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Demo for training a LAEO-Net++ model on AVA-LAEO

We describe here how to train a model with already preprocessed data.

Data preparation

Download AVA-LAEO preprocessed samples: train (3.58 GB), val (940 MB).

The training code assumes that the previous tar files are not unpacked. So, just place them in a couple of directories and update the code to point to those directories.
For example, in file ln_train3DconvModelGeomMBranchCropMapAVA.py, there is a variable named tardir that can be customized. Currently, it points to /experiments/ava/preprocdata/w10_mw10/train for training data. Similarly, use val for the validation data.

Download additional annotation data and place it in subdirectory data: pkl with tracks (624 MB)

AFLW head crops

We are not sure whether we can distribute the preprocessed set of AFLW heads used in our experiments. Therefore, such data is not currently available.

Run the training script

From the root directory of the project, run the following command:

python train/ln_train3DconvModelGeomMBranchCropMapAVA.py -g 0.50 -e 60 -l 0.0001 
-d 0.2 -n 1 -s 32 -a 1 -z 0 -b 8 -f 1 -F 0 -k 0.3 -c 0 -R 2 -L 1 -m 0 -u 0 
--useframecrop=0 --usegeometry=0 --initfilefm="" --usemap=1 --mapwindowlen=10 -S 0 
--DEBUG=0 --trainuco=0 --testuco=0 --infix=_ss64jitter 
-w ./models/model-init-ssheadbranch_py36.hdf5 --useself64=1 
--avalaeodir=./data

Parameters:

  • -S: use synthetic data? Since AFLW preprocessed data is not released, we set it to 0 in the example.
  • --testuco: we set it to 0 in the example to use a portion of AVA-LAEO training data for validation.
  • --avalaeodir: points to the directory containing the pkl tracks file (see Data preparation).