Spot the Mask Challenge - Classification Results

Evaluation Metric

The competition will be evaluated based on the Area under curve (opens in a new tab). The Area under curve is a metric that considers both recall(TPR) and Fall Out (FPR), making it suitable for binary classification tasks. After running only 100 epochs there are the results

Results

Training Performance

• Training Accuracy: 0.9869
• Training Loss: 0.0953

Validation Performance

• Validation Accuracy: 0.9922
• Validation Loss: 0.0953

Test Set Performance

• Test Set Accuracy (after submission): 0.9946

Training and Loss Curves

Training Curve and Loss Curve

Train Curve The training curve provides insights into how the model's accuracy improves over successive epochs during the training phase. The training curve illustrates the progressive increase in training accuracy as the model learns from the training dataset. A rising curve indicates that the model is effectively adapting to the patterns present in the training data.

Loss Curve The loss curve shows how the loss function, a measure of the model's error, changes over epochs during training. A decreasing loss curve is indicative of the model minimizing its error on the training data. The goal is to reach a point where the loss is sufficiently low, indicating that the model has learned to generalize well to unseen data.

Analysis

• Training Curve Observation: The training curve demonstrates a steady increase in accuracy, suggesting that the model is effectively learning from the training data.

• Loss Curve Observation: The loss curve shows a consistent decrease, indicating that the model is converging and improving its predictive performance.

Overfitting Consideration

It's essential to monitor the training and validation curves for signs of overfitting, where the model may become too specialized to the training data and perform poorly on new, unseen data. If there is a significant gap between the training and validation curves, additional regularization techniques may be necessary.

Recommendations

• Early Stopping: Implement early stopping to prevent overfitting by monitoring the validation loss and stopping training when it starts to increase.

• Learning Rate Adjustment: Experiment with adjusting the learning rate during training to optimize convergence and prevent overshooting.

By analyzing these curves, you can gain valuable insights into how well your model is learning and whether adjustments are needed to improve generalization performance.

Conclusion

The classification model performed well on the Spot the Mask Challenge, achieving a high accuracy and AUC on the test set. But we can stop there more can be done to improve more the model with the following future works.

Future Work

• Investigate misclassifications and fine-tune the model accordingly.
• Explore data augmentation techniques to improve model generalization.
• Consider ensemble methods for boosting overall performance.

Feel free to customize this template based on your actual results and the specifics of your classification model.