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Training using own Dataset #147
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I just noticed that max number of frames a video can have for us to use it |
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Hello there,
Thanks for the great work, I have tested it with 101 Dataset and all went good.
Now I want to train a model using my dataset, I converted all videos to .avi (from mp4) using ffmpeg.
I followed a similar naming structure :
testlist01.txt containing 30 videos [10 from each category]
`Fighting/v_Fighting_g01_c01.avi
Fighting/v_Fighting_g01_c02.avi
Fighting/v_Fighting_g01_c03.avi
Fighting/v_Fighting_g02_c01.avi
Fighting/v_Fighting_g02_c02.avi
...
Normal/v_Normal_g01_c01.avi
Normal/v_Normal_g01_c02.avi
Normal/v_Normal_g01_c03.avi
Normal/v_Normal_g02_c01.avi
Normal/v_Normal_g02_c02.avi
...
Shooting/v_Shooting_g01_c01.avi
Shooting/v_Shooting_g01_c02.avi
Shooting/v_Shooting_g01_c03.avi
Shooting/v_Shooting_g02_c01.avi
Shooting/v_Shooting_g02_c02.avi
...
my trainlist01.text contains:
Fighting/v_Fighting_g04_c01.avi 1
Fighting/v_Fighting_g04_c02.avi 1
Fighting/v_Fighting_g04_c03.avi 1
Fighting/v_Fighting_g04_c04.avi 1
Fighting/v_Fighting_g05_c01.avi 1
Fighting/v_Fighting_g05_c02.avi 1
...
Normal/v_Normal_g04_c01.avi 2
Normal/v_Normal_g04_c02.avi 2
Normal/v_Normal_g04_c03.avi 2
Normal/v_Normal_g04_c04.avi 2
Normal/v_Normal_g05_c01.avi 2
Normal/v_Normal_g05_c02.avi 2
...
Shooting/v_Shooting_g04_c01.avi 3
Shooting/v_Shooting_g04_c02.avi 3
Shooting/v_Shooting_g04_c03.avi 3
Shooting/v_Shooting_g04_c04.avi 3
Shooting/v_Shooting_g05_c01.avi 3
Shooting/v_Shooting_g05_c02.avi 3
...
my classInd.text contains:
1 Fighting
2 Normal
3 Shooting
when I run 1_move_files.py and 2_extract_files.py I get the expected results.
My issue is when I run extract_features.py I only get this files generated:
v_Normal_g04_c04-40-features.npy
v_Normal_g10_c02-40-features.npy
v_Shooting_g01_c01-40-features.npy
there is no errors, but only 3 features files are extracted from my dataset, may I know why? and what should I do?
my dataset have 3 categories, each with 50 videos, divided into 20% testing 80% training.
your help is highly appreciated!
Thanks
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