This project is a Handwritten Digits Classifier implemented using PyTorch, a powerful deep learning library. The classifier aims to accurately predict the digit represented by an input image of handwritten digits.
The architecture of the Handwritten Digits Classifier utilizes a Transformer Compose pipeline, which consists of several layers for feature extraction, transformation, and classification. The model architecture is designed to effectively process and analyze input images to predict the corresponding digit.
The loss function employed in this project is the CrossEntropyLoss function. Cross-entropy loss is commonly used in classification tasks, as it calculates the difference between the predicted probability distribution and the true distribution of the labels.
The optimizer used for training the model is the Adam optimizer. Adam is an adaptive learning rate optimization algorithm that efficiently updates the parameters of the model during training. It is well-suited for deep learning tasks due to its ability to handle sparse gradients and noisy data.
The accuracy achieved by the Handwritten Digits Classifier is an impressive 97.42%. This accuracy rate reflects the effectiveness of the model in accurately classifying handwritten digits.
To use the Handwritten Digits Classifier, follow these steps:
- Prepare the dataset: Ensure that you have a dataset of handwritten digits images for training and testing the model.
- Preprocess the data: Preprocess the images to prepare them for training, including normalization and resizing if necessary.
- Train the model: Use the provided PyTorch scripts to train the model on the prepared dataset.
- Evaluate the model: Evaluate the trained model on a separate test dataset to assess its accuracy and performance.
Contributions to this project are welcome! If you have any suggestions, improvements, or bug fixes, feel free to open an issue or submit a pull request.