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