README.md

---
language: en
datasets:
- mnist
metrics:
- accuracy
model-index:
  - name: EfficientNet-DigitClassifier-99Acc
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: "MNIST (Mixed National Institute of Standards and Technology) database"
          type: mnist
          args: mnist
        metrics:
          - name: Accuracy
            type: accuracy
            value: 99.58
tags:
- computer-vision
- cnn
- mnist
- educational
- efficientnet
---

# EfficientNet-DigitClassifier-99Acc

## Overview

This model card introduces the EfficientNet-DigitClassifier-99Acc, a high-accuracy Convolutional Neural Network (CNN) designed for digit classification. Achieving an impressive accuracy of 99.58%, this model stands out as a reliable tool for recognizing handwritten digits, trained and validated on a large-scale digit dataset with 240,000 samples for training and 40,000 for testing.

## Model Architecture

The EfficientNet-DigitClassifier-99Acc features a sequential CNN architecture optimized for 28x28 pixel grayscale images. The architecture includes:

- **Conv2D Layers:** Two convolutional layers with 32 and 64 filters, respectively, using 3x3 kernels and ReLU activation.
- **MaxPooling2D Layers:** Pooling layers following each Conv2D layer to reduce spatial dimensions.
- **Flatten Layer:** Converts the 2D matrix data into a vector for processing in dense layers.
- **Dropout Layer:** A dropout rate of 0.5 to mitigate overfitting.
- **Output Layer:** A dense layer with 10 units and softmax activation, corresponding to the ten digit classes.

## Dataset

Training and testing were performed on a specially curated digit dataset derived from the MNIST database, featuring 240,000 training samples and 40,000 test samples. Each image underwent preprocessing to scale pixel values to the [0, 1] range and reshape to 28x28 pixels.

## Performance

- **Test Loss:** 0.0169
- **Test Accuracy:** 99.58%

This model exemplifies state-of-the-art performance in digit classification, providing a robust solution for applications requiring high accuracy in digit recognition.