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Transfer Learning

Transfer learning aims at learning new knowledge from existing knowledge. For example, take pretrained model from ImageNet to initialize detection models, or take pretrained model from COCO dataset to initialize train detection models in PascalVOC dataset.

In transfer learning, if different dataset and the number of classes is used, the dimensional inconsistency will causes in loading parameters related to the number of classes; On the other hand, if more complicated model is used, need to motify the open-source model construction and selective load parameters. Thus, PaddleDetection should designate parameter fields and ignore loading the parameters which match the fields.

Use custom dataset

Transfer learning needs custom dataset and annotation in COCO-format and VOC-format is supported now. The script converts the annotation from voc, labelme or cityscape to COCO is provided in tools/x2coco.py. More details please refer to READER. After data preparation, update the data parameters in configuration file.

COCO-format dataset, take yolov3_darknet.yml for example, modify the COCODataSet in yolov3_reader:

  dataset:
    !COCODataSet
      dataset_dir: custom_data/coco # directory of custom dataset
      image_dir: train2017 # custom training dataset which is in dataset_dir
      anno_path: annotations/instances_train2017.json # custom annotation path which is in dataset_dir
      with_background: false

VOC-format dataset, take yolov3_darknet_voc.yml for example, modify the VOCDataSet in the configuration:

  dataset:
    !VOCDataSet
      dataset_dir: custom_data/voc # directory of custom dataset
      anno_path: trainval.txt # custom annotation path which is in dataset_dir
      use_default_label: true
      with_background: false

Load pretrained model

In transfer learning, it's needed to load pretrained model selectively. Two methods are provided.

Load pretrained weights directly (recommended)

The parameters which have diffierent shape between model and pretrain_weights are ignored automatically. For example:

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml \
                      -o pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar

Use `finetune_exclude_pretrained_params` to specify the parameters to ignore.

The parameters which need to ignore can be specified explicitly as well and arbitrary parameter names can be added to finetune_exclude_pretrained_params. For this purpose, several methods can be used as follwed:

Set finetune_exclude_pretrained_params in YAML configuration files. Please refer to configure file
Set finetune_exclude_pretrained_params in command line. For example:

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml \
                        -o pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar \
                           finetune_exclude_pretrained_params=['cls_score','bbox_pred'] \

Note:

The path in pretrain_weights is the open-source model link of faster RCNN from COCO dataset. For full models link, please refer to MODEL_ZOO
The parameter fields are set in finetune_exclude_pretrained_params. If the name of parameter matches field (wildcard matching), the parameter will be ignored in loading.

If users want to fine-tune by own dataset, and remain the model construction, need to ignore the parameters related to the number of classes. PaddleDetection lists ignored parameter fields corresponding to different model type. The table is shown below:

model type	ignored parameter fields
Faster RCNN	cls_score, bbox_pred
Cascade RCNN	cls_score, bbox_pred
Mask RCNN	cls_score, bbox_pred, mask_fcn_logits
Cascade-Mask RCNN	cls_score, bbox_pred, mask_fcn_logits
RetinaNet	retnet_cls_pred_fpn
SSD	^conv2d_
YOLOv3	yolo_output

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