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README.md

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+---
+tags:
+- image-classification
+- pytorch
+- waste-classification
+- mobilenetv2
+- computer-vision
+- recycling
+license: mit
+metrics:
+- accuracy
+pipeline_tag: image-classification
+---
+
+# 🗑️ Smart Waste Classification Model
+
+A fine-tuned **MobileNetV2** model for classifying waste items into 6 categories using computer vision.
+
+## Model Performance
+- **Validation Accuracy**: 97.46%
+- **Framework**: PyTorch
+- **Architecture**: MobileNetV2
+
+## Classes
+
+| Class | Description | Color |
+|-------|-------------|-------|
+| 🔵 **plastic** | Bottles, bags, containers | Blue |
+| 📄 **paper** | Newspapers, cardboard, magazines | Brown |
+| 🔘 **metal** | Cans, foil, batteries | Gray |
+| 💚 **glass** | Bottles, jars | Green |
+| 🟢 **organic** | Food waste, plant matter | Dark Green |
+| ⚫ **non-recyclable** | Mixed/contaminated waste | Black |
+
+## Quick Usage
+
+```python
+import torch
+from torchvision import models, transforms
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(repo_id="karthikeya09/smart_image_recognation", filename="best_model.pth")
+
+# Load model
+model = models.mobilenet_v2(weights=None)
+model.classifier = torch.nn.Sequential(
+    torch.nn.Dropout(p=0.2),
+    torch.nn.Linear(1280, 6)
+)
+checkpoint = torch.load(model_path, map_location='cpu')
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+# Define transforms
+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])
+])
+
+# Predict
+classes = ['glass', 'metal', 'non-recyclable', 'organic', 'paper', 'plastic']
+image = Image.open('your_image.jpg').convert('RGB')
+input_tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    outputs = model(input_tensor)
+    probs = torch.nn.functional.softmax(outputs, dim=1)
+    confidence, predicted = torch.max(probs, 1)
+
+print(f'Predicted: {classes[predicted.item()]} ({confidence.item()*100:.1f}%)')
+```
+
+## Training Details
+
+- **Dataset**: ~21,000 waste images
+- **Training Split**: 70% train, 15% val, 15% test
+- **Optimizer**: Adam (lr=0.001)
+- **Class Weights**: Used to handle class imbalance
+- **Data Augmentation**: Random crop, flip, rotation, color jitter
+- **Input Size**: 224x224 RGB
+
+## Dataset Distribution
+
+| Category | Images |
+|----------|--------|
+| Organic | 6,620 |
+| Glass | 4,022 |
+| Paper | 3,882 |
+| Metal | 3,428 |
+| Plastic | 1,870 |
+| Non-recyclable | 1,394 |
+
+## Model Architecture
+
+```
+MobileNetV2 (pretrained on ImageNet)
+└── classifier
+    ├── Dropout(p=0.2)
+    └── Linear(1280, 6)
+```
+
+## License
+
+MIT License
+
+## Author
+
+**K Karthikeya Gupta**