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---
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. |