Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+### **MNIST CNN Classifier**
+- Adapted from Pytorch Examples
+
+#### **Model Overview**
+- **Model Name**: MNIST CNN Classifier
+- **Model Type**: Convolutional Neural Network (CNN)
+- **Dataset**: MNIST (Modified National Institute of Standards and Technology)
+  - A dataset of 70,000 handwritten digits (28x28 grayscale images) of digits 0-9.
+- **Task**: Image classification (Digit recognition)
+- **Framework**: PyTorch
+- **Preprocessing**: 
+  - Images are normalized and transformed before feeding into the model (e.g., scaling pixel values to a [0, 1] range).
+
+#### **Model Architecture**
+- **Input Layer**: 28x28 grayscale images.
+- **Convolutional Layers**:
+  - Conv1: 32 filters of size 3x3, applied to the input image (1 channel).
+  - Conv2: 64 filters of size 3x3, applied to the output of Conv1.
+- **Activation Functions**:
+  - ReLU activations after each convolutional layer.
+- **Pooling Layer**:
+  - Max pooling with a 2x2 window after Conv2.
+- **Dropout**:
+  - Dropout rate of 25% after the first convolutional layer and 50% after the fully connected layer to prevent overfitting.
+- **Fully Connected Layers**:
+  - fc1: A fully connected layer with 128 neurons.
+  - fc2: A fully connected layer with 10 output units (one for each digit).
+- **Output**:
+  - Log-softmax activation applied to the final layer to produce log-probabilities for the 10 classes.
+
+#### **Training Details**
+
+- **Batch Size**: (60000)
+- **Epochs**: (14)
+- **Learning Rate**: (1.0 Adadelta)
+- **Hardware Used**: (Google Colab T4 GPU)
+
+#### **Performance Metrics**
+- **Training Accuracy**: (99%)
+- **Training Loss**: (0.0273)
+
+#### **Usage Instructions**
+- **How to Use**: 
+  - Clone the repo
+  - **Dependencies**:
+  - pip install transformers torch torchvision==0.20.0
+  - ``` python app.py ```
+
+#### **Model Limitations**
+- This model may struggle with noise or distorted images and is trained on relatively clean data.
+- The model is limited to the MNIST digit set and may not perform well on other datasets or noisy, out-of-distribution data.
+