|
|
---
|
|
|
library_name: pytorch
|
|
|
tags:
|
|
|
- waste-classification
|
|
|
- mae
|
|
|
- vision-transformer
|
|
|
- environmental
|
|
|
- recycling
|
|
|
license: mit
|
|
|
datasets:
|
|
|
- RealWaste
|
|
|
metrics:
|
|
|
- accuracy
|
|
|
model-index:
|
|
|
- name: MAE Waste Classifier
|
|
|
results:
|
|
|
- task:
|
|
|
type: image-classification
|
|
|
name: Waste Classification
|
|
|
dataset:
|
|
|
type: RealWaste
|
|
|
name: RealWaste Dataset
|
|
|
metrics:
|
|
|
- type: accuracy
|
|
|
value: 0.9327
|
|
|
name: Validation Accuracy
|
|
|
---
|
|
|
|
|
|
# MAE Waste Classifier
|
|
|
|
|
|
A finetuned MAE (Masked Autoencoder) ViT-Base model for waste classification achieving **93.27% validation accuracy** on 9 waste categories.
|
|
|
|
|
|
## Model Details
|
|
|
|
|
|
- **Architecture**: Vision Transformer (ViT-Base) with MAE pretraining
|
|
|
- **Parameters**: ~86M
|
|
|
- **Input Size**: 224x224 RGB images
|
|
|
- **Classes**: 9 waste categories
|
|
|
- **Validation Accuracy**: 93.27%
|
|
|
|
|
|
## Categories
|
|
|
|
|
|
1. **Cardboard** - Flatten and place in recycling bin. Remove any tape or staples.
|
|
|
2. **Food Organics** - Compost in organic waste bin or home composter.
|
|
|
3. **Glass** - Rinse and place in glass recycling. Remove lids and caps.
|
|
|
4. **Metal** - Rinse aluminum/steel cans and place in recycling bin.
|
|
|
5. **Miscellaneous Trash** - Dispose in general waste bin. Cannot be recycled.
|
|
|
6. **Paper** - Place clean paper in recycling. Remove plastic windows from envelopes.
|
|
|
7. **Plastic** - Check recycling number. Rinse containers before recycling.
|
|
|
8. **Textile Trash** - Donate if reusable, otherwise dispose in textile recycling.
|
|
|
9. **Vegetation** - Compost in organic waste or use for mulch in garden.
|
|
|
|
|
|
## Usage
|
|
|
|
|
|
```python
|
|
|
import torch
|
|
|
import timm
|
|
|
from PIL import Image
|
|
|
from torchvision import transforms
|
|
|
|
|
|
# Load model
|
|
|
model = timm.create_model('vit_base_patch16_224', pretrained=False, num_classes=9)
|
|
|
checkpoint = torch.load('best_model.pth', map_location='cpu')
|
|
|
model.load_state_dict(checkpoint['model_state_dict'])
|
|
|
model.eval()
|
|
|
|
|
|
# Preprocessing
|
|
|
transform = transforms.Compose([
|
|
|
transforms.Resize((224, 224)),
|
|
|
transforms.ToTensor(),
|
|
|
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
|
|
])
|
|
|
|
|
|
# Inference
|
|
|
image = Image.open('waste_item.jpg').convert('RGB')
|
|
|
input_tensor = transform(image).unsqueeze(0)
|
|
|
|
|
|
with torch.no_grad():
|
|
|
outputs = model(input_tensor)
|
|
|
probabilities = torch.nn.functional.softmax(outputs, dim=1)
|
|
|
predicted_class = torch.argmax(probabilities, dim=1).item()
|
|
|
|
|
|
categories = ['Cardboard', 'Food Organics', 'Glass', 'Metal', 'Miscellaneous Trash', 'Paper', 'Plastic', 'Textile Trash', 'Vegetation']
|
|
|
print(f"Predicted: {categories[predicted_class]}")
|
|
|
```
|
|
|
|
|
|
## Training Details
|
|
|
|
|
|
- **Dataset**: RealWaste (4,752 images)
|
|
|
- **Pretraining**: MAE on ImageNet
|
|
|
- **Finetuning**: 15 epochs on RealWaste
|
|
|
- **Optimizer**: AdamW
|
|
|
- **Hardware**: NVIDIA RTX 3080 Ti
|
|
|
|
|
|
## Performance
|
|
|
|
|
|
- **Validation Accuracy**: 93.27%
|
|
|
- **Training Accuracy**: 99.89%
|
|
|
- **Model Size**: ~350MB
|
|
|
- **Inference Speed**: ~50ms per image (GPU)
|
|
|
|
|
|
## Environmental Impact
|
|
|
|
|
|
This model helps improve recycling efficiency by providing accurate waste classification and proper disposal instructions.
|
|
|
|
