| # MNIST Digit Classifier | |
| **Model type**: Convolutional Neural Network (CNN) | |
| **Model Architecture**: 3 Convolutional Layers, 1 Adaptive Pooling Layer, 1 Fully Connected Layer | |
| **Framework**: PyTorch | |
| **Task**: Image Classification (Digits 0-9) | |
| ## Model Description | |
| This model is a Convolutional Neural Network (CNN) trained on the MNIST dataset, which consists of handwritten digits (0-9). It is designed to classify images of handwritten digits, making it suitable for applications that require digit recognition, such as form scanning, document analysis, and real-time digit detection. | |
| ### Model Architecture: | |
| - **Convolutional Layers**: 3 convolutional layers with ReLU activations. | |
| - **Adaptive Pooling**: Adaptive Average Pooling is used to ensure dynamic input handling. | |
| - **Fully Connected Layer**: The output from the convolutional layers is flattened and fed into a fully connected layer that outputs 10 logits corresponding to the digits 0-9. | |
| ## Training Data | |
| The model was trained on the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), which contains 60,000 training examples and 10,000 test examples of 28x28 grayscale images of handwritten digits. | |
| ### Data Preprocessing: | |
| - **Data Augmentation**: Random rotations (up to 10 degrees) and translations (up to 10% shift) were applied to the training data to improve generalization and robustness. | |
| - **Normalization**: Each pixel was normalized to the range [-1, 1] by using the following normalization parameters: | |
| - Mean: 0.5 | |
| - Standard Deviation: 0.5 | |
| ## Intended Use | |
| This model is suitable for: | |
| - Recognizing handwritten digits in real-world applications such as scanned documents, forms, or digit-based input systems. | |
| - Educational purposes to demonstrate digit classification using neural networks. | |
| **How to use**: | |
| The model can be loaded using PyTorch, and an image can be classified by following this code snippet: | |
| \`\`\`python | |
| import torch | |
| from torchvision import transforms | |
| from PIL import Image | |
| # Load the model | |
| model = YourModelClass() | |
| model.load_state_dict(torch.load('mnist_classifier.pth')) | |
| model.eval() | |
| # Preprocess image | |
| transform = transforms.Compose([ | |
| transforms.Resize((28, 28)), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.5,), (0.5,)) | |
| ]) | |
| img = Image.open('path_to_image').convert('L') | |
| img_tensor = transform(img).unsqueeze(0) | |
| # Predict | |
| with torch.no_grad(): | |
| output = model(img_tensor) | |
| predicted_label = torch.argmax(output, dim=1).item() | |
| print(f"Predicted Label: {predicted_label}") | |
| \`\`\` | |
| ## Evaluation Results | |
| ### Metrics: | |
| The model achieved the following performance metrics on the MNIST test dataset: | |
| - **Accuracy**: ~98% | |
| - **Loss**: Cross-entropy loss during training converged to a low value (~0.15 after 10 epochs). | |
| ### Noisy Image Performance: | |
| The model was also tested on noisy digit images and successfully classified digits with preprocessing applied (e.g., Gaussian blur and thresholding). | |
| ## Limitations | |
| - **Noisy Inputs**: The model might still struggle with images that are heavily noisy or distorted, though preprocessing techniques like Gaussian blur and normalization help mitigate these issues. | |
| - **Generalization**: The model is designed specifically for MNIST-like digits and might not generalize well to digit styles that are too different from the MNIST dataset (e.g., digits from different cultures or handwriting styles). | |
| ## Training Details | |
| ### Hyperparameters: | |
| - **Optimizer**: Adam | |
| - Learning Rate: 0.001 | |
| - **Loss Function**: Cross-entropy Loss | |
| - **Batch Size**: 32 | |
| - **Epochs**: 10 | |
| - **Data Augmentation**: Random rotations and translations during training | |
| ## Ethical Considerations | |
| While this model does not have significant ethical concerns, users should be aware that it is trained on a specific dataset (MNIST) that consists only of simple, grayscale digits. It may not perform well on digits outside of this domain (e.g., digits from other scripts or more complex scenarios). | |
| ## Model Card Contact | |
| If you have any questions, feedback, or inquiries about this model, feel free to reach out to the author via [[email protected]]. | |