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---
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language: en
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tags:
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- image-classification
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- nameplate-detection
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- industrial
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- pytorch
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- computer-vision
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license: mit
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datasets:
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- kahua-ml/nameplate-classification
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base_model: mobilenet_v2
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pipeline_tag: image-classification
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---
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# Nameplate Classifier
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## Model Description
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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.
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## Model Details
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- **Model Type**: Image Classification (Binary)
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- **Architecture**: MobileNetV2 backbone with custom classifier
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- **Framework**: PyTorch
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- **Model Size**: ~9.4MB
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- **Input Size**: 224x224 RGB images
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- **Classes**:
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- `no_nameplate` (0): Image does not contain a readable nameplate
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- `has_nameplate` (1): Image contains a readable nameplate
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## Training Data
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The model was trained on the `kahua-ml/nameplate-classification` dataset, which contains:
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- **Positive examples**: 3,456 images with nameplates from industrial equipment
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- **Negative examples**: 826 images without nameplates from construction safety and industry datasets
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- **Total**: 4,282 images
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## Performance
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- **Expected Accuracy**: 85-95%
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- **Training Time**: ~10-15 minutes
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- **Inference Speed**: Very fast (optimized for real-time applications)
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## Usage
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```python
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import torch
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import torchvision.transforms as transforms
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from PIL import Image
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# Load model
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model = torch.load("model.pth", map_location='cpu')
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model.eval()
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# Prepare image
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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])
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# Predict
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image = Image.open("your_image.jpg")
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input_tensor = transform(image).unsqueeze(0)
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with torch.no_grad():
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outputs = model(input_tensor)
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probabilities = torch.nn.functional.softmax(outputs, dim=1)
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predicted = torch.max(outputs, 1)[1].item()
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confidence = probabilities[0][predicted].item()
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result = "has_nameplate" if predicted == 1 else "no_nameplate"
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print(f"Prediction: {result} (Confidence: {confidence:.3f})")
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```
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## Applications
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- Industrial equipment documentation
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- Asset management systems
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- Quality control in manufacturing
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- Automated inventory systems
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## Limitations
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- Optimized for industrial nameplates
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- May not work well on severely damaged or obscured nameplates
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- Requires clear, readable text on nameplates
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## Training Details
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- **Optimizer**: Adam
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- **Loss Function**: CrossEntropyLoss
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- **Epochs**: 5
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- **Batch Size**: 32
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- **Learning Rate**: 0.001
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- **Data Augmentation**: Random horizontal flip, rotation, color jitter
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## Model Architecture
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```
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LightweightNameplateClassifier(
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(backbone): MobileNetV2(
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(classifier): Sequential(
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(0): Dropout(p=0.2)
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(1): Linear(in_features=1280, out_features=128)
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(2): ReLU()
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(3): Dropout(p=0.3)
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(4): Linear(in_features=128, out_features=2)
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)
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)
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)
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```
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## Citation
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If you use this model in your research, please cite:
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```
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@misc{kahua-nameplate-classifier,
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title={Lightweight Nameplate Classifier},
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author={Kahua ML Team},
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year={2024},
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howpublished={\url{https://huggingface.co/kahua-ml/nameplate-classifier}}
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}
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```
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