| --- |
| 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. |
