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--- |
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license: apache-2.0 |
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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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- Waste |
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- Classification |
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--- |
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# Augmented-Waste-Classifier-SigLIP2 |
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> **Augmented-Waste-Classifier-SigLIP2** 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 waste types using the **SiglipForImageClassification** architecture. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Battery 0.9987 0.9987 0.9987 3840 |
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Biological 0.9998 0.9960 0.9979 4036 |
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Cardboard 0.9956 0.9909 0.9932 3628 |
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Clothes 0.9957 0.9914 0.9935 5336 |
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Glass 0.9800 0.9914 0.9856 4048 |
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Metal 0.9892 0.9965 0.9929 3136 |
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Paper 0.9937 0.9891 0.9914 4308 |
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Plastic 0.9865 0.9798 0.9831 3568 |
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Shoes 0.9876 0.9990 0.9933 3990 |
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Trash 1.0000 0.9939 0.9970 2796 |
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accuracy 0.9926 38686 |
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macro avg 0.9927 0.9927 0.9927 38686 |
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weighted avg 0.9926 0.9926 0.9926 38686 |
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``` |
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The model categorizes images into 10 waste classes: |
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Class 0: "Battery" |
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Class 1: "Biological" |
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Class 2: "Cardboard" |
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Class 3: "Clothes" |
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Class 4: "Glass" |
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Class 5: "Metal" |
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Class 6: "Paper" |
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Class 7: "Plastic" |
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Class 8: "Shoes" |
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Class 9: "Trash" |
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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/Augmented-Waste-Classifier-SigLIP2" |
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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 waste_classification(image): |
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"""Predicts waste classification for an 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": "Battery", "1": "Biological", "2": "Cardboard", "3": "Clothes", |
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"4": "Glass", "5": "Metal", "6": "Paper", "7": "Plastic", |
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"8": "Shoes", "9": "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=waste_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="Augmented Waste Classification", |
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description="Upload an image to classify the type of waste." |
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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 **Augmented-Waste-Classifier-SigLIP2** model is designed to classify different types of waste based on images. Potential use cases include: |
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- **Waste Management:** Identifying and categorizing waste materials for proper disposal. |
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- **Recycling Assistance:** Helping users determine recyclable materials. |
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- **Environmental Monitoring:** Automating waste classification for smart cities. |
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- **AI-Powered Sustainability Solutions:** Supporting AI-based waste sorting systems to improve recycling efficiency. |
