---
language: en
tags:
- image-classification
- nameplate-detection
- industrial
- pytorch
- computer-vision
license: mit
datasets:
- kahua-ml/nameplate-classification
base_model: mobilenet_v2
pipeline_tag: image-classification
---

# Nameplate Classifier

## Model Description

This is a lightweight binary image classifier that determines whether an image contains a readable nameplate or not. The model is based on MobileNetV2 architecture and is optimized for industrial equipment nameplate detection.

## Model Details

- **Model Type**: Image Classification (Binary)
- **Architecture**: MobileNetV2 backbone with custom classifier
- **Framework**: PyTorch
- **Model Size**: ~9.4MB
- **Input Size**: 224x224 RGB images
- **Classes**: 
  - `no_nameplate` (0): Image does not contain a readable nameplate
  - `has_nameplate` (1): Image contains a readable nameplate

## Training Data

The model was trained on the `kahua-ml/nameplate-classification` dataset, which contains:
- **Positive examples**: 3,456 images with nameplates from industrial equipment
- **Negative examples**: 826 images without nameplates from construction safety and industry datasets
- **Total**: 4,282 images

## Performance

- **Expected Accuracy**: 85-95%
- **Training Time**: ~10-15 minutes
- **Inference Speed**: Very fast (optimized for real-time applications)

## Usage

```python
import torch
import torchvision.transforms as transforms
from PIL import Image

# Load model
model = torch.load("model.pth", map_location='cpu')
model.eval()

# Prepare image
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
image = Image.open("your_image.jpg")
input_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    outputs = model(input_tensor)
    probabilities = torch.nn.functional.softmax(outputs, dim=1)
    predicted = torch.max(outputs, 1)[1].item()
    confidence = probabilities[0][predicted].item()

result = "has_nameplate" if predicted == 1 else "no_nameplate"
print(f"Prediction: {result} (Confidence: {confidence:.3f})")
```

## Applications

- Industrial equipment documentation
- Asset management systems
- Quality control in manufacturing
- Automated inventory systems

## Limitations

- Optimized for industrial nameplates
- May not work well on severely damaged or obscured nameplates
- Requires clear, readable text on nameplates

## Training Details

- **Optimizer**: Adam
- **Loss Function**: CrossEntropyLoss
- **Epochs**: 5
- **Batch Size**: 32
- **Learning Rate**: 0.001
- **Data Augmentation**: Random horizontal flip, rotation, color jitter

## Model Architecture

```
LightweightNameplateClassifier(
  (backbone): MobileNetV2(
    (classifier): Sequential(
      (0): Dropout(p=0.2)
      (1): Linear(in_features=1280, out_features=128)
      (2): ReLU()
      (3): Dropout(p=0.3)
      (4): Linear(in_features=128, out_features=2)
    )
  )
)
```

## Citation

If you use this model in your research, please cite:

```
@misc{kahua-nameplate-classifier,
  title={Lightweight Nameplate Classifier},
  author={Kahua ML Team},
  year={2024},
  howpublished={\url{https://huggingface.co/kahua-ml/nameplate-classifier}}
}
```