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--- |
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license: mit |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': apples |
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'1': bananas |
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'2': bottles |
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'3': cans |
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'4': cardboard |
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'5': cups |
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'6': eggshells |
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'7': generalcompost |
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'8': mixers |
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'9': peels |
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'10': plasticbags |
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'11': plastics |
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'12': tissues |
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splits: |
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- name: train |
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num_bytes: 122444841 |
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num_examples: 14651 |
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download_size: 2050293304 |
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dataset_size: 122444841 |
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--- |
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The dataset has images collected from publicly available resources like Kaggle and Roboflow, and some photos that I clicked.</br> |
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Feel free to expand on the ones available and add more directories.</br> |
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To get an idea of which additional directories could be useful refer recycle.jpeg and compost.jpeg.</br> |
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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> |
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To use this dataset in your python project use: |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("dvk65/TrashTypes", split="train") |
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label_names = dataset.features["label"].names |
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``` |
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Currently, it is in a single train split. |
