|
|
--- |
|
|
license: apache-2.0 |
|
|
datasets: |
|
|
- viola77data/recycling-dataset |
|
|
library_name: transformers |
|
|
language: |
|
|
- en |
|
|
base_model: |
|
|
- google/siglip2-so400m-patch14-384 |
|
|
pipeline_tag: image-classification |
|
|
tags: |
|
|
- Waste |
|
|
- Recycling |
|
|
- Net |
|
|
- '11' |
|
|
- Image |
|
|
- SigLIP2 |
|
|
--- |
|
|
|
|
|
 |
|
|
|
|
|
# **Recycling-Net-11** |
|
|
|
|
|
> **Recycling-Net-11** is an image classification model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. The model classifies images into 11 categories related to recyclable materials, helping to automate and enhance waste sorting systems. |
|
|
|
|
|
```py |
|
|
Classification Report: |
|
|
precision recall f1-score support |
|
|
|
|
|
aluminium 0.9213 0.9145 0.9179 269 |
|
|
batteries 0.9833 0.9933 0.9883 297 |
|
|
cardboard 0.9660 0.9343 0.9499 274 |
|
|
disposable plates 0.9078 0.9744 0.9399 273 |
|
|
glass 0.9621 0.9490 0.9555 294 |
|
|
hard plastic 0.8675 0.7250 0.7899 280 |
|
|
paper 0.8702 0.8941 0.8820 255 |
|
|
paper towel 0.9333 0.9622 0.9475 291 |
|
|
polystyrene 0.8188 0.8385 0.8285 291 |
|
|
soft plastics 0.8425 0.8693 0.8557 283 |
|
|
takeaway cups 0.9575 0.9767 0.9670 300 |
|
|
|
|
|
accuracy 0.9128 3107 |
|
|
macro avg 0.9119 0.9119 0.9111 3107 |
|
|
weighted avg 0.9127 0.9128 0.9119 3107 |
|
|
``` |
|
|
|
|
|
 |
|
|
|
|
|
The model categorizes images into the following classes: |
|
|
|
|
|
- **0:** aluminium |
|
|
- **1:** batteries |
|
|
- **2:** cardboard |
|
|
- **3:** disposable plates |
|
|
- **4:** glass |
|
|
- **5:** hard plastic |
|
|
- **6:** paper |
|
|
- **7:** paper towel |
|
|
- **8:** polystyrene |
|
|
- **9:** soft plastics |
|
|
- **10:** takeaway cups |
|
|
|
|
|
--- |
|
|
|
|
|
# **Run with Transformers 🤗** |
|
|
|
|
|
```python |
|
|
!pip install -q transformers torch pillow gradio |
|
|
``` |
|
|
|
|
|
```python |
|
|
import gradio as gr |
|
|
from transformers import AutoImageProcessor, SiglipForImageClassification |
|
|
from PIL import Image |
|
|
import torch |
|
|
|
|
|
# Load model and processor |
|
|
model_name = "prithivMLmods/Recycling-Net-11" # Update with your actual Hugging Face model path |
|
|
model = SiglipForImageClassification.from_pretrained(model_name) |
|
|
processor = AutoImageProcessor.from_pretrained(model_name) |
|
|
|
|
|
# Label mapping |
|
|
id2label = { |
|
|
0: "aluminium", |
|
|
1: "batteries", |
|
|
2: "cardboard", |
|
|
3: "disposable plates", |
|
|
4: "glass", |
|
|
5: "hard plastic", |
|
|
6: "paper", |
|
|
7: "paper towel", |
|
|
8: "polystyrene", |
|
|
9: "soft plastics", |
|
|
10: "takeaway cups" |
|
|
} |
|
|
|
|
|
def classify_recyclable_material(image): |
|
|
"""Predicts the type of recyclable 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() |
|
|
|
|
|
predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))} |
|
|
return predictions |
|
|
|
|
|
# Gradio interface |
|
|
iface = gr.Interface( |
|
|
fn=classify_recyclable_material, |
|
|
inputs=gr.Image(type="numpy"), |
|
|
outputs=gr.Label(label="Recyclable Material Prediction Scores"), |
|
|
title="Recycling-Net-11", |
|
|
description="Upload an image of a waste item to identify its recyclable material type." |
|
|
) |
|
|
|
|
|
# Launch the app |
|
|
if __name__ == "__main__": |
|
|
iface.launch() |
|
|
``` |
|
|
|
|
|
--- |
|
|
|
|
|
# **Intended Use** |
|
|
|
|
|
**Recycling-Net-11** is ideal for: |
|
|
|
|
|
- **Smart Waste Sorting:** Automating recycling processes in smart bins or factories. |
|
|
- **Environmental Awareness Tools:** Helping people learn how to sort waste correctly. |
|
|
- **Municipal Waste Management:** Classifying and analyzing urban waste data. |
|
|
- **Robotics:** Assisting robots in identifying and sorting materials. |
|
|
- **Education:** Teaching children and communities about recyclable materials. |
