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

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}}
}

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