Model Card for Model ID

This model is a fine-tuned variant of LLaVA 1.6, now at MK4, trained to detect and infer specific details from screenshots of EA's title FC 24.

Model Details

The MK4 model of LLaVA is trained to accurately infer the following details from screenshots of FC 24:

The model is designed for direct use in detecting and extracting match-related information from screenshots of FC 24.

Further fine-tuning or integration into applications for gaming analytics or automated reporting.

Misuse scenarios where the model's output might not be reliable, such as non-standard screenshots or modified game environments.

Users should be aware of potential biases in inference accuracy related to changes in game updates or variations in screenshot quality.

Users should ensure screenshots are of standard quality and from up-to-date game versions for optimal performance.

Use the provided inference code to integrate the model into your application.

Training data consisted of annotated screenshots from FC 24 matches and menus.

Screenshots were preprocessed to enhance readability and standardize input features.

Annotated testing dataset reflecting diverse gameplay scenarios.

Evaluation metrics include accuracy, precision, recall for each inferred category.

Detailed evaluation results are available upon request.

Carbon emissions and environmental considerations are minimal due to the computational efficiency of the model.

The model architecture integrates deep learning for feature extraction and classification of game-related data from screenshots.

Training and inference were conducted on an RTX 3090 ~8 hours.

Python, TensorFlow, and additional libraries for machine learning.

BibTeX: