-
Notifications
You must be signed in to change notification settings - Fork 502
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Prediction accuracy & over-fitting #45
Comments
Yes, the accuracy is very low because it is calculated as a classification problem. However, in prediction, the estimated age is calculated as an expected value instead of the age with maximum probability. Then, mean square error (MSE) or mean absolute error (MAE) is used as a performance indicator. Overfitting may be reduced by augmentation. Please check |
It is difficult to guess what's going on from the above information. The pred_age seems to be always the same value... |
I changed the input into 0-1 and this problem solved. What's more, the result seems better than the first time I use the original 0-255 image as input. I'm not sure if you ever met this situation before but it is worth a try. |
Thank you for your information! |
Hi ! I met the same problem that the pred_acc of age is 0 all the time. I changed the input into 0-1 and it did solve the 0 problem, but after training with data augmentation for 30 epochs, I only achieved 4.64 val_loss. I was wondering if you have some solutions. |
Thank you for your report. In terms of augmentation, random erasing might cause problem because it assumes the input range of [0. 255].
to
may fix the problem. |
Thanks for your reply! |
Yes, the evaluation script for the APPA-REAL dataset assumes inputs ranging from 0 to 255. https://github.com/yu4u/age-gender-estimation/blob/master/evaluate_appa_real.py#L54 If you used normalized images for training, you should do the same thing in test time like (I did not test the code);
|
Hi, I really appreciate your neat code and it works perfectly. However, it seems the age estimation accuracy on validation set is quite low and seems only attend 0.04. I guess it is due to the number of classes. But would the model predict better if I reduce the class number?
In addition, I re-checked your accuracy curve and observed that it might overfit when epoch over 25.
Thanks
The text was updated successfully, but these errors were encountered: