README.md

---
title: Waste Classify
emoji: 📈
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 6.19.0
python_version: '3.13'
app_file: app.py
pinned: false
license: apache-2.0
---

# Waste Classification – Organic vs Recyclable (VGG16)

This repository contains two Keras/TensorFlow models for classifying images of waste into **Organic (O)** and **Recyclable (R)** categories. Both models are based on a pre‑trained VGG16 backbone, but they differ in the training approach:

- **`O_R_tlearn_vgg16_final.keras`** – Feature‑extraction model: the VGG16 base is frozen and only the newly added dense layers are trained.
- **`O_R_tlearn_fine_tune_vgg16_final.keras`** – Fine‑tuned model: after feature extraction, the last convolutional block of VGG16 is unfrozen and the whole network is trained further, yielding slightly better performance.

**Inference is performed using the fine‑tuned model** (`...fine_tune...`), as it achieves the highest accuracy on the test set.

---

## Model Performance

| Model                    | Test Accuracy |
|--------------------------|---------------|
| Feature Extraction       | 80%           |
| **Fine‑tuned** (recommended) | **81%**   |

---

## Files

- `O_R_tlearn_vgg16_final.keras` – feature‑extraction model
- `O_R_tlearn_fine_tune_vgg16_final.keras` – fine‑tuned model (used for predictions)

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## How to Use

### 1. Load the Fine‑Tuned Model

```python
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing.image import load_img, img_to_array

model = tf.keras.models.load_model("O_R_tlearn_fine_tune_vgg16_final.keras")
```

### 2. Preprocess an Image

```python
IMG_SIZE = (150, 150)

def preprocess_image(image_path):
    img = load_img(image_path, target_size=IMG_SIZE)
    img_array = img_to_array(img)
    img_array = img_array / 255.0
    return np.expand_dims(img_array, axis=0)
```

### 3. Make a Prediction

```python
image_path = "path/to/your/waste_image.jpg"
input_data = preprocess_image(image_path)
prediction = model.predict(input_data)

# Output class
if prediction[0][0] < 0.5:
    print("Predicted: Organic (O)")
else:
    print("Predicted: Recyclable (R)")
```

> **Note:** The model outputs a single sigmoid value. Values below 0.5 are classified as Organic, above as Recyclable.

---

## Training Details

- Base model: VGG16 (weights = `imagenet`)
- Input size: 150×150 pixels
- Optimizer: RMSprop with learning rate decay
- Loss: binary cross‑entropy
- Early stopping and model checkpointing were used to prevent overfitting

The dataset was split into training (80%) and validation (20%). Data augmentation (shifts, flips) was applied during training.

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## Citation

If you use this model, please cite this repository.

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## License

This model is released under the [MIT License](LICENSE).
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