Refactor all the project ! Now it's more efficient and the structure is more clear.
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Get your dataset annotations file, parse and save it to two files
trainval_annotations.txt
andtest_annotations.txt
, file name doesn't matter. -
Your annotations file must have the format like this:
image_full_path object1_class x1_min y1_min x1_max y1_max object2_class x2_min y2_min x2_max y2_max...
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You can check
examples/Train_annotation.txt
file to understand the annotation format more clearly, this is the INRIA annotations file after my processing, your annotations file should be like this. -
You should write your own dataset annotation process program, I just write for INRIA dataset and you can reference it in
preprocess/inria_preprocess.py
. -
If I have more time, I will write more process program, you can send your requires in issues.
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Edit your dataset config and run file;
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Check the
examples/inria_example.py
to understand how to call thePASCALVOC07
class -
Config your own information in your pascal voc dataset
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Set the dataset directory, annotations file and output directory, then just run
build
, wait for your own pascal voc dataset.
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- I have writen an example of the INRIA dataset:
python preprocess/inria_preprocess.py /path/to/INRIAPerson
python examples/inria_example.py /path/to/INRIAPerson /path/to/output
Anything can be send to issues and forgive my poor English...