Error in `python3': double free or corruption (!prev): about age-gender-estimation HOT 9 OPEN

tensorflow/tensorflow#6968 related this issue?

from age-gender-estimation.

Thanks a lot. I have solved this problem. And one more question, how to get the model graph?

from age-gender-estimation.

What did you mean by "the model graph"? The model graph for TensorFlow?

from age-gender-estimation.

What did you mean by "the model graph"? The model graph for TensorFlow?
Thanks for your reply. Yes. I want to check the model graph for TensorFlow .

from age-gender-estimation.

You can search the way to do so.
keras-team/keras#3223

from age-gender-estimation.

Thanks you for your advice.
There is another doubt, would you please give some suggestion.
You trained the network by "python3 train.py --input data/imdb_db.mat" from scratch and didn't finetune it by other data set and the best val_loss is to 3.969.
The best val_loss was improved from 3.969 to 3.731:

Without data augmentation: 3.969
With standard data augmentation: 3.799
With mixup and random erasing: 3.731

How about the finetuned result and in this project there is no script for training on pretrained models, right?
Best wishes.

from age-gender-estimation.

All the above results were obtained by training from scratch.
There is no script for fine-tuning.

Without data augmentation: 3.969
-> python3 train.py --input data/imdb_db.mat

With standard data augmentation: 3.799
-> N/A

With mixup and random erasing: 3.731
-> python3 train.py --input data/imdb_db.mat --aug

from age-gender-estimation.

Thanks a lot.
when evaluating the trained model, the result is:
The results of pretrained model is:

MAE Apparent: 6.06
MAE Real: 7.38

The best result reported in [5] is:

MAE Apparent: 4.08
MAE Real: 5.30
So I doubt that you first pretrained and finetuned and achieve the MAE Apparent :6.06, and this is only for age ,not for gender. To evaluate a age-gender-estimation model , MAE and delta-error are both applicable .Any way , I am confused about the quantitative evaluation protocol about age and gender respectively.

from age-gender-estimation.

The APPA-REAL dataset is used to obtain the above results, and the dataset does not include gender lables. Thus only MAE for age estimation is shown.

from age-gender-estimation.