Hi,
@dutran, thank you for your great work.
I finetuned the r2+1d model on my own dataset using train_net.py, then I got the best test accuracy 0.72 and the corresponding model r2plus1d_3.mdl. However, when I use the same test dataset and the r2plus1_3.mdl to run test_net.py, the test accuracy is low, it is about 0.2. And I also tried to extract features using extract_features.py and then got the test accuracy using dense_prediction_aggregation.py. The test accuracy is low too, it is at most 0.11.
It makes me feel confused. Why is the test accuracy so different?
I know the value of the decode_type may influence the test accuracy, but I wonder if there are any other reasons that could affect the test accuracy? Could you give me some advice? Thank you.
Hi, @dutran, thank you for your great work. I finetuned the r2+1d model on my own dataset using train_net.py, then I got the best test accuracy 0.72 and the corresponding model r2plus1d_3.mdl. However, when I use the same test dataset and the r2plus1_3.mdl to run test_net.py, the test accuracy is low, it is about 0.2. And I also tried to extract features using extract_features.py and then got the test accuracy using dense_prediction_aggregation.py. The test accuracy is low too, it is at most 0.11. It makes me feel confused. Why is the test accuracy so different? I know the value of the decode_type may influence the test accuracy, but I wonder if there are any other reasons that could affect the test accuracy? Could you give me some advice? Thank you.