Pre-Train General FPS Model

[jaredb1011]

Description:

Train the implemented video masked autoencoder model on a dataset of general gameplay clips from first-person shooter (FPS) games.

Requirements:

• Split the dataset into training, validation, and testing sets.
• Train the video masked autoencoder model on the training set and validate its performance on the validation set.
• Evaluate the final model's performance using appropriate metrics (e.g., reconstruction error, SSIM, etc.) on the testing set.

Acceptance Criteria:

• Prepare a dataset of general FPS gameplay clips from the Waldo Vision database.
• Preprocess the dataset appropriately, including resizing, normalization, and data augmentation if necessary.
• Split the dataset into training, validation, and testing sets.
• Train the video masked autoencoder model on the training set, achieving satisfactory performance as indicated by appropriate metrics.
• Monitor training progress and adjust hyperparameters as needed to optimize model performance.
• Evaluate the final model's performance using appropriate metrics (e.g., reconstruction error, SSIM, etc.) on the testing set.
• Provide clear instructions on how to reproduce the training process and any customization options.

Notes:

Monitor training progress and be prepared to adjust hyperparameters, such as learning rate, batch size, or other factors, to optimize model performance.

Blocked by #5