English | [简体中文](TRANSFER_LEARNING_cn.md)

# 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.

## Transfer Learning in PaddleDetection

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

#### Load pretrain weights directly (**recommended**)

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

```python
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](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_mobilenet_v1_fruit.yml#L15)
- Set `finetune_exclude_pretrained_params` in command line. For example:

```python
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:

1. 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](../MODEL_ZOO.md)
2. 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: </br>

|      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                 |
