Comments (1)
thuyngch
commented on June 27, 2024
As mentioned in the technical report, we trained models using the CASIA2 dataset. Therefore, we just limit the scope of the project in this dataset for benchmarking.
If you test images come from the CASIA2 dataset, the performance will be acceptable. In the case of another dataset (i.e., your prepared dataset), the distribution may be different from one of CASIA2. Thus, if you want to make the model more generalized, you should try to train it with more data.
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Related Issues (10)
- call for help HOT 5
- Getting different results in different systems
- Error in runing train_MBN2_mod.py HOT 1
- "Prediction: Positive ==> True" HOT 4
- i have no idea about Positive and Negative HOT 1
- Foregeryb detection test HOT 7
- No module named 'libs' HOT 1
- Execution problem HOT 1
- scanned document Forgery
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