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--- |
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datasets: |
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- mnist |
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- emnist |
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metrics: |
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- accuracy |
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library_name: keras |
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license: gpl-3.0 |
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--- |
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# Model Card for Model ID |
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This model is a deep neural network for classifying handwritten digits (0-9) from images. |
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It was a submission for a coursework assignment and is built using Keras. |
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## Model Details |
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### Model Description |
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This model is designed to classify handwritten digits from the MNIST dataset. |
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It is a basic implementation and can be a starting point for further exploration and improvement. |
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- **Developed by:** Paul J. Aru |
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- **Model type:** Convolutional Neural Network (CNN) |
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- **License:** GNU GPLv3 |
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<!--### Model Sources [optional] |
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Provide the basic links for the model. |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] --> |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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This model can be used to classify handwritten digits from images. |
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However, it is important to note that its performance may not be optimal and can be further improved. |
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### Out-of-Scope Use |
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This model is not intended for real-world applications where high accuracy and robustness are critical. |
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It is for learning purposes and serves as an example for my portfolio. |
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## Bias, Risks, and Limitations |
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The model may exhibit bias depending on the training data used. |
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The MNIST and EMNIST dataset might contain inherent biases, and the model might learn these biases. |
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The model might not perform well on unseen data, especially if the handwriting styles differ significantly from the training data. |
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This is a basic implementation and likely has limitations in accuracy and generalizability. |
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It serves as a starting point for further exploration and can be improved by experimenting with different architectures, hyperparameters, and data augmentation techniques. |
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### Recommendations |
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Users should be aware of the limitations of this model and not rely on it for critical tasks. |
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The model can be a good foundation for further development and experimentation in deep learning for handwritten digit classification. |
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## How to Get Started with the Model |
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``` |
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from tensorflow.keras.models import load_model |
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import os |
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model=load_model("Best_Model.h5") |
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``` |
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## Training Details |
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### Training Data |
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The model is trained on the MNIST and EMNIST dataset, a standard dataset for handwritten digit classification. |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing |
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The images were preprocessed using data augmentation techniques such as shifting, rotation, resizing and introducing noise. |
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#### Training Hyperparameters |
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- Epoch: 50 |
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- Batch Size: 32 |
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#### Speeds, Sizes, Times |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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The datasets used for testing include: |
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- the MNIST dataset |
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- the EMNIST dataset |
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- a combined dataset of MNIST and EMNIST |
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- an augmented combined dataset of MNIST and EMNIST |
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#### Factors |
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The factors considered in the testing process are the misclassification errors, which indicate the percentage of incorrectly classified samples in each dataset. |
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The metrics used to measure the performance of the models are the percentage of misclassifications for each dataset. |
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#### Metrics |
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After testing all the models, the misclassification errors for each model are plotted using a bar chart. |
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The range between the best and worst errors is calculated, and the model with the lowest maximum error is identified as the best model. |
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### Results |
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#### Summary |
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In summary, my testing approach involves evaluating the models on different datasets, considering misclassification errors as the primary metric, and comparing the performance of the models to determine the best model. |
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## Environmental Impact |
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- **Hardware Type:** Apple M2 Max |
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- **Hours used:** 93min (*last Model*) |
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## Model Card Authors |
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Paul J. Aru |
