update model card

README.md

@@ -1,5 +1,96 @@
-Changes from previous author:
-- Updated Architecture: Using AdaptiveAvgPool2d ensures that the fully connected layer receives a consistent input size, regardless of the input dimensions.
-- Data Augmentation: Training with rotated and shifted images ensures the model becomes more robust to variations, improving generalization.
-- Noise Reduction: Preprocessing the image by removing noise helps the model focus on the digit itself.
 
+# 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]].