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--- |
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language: en |
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datasets: |
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- mnist |
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metrics: |
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- accuracy |
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model-index: |
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- name: EfficientNet-DigitClassifier-99Acc |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: "MNIST (Mixed National Institute of Standards and Technology) database" |
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type: mnist |
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args: mnist |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 99.58 |
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tags: |
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- computer-vision |
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- cnn |
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- mnist |
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- educational |
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- efficientnet |
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--- |
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# EfficientNet-DigitClassifier-99Acc |
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## Overview |
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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. |
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## Model Architecture |
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The EfficientNet-DigitClassifier-99Acc features a sequential CNN architecture optimized for 28x28 pixel grayscale images. The architecture includes: |
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- **Conv2D Layers:** Two convolutional layers with 32 and 64 filters, respectively, using 3x3 kernels and ReLU activation. |
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- **MaxPooling2D Layers:** Pooling layers following each Conv2D layer to reduce spatial dimensions. |
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- **Flatten Layer:** Converts the 2D matrix data into a vector for processing in dense layers. |
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- **Dropout Layer:** A dropout rate of 0.5 to mitigate overfitting. |
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- **Output Layer:** A dense layer with 10 units and softmax activation, corresponding to the ten digit classes. |
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## Dataset |
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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. |
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## Performance |
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- **Test Loss:** 0.0169 |
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- **Test Accuracy:** 99.58% |
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This model exemplifies state-of-the-art performance in digit classification, providing a robust solution for applications requiring high accuracy in digit recognition. |
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