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.