WebP
- Fuente original: smarthkaushal/waste-segregation en Kaggle
Clases
| Clase | Descripción |
|---|---|
| Cardboard | Cartón |
| Food Organics | Residuos orgánicos de alimentos |
| Glass | Vidrio |
| Metal | Metal |
| Miscellaneous Trash | Basura miscelánea |
| Paper | Papel |
| Plastic | Plástico |
| Textile Trash | Residuos textiles |
| Vegetation | Vegetación |
Balance de Clases
El dataset presenta desbalance moderado: Plastic es la clase más representada (19%)
y Textile Trash la menos representada (6.6%). La razón de desbalance (max/min) es ~2.89x.
Uso
from datasets import load_dataset
ds = load_dataset("ddompe/waste-segregation-dataset")
Licencia
CC BY 4.0 — Se permite uso, distribución y modificación con atribución.
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