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--- |
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license: mit |
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datasets: |
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- dvk65/TrashTypes |
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language: |
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- en |
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base_model: |
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- microsoft/resnet-50 |
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--- |
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This model is trained on a curated dataset of most frequently seen trash items in our college.</br> |
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## Model Details |
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- **Backbone**: ResNet50 (ImageNet pre-trained, fine-tuned) |
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- **Classes**: 13 trash / recycling / compost categories |
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- **Input size**: 224×224 RGB |
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- **Loss**: sparse_categorical_crossentropy |
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- **Optimizer**: Adam |
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## Dataset |
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Processed training, validation, and test splits are included in the `*_processed` directories. |
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Original dataset: [`dvk65/TrashTypes`](https://huggingface.co/dvk65/TrashTypes) |
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## Usage |
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```python |
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from huggingface_hub import hf_hub_download |
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import tensorflow as tf # tensorflow version above 2.20.0 |
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REPO_ID = "dvk65/trash-classifier-resnet50" |
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FILENAME = "trashclassify_13.keras" |
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model_path = hf_hub_download( |
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repo_id=REPO_ID, |
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filename=FILENAME, |
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) |
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model = tf.keras.models.load_model(model_path) |
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``` |
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The current target values are: |
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1. apples |
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2. bananas |
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3. bottles |
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4. cans |
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5. cardboard |
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6. cups |
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7. eggshells |
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8. mixed leftover food (labeled as generalcompost) |
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9. wooden coffee stirrers (labeled as mixers) |
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10. oranges (labeled as peels) |
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11. platicbags |
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12. plastic wrappers (labeled as plastics) |
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13. tissue papers |
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To help with expanding the dataset, feel free to contribute to: https://huggingface.co/datasets/dvk65/TrashTypes </br> |
