---
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.