Closed tyiannak closed 3 years ago
Steps to create the dataset: 1) Download shots and extract them 2) Create 2 folders (static, non_static) 3) Move all shots to folders (2 times one per folder) 4) Run the script create_dataset.py
PS The script creates 2 different csv files. The first contains the name of static shots that have 100 confidence and have been annotated by at least 2 annotators. The second contains the name of non-statics shots that gave 100 confidence, have been annotated by at least 2 annotators.
It is not productive to ask the user of create_dataset to download the data and duplicate them into 2 folders and then the script will delete the respective files from each folder. What create_dataset.py should do:
e.g. create_dataset('annotations.csv', '/home/ubuntu/data/all_videos', '/home/ubuntu/data/classes')
The result of this should be a set of folders-classes in '/home/ubuntu/data/classes' containing the corresponding video files , e.g. /home/ubuntu/data/classes/static /home/ubuntu/data/classes/travelling_in etc
Then you can copy the contents from particular folders to run your experiment.
@apoman38 checkout the latest commits that fix the bugs on loading/saving the npy feature files.
Please add the following before proceeding to PR:
Three more classifiers (Adaboost, Extratrees, RandomForest) were added to the train.py code. Hyperparameter tuning was performed to find the parameters that give the best results in each algorithm separately. In addition, the confusion matrix is stored as an image for each algorithm separately.
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