dev: update model card
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README.md
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##
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---
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title: Cat and Dog Sketch Classifier
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emoji: 🐱🐶
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tags:
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- image-classification
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- quickdraw
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- cat
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- dog
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license: mit
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---
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# Cat and Dog Sketch Classifier
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This is a machine learning model trained to differentiate between sketches of cats and dogs. It was built as part of a learning project to understand how AI models work and how to train them.
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## Model Details
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- **Model Type**: Convolutional Neural Network (CNN)
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- **Training Data**: Quick, Draw! dataset (cat and dog sketches)
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- **License**: MIT License
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- **Supported Tasks**: Image Classification
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## Usage
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To use this model, you can follow these steps:
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1. **Load the Model**:
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```python
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import torch
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from model import SimpleCNN
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model = SimpleCNN()
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model.load_state_dict(torch.load('cat_dog_classifier.bin'))
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model.eval()
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```
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2. **Predict an Image**:
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```python
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from PIL import Image
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import numpy as np
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import torch
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def predict_image(model, image):
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# Preprocess the image
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if isinstance(image, Image.Image):
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image = image.resize((28, 28)).convert('L')
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image = np.array(image).astype('float32') / 255.0
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elif isinstance(image, np.ndarray):
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if image.shape != (28, 28):
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image = Image.fromarray(image).resize((28, 28)).convert('L')
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image = np.array(image).astype('float32') / 255.0
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else:
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raise ValueError("Image must be a PIL Image or NumPy array.")
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image = image.reshape(1, 1, 28, 28)
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image_tensor = torch.tensor(image).to(device)
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# Get prediction
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model.eval()
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with torch.no_grad():
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output = model(image_tensor)
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_, predicted = torch.max(output.data, 1)
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return 'cat' if predicted.item() == 0 else 'dog'
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# Example usage
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image = Image.open('path/to/your/image.png')
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prediction = predict_image(model, image)
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print(prediction)
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```
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## Training the Model
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To train the model yourself, use the provided `train_cat_dog_classifier.py` script.
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## License
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
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