cpoisson/trash-optimizer-dataset

Trash Optimizer Dataset

Assembled waste classification dataset covering 18 categories, used to train the cpoisson/trash-optimizer-models series.

Built with a custom pipeline that normalises, filters and balances images from three sources:

Source	Categories contributed	License
RealWaste (UCI / Kaggle)	cardboard, food_organics, glass, metal, miscellaneous_trash, paper, plastic, textile_trash, vegetation	CC BY 4.0
RHWC (Kaggle)	cardboard, food_organics, glass, metal, paper, plastic, textile_trash	CC BY-SA 4.0
Custom (web-scraped)	battery, car_battery, light_bulb, mirror, neon, pharmacy, printer_cartridge, tire, wood	⚠️ Mixed — images sourced from the public web, licenses vary

Note: The custom category images were collected from publicly accessible web sources. Their individual licenses may vary. Use for research and non-commercial purposes.

Structure

dataset/
  battery/          277 images
  car_battery/      260 images
  cardboard/        500 images
  food_organics/    500 images
  glass/            500 images
  light_bulb/       157 images
  metal/            500 images
  mirror/           194 images
  miscellaneous_trash/ 500 images
  neon/             190 images
  paper/            500 images
  pharmacy/         257 images
  plastic/          500 images
  printer_cartridge/ 357 images
  textile_trash/    500 images
  tire/             353 images
  vegetation/       436 images
  wood/             245 images

Total: 6,229 images across 18 categories

Images are JPEG/PNG, minimum size 150×150px, maximum 500 images per category. Filename prefix indicates source: realwaste_*, rhwc_*, customdataset_*.

Dataset builder

The assembly pipeline is available in the trash-optimizer repository under dataset/datasetbuilder.py, configured via trash-optimizer-datasetbuilder.toml.

Model trained on this dataset

→ cpoisson/trash-optimizer-models — EfficientNet-B0, 95.07% test accuracy across 18 categories.

Citation

If you use the RealWaste portion, please cite:

@misc{realwaste,
  author    = {Majchrowska, Sylwia and others},
  title     = {RealWaste},
  year      = {2023},
  publisher = {UCI Machine Learning Repository},
  doi       = {10.24432/C5SS4S}
}