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--- |
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license: apache-2.0 |
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datasets: |
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- garythung/trashnet |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Trash |
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- Classification |
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- Net |
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- biology |
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- SigLIP2 |
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--- |
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# **Trash-Net** |
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> **Trash-Net** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify images of waste materials into different categories using the **SiglipForImageClassification** architecture. |
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The model categorizes images into six classes: |
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- **Class 0:** "cardboard" |
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- **Class 1:** "glass" |
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- **Class 2:** "metal" |
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- **Class 3:** "paper" |
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- **Class 4:** "plastic" |
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- **Class 5:** "trash" |
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```py |
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Classification Report: |
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precision recall f1-score support |
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cardboard 0.9912 0.9739 0.9825 806 |
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glass 0.9564 0.9641 0.9602 1002 |
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metal 0.9523 0.9744 0.9632 820 |
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paper 0.9520 0.9848 0.9681 1188 |
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plastic 0.9835 0.9274 0.9546 964 |
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trash 0.9127 0.9161 0.9144 274 |
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accuracy 0.9626 5054 |
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macro avg 0.9580 0.9568 0.9572 5054 |
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weighted avg 0.9631 0.9626 0.9626 5054 |
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``` |
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# **Run with Transformers🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor |
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from transformers import SiglipForImageClassification |
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from transformers.image_utils import load_image |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Trash-Net" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def trash_classification(image): |
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"""Predicts the category of waste material in the image.""" |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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labels = { |
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"0": "cardboard", |
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"1": "glass", |
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"2": "metal", |
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"3": "paper", |
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"4": "plastic", |
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"5": "trash" |
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} |
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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=trash_classification, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Trash Classification", |
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description="Upload an image to classify the type of waste material." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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# **Intended Use:** |
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The **Trash-Net** model is designed to classify waste materials into different categories. Potential use cases include: |
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- **Waste Management:** Assisting in automated waste sorting and recycling. |
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- **Environmental Monitoring:** Identifying and categorizing waste in public spaces. |
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- **Educational Purposes:** Teaching waste classification and sustainability. |
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- **Smart Cities:** Enhancing waste disposal systems through AI-driven classification. |
