README_PD.md

Person detection

Data preparation

Prepare dataset follow instruction

Note 1: To significantly speedup training you can initialize your model from our distributed .caffemodel snapshots:

• $REPO_ROOT/models/init_weights/person_detection_0022.caffemodel - for training Person Detection model

Person Detection training

On first stage you should train the SSD-based person (two class) detector. To do this you should run single-GPU (python layers does not allow to run on multiple GPUs) training procedure (specify GPU_ID):

cd ./models
python3 train.py --model person_detection \                   # name of model
                --weights person_detection_0022.caffemodel \  # initialize weights from 'init_weights' directory
                --data_dir <PATH_TO_DATA> \                   # path to directory with dataset
                --work_dir <WORK_DIR>                         # directory to collect file from training process

If it's needed the model evaluation can be performed by default pipeline in the original SSD repository. Moreover the training process of PD model can be carried out using SSD-original environment without any changes and after this the weights of trained model can be used as an initialization point on next stage.

Note: to get more accurate model it's recommended to use pre-training of backbone on default classification or detection datasets.

Export to IR format

cd ./models
python3 mo_convert.py --name face_detection \
    --dir <WORK_DIR>/person_detection/<EXPERIMENT_NUM> \
    --iter <INTERATION> \
    --data_type FP32