---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': apples
          '1': bananas
          '2': bottles
          '3': cans
          '4': cardboard
          '5': cups
          '6': eggshells
          '7': generalcompost
          '8': mixers
          '9': peels
          '10': plasticbags
          '11': plastics
          '12': tissues
  splits:
  - name: train
    num_bytes: 122444841
    num_examples: 14651
  download_size: 2050293304
  dataset_size: 122444841
---
The dataset has images collected from publicly available resources like Kaggle and Roboflow, and some photos that I clicked.</br>
Feel free to expand on the ones available and add more directories.</br>
To get an idea of which additional directories could be useful refer recycle.jpeg and compost.jpeg.</br>
The notebook used to train the dataset and the best performing model with 98.2947% accuracy is saved at https://huggingface.co/dvk65/trash-classifier-resnet50. </br>
To use this dataset in your python project use:
```
from datasets import load_dataset

dataset = load_dataset("dvk65/TrashTypes", split="train")
label_names = dataset.features["label"].names
```
Currently, it is in a single train split.