Quick Model Card

README.md

@@ -5,4 +5,131 @@ datasets:
 metrics:
 - accuracy
 library_name: keras
----
+license: gpl-3.0
+---
+# Model Card for Model ID
+
+This model is a deep neural network for classifying handwritten digits (0-9) from images.
+It was a submission for a coursework assignment and is built using Keras.
+
+## Model Details
+
+### Model Description
+
+This model is designed to classify handwritten digits from the MNIST dataset.
+It is a basic implementation and can be a starting point for further exploration and improvement.
+
+
+
+- **Developed by:** Paul J. Aru
+- **Model type:** Convolutional Neural Network (CNN)
+- **License:** GNU GPLv3
+
+<!--### Model Sources [optional]
+
+ Provide the basic links for the model.
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed] -->
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+
+This model can be used to classify handwritten digits from images.
+However, it is important to note that its performance may not be optimal and can be further improved.
+
+### Out-of-Scope Use
+
+This model is not intended for real-world applications where high accuracy and robustness are critical.
+It is for learning purposes and serves as an example for my portfolio.
+
+## Bias, Risks, and Limitations
+
+The model may exhibit bias depending on the training data used.
+The MNIST and EMNIST dataset might contain inherent biases, and the model might learn these biases.
+The model might not perform well on unseen data, especially if the handwriting styles differ significantly from the training data.
+This is a basic implementation and likely has limitations in accuracy and generalizability.
+It serves as a starting point for further exploration and can be improved by experimenting with different architectures, hyperparameters, and data augmentation techniques.
+
+### Recommendations
+
+Users should be aware of the limitations of this model and not rely on it for critical tasks.
+The model can be a good foundation for further development and experimentation in deep learning for handwritten digit classification.
+
+## How to Get Started with the Model
+
+```
+from tensorflow.keras.models import load_model
+import os
+
+model=load_model("Best_Model.h5")
+```
+
+## Training Details
+
+### Training Data
+
+The model is trained on the MNIST and EMNIST dataset, a standard dataset for handwritten digit classification.
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing
+
+The images were preprocessed using data augmentation techniques such as shifting, rotation, resizing and introducing noise.
+
+#### Training Hyperparameters
+
+- Epoch: 50
+- Batch Size: 32
+
+#### Speeds, Sizes, Times
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f32a02b3338b9643e7eece/cgfXMmfcm14Ccgr4lH_wR.png)
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+The datasets used for testing include:
+- the MNIST dataset
+- the EMNIST dataset
+- a combined dataset of MNIST and EMNIST
+- an augmented combined dataset of MNIST and EMNIST
+
+#### Factors
+
+The factors considered in the testing process are the misclassification errors, which indicate the percentage of incorrectly classified samples in each dataset.
+The metrics used to measure the performance of the models are the percentage of misclassifications for each dataset.
+
+#### Metrics
+
+After testing all the models, the misclassification errors for each model are plotted using a bar chart.
+The range between the best and worst errors is calculated, and the model with the lowest maximum error is identified as the best model.
+
+### Results
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f32a02b3338b9643e7eece/aMGA_CaHmP9J39nCssvLS.png)
+
+#### Summary
+
+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.
+
+## Environmental Impact
+
+- **Hardware Type:** Apple M2 Max
+- **Hours used:** 93min (*last Model*)
+
+## Model Card Authors
+
+Paul J. Aru