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Hi, thank you for your dataset and the source code, I wanna replicate this work with your code, but I am confused about the paths in config.py. I want to know what kinds of data has been used to train the model. In your paper Revisiting Video Saliency: A Large-scale Benchmark and a New Model ,you said that you have used the static dataset SALICON to train the attention module, and in your code there are several paths. Could you tell me:
which paths are the video dataset's path and which is for the SALICON? Do you mean that frames_path is all the frames extracted from the video, and imgs_path is for the data in SALICON?
do I need to extract all frames from the videos by myself?
related code are as follows:
# path of training videos
videos_train_paths = ['D:/code/attention/DHF1K/training/']
# path of validation videos
videos_val_paths = ['D:/code/attention/DHF1K/val/']
videos_test_path = 'D:/code/attention/DHF1K/testing/'
# path of training maps
maps_path = '/maps/'
# path of training fixation maps
fixs_path = '/fixation/maps/'
frames_path = '/images/'
# path of training images
imgs_path = 'D:/code/attention/staticimages/training/'
Thankyou.
The text was updated successfully, but these errors were encountered:
Hi, thank you for your dataset and the source code, I wanna replicate this work with your code, but I am confused about the paths in config.py. I want to know what kinds of data has been used to train the model. In your paper Revisiting Video Saliency: A Large-scale Benchmark and a New Model ,you said that you have used the static dataset SALICON to train the attention module, and in your code there are several paths. Could you tell me:
related code are as follows:
Thankyou.
The text was updated successfully, but these errors were encountered: