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README.md

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+---
+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.