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Before installation create and activate virtual environment
python3 -m venv venv
source venv/bin/activate
Install the dependencies
pip install -r requirements.txt
For training run jupyter notebook
We use the NUS-WIDE dataset for this tutorial. Instead of parsing Flickr for image downloading we use a dump from this github repository Download and extract it.
Also, we added pre-processed annotations:
nus_wide/train.json
nus_wide/test.json
If you want to create them yourself, run the command:
python split_data_nus.py -i images
where -i images is the path to the folder with extracted images
You can train the model for the entire data set, but it takes a lot of time. For this tutorial we use part of this data.
For subset creation run the command:
python create_subset.py -i images
where -i images is the path to the folder with extracted images
Additional options:
python create_subset.py -h
usage: Subset creation [-h] -i IMG_PATH [-v VAL_SIZE] [-t TRAIN_SIZE]
[--shuffle] [-l LABELS [LABELS ...]]
optional arguments:
-h, --help show this help message and exit
-i IMG_PATH, --img-path IMG_PATH
Path to the "images" folder
-v VAL_SIZE, --val-size VAL_SIZE
Size of the validation data
-t TRAIN_SIZE, --train-size TRAIN_SIZE
Size of the train data
--shuffle Shuffle samples before splitting
-l LABELS [LABELS ...], --labels LABELS [LABELS ...]
Subset labels
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