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- # Tutorial 7: Finetuning Models
-
- Detectors pre-trained on the COCO dataset can serve as a good pre-trained model for other datasets, e.g., CityScapes and KITTI Dataset.
- This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance.
-
- There are two steps to finetune a model on a new dataset.
-
- - Add support for the new dataset following [Tutorial 2: Customize Datasets](customize_dataset.md).
- - Modify the configs as will be discussed in this tutorial.
-
- Take the finetuning process on Cityscapes Dataset as an example, the users need to modify five parts in the config.
-
- ## Inherit base configs
-
- To release the burden and reduce bugs in writing the whole configs, MMDetection V2.0 support inheriting configs from multiple existing configs. To finetune a Mask RCNN model, the new config needs to inherit
- `_base_/models/mask_rcnn_r50_fpn.py` to build the basic structure of the model. To use the Cityscapes Dataset, the new config can also simply inherit `_base_/datasets/cityscapes_instance.py`. For runtime settings such as training schedules, the new config needs to inherit `_base_/default_runtime.py`. This configs are in the `configs` directory and the users can also choose to write the whole contents rather than use inheritance.
-
- ```python
- _base_ = [
- '../_base_/models/mask_rcnn_r50_fpn.py',
- '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
- ]
- ```
-
- ## Modify head
-
- Then the new config needs to modify the head according to the class numbers of the new datasets. By only changing `num_classes` in the roi_head, the weights of the pre-trained models are mostly reused except the final prediction head.
-
- ```python
- model = dict(
- pretrained=None,
- roi_head=dict(
- bbox_head=dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=8,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=False,
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
- mask_head=dict(
- type='FCNMaskHead',
- num_convs=4,
- in_channels=256,
- conv_out_channels=256,
- num_classes=8,
- loss_mask=dict(
- type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))))
- ```
-
- ## Modify dataset
-
- The users may also need to prepare the dataset and write the configs about dataset. MMDetection V2.0 already support VOC, WIDER FACE, COCO and Cityscapes Dataset.
-
- ## Modify training schedule
-
- The finetuning hyperparameters vary from the default schedule. It usually requires smaller learning rate and less training epochs
-
- ```python
- # optimizer
- # lr is set for a batch size of 8
- optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
- optimizer_config = dict(grad_clip=None)
- # learning policy
- lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.001,
- step=[7])
- # the max_epochs and step in lr_config need specifically tuned for the customized dataset
- runner = dict(max_epochs=8)
- log_config = dict(interval=100)
- ```
-
- ## Use pre-trained model
-
- To use the pre-trained model, the new config add the link of pre-trained models in the `load_from`. The users might need to download the model weights before training to avoid the download time during training.
-
- ```python
- load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' # noqa
-
- ```
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