---
language: en
license: mit
library_name: transformers
pipeline_tag: image-classification
tags:
  - vision
  - damage-detection
  - classification
  - vit
  - household-items
datasets:
  - custom
---

# damage-classifier-multi-task

## Home Damage Classification Model

This model was trained to classify damage to household items, identifying both the item category, damage type, and damage severity.

### Model Type

- **Architecture**: Vision Transformer (ViT)
- **Classification Approach**: Multi_Task
- **Base Model**: google/vit-base-patch16-224

### Supported Categories

#### Item Categories

- Microwave
- Wall
- Window
- Fence
- Glass
- Fishbowl

#### Damage Types

- Scratch
- Dent
- Break
- Burn
- Water Damage

#### Severity Levels

- No Damage
- Minor Damage
- Moderate Damage
- Severe Damage

### Multi-Task Architecture

This model uses a multi-task learning approach with:

1. A shared Vision Transformer (ViT) backbone that extracts features from the input image
2. Separate classification heads for:
   - Item category identification
   - Damage type classification
   - Damage severity assessment

This approach allows the model to share knowledge between related tasks while making separate predictions for each aspect.

#### Advantages of Multi-Task Learning

- Shares knowledge across related tasks
- Requires fewer examples per combination
- Can perform well even with missing combinations
- Independent predictions for each aspect

### Usage

```python
from transformers import ViTFeatureExtractor
from PIL import Image
import torch

# Load model and feature extractor
model = torch.load("pytorch_model.bin")  # Or use your preferred loading method
feature_extractor = ViTFeatureExtractor.from_pretrained("USER/REPO_NAME")

# Prepare image
image = Image.open("path/to/image.jpg").convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt")

# Get predictions
outputs = model(**inputs)

# Process multi-task outputs
item_logits = outputs['item_logits']
damage_logits = outputs['damage_type_logits']
severity_logits = outputs['severity_logits']

# Get predicted classes
item_class = torch.argmax(item_logits, dim=1).item()
damage_class = torch.argmax(damage_logits, dim=1).item()
severity_class = torch.argmax(severity_logits, dim=1).item()

# Map to class names (replace with your class mappings)
item_categories = ["microwave", "wall", "window", "fence", "glass", "fishbowl"]
damage_types = ["scratch", "dent", "break", "burn", "water_damage"]
severity_levels = ["no_damage", "minor_damage", "moderate_damage", "severe_damage"]

print(f"Item: {item_categories[item_class]}")
print(f"Damage Type: {damage_types[damage_class]}")
print(f"Severity: {severity_levels[severity_class]}")
```

For a more complete example, see the inference script in the [GitHub repository](https://github.com/yourusername/home-damage-classifier).