I need help with an assignment where the goal is to train machine learning models to predict the age and gender of individuals from facial images using the UTKFace dataset. This involves data preprocessing, model training, evaluation, and interpreting the results. Additionally, I need to compare the performance of Convolutional Neural Networks $($CNNs$)$ with Transformers for these tasks.

Dataset:

The UTKFace dataset contains over $20,000$ face images with annotations of age and gender. Each image file is named in the format age$\_$gender$\_$ethnicity$\_$date.jpg$.$

Age: $0-116$

Gender: $0($male$)$ or $1($female$)$

Tasks:

Data Preprocessing:

Download the UTKFace dataset.

Load the images and corresponding labels $($age$,$ gender$)$ into a data structure suitable for training machine learning models.

Split the dataset into training, validation, and test sets $($e$.$g$.,70\%$ training, $15\%$ validation, $15\%$ test$).$

Exploratory Data Analysis $($EDA$)$:

Perform EDA to understand the distribution of age and gender in the dataset.

Visualize some example images from each class.

Discuss any potential biases in the dataset and how they might affect model performance.

Model Training:

Convolutional Neural Networks $($CNN$)$:

Train a CNN model to predict the age and gender of individuals.

Evaluate the model using appropriate metrics $($e$.$g$.,$ Mean Absolute Error for age, Accuracy for gender$).$

Transformers:

Train a Transformer$-$based model to predict the age and gender of individuals.

Evaluate the model using the same metrics as used for the CNN model.

Model Comparison:

Compare the performance of the CNN and Transformer models on the validation and test sets.

Analyze the training time, computational resources required, and any differences in model architecture complexity.

Discuss the strengths and weaknesses of each approach in the context of the given tasks.

Model Evaluation:

Plot the training and validation metrics over epochs to check for overfitting$/$underfitting for both CNN and Transformer models.

Analyze the confusion matrix for gender predictions to identify common misclassifications for both models.

Discuss the overall performance of the models and potential improvements.

Could someone please guide me through these tasks or provide examples of how to approach each step? Any help with code examples, libraries to use, or tips for handling this dataset would be greatly appreciated!