positive ratio for datasets with more faces.

3. Use a lower learning rate and an optimizer such as Adam or SGD for

datasets with facial landmarks.

Test task: FDDB-360

1. For datasets with more faces, use a larger crop size and a higher

2. For datasets with fewer faces, use a smaller crop size and a lower

anchor matching IoU threshold.

anchor matching IoU threshold.

3. For datasets with no facial landmarks, use a lower location loss

weight and a higher negative to positive ratio.

4. For datasets with facial landmarks, use a higher location loss

weight and a lower negative to positive ratio.

5. For datasets with more faces, use a higher learning rate and an

6. For datasets with fewer faces, use a lower learning rate and an

SGD optimizer.

Adam optimizer.

Test task: MAFA

1. Set the crop size and anchor matching IoU threshold according to
MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks