Comments (1)
Hey @Dero19xx ,
The embedding_routine
function is used during the training loop for computing the embeddings (i.e., the features extracted by the network) for all samples of the training and validation set.
These embeddings are then plotted using Tensorboard and in this way we can "see" how the features of REAL and FAKE videos separate as the training converges and the network learns to discriminate between them.
Hope this is clear, but in case just ask whatever question you have in mind!
Bests
from icpr2020dfdc.
Related Issues (20)
- What is the Training set of those pre-trianed models that provided by your dropbox link HOT 1
- Analyze results HOT 5
- ERROR conda.core.link:_execute(507): An error occurred while installing package 'conda-forge::async_generator-1.10-py_0' HOT 4
- training using the train_triplet file HOT 11
- Facing error while running train_all.sh HOT 2
- Celebdf dataset HOT 1
- Can't find the file data_frame_df.pkl HOT 3
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- Query HOT 4
- Unable to find "List_of_testing_videos.txt" HOT 1
- Source code of Ensemble models HOT 18
- index_dfdc errror HOT 2
- DFDC split HOT 1
- Question about the returned RuntimeError when using the Celeb-DF dataset HOT 7
- how to set validation split for dfdc? HOT 4
- use pre-trained model to detect my videos, but filter out some specific datasets on the pre-train which may overlap HOT 2
- regarding attention heatmap HOT 1
- Training dataset for DFDC HOT 1
- models pretrained on DFDC works well, but why models pertrained on FFPP always give a low score? HOT 1
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