# Person detection## Data preparationPrepare dataset follow [instruction](./README_DATA.md)**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 trainingOn 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`):```Shellcd ./modelspython 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](https://github.com/weiliu89/caffe/tree/ssd). 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](#action-recognition-training).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```Shellcd ./modelspython mo_convert.py --name face_detection \ --dir <WORK_DIR>/person_detection/<EXPERIMENT_NUM> \ --iter <INTERATION> \ --data_type FP32```