yaml added

README.md

@@ -1,5 +1,19 @@
 
-# MNIST Digit Classifier
+---
+language: en
+tags:
+- mnist
+- cnn
+- pytorch
+- image-classification
+license: apache-2.0
+datasets:
+- mnist
+metrics:
+- accuracy
+---
+
+# MNIST Digit Classifier with Noise Reduction
 
 **Model type**: Convolutional Neural Network (CNN)  
 **Model Architecture**: 3 Convolutional Layers, 1 Adaptive Pooling Layer, 1 Fully Connected Layer  
@@ -8,43 +22,42 @@
 
 ## 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.
+This model is a Convolutional Neural Network (CNN) trained on the MNIST dataset and designed to classify handwritten digits from 0 to 9. The model uses data augmentation to improve its robustness, especially for noisy or rotated images. The preprocessing step includes Gaussian blur for noise reduction, making the model more resilient to outliers and noisy digit inputs.
 
 ### 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.
+- **Adaptive Pooling**: Adaptive Average Pooling to ensure the model handles dynamic input sizes.
+- **Fully Connected Layer**: The output from the convolutional layers is flattened and passed through a fully connected layer to predict the digit.
 
 ## 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.
+The model was trained on the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), which consists of 60,000 training images and 10,000 test images of handwritten digits. The images are 28x28 pixels in grayscale.
 
 ### 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
+- **Data Augmentation**: Random rotations (up to 10 degrees) and random translations (up to 10%) were applied during training to make the model more robust to variations.
+- **Normalization**: The pixel values were normalized to the range [-1, 1].
 
 ## 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.
+This model is designed for:
+- Recognizing handwritten digits in applications like form scanning, document analysis, or real-time digit detection.
+- Educational purposes to demonstrate CNN-based image classification.
 
-**How to use**:  
-The model can be loaded using PyTorch, and an image can be classified by following this code snippet:
+### How to use the model
 
-\`\`\`python
+You can load the trained model in PyTorch and use it to classify digit images as shown below:
+
+```python
 import torch
 from torchvision import transforms
 from PIL import Image
 
 # Load the model
-model = YourModelClass()
+model = ImageClassifier()
 model.load_state_dict(torch.load('mnist_classifier.pth'))
 model.eval()
 
-# Preprocess image
+# Preprocess an input image
 transform = transforms.Compose([
     transforms.Resize((28, 28)),
     transforms.ToTensor(),
@@ -54,28 +67,28 @@ transform = transforms.Compose([
 img = Image.open('path_to_image').convert('L')
 img_tensor = transform(img).unsqueeze(0)
 
-# Predict
+# Perform inference
 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:
+The model was evaluated on the MNIST test set, achieving the following results:
 - **Accuracy**: ~98%
-- **Loss**: Cross-entropy loss during training converged to a low value (~0.15 after 10 epochs).
+- **Loss**: Cross-entropy loss decreased to a value of approximately 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).
+### Performance on Noisy Inputs:
+The model was tested on noisy images (e.g., images with added noise or distortions), and the preprocessing steps (Gaussian blur, resizing) helped improve the model’s performance on such inputs.
 
 ## 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).
+- **Noisy Inputs**: Although preprocessing helps, very noisy or distorted inputs might still be challenging for the model.
+- **Generalization**: This model is primarily trained on MNIST digits. It may not generalize well to digits from different writing styles or other number systems.
 
 ## Training Details
 
@@ -85,12 +98,12 @@ The model was also tested on noisy digit images and successfully classified digi
 - **Loss Function**: Cross-entropy Loss
 - **Batch Size**: 32
 - **Epochs**: 10
-- **Data Augmentation**: Random rotations and translations during training
+- **Data Augmentation**: Random rotations and translations
 
 ## 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).
+There are no significant ethical concerns related to this model. However, users should be aware that the model is specifically trained on simple MNIST digits and may not perform well in more complex scenarios.
 
-## Model Card Contact
+## Contact
 
-If you have any questions, feedback, or inquiries about this model, feel free to reach out to the author via [[email protected]].
+For any questions or feedback, please reach out to the model author via [[email protected]].