Add nameplate classifier model

README.md

@@ -0,0 +1,128 @@
+---
+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}}
+}
+```

config.json

@@ -0,0 +1,19 @@
+{
+  "architectures": [
+    "LightweightNameplateClassifier"
+  ],
+  "model_type": "image-classification",
+  "num_classes": 2,
+  "id2label": {
+    "0": "no_nameplate",
+    "1": "has_nameplate"
+  },
+  "label2id": {
+    "no_nameplate": 0,
+    "has_nameplate": 1
+  },
+  "image_size": 224,
+  "backbone": "mobilenet_v2",
+  "framework": "pytorch",
+  "task": "image-classification"
+}

model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7976def7f4d156f56251a1e661fa73f0603ea674f5e6c74ee8218772bd97429
+size 9813242

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:531e5aeca5016aa40ad162d7fbb635edacb03b790ff60b820d4afb8055167135
+size 9799674